Patent Publication Number: US-2019175026-A1

Title: Cardiac and sleep monitoring

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application is a National Stage Application that claims priority to PCT Application No. PCT/US2016/061672, entitled “CARDIAC AND SLEEP MONITORING,” having a filing date of Nov. 11, 2016 which is a Non-Provisional application which claims the benefit of Provisional U.S. Patent Application Ser. No. 62/253,803, entitled “CARDIAC MONITORING IN ASSOCIATION WITH SLEEP DISORDERED BREATHING-RELATED DEVICE,” having a filing date of Nov. 11, 2015, both of which are incorporated herein by reference. 
    
    
     BACKGROUND 
     Treating sleep disordered breathing has led to improved sleep quality for some patients. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a block diagram schematically representing an arrangement including a monitoring resource for cardiac-related information, according to one example of the present disclosure. 
         FIG. 2A  is a block diagram schematically representing a cardiac disorder parameter, according to one example of the present disclosure. 
         FIG. 2B  is a block diagram schematically representing a cardiac health parameter, according to one example of the present disclosure. 
         FIG. 3A  is a block diagram schematically representing an arrangement including a monitoring resource, according to one example of the present disclosure. 
         FIG. 3B  is a block diagram schematically representing access tools, according to one example of the present disclosure. 
         FIG. 3C  is a block diagram schematically representing a user interface, according to one example of the present disclosure. 
         FIG. 4A  is a diagram schematically representing one instance of a clinician user interface, according to one example of the present disclosure. 
         FIG. 4B  is a table schematically representing aspects of an import function associated with a clinician user interface, according to one example of the present disclosure. 
         FIG. 4C  is a table schematically representing aspects of a filter function associated with a clinician user interface, according to one example of the present disclosure. 
         FIG. 4D  is a table schematically representing a correlation coefficient array for a plurality of sleep parameters and a plurality of cardiac parameters, according to one example of the present disclosure. 
         FIG. 4E  is a table schematically representing a correlation coefficient relationship for one sleep parameter and one cardiac parameter, according to one example of the present disclosure. 
         FIG. 4F  is a diagram including a pair of graphs schematically representing the information in the table of  FIG. 4E . 
         FIG. 5A  is a table schematically representing information regarding a cardiac parameter and a sleep parameter in one instance of a patient user interface, according to one example of the present disclosure. 
         FIG. 5B  is a graph schematically representing information regarding a cardiac parameter and a sleep parameter in one instance of a patient user interface, according to one example of the present disclosure. 
         FIG. 6A  is a block diagram schematically representing stimulation circuitry, according to one example of the present disclosure. 
         FIG. 6B  is a block diagram schematically representing upper-airway-related body tissue, according to one example of the present disclosure. 
         FIG. 6C  is a block diagram schematically representing a non-cardiac pulse generator, according to one example of the present disclosure. 
         FIG. 7  is a block diagram schematically representing stimulation therapy components, according to one example of the present disclosure. 
         FIG. 8  is a block diagram schematically representing therapy modalities, according to one example of the present disclosure. 
         FIG. 9  is a block diagram schematically representing types of information associated with a therapy device, according to one example of the present disclosure. 
         FIG. 10A  is a block diagram schematically representing a stimulation therapy device including a sensor, according to one example of the present disclosure. 
         FIG. 10B  is a block diagram schematically representing a stimulation therapy device separate from a sensor, according to one example of the present disclosure. 
         FIG. 11  is a block diagram schematically representing sensors, according to one example of the present disclosure. 
         FIG. 12  is a block diagram schematically representing sensor types, according to one example of the present disclosure. 
         FIG. 13A  is a diagram schematically representing some aspects of accelerometer sensing in association with some aspects of sleep quality, according to one example of the present disclosure. 
         FIG. 13B  is a diagram schematically representing some aspects of accelerometer sensing in association with some aspects of sleep quality, according to one example of the present disclosure. 
         FIG. 13C  is a diagram schematically representing some aspects of acoustic sensing of cardiac information and respiratory information, according to one example of the present disclosure. 
         FIG. 13D  is a diagram schematically representing a Wiggers Diagram, according to one example of the present disclosure. 
         FIG. 13E  is a diagram schematically representing non-contact sensing of respiratory information, according to one example of the present disclosure. 
         FIG. 13F  is a diagram schematically representing derivation of respiratory information from a cardiac waveform, according to one example of the present disclosure. 
         FIG. 13G  is a diagram schematically representing a juxtaposition of cardiac timing information and respiratory information, according to one example of the present disclosure. 
         FIG. 13H  is a diagram schematically representing a juxtaposition of respiratory information, cardiac information, and sleep information, according to one example of the present disclosure. 
         FIG. 13I  is a diagram schematically representing an overnight patient report including cardiac information, respiratory information, and sleep information, and, according to one example of the present disclosure. 
         FIG. 14A  is a block diagram schematically representing an array of sensor modalities, according to one example of the present disclosure. 
         FIG. 14B  is a block diagram schematically representing a sensor profile manager associated with a therapy device, according to one example of the present disclosure. 
         FIG. 15A  is a block diagram schematically representing a cardiac condition array, according to one example of the present disclosure. 
         FIG. 15B  is a block diagram schematically representing a cardiac condition determination engine, according to one example of the present disclosure. 
         FIG. 15C  is a block diagram schematically representing a determination engine, according to one example of the present disclosure. 
         FIG. 16A  is a block diagram schematically representing a therapy system including cardiac monitoring, according to one example of the present disclosure. 
         FIG. 16B  is a diagram schematically representing a therapy system as deployed on a patient, according to one example of the present disclosure. 
         FIG. 16C  is a block diagram schematically representing at least some components of a pulse generator, according to one example of the present disclosure. 
         FIG. 17A  is a block diagram schematically representing a monitoring resource including a sensor, according to one example of the present disclosure. 
         FIG. 17B  is a block diagram schematically representing a monitoring resource, according to one example of the present disclosure. 
         FIG. 18A  is a block diagram schematically representing a manager, according to one example of the present disclosure. 
         FIG. 18B  is a table listing at least some sleep quality parameters, at least some cardiac parameters, and other parameters, according to one example of the present disclosure. 
         FIG. 18C  is a diagram of a correlation graphing tool, according to one example of the present disclosure. 
         FIG. 19  is a block diagram schematically representing a therapy device, according to one example of the present disclosure. 
         FIG. 20  is a block diagram schematically representing a wireless communication link, according to one example of the present disclosure. 
         FIG. 21  is a block diagram schematically representing a sensor, according to one example of the present disclosure. 
         FIG. 22  is a block diagram schematically representing an evaluation engine, according to one example of the present disclosure. 
         FIG. 23  is a block diagram schematically representing a control portion, according to one example of the present disclosure. 
         FIG. 24A  is block diagram schematically representing instructions for cardiac monitoring, according to one example of the present disclosure. 
         FIG. 24B  is block diagram schematically representing instructions for cardiac monitoring, according to one example of the present disclosure. 
         FIG. 25  is a flow diagram schematically representing instructions for cardiac monitoring, according to one example of the present disclosure. 
         FIGS. 26-28  are block diagrams schematically representing instructions for sleep parameter monitoring and/or cardiac parameter monitoring, according to some examples of the present disclosure. 
         FIG. 29  is a block diagram schematically representing instructions for displaying information, according to one example of the present disclosure. 
     
    
    
     DETAILED DESCRIPTION 
     In the following Detailed Description, reference is made to the accompanying drawings which form a part hereof, and in which is shown by way of illustration specific examples of the present disclosure which may be practiced. In this regard, directional terminology, such as “top,” “bottom,” “front,” “back,” “leading,” “trailing,” etc., is used with reference to the orientation of the Figure(s) being described. Because components of at least some examples of the present disclosure can be positioned in a number of different orientations, the directional terminology is used for purposes of illustration and is in no way limiting. It is to be understood that other examples may be utilized and structural or logical changes may be made without departing from the scope of the present disclosure. The following detailed description, therefore, is not to be taken in a limiting sense. 
     At least some examples of the present disclosure are directed to cardiac monitoring and/or sleep monitoring. In at least some examples of the present disclosure, cardiac monitoring may be employed in association with a therapy for sleep disordered breathing. In some instances, such cardiac monitoring may help demonstrate long term efficacy of sleep disordered breathing therapy in improving cardiac health or in slowing down progression of negative cardiac conditions (e.g. cardiac disorders). In some instances, such cardiac monitoring may help identify negative cardiac conditions which are not alleviated despite efficacious sleep disordered breathing therapy, and thereby facilitate the diagnosis and treatment of such cardiac conditions. 
     In some examples, such cardiac monitoring is performed via obtaining physiologic-related information. In some examples, the cardiac monitoring is performed in association with the sleep disordered breathing treatment and then deriving or extracting cardiac information from the physiologic-related information. In some examples, this physiologic-related information may include at least respiratory information. Accordingly, in some examples, cardiac monitoring is performed via at least some of the components associated with a therapy device for treating sleep disordered breathing. 
     However, in some examples, such cardiac monitoring is performed via obtaining cardiac information independent of obtaining other physiologic-related information. Accordingly, in some examples, cardiac monitoring is performed via devices or components separate from, and independent of, a therapy device for treating sleep disordered breathing. 
     For instance, in some examples such cardiac monitoring may be performed via a monitoring resource, whether or not a therapy device is involved. In at least some examples, a monitoring resource can take a variety of forms. In some examples, at least a portion of the monitoring resource is located within an implantable element the patient and/or within the presence of the patient, such as within components external to, but near, the patient. In some examples, at least a portion of the monitoring resource is located remotely from the patient, such as in an implementation via a server, other computing device, which may be located in the cloud (e.g. web-based computing resource) or in a monitoring facility (e.g. clinic, device manufacturer facility, hospital, etc.). 
     Whether or not near the patient, in some examples at least a portion of the monitoring resource may be located in and/or accessible via a dedicated mobile device (e.g. patient or clinician remote control) or a non-dedicated mobile device (e.g. smart phone, tablet, etc.). In some such examples, the monitoring resource may be implemented via an app (e.g. mobile application), widget, and/or other computing/communication resource operable via such mobile devices. In some examples, regardless of location at least a portion of the monitoring resource may be implemented via a stationary device, e.g. a workstation. 
     For instance, in some examples a monitoring resource monitors information without displaying the monitored information. However, in some examples, at least some of the monitored information is displayable. Accordingly, in some examples, monitoring information does not necessitate displaying such information. 
     In some examples, regardless of location, the monitoring resource may be at least partially implemented via a user interface through which at least some features, functions, and attributes of the monitoring resource may be displayed, accessed, engaged, etc. In some such examples, the user interface is accessible as a clinician user interface, a patient interface, etc. whether available via a web interface, mobile app, program (e.g. desktop, notebook computer), etc. 
     In some examples, monitoring via the monitoring resource comprises observing a parameter (e.g. sleep, cardiac, etc.) over a period of time. In some instances, the monitoring of one or more parameters over a period of time may sometimes be referred to as tracking the parameter at least in the sense the parameters are observed over time. 
     In some examples, the monitoring comprises receiving information regarding the parameter without performing a measurement. In some such examples, the information may be received from an external source, such as environmental information, patient history, etc. However, in some examples, the monitoring comprises monitoring the parameter via sensing information via at least one sensor. In some instances, the sensing may include or be associated with measuring. 
     In some examples, the monitoring comprises determining further information or drawing a conclusion, such as whether a particular parameter may be associated with or at least partially define a condition. For instance, upon monitoring a particular cardiac parameter, the monitoring may determine that a cardiac condition (e.g. atrial fibrillation) is exhibited. It will be understood that in at least some examples, the cardiac condition may be considered part of and/or encompassed by an associated cardiac parameter. Similarly, upon monitoring a particular sleep parameter, the monitoring may determine that a sleep condition (e.g. obstructive sleep apnea) is exhibited. In some examples, such determining may include determining correlations, trends between among different monitored parameters, determining to provide a notification to a patient or clinician, etc. 
     Accordingly, it will be understood that in at least some examples, the term “monitoring” and the term “monitoring resource” may broadly encompass determining, observing, receiving, sensing, measuring, tracking, displaying, etc. a parameter relating to at least sleep parameters and/or cardiac parameters. However, it will be understood that the various different features, functions, attributes, etc. associated with the term “monitoring” and/or “monitoring resource” may be distinct from each other, while existing in a complementary manner in at least some examples. 
     In some examples, “monitoring” and/or “the monitoring resource” are associated with a monitoring period. However, in some examples, “monitoring” and/or “a monitoring resource” are not associated with a particular monitoring period. 
     Moreover, at least some of these features, functions, and attributes of a monitoring resource, and/or additional features, functions, and attributes of a monitoring resource, are further defined in the context of at least some examples of the present disclosure in association with  FIGS. 1-29 . 
     These examples, and additional examples, are described in more detail in association with at least  FIGS. 1-29 . 
       FIG. 1  is a block diagram  51  schematically representing a monitoring resource  60  in an arrangement  50 , according to one example of the present disclosure. As shown in  FIG. 1 , in some examples arrangement  50  comprises a monitoring resource  60  to monitor and/or evaluate information regarding a patient  72 . In some examples, the information may comprise physiologic-related information and/or other information (e.g. environmental information) indicative of cardiac-related information. In some examples, the information also may comprise information regarding sleep quality, which may include information regarding sleep disordered breathing (SDB) behavior. In some examples, SDB behavior comprises obstructive sleep apneic behavior. In some examples, the information may comprises at least one of the types of information as further described later in association with at least  FIG. 9 . 
     In some examples, monitoring resource  60  obtains such information via at least one sensor  74 . The sensor(s)  74  may be implantable, external, contact, non-contact, etc. as further described later in association with at least  FIGS. 11-12 , and may be in wired or wireless communication with monitoring resource  60 . In some instances, the sensor(s)  74  may be incorporated into monitoring resource  60 . 
     In some examples, the arrangement  50  may comprise a therapy device  70 . In such arrangements, in some examples the monitoring resource  60  may receive information from the therapy device  70  regarding the patient  72  and/or a therapy applied to the patient. In some examples, monitoring resource  60  may communicate information to the therapy device  70 , which may be used in some examples to determine therapy parameters. In some examples, monitoring resource  60  may communicate wirelessly with therapy device  70 . 
     In some examples, information from sensor(s)  74  may be received by therapy device  70 , which in turn may be communicated to the monitoring resource  60  in some examples. 
     In some examples, monitoring resource  60  receives patient-related information from external sources other than sensor(s)  74  and/or therapy device  40 . 
     In general terms, the therapy device  70  can take a variety of forms provided that it works toward alleviating sleep disordered breathing (e.g. obstructive sleep apneas) in the patient  72 . In some examples, therapy device  70  provides neurostimulation to upper-airway-related body tissue to address sleep disordered breathing. At least some examples of such neurostimulation are later described and illustrated in association with  FIGS. 3A-29 . In some examples, therapy device  70  comprises an external therapy device, such as a device to provide airflow therapy (e.g. Continuous Positive Airway Pressure—CPAP) to address the sleep disordered breathing. 
     In some examples, monitoring resource  60  monitors a cardiac parameter  62  regarding the patient. In some examples, the cardiac parameter  62  is indicative of cardiac disorders as represented by cardiac disorder parameter  64  in  FIG. 2A . In some examples, cardiac parameter  62  is indicative of cardiac health, as represented by cardiac health parameter  66  in  FIG. 2B . In some examples, cardiac parameter  62  is indicative of both at least some aspects of cardiac disorders and at least some aspects of cardiac health. 
     In some examples, arrangement  50  enables treating the patient&#39;s sleep disordered breathing while also monitoring the patient for cardiac parameters. As more fully described later, such monitoring enables determining positive indications (e.g. enhanced cardiac health) and/or negative indications (e.g. evidence of cardiac disorders). In some examples, the indications regarding cardiac parameters may be short term, and in some examples, the indications regarding cardiac parameters may occur over the long term. 
     In some examples, monitoring resource  60  is implemented as monitoring resource  60  in association with a therapy manager  110  as shown in  FIG. 3A , according to one example of the present disclosure. 
     In some examples, as represented by arrangement  100  in  FIG. 3A , via therapy manager  110 , treatment of sleep disordered breathing (via therapy device  70 ) may occur according to a treatment period  112  in  FIG. 3A , while the cardiac parameter  62  may be monitored and/or evaluated via monitoring resource  60  according to a monitoring period  124  separate from, and independent of, the treatment period  112 . 
     In general terms, the treatment period  112  refers to a time period during which treatment or therapy occurs. For instance, because sleep disordered breathing is generally associated with sleep periods of the patient, in some examples the treatment period  112  coincides with a daily sleep period of the patient. In some instances, the daily sleep period is identified via sensing technology which detects motion, activity, posture, position of the patient, as well as other indicia, such as heart rate, breathing patterns, etc. In some instances, the daily sleep period is selectably preset, such from 10 pm to 6 am or other suitable times. 
     However, in some examples, the treatment period  112  could be implemented intermittently, such as every other day or every third day, and the like. Moreover, in some examples, the treatment period  112  can be shorter or longer than the sleep period of the patient. 
     In some examples, commencement of a treatment period  112  does not necessarily mean that continuous stimulation is applied during the treatment period  112 . Rather, various stimulation protocols can be implemented during a treatment period  112 . In some implementations, the stimulation protocol includes stimulating pertinent body tissues (e.g. upper-airway-related body tissues) upon identification of a fiducial from a respiratory waveform and/or other information sensed at the patient, wherein the fiducial may be indicative of sleep disordered breathing. 
     In some instances, stimulation is generally synchronized with inspiration. 
     In some instances, whether or not stimulation is synchronized with inspiration, stimulation is triggered in association with at least one of a beginning of inspiration, an end of inspiration, a beginning of expiration, and/or an end of expiration. 
     In some instances, initiation, termination, and/or duration of stimulation are based on a sensed respiratory waveform but are not synchronized relative to each inspiratory phase. 
     In some examples, a stimulation protocol includes stimulating pertinent body tissues without sensing respiratory information and/or without being synchronized relative to inspiration. 
     In some of these examples, the monitoring period  124  may have a duration on the same order of magnitude as the treatment period  112 . For instance, if the treatment period  112  occurs daily (or every other day, every third day, etc.), the monitoring period  124  may be daily or some number (e.g. 2, 3, 4, 5, 6, 7) of days. 
     However, in some examples, the monitoring period  124  may have a duration on a different order of magnitude than the treatment period  112 . In some examples, the monitoring period  124  has a duration that is at least one order of magnitude greater than the duration of the treatment period  112 . Accordingly, the monitoring period  124  may be ten days, two weeks, several weeks, a month, a quarter, one-half year, a year, etc. 
     In some examples, a duration of the monitoring period  124  is based on each particular diagnosable cardiac disorder. In particular, the duration of the monitoring period  124  is selected to correspond to a period of time by which one can observe indicia of the absence, presence, increase, or decrease of the particular cardiac disorder. 
     In some examples, a duration of the monitoring period  124  is based on each particular cardiac health parameter. In particular, the duration of the monitoring period  124  is selected to correspond to a period of time by which one can observe indicia of the absence, presence, increase, or decrease of the particular cardiac health. 
     In some examples, a monitoring resource  60  comprises part of or is incorporated within the therapy device  70 . As such, some example monitors may sometimes be referred to as being “on board” the therapy device  70 . In some examples, monitor is external to the therapy device  70  but is coupled to and/or in communication with therapy device  70 . In some examples, monitoring resource  60  is dedicated to monitoring and/or evaluating the cardiac parameter  62 . In some examples, monitoring and/or evaluating the cardiac parameter  62  are just some functions of multiple functions of monitoring resource  60 . In some examples, monitoring resource  60  may support managing at least some general operations of therapy device  70 . 
     In some examples, monitoring resource  60  cooperates with and/or forms part of a control portion, such as but not limited to, control portion  880  as later described in association with at least  FIG. 23 . In some examples, the monitoring resource  60  at least partially fulfills the role of engine  885  in  FIG. 23 . In some examples, monitoring resource  60  completely fulfills the role of engine  885  in control portion  880  ( FIG. 23 ). 
     In some examples, therapy manager  110  in  FIG. 3A  cooperates with and/or forms part of a control portion, such as but not limited to, control portion  880  as later described in association with at least  FIG. 23 . In some examples, the therapy manager  110  ( FIG. 3A ) at least partially fulfills the role of engine  885  in  FIG. 23 . In some examples, therapy manager  110  completely fulfills the role of engine  885  in control portion  880  ( FIG. 23 ). 
     In some examples, both monitoring resource  60  and therapy manager  110  work together in a complementary manner to at least partially fulfill the role of engine  885  of control portion  880  in  FIG. 23 . 
     With this general arrangement of system  50  in  FIG. 1  in mind, it will be understood that at least some implementations associated with  FIGS. 3B-23  provide more specific examples of various implementations and details regarding the operation and interaction of at least some aspects of monitoring resource  60  and/or therapy device  70 . 
       FIG. 3B  is block diagram schematically representing an array  130  of access tools  131 - 135 , according to one example of the present disclosure.  FIG. 3C  is a block diagram schematically representing user interface  140 , according to one example of the present disclosure. In some examples, at least some of the access tools  131 - 135  include user interface  140 . 
     In some examples, user interface  140  comprises a user interface or other display that provides for the simultaneous display, activation, and/or operation of at least some of the various components, elements, engine, functions, parameters, features, and attributes of monitoring resource  60  and/or therapy manager  110  and/or control portion  880  ( FIG. 23 ). In some examples, at least some portions or aspects of the user interface  140  are provided via a graphical user interface (GUI). In some examples, as shown in  FIG. 3C , user interface  140  includes display  142  and input  144 . 
     With further reference to  FIG. 3B , in some examples the access tools comprise a mobile device  131  dedicated to facilitating operation of and/or monitoring operation of at least some aspects of therapy device  70  ( FIG. 1 ). In some instances, at least some components of monitoring resource  60  and/or therapy manager  110  reside in the dedicated mobile device  131 . In some instances, dedicated mobile device  131  may be embodied as or referred to as a patient remote, patient programmer, or patient controller. 
     In some examples, the access tools in  FIG. 3B  comprise a mobile device  132  not dedicated to, but capable of, facilitating operation of and/or monitoring operation of at least some aspects of monitoring resource  60  and/or therapy device  70  ( FIG. 1 ). In some instances, at least some components of monitoring resource  60  and/or therapy manager  110  may be stored in the non-dedicated mobile device  132 . In some instances, non-dedicated mobile device  132  may be embodied as or referred to as a smart phone, tablet, phablet, notebook computer, watch phone, etc. In some examples, at least some components of monitoring resource  60  and/or therapy manager  110  may be arranged as a function, widget, and/or application (i.e. mobile app), etc. on the non-dedicated mobile device  132 . In some instances, the non-dedicated mobile device  132  may be used for functions (e.g. phone, computing, web browsing, texting, etc.) separate from, and independent of, operation of monitoring resource  60  and/or therapy manager  110 . 
     In some examples, any one of the mobile devices  131 ,  132  and dedicated station  133  may include at least one of the sensors as later described in association with  FIGS. 11-12  and/or may receive at least some of information  300  ( FIG. 9 ). 
     For instance, in some examples, many commercially available non-dedicated mobile devices  132  include photographic and/or video recording capabilities which can be used to take still and/or moving images of a patient before, during, and after sleep. In some examples, this imaging functionality is embodied in image sensor  419  in  FIG. 12 . Similarly, in some examples, the commercially available, non-dedicated mobile devices  132  may include audio recording equipment, which can record snoring or other breathing sounds, patient activity, and sounds in the patient&#39;s sleep environment. In some examples, this audio functionality is embodied in acoustic sensor  418  in  FIG. 12 . 
     In some examples, dedicated station  133  comprises any device or instrument locatable within a patient&#39;s sleep environment, and which is dedicated to facilitating operation of and/or monitoring operation of at least some aspects of therapy device  70  ( FIG. 1 ). In some instances, at least some components of monitoring resource  60  and/or therapy manager  110  reside in the dedicated station  133 . In some instances, dedicated station  133  may be embodied as or referred to as a patient remote, patient programmer, or patient controller. In some examples, dedicated station  133  comprises at least some of substantially the same features and attributes as mobile devices  131 ,  132 . 
     In some examples, clinician portal  135  facilitates operation of and/or monitoring operation of at least some aspects of therapy device  70  ( FIG. 1 ) by a clinician. In some instances, at least some components of monitoring resource  60 , and/or therapy manager  110  are accessible via clinician portal  133 . In some instances, clinician portal  133  may be embodied as or referred to as a clinician remote, clinician programmer, or clinician controller. 
     In some examples, regardless of the form of the access tool, at least some of the features and functions of monitoring resource  60 , and/or therapy manager  110  are accessible via a web-centric model. 
     In some examples, at least one of the access tools  131 - 135  for facilitating operation of monitoring resource  60  and/or therapy manager  110  are cooperable with the therapy devices/systems (e.g.  170  in  FIG. 6A ;  340  in  FIG. 10A ;  350  in  FIG. 10B ;  650  in  FIG. 16A ;  670  in  FIGS. 16B-16C ;  765  in  FIG. 19 ). In some examples, access tools  131 - 135  employed in association with monitoring resource  60  and/or therapy manager  110  may include or be cooperable with determination engine (e.g.  570  in  FIG. 15C ;  704  in  FIG. 17A ; and  752  in  FIG. 18A ), cooperable with monitoring resources (e.g.  700  in  FIG. 17A ;  710  in  FIG. 17B ;  750  in  FIG. 18A ) and/or cooperable with evaluation engine ( 770  in  FIG. 22 ), as described in the examples throughout the present disclosure. 
       FIGS. 4A-5B  include diagrams schematically representing at least some instances of a user interface associated with cardiac-related monitoring according to at least some examples of the present disclosure. It will be understood that in some examples, user interface portions represented in one Figure may be combined in a complementary manner with user interface portions in another Figure or user interface portions among several Figures. Likewise, in some examples, some user interface portions represented in  FIGS. 4A-5B  may be combined in a complementary manner with some user interface portions throughout various examples in at least  FIGS. 13A-13I . 
       FIG. 4A  is a diagram schematically representing one instance of a clinician user interface  1000 , according to one example of the present disclosure. In some examples, a clinician user interface  1000  may include all of the components shown in  FIG. 4A , while in some examples, a clinician user interface  1000  includes just some of the components shown in  FIG. 4A . 
     In some examples, clinician user interface  1000  comprises at least some of substantially the same features as user interface  140  in  FIG. 3C . In some examples, clinician user interface  140  may be accessed via at least one of the access tools  131 - 135  in  FIG. 3B . 
     In some examples, clinician user interface  1000  displays information about a particular patient, and as such includes a patient table  1010  reporting several parameters  1012 - 1016  regarding a physiologic state of a patient. In some examples, parameters  1012 - 1016  include a cardiac parameter  1012 , a sleep parameter  1014 , and/or a self-developing vector  1016 . It will be understood that greater or fewer than three parameters  1012 - 1016  can be monitored and displayed in table  1010 . 
     Table  1010  further includes a trending column  1020 , which indicates whether a particular parameter  1012 - 1016  is trending upward, downward, or is steady as represented by corresponding directional arrows. A score column  1022  indicates a score according to an alphanumeric scoring scale, which in some instances, indicates a relative value for a particular parameter  1012 - 1016 . In some instances, an absolute value may be displayed. 
     As further shown in  FIG. 4A , in some examples clinician user interface  1000  includes an import data function  1040  and/or a graph function  1042 . In general terms, import data function  1040  initiates and/or controls importing of data from patient device(s) and/or other patient-related information databases. One example implementation of the import data function  1040  is later described and illustrated in association with at least  FIG. 4B . 
     In some examples, clinician user interface  1000  includes an observation log element  1050  to display therapy-related information  1052 . In some examples, the particular types of information displayed are selectable by the clinician and in some examples, the particular types of information are fixed by the device manufacturer. 
     As shown in  FIG. 4A , in some examples, information  1052  includes average treatment usage, such as the number of hours per night that therapy was applied. In some examples, information  1052  includes episodal information, which may include episodes regarding obstructive sleep apnea, cardiac disorder episodes, and/or pulmonary episodes, etc. In the particular example shown in  FIG. 4A , information  1052  includes a cardiac episode, such as an instance of atrial fibrillation along with the date of occurrence. 
     In some examples, the information  1052  may be hourly, daily, weekly, monthly, etc. 
     As further shown in  FIG. 4A , in some examples clinician user interface  1000  includes a graph  1060  displaying physiologic information, such as but not limited to a cardiac waveform. In some examples, the cardiac waveform may reveal an episode indicative of a cardiac disorder. For instance, the cardiac waveform in graph may reveal a potential episode of atrial fibrillation (AF), and which may be noted in LOG  1050 . 
     As further shown in  FIG. 4A , in some examples a clinician user interface  1000  includes a graph  1070  displaying sleep information  1072 . In some examples, such sleep information  1072  is plotted according to an x-axis  1072  representing a date and a first y-axis  1074 A representing a time of day, and a second y-axis  1074 B representing a duration (e.g. in hours). 
     In some examples, sleep information  1072  plotted on graph  1070  includes a series  1080  of daily sleep periods  1082 , illustrating whether sleep is generally continuous or broken and the start time (e.g. about 11 pm) and end time (e.g. about 7 am) of the daily sleep period  1082  for a particular date. In some examples, sleep information  1072  plotted on graph  1070  includes a series  1090  of durations  1092  (e.g. 7.5 hours) of the daily sleep periods. 
       FIG. 4B  is a table schematically representing aspects of an import function  1150  associated with a clinician user interface, according to one example of the present disclosure. In some examples, import function  1150  comprises at least some of substantially the same features and attributes as import data function  1040  in  FIG. 4A . In some examples, import data function  1150  monitors and controls the importing of data from patient devices into clinician user interface  1000  and/or other clinician management tools. As further shown in  FIG. 4B , import function  1150  can utilize a table  1152 , which includes a device column  1160 , a status column  1170 , and an action column  1180 . The device column  1160  lists which devices, by type and/or patient identity, for which therapy is to be monitored and/or evaluated via clinician user interface  1000 . The status column  1170  lists an upload data status regarding each listed device, while the action column  1180  lists potential actions that can be taken regarding a particular listed device. It will be understood that import function  1150  includes the ability to add or remove devices from table  1152 . 
       FIG. 4C  is a table  1202  schematically representing aspects of a filter function  1200  associated with a clinician user interface, according to one example of the present disclosure. In some examples, filter function  1200  works in association with import function  1150  to facilitate a clinician decision whether data from a particular device (and therefore a particular patient) listed in column  1210  is to be included in subsequent data analysis, as indicated via column  1220 . 
     In some examples, table  1152  or table  1202  comprise a displayable user interface or may form part of a clinician user interface  1000 , such as upon engagement of the import data function  1040 . In some examples, tables  1152 ,  1202  comprise at least some of substantially the same features and attributes as user interface  140  in  FIG. 3C . 
       FIGS. 4D-4F  provide further tables and graphs, which may form part of a clinician user interface, such as but not limited to clinician user interface  1000 , and may comprise at least some of substantially the same features and attributes as user interface  140  in  FIG. 3C . 
       FIG. 4D  is a table  1300  schematically representing a correlation coefficient array  1302  for a plurality of sleep parameters  1306  and a plurality of cardiac parameters  1304 , according to one example of the present disclosure. As shown in  FIG. 4D , various sleep parameters (e.g. 1, 2, 3, 4) and various cardiac parameters (e.g. 1, 2, 3, 4) are mapped relative to each other and from which a correlation coefficient (e.g. 0.94) can be determined for each pairing of sleep parameters (e.g. 1) and cardiac parameters (e.g. 1). In some examples, the sleep parameters relate to aspects of sleep quality and one of the parameters also may represent an overall sleep quality parameter, which is a combination of various sleep parameters. In some examples, the cardiac parameters relate to aspects of cardiac disorder (or cardiac health) and one of the parameters in table  1330  also may represent an overall cardiac disorder parameter (alternatively, a cardiac health parameter), which is a combination of various cardiac parameters. The correlation coefficients facilitate identification of a relationship between various sleep parameters and cardiac parameters, such that a clinician may identify and monitor cardiac disorders (alternatively, cardiac health) in association with sleep parameters. 
     In some examples, the table  1300  in  FIG. 4D  provides one example implementation of the array  754  of sleep quality parameters and the array  756  of cardiac disorder parameters determined via determination engine  752 , as later described in association with at least  FIG. 18A . 
       FIG. 4E  is a table  1330  schematically representing a correlation coefficient table for one sleep parameter and one cardiac parameter, according to one example of the present disclosure. Scores for a sleep parameter  1338  and scores for a cardiac parameter  1340  are determined through different monitoring periods  1336 . In some examples, the overall correlation coefficient between sleep parameter 1 and cardiac parameter 1 is 0.94. Each monitoring period (e.g. 1, 2, 3, 4, 5, etc.) may be hourly, daily, weekly, monthly, quarterly, yearly, etc. In some examples, some monitoring periods may have a duration different than other monitoring periods within a group of monitoring periods. 
       FIG. 4F  is a diagram  1360  including a pair of graphs  1362 A,  1362 B schematically representing the information in the table of  FIG. 4E , with graph  1362 A including an array  1365  of the sleep parameter scores ( 1338  in  FIG. 4E ) and an array  1366  of the cardiac parameter scores ( 1340  in  FIG. 4E ). Graph  1362 B maps the same parameters according to a sleep parameter trend  1375  and a cardiac parameter trend  1376 . In one aspect, the generally matching downward trajectory of both parameters may be interpreted, in some examples, that as the sleep parameter declines so does the cardiac parameter. Depending on whether the sleep parameter is determined to be a positive trait or a negative trait and depending on whether the cardiac parameter is determined to be a positive trait or a negative trait, the matching trendlines may indicate different types of association (e.g. positive, negative) between the particular sleep parameter and the particular cardiac parameter. 
     In some examples, at least one of the different types of sleep parameters (e.g. quality or disorder) may correspond to obstructive-sleep-apnea (OSA)-related parameters. In some examples, the OSA-related parameters may comprise a number of obstructive sleep apnea events or a severity of obstructive sleep apnea behavior. 
       FIGS. 5A-5B  provide patient-oriented tables and graphs, which may form part of a patient user interface, which may comprise at least some of substantially the same features and attributes as user interface  140  in  FIG. 3C . In some examples, the aspects of a patient user interface represented by  FIGS. 5A-5B  are accessible via one of the access tools  131 - 135  represented in  FIG. 3B . 
       FIG. 5A  is a table  1400  schematically representing information regarding a cardiac parameter and a sleep parameter in one instance of a patient user interface, according to one example of the present disclosure. In some examples, table  1400  comprises at least some of substantially the same features and attributes as table  1010  ( FIG. 4A ) except in a simplified form that omits self-developing vector  1016 . However, table  1400  does include a trend column  1410  and a score column  1412  by which a cardiac parameter  1402  (e.g. health or disorder) and a sleep parameter  1404  (e.g. quality or disorder) may be monitored. 
       FIG. 5B  is a graph  1430  schematically representing information regarding a sleep parameter in one instance of a patient user interface, according to one example of the present disclosure. While graph  1430  can take many forms and can represent many different kinds of patient information, in some examples graph  1430  maps instances of sleep duration (e.g. y-axis  1434 ) relative to days (x-axis  1432 ), thereby providing a map of trends. With or without other sleep parameters, sleep duration may provide an indication of sleep quality. 
       FIG. 6A  is a block diagram schematically representing a therapy device  171 , according to one example of the present disclosure. In some examples, therapy device  171  comprises at least some of substantially the same features and attributes as therapy device  70  ( FIG. 1 ), and in some examples, therapy device  171  may act as therapy device  70  in  FIG. 1 . 
     As shown in  FIG. 6A , therapy device  171  comprises stimulation circuitry  170  which includes a non-cardiac pulse generator  172  and a stimulation element  174 . In some examples, the non-cardiac pulse generator  172  and stimulation element  174  are formed as a single unit, which can be multiple, separate components joined together or which may be a monolithic formation including both the non-cardiac pulse generator  172  and the stimulation element  174 . 
     The therapy device  171  enables electrically stimulating upper-airway-related body tissue  180 , such as schematically represented in  FIG. 6B . In general terms, upper-airway-related body tissue comprises any body tissue which can affect the function and/or operation of the upper airway, and which can be stimulated in some form to efficaciously address sleep disordered breathing, such as via restoring upper airway patency or via other physiologic mechanisms. In some examples, the body tissue includes nerve(s)  182 , muscle  184 , or a combination  186  of nerve and muscle. In some examples, the particular nerve(s)  182  and/or muscle(s)  184 , upon stimulation, restore patency of the upper airway and thereby alleviate obstructive sleep apnea. 
       FIG. 6C  is a block diagram schematically representing a non-cardiac pulse generator  200 , according to one example of the present disclosure. In some examples, non-cardiac pulse generator  200  may act as a non-cardiac pulse generator (e.g.  172  in  FIG. 6A ;  200  in  FIG. 7 ;  652  in  FIG. 16A ), and/or may be incorporated into therapy device (e.g.  70  in  FIG. 1 ;  340  in  FIG. 10A ;  350  in  FIG. 10B ;  765  in  FIG. 19 ). 
     In some examples, the non-cardiac pulse generator  200  includes entirely implantable components  202 . In some examples, the non-cardiac pulse generator  200  includes some implantable components  202  and some external components  204  to form a combination  206 . In some examples, the non-cardiac pulse generator  200  is wholly external to the patient&#39;s body. 
     In general terms, the non-cardiac pulse generator  200  can generate electrical signals deliverable through a stimulation element (e.g.  174  in  FIG. 6A ;  216  in  FIG. 7 ) suitable for exciting body tissue  180  to restore airway patency. In some examples, the signals are adapted to directly stimulate upper-airway-related muscles  184  and/or to stimulate nerves  182  innervating such muscles  184 . In some examples, such as the case of obstructive sleep apnea, the nerves  182  may include (but are not limited to) the nerve  182  and the muscles  184  related to causing movement of the tongue and related musculature to restore airway patency. In some examples, the nerves  182  may include (but are not limited to) the hypoglossal nerve and the muscles  184  may include (but are not limited to) the genioglossus muscle. 
     In some examples, the non-cardiac pulse generator  200  forms part of the INSPIRE® Upper Airway Stimulation system, available from Inspire Medical, Inc. of Maple Grove, Minn. In some examples, the pulse generator  200  comprises a pulse generator available from other vendors. 
     Further examples of the non-cardiac pulse generator  200  are later described in association with at least  FIGS. 7 and 16A . 
       FIG. 7  is a block diagram schematically representing components of a therapy device  210 , according to one example of the present disclosure. As shown in  FIG. 7 , therapy device  210  includes a non-cardiac pulse generator  200  and stimulation element  216 . In some examples, pulse generator  200  includes at least some of substantially the same features and attributes as pulse generator  200 , as previously described in association with at least  FIG. 6C . In some examples, stimulation element  216  comprises at least some of substantially the same features and attributes as stimulation element  174 , as previously described in association with at least  FIG. 6A . 
     In general terms, therapy device  210  enables stimulation of upper-airway-related body tissue  180  ( FIG. 6B ). In some examples, the pulse generator  200  and stimulation element  216  are not necessarily physically co-located. However, in some examples pulse generator  200  and stimulation element  215  may be co-located in close physical proximity to each other. For instance, in some examples, both the pulse generator  200  and stimulation element may be located in proximity to a target stimulation site. However, in some examples, the stimulation element  216  is located at or near a target stimulation site while the pulse generator  200  is located remotely from the target stimulation site. In some examples, pulse generator  200  and/or stimulation element  216  include wireless communication elements to enable wireless communication therebetween. 
     In some examples, the pulse generator  200  is implanted within a pectoral region and the stimulation element  216  comprises a cuff electrode coupled relative to a nerve, such as the hypoglossal nerve. Further details regarding such examples are provided later in association with at least  FIGS. 16A-16B . 
     In one aspect, pulse generator  200  is at least electrically coupled relative to stimulation element  216  and is also physically coupled relative to stimulation element  216 , such as via a lead extending between the pulse generator  200  and the stimulation element  200 . However, in some examples, pulse generator  200  is physically coupled relative to stimulation element  216  via structures other than an electrical lead. 
       FIG. 8  is a block diagram  220  schematically representing various modalities of restoring airway patency, according to one example of the present disclosure. As shown in  FIG. 8 , such modalities include stimulation  222 , structural  224 , and chemical  226 . The stimulation modality  222  is described throughout the present disclosure in association with at least  FIGS. 6A, 6C, 7, 10A-10B, 16A-16B , and  19 . Structural modality  224  comprises installing a structural component within the upper airway or nearby bodily structures to at least partially modify or influence the patency of the upper airway. In some examples, the various modalities  222 ,  224 , and  226  may be implemented in different combinations, such as but not limited to, employing both a stimulation modality  222  and a structural modality  224 . 
       FIG. 9  is a block diagram schematically representing patient information  300 , according to one example of the present disclosure. In some examples, patient information  300  includes respiratory information  302 , cardiac information  304 , sleep quality information  306 , sleep disordered breathing (SDB) information  308 , and/or other information  310 . In some examples, various combinations of information  302 ,  304 ,  306 ,  308 , and  310  may be used, as represented via combination information  312 . 
     Information  300  may be obtained via a sensor coupled to or in proximity to a patient or may be obtained via other sources. Various examples of a sensor are later described in association with at least  FIGS. 11-12, 13A-13I, 14A-14B, 16A-16C, 17A-17B, and 21 . 
     In some examples, one type of information may be derived from another type of information. For instance, via filtering or other processing mechanisms, at least some forms of cardiac information  304  (e.g. heart rate) may be determined or derived from respiratory information  302 , where the respiratory information  302  is determined via a sensor. By looking at the behavior (e.g. increasing, decreasing, stable, high variability, low variability, high disorganization, low disorganization, etc.) of this derived/determined heart rate information alone and/or with other factors, one may determine a cardiac condition. 
     In some examples, respiratory information  302  is gathered during daytime (e.g. non-sleep) activities to detect the potential presence or worsening of non-cardiac diseases, such as but not limited to, pulmonary diseases (in addition to the particular pulmonary issues directly associated with sleep disordered breathing). In some examples, other information  310  may gather information and/or determine information regarding such non-cardiac physiologic conditions and/or diseases. In one example, such other information  310  includes pulmonary information. In some examples, such pulmonary information includes pulmonary disease information, such as but not limited to, chronic obstructive pulmonary disease (COPD), exacerbation of chronic obstructive pulmonary disease (ECOPD), etc. 
     In some examples, a change in respiratory information  302  may be indicative of future changes in cardiac information  304 , sleep quality information  306 , and sleep disordered breathing (SDB) information  308 . In some examples, a change in respiratory information  302  may be indicative of future changes in other information  310 , such as pulmonary disease information. For instance, for a patient already known to have chronic obstructive pulmonary disease (COPD), an increase in a patient&#39;s respiratory rate (e.g. one type of respiratory information  302 ) and/or reduced tidal volume may signal a forthcoming exacerbation of chronic obstructive pulmonary disease (ECOPD). Accordingly, in some examples, a therapy device and/or monitoring resource ( 70 ,  60  in  FIG. 1 ) may be programmed to store a known non-cardiac disease information in other information  310  along with an association with another type of information, such as respiratory information  302 , such that when the therapy device and/or monitoring resource detects a change in respiratory information  302  (e.g. increase in respiratory rate  302 ), the therapy device and/or monitoring resource automatically provides a notification for a clinician/patient that evaluation and/or intervention of the patient may be warranted regarding their pulmonary disease state in order to prevent or mitigate the pulmonary disease, such as preventing or mitigating ECOPD. 
     Accordingly, in such examples, the therapy device and/or manager may be programmed regarding various disease states of the patient to enable the therapy device and/or monitoring resource to act as an early warning system for non-cardiac conditions and/or non-OSA conditions upon detection of a change in respiratory information  302  or other types of information  300  monitored (e.g. gathered, determined, etc.) via a therapy device and/or monitoring resource. 
     In some examples, information  300 , including other information  310 , may be uploaded from an external source into a therapy device and/or manager. With further reference to  FIG. 9 , in some examples, sleep disordered breathing (SDB) information  308  is derived or determined from cardiac information  304 . For instance, one example may comprise performing apnea detection from an electrocardiogram signal and/or other signals sensing cardiac activity. 
     In some examples, sensor  344  for obtaining information  300  ( FIG. 9 ) may form part of a therapy device  340 , as shown in  FIG. 10A  according to one example of the present disclosure. Therapy device  340  also comprises stimulator circuitry  342 , which may take the forms described in association with at least  FIGS. 6A, 7, 10A-10B, 19 . 
     However, in some examples, sensor  354  for obtaining information  300  ( FIG. 9 ) may be separate from, and independent of a therapy device  350  as shown in  FIG. 10B , according to one example of the present disclosure. Like therapy device  340  in  FIG. 10A , therapy device  350  in  FIG. 10B  also comprises stimulator circuitry  342 . In some examples, while separate from and independent of therapy device  350 , sensor  354  is dedicated to providing sensed information to therapy device  350 . In some examples, sensor  354  is not dedicated to providing sensed information to therapy device  350 . As such, sensor  354  may be part of a system which is independent of therapy device  350  or sensor  354  may be a standalone sensor not associated with any other system or device. 
       FIG. 11  is a block diagram schematically representing sensors  370 , according to one example of the present disclosure. In some examples, sensors  370  may correspond to the sensors (e.g.,  344  in  FIG. 10A ;  354  in  FIG. 10B ;  400  in  FIG. 12 ;  654  in  FIG. 16A ;  702  in  FIG. 17A ;  712  in  FIG. 17B ; and  769  in  FIG. 21 ) as previously described or later described in the examples of the present disclosure. 
     In some examples, sensor  370  is an implantable sensor  372  which is couplable relative to a patient&#39;s body via being implanted within a patient&#39;s body. Via such implantation, the sensor  372  is at least coupled mechanically relative to the patient&#39;s body. Moreover, via such implantation, the sensor  372  is further coupled relative to the patient&#39;s body according to the particular sensor modality (e.g.  FIG. 12 ) of the implantable sensor  372 , which may be electrical (e.g. impedance, etc.), mechanical (e.g. pressure, motion, etc.), chemical, etc. 
     In some examples, implantable sensor  372  forms part of another component implanted within the patient&#39;s body, such as a pulse generator (e.g. pulse generator  200  in  FIG. 6C ). In such examples, the sensor  372  may form part of the housing of the pulse generator and therefore may be exposed to the internal environment of the patient. On the other hand, in such examples, the sensor  370  may be housed internally within the pulse generator and be isolated from the internal environment of the patient. While a fuller discussion of sensor types  400  is reserved until a later discussion of  FIG. 12 , it will be noted that an accelerometer (e.g.  406  in  FIG. 12 ) is one example of an implantable sensor, which is internally housed within a pulse generator. 
     In some examples, implantable sensor  372  may comprise stand-alone implantable sensors distributed throughout the patient&#39;s body and which communicate wirelessly to a SDB therapy device or to an external device that integrates the sensed data. For instance, one stand-alone implantable sensor may comprise an oxygen sensor. 
     In some examples, sensor  370  comprises an external sensor  374  that remains external to a patient&#39;s body. The external sensor  374  may be a wearable sensor  380 , and therefore may at least releasably couplable relative to the patient&#39;s body. In some examples, the external sensor  374  comprises an environment sensor  382 , which is present in and/or part of the patient&#39;s environment  382  and which senses information from the patient and/or regarding the environment in which the patient is present. However, in some instances, the environment sensor  382  is not couplable relative to the patient&#39;s body while in other instances, the environment sensor  382  is couplable relative the patient&#39;s body. 
     In some examples, a wearable sensor  380  may be used to sense physiologic information (such as heart rate variability) such that the wearable sensor  380  need not be part of an implantable therapy device or external therapy device. Rather, one may simply add the wearable sensor  380  at a later time to monitor cardiac parameters in association with a therapy performed to alleviate sleep disordered breathing. 
     In some examples, a wearable sensor  380  may comprise a commercially available wearable sensor which includes an array of sensors for measuring heart rate (e.g. LED, optical sensor), sleep quality/motion (e.g. 3D accelerometer), ambient light, In some instances, the wearable sensor  380  includes a touchscreen display to facilitate monitoring the sensed conditions. In some instances, the wearable sensor  380  includes a wireless communication tool for communicating with a dongle, mobile device, etc. via a wireless communication protocol (e.g. Bluetooth, NFC, etc.). In one instance, such a wearable sensor  380  is available from FitBit, Inc. of San Francisco, Calif. In some examples, such a system may include a single sensor or array of sensors which provide respiratory information  302 , cardiac information  304 , sleep quality information  306 , sleep disordered breathing (SDB) information  308 , and/or other information  310  ( FIG. 9 ). In some examples, this information may be coordinated with information sensed or determined via a sleep disordered breathing therapy device. For instance, in some examples, such wearable sensor arrangements cooperate with a sensor profile manager  450 , as later further described in association with at least  FIG. 14B . 
     In some examples, external sensor(s)  374  may be used to measure parameters, such as blood pressure, weight, etc. which may be used to identify a drug-resistant hypertension and any potential correlation or link between sleep disordered breathing (e.g. obstructive sleep apnea) and drug-resistant hypertension. 
     In some examples, information from external sensors  374  can be coordinated with information from implantable sensors  372 . For instance, information from external sensors  374  or other external information sources, such as weather/environmental reports, can be coordinated with information from implanted sensors  372  to provide guidance to asthmatic patients on whether it&#39;s safe to go outside based on previous respiratory/weather correlations and situations. 
     In some examples, external sensor  374  comprises an integrated external sensing system for monitoring sleep quality, heart rate, breathing rhythm, movement, sleep stages, snoring, and sleep environment (e.g., noise level and light). One example system comprises the Beddit® system available from www.beddit.com. In some examples, such a system may provide respiratory information  302 , cardiac information  304 , sleep quality information  306 , sleep disordered breathing (SDB) information  308 , and/or other information  310  ( FIG. 9 ). In some examples, this information may be coordinated with information sensed or determined via a sleep disordered breathing therapy device. For instance, in some examples, such external sensor arrangements cooperate with a sensor profile manager  450 , as later further described in association with at least  FIG. 14B . 
     In some examples, an external sensor(s)  374  may comprise clinically available diagnostic equipment such as ECG sensors, a blood pressure cuff, oxygen sensor, etc. 
     In some examples, external sensor(s)  374  may be incorporated into a patient remote, such as one of the access tools  131 - 133 . In some examples, external sensor(s)  374  can measure parameters associated with an apnea-hypopnea index (AHI). In such examples, the external sensor  374  can sense pulse transit times, which vary during respiration. 
     In some instances, the environment sensor  382  shown in  FIG. 11  comprises a non-contact sensor  384 , which does not make contact with the patient. Accordingly, in such instances, the non-contact sensor  384  is at least not mechanically couplable relative to the patient&#39;s body. However, in some examples, the non-contact sensor  384  may be couplable relative to the patient&#39;s body in at least the sense that the particular sensor modality can relate to the patient&#39;s body in at least some fashion to obtain physiologic information regarding the patient. 
     For instance, at least some types of a non-contact sensor  384  are later described more fully regarding sensor type  400  in association with at least  FIG. 12 . In one instance, non-contact sensor  384  comprises at least some of substantially the same features and attributes as the non-contact sensor paradigm described in Heneghan et al. U.S. Pat. No. 5,562,526, which may provide respiratory information  302 , cardiac information  304 , sleep quality information  306 , sleep disordered breathing (SDB) information  306 , and/or other information. In one instance, one such system is available from Resmed Corporation of San Diego, Calif. 
     In some instances, the non-contact sensor  384  incorporates or cooperates with one of the sensor modalities described in association with at least  FIG. 12 , such as but not limited to, a radiofrequency sensor  408 . The signal produced by sensing via the radiofrequency sensor  408  (also a non-contact sensor  384 ) may be processed to detect patient motion/activity, breathing (e.g. respiratory rate), heart rate, and/or a sleep stage of the patient. In some instances, physiologic information, such as cardiac information, detected via the radiofrequency sensor  408  (a non-contact sensor  384 ) may take the form of a ballistocardiogram or seismocardiogram, which are both further described later in association with at least  FIG. 14A . Among other attributes, in at least some examples the ballistocardiogram and/or seismocardiogram may obtain at least cardiac information without contacting the patient and thus may sometimes be referred to as unobtrusive cardiac sensing. 
     In some examples, sensor  370  may comprise a sensor providing a combination sensor  376 , which combines at least some aspects of the various implantable sensor  372  and external sensor  374 . 
       FIG. 12  is a block diagram schematically representing a sensor type  400  according to one example of the present disclosure. In some examples, sensor type  400  corresponds to a sensor (e.g.,  344  in  FIG. 10A ;  354  in  FIG. 10B ;  370  in  FIG. 11 ;  654  in  FIG. 16A ;  702  in  FIG. 17A ;  712  in  FIG. 17B ;  769  in  FIG. 21 ) as previously described or later described in the examples of the present disclosure. 
     As shown in  FIG. 12 , sensor type  400  comprises various types of sensor modalities  402 - 422 , any one of which may be used for determining, obtaining, and/or monitoring respiratory information  302 , cardiac information  304  (e.g. positive cardiac conditions and/or negative cardiac conditions), sleep quality information  306 , sleep disordered breathing-related information  308 , and/or other information  310  as previously described in association with at least  FIG. 9 . 
     As shown in  FIG. 12 , in some examples sensor type  400  comprises the modalities of pressure  402 , impedance  404 , accelerometer  406 , airflow  407 , radiofrequency (RF)  408 , optical  410 , electromyography (EMG)  412 , electrocardiography (ECG)  414 , ultrasonic  416 , acoustic  418 , image  419 , and/or other  420 . In some examples, sensor type  400  comprises a combination  422  of at least some of the various sensor modalities  402 - 420 . 
     It will be understood that, depending upon the attribute being sensed, in some instances a given sensor modality identified within  FIG. 12  may include multiple sensing components while in some instances, a given sensor modality may include a single sensing component. Moreover, in some instances, a given sensor modality identified within  FIG. 12  may include monitoring circuitry and/or communication circuitry. However, in some instances a given sensor modality in  FIG. 12  may omit such monitoring and/or communication circuitry but may cooperate with such monitoring or communication circuitry located elsewhere. 
     In some examples, a pressure sensor  402  may sense pressure associated with respiration and can be implemented as an external sensor  374  ( FIG. 11 ) and/or an implantable sensor  372  ( FIG. 11 ). In some instances, such pressures may include an extrapleural pressure, intrapleural pressures, etc. For example, one pressure sensor  402  may comprise an implantable respiratory sensor, such as that disclosed in Ni et al. U.S. Patent Publication 2011-0152706, published on Jun. 23, 2011, titled METHOD AND APPARATUS FOR SENSING RESPIRATORY PRESSURE IN AN IMPLANTABLE STIMULATION SYSTEM. 
     In some instances, pressure sensor  402  may include a respiratory pressure belt worn about the patient&#39;s body. 
     In some examples, a pressure sensor  402  can sense sound and/or pressure waves at a different frequency than occur for respiration (e.g. inspiration, exhalation, etc.). In some instances, this data can be used to monitor cardiac parameters of patients via a respiratory rate and/or a heart rate. In some instances, such data can be used to approximate electrocardiogram information, such as a QRS complex. In some instances, the detected heart rate is used to identify a relative degree of organized heart rate variability, in which organized heart rate variability may enable detecting apneas or other sleep disordered breathing events, which may enable evaluating efficacy of sleep disordered breathing. In some instances, the detected heart rate is used to identify disorganized heart rate variability, which may enable detecting cardiac disorders, such as arrhythmias (e.g. atrial fibrillation, ventricular tachycardia, etc.), for which cardiac intervention (e.g. ablation, drug therapy, etc.) may be appropriate. 
     In some examples, pressure sensor  402  comprises an implantable blood pressure sensor which is separate from a therapy device and which may be used to monitor cardiac parameters. 
     In some examples, pressure sensor  402  is locatable in close proximity to the patient&#39;s heart to optimize detection of cardiac information  304 . 
     In some examples, pressure sensor  402  comprises an intracardiac absolute pressure sensor. In some instances, this pressure sensor is used to detect respiration and/or arterial pressure. This pressure sensor also may involve a training mode in which field calibration is applied via use of an external sensor (wearable atmospheric blood pressure), thereby ensuring accuracy of the intracardiac absolute pressure sensor. Due to component sensitivity, manufacturing variability, implant variability, and/or system interactions, in at least some instances, it may be more accurate and simpler to perform a field calibration (such as but not limited to the above-described field calibration) with the sensor in its final functional state rather than trying to calibrate the sensor to an absolute scale at the component level in the manufacturing environment. In this way, an implantable pressure sensor in accordance with at least some examples of the present disclosure may be utilized with simpler manufacturing processes than if a pre-calibrated sensor were implanted. 
     In some examples, use of the pressure sensor  402  is paired with obtaining a far field ECG, in which the ECG signal is used to filter out or blank out cardiac artifacts from the pressure sensor signal. 
     In some examples, the pressure sensor  402  is used to determine minute ventilation. Among other benefits, the determined minute ventilation may be used to make long term evaluations regarding pulmonary disease. 
     As shown in  FIG. 12 , in some examples one sensor modality includes air flow sensor  407 , which can be used to sense respiratory information  302 , sleep disordered breathing-related information  308 , sleep quality information  306 , etc. In some instances, air flow sensor  407  detects a rate or volume of upper respiratory air flow. 
     As shown in  FIG. 12 , in some examples one sensor modality includes impedance sensor  404 , which may be implemented in some examples via various sensors distributed about the upper body for measuring a bio-impedance signal, whether the sensors are internal and/or external. In some instances, the sensors are positioned about a chest region to measure a trans-thoracic bio-impedance. In some instances, at least one sensor involved in measuring bio-impedance can form part of a pulse generator, whether implantable or external. In some instances, at least one sensor involved in measuring bio-impedance can form part of a stimulation element and/or stimulation circuitry. In some instances, at least one sensor forms part of a lead extending between a pulse generator and a stimulation element. 
     In some examples, impedance sensor  404  may take the form of electrical components not used in a SDB therapy device. For instance, some patients may already have a cardiac therapy device (e.g. pacemaker, defibrillator, etc.) implanted within their bodies, and therefore have some cardiac leads implanted within their body. Accordingly, the cardiac leads may function together or in cooperation with other resistive/electrical elements to provide impedance sensing. 
     In some examples, whether internal and/or external, impedance sensor(s)  404  may be used to sense an electrocardiogram (ECG) signal. 
     As shown in  FIG. 12 , in some examples one sensor modality includes an accelerometer  406 . In some instances, accelerometer  406  is generally incorporated within or associated with device  171 ,  210  or may be incorporated within or form part of a pulse generator (e.g.  200  in  FIG. 6C ). For instance, in some examples of a pulse generator, a housing (e.g. can) contains numerous components such as control circuitry, stimulation, and also may contain an accelerometer  406  within the housing. However, in some examples, the accelerometer  406  may be separate from, and independent of, the pulse generator (e.g.  200  in  FIG. 6C ). In some examples, accelerometer  406  can enable sensing body position, body posture, and/or body activity regarding the patient, which may be indicative of behaviors from which sleep quality information  306  or sleep disordered breathing (SDB) information  308  may be determined. For instance, sleep position (e.g. left side, right side, supine, etc.) may be used to determine the effectiveness of SDB therapy according to sleep position, and in some instances, the SDB therapy may be automatically adjusted based on the orientation (i.e. sleep position) of the patient. In some instances, this information regarding sleep position may be communicated to the patient during a sleep period in order to induce the patient to change their sleep position into one more conducive to efficacious SDB therapy. In some examples, the communication may occur by an audible or vibratory alarm implemented via wireless communication to a patient remote or via direct muscle stimulation via wireless communication to a wearable muscle stimulation device. 
       FIG. 13A  is a diagram  2000  schematically representing some aspects of accelerometer sensing in association with some aspects of sleep quality, according to one example of the present disclosure. As shown in  FIG. 13A , diagram  2000  juxtaposes several different types of information/waveforms, such as a snoring intensity waveform  2010 , a respiratory waveform  2020 , a stimulation profile  2025 , a sleep position profile  2030 , and a sleep apnea index waveform (e.g. AHI)  2040 . In some examples, the sleep apnea index waveform provides at least one measure of sleep quality among several potential measures of sleep quality. 
     In one aspect, the information shown in diagram  2000  corresponds to information obtained via automatic storage of at least minute-by-minute sleep data, therapy data, positional data, etc. 
     In some examples, at least one accelerometer  406  can be used to obtain the snoring intensity waveform  2010 , respiratory waveform  2020 , and/or sleep position profile  2030 . In some examples, other sensing elements are used to obtain such information as described within at least some examples throughout the present disclosure. 
     In some examples, the snoring intensity waveform  2010  includes a first portion  2011  having a first generally constant value and a second portion  2012  having a second value generally higher than the first value. In some examples, the respiratory waveform  2020  includes a first portion (e.g. series of respiratory cycles) of generally normal respiration followed by a second portion  2022  of irregular respiratory cycles  2023 ,  2024 ,  2029 , etc. Accordingly, the increased snoring intensity generally coincides with the second portion  2022  representing irregular breathing. 
     In some examples, stimulation profile  2025  includes a series of stimulation pulses at a particular intensity (e.g. 2.1 V) with some stimulation pulses  2026  being of longer duration and less frequency and some stimulation pulses  2027  of shorter duration and higher frequency. In one aspect, the shorter, more frequent stimulation pulses are applied during the irregular respiratory cycles  2023 ,  2024 ,  2029 . 
     In some examples, sleep position profile  2030  includes a first sleep position  2032  (e.g. left side) and a second sleep position  2034  (e.g. supine). It can be observed that the second sleep position  2034  generally coincides with the elevated snoring intensity  2012  and irregular respiratory cycles  2023 ,  2024 ,  2029 . 
     In some examples, sleep apnea index waveform  2040  includes a first portion  2042  having a generally constant value and a second portion  2044  in which the index (e.g. AHI) increases over time. It can be observed that the supine sleep position  2034  generally coincides with the elevated snoring intensity  2012 , irregular respiratory cycles  2023 ,  2024 ,  2029 , and supine sleep position  2034 . 
     Among other uses, the information in diagram  2000  may be employed by a clinician to adjust stimulation therapy and/or employed by a therapy device (and/or manager) to automatically adjust stimulation therapy to cause a decrease in the moving average of the sleep apnea index (e.g. AHI) represented by waveform  2040 . Moreover, as previously mentioned this information may be used to communicate to the patient via audio or non-audio techniques to change their sleep position to a position (e.g. left side) more amenable to regular respiration (e.g. portion  2021 ). 
     In some examples, some portions schematically represented in  FIGS. 13A-13I  may function as (or correspond to) at least some instances of a user interface for cardiac-related monitoring according to at least some examples of the present disclosure, whether the user interface is a clinician user interface or a patient user interface. It will be understood that in some examples, user interface portions represented in one Figure within  FIGS. 13A-13I  may be combined in a complementary manner with user interface portions in another one of  FIGS. 13A-13I  or with user interface portions among several figures within  FIGS. 13A-13I  and/or  FIGS. 4A-5B . 
       FIG. 13B  is a diagram  2050  schematically representing some aspects of accelerometer sensing in association with some aspects of sleep quality, according to one example of the present disclosure. In general terms, diagram  2050  maps several waveforms over an entire night of sleep. Among other things, relative to a general timeline  2055 ,  FIG. 13B  provides a juxtaposition of a sleep apnea index waveform  2060 , a stimulation profile  2070 , and a sleep position profile  2080 . As can be seen via  FIG. 13B , in some examples a supine sleep position ( 2082 ) results in increases in amplitude ( 2062 ) of the apnea index (e.g. AHI), and which is generally matched via a therapy device with an increase in the intensity (e.g. amplitude) of stimulation ( 2072 ). However, upon the patient switching to a side sleep position (e.g. left side)  2084 , the apnea index decreases ( 2064 ), thereby resulting in the therapy device reducing stimulation intensity ( 2074 ). It will be understood that in some examples stimulation intensity may be adjusted via other parameters, such as pulse width, frequency, etc. in combination with or separate from amplitude adjustments. 
     In some examples, accelerometer  406  enables acoustic detection of cardiac information  304 , such as heart rate and/or electrocardiogram (ECG) waveforms, including QRS complexes. In some examples, measuring the heart rate includes sensing heart rate variability. In some examples, accelerometer  406  can sense respiratory information, such as but not limited to, a respiratory rate. In some examples, whether sensed via an accelerometer  406  alone or in conjunction with other sensors, one can monitor cardiac information  304  and respiratory information  302  simultaneously by exploiting the behavior of ECG signal in which an ECG waveform can vary with respiration. 
       FIG. 13C  is a diagram  2200  schematically representing some aspects of acoustic sensing of cardiac information and respiratory information, according to one example of the present disclosure. In some examples, the acoustic sensing demonstrated in  FIG. 13C  is performed via accelerometer  406 . Among other things, the accelerometer  406  can enable various forms of cardiac timing measurements, such as but not limited to, heart rate detection, QT timing detection, etc. This cardiac timing, in turn, enables heart rate variability measurements. 
     As shown in diagram  2200 , accelerometer  406  produces a raw output waveform  2210 , which is split ( 2212 ) via filtering with a high pass filter  2220  to produce a phonocardiogram waveform  2222  and via filtering with a low pass filter  2230  to produce a respiratory waveform  2232 . Among other features, the phonocardiogram waveform  2222  includes an S1 component, which correlates with a QRS complex in an ECG waveform, and a S2 component, which correlates with a T-wave component in an ECG waveform  2224 . Accordingly, via this arrangement the accelerometer  406  may sense both cardiac motion and respiratory motion, which may be differentiated and identified via application of the respective different frequency filters  2220  and  2230 . In one aspect, as shown in  FIG. 13D , a Wiggers diagram  2250  illustrates (among other things), portions of the phonocardiogram which coincide with or correspond with portions of an electrocardiogram (ECG). In one aspect, this Wiggers Diagram may be obtained at https://commons.wikimedia.org/wiki/File:Wiggers Diagram.svg#filelinks. 
     In some examples, accelerometer  406  enables detection of sleep/awake via the sensing of motion, position, posture and/or activity of the patient, along with other parameters determinable via the accelerometer  406 . In some instance, this information may be used to implement automatic control of SDB therapy to enhance therapeutic efficacy. 
     In some examples, the accelerometer  406  comprises an external sensor  374 . In some instances, when embodied as an external sensor, the accelerometer  406  may comprise a wearable sensor, such as an accelerometer incorporated into a band or belt worn about a portion of the body (e.g. wrist, chest, arm, leg, torso, etc.). 
     In some examples, the accelerometer  406  may be used to detect sleep disordered breathing events during the sleep period and may be used continuously to detect arrhythmias. 
     In some examples, radiofrequency sensor  408  shown in  FIG. 12  enables non-contact sensing of various physiologic parameters and information, such as but not limited to cardiac information  304 , respiratory information  302 , motion/activity, and/or sleep quality, such as previously described regarding non-contact sensor  384  in association with at least  FIG. 11 . In some examples, radiofrequency sensor  408  enables non-contact sensing of other physiologic information. 
     Accordingly,  FIG. 13E  is a diagram  2400  schematically representing RF-based non-contact sensing of respiratory information, according to one example of the present disclosure. As shown in diagram  2400  of  FIG. 13E , a sensing arrangement  2410  includes a radio-frequency (RF) sensor  2412  which determines chest motion based on Doppler principles  2420  via signals sent and received by sensor  2412  relative to the chest of the patient  2414 . The sensor  2412  can be located anywhere within the vicinity of the patient  2414 , such as various locations within the room (e.g. bedroom) in which the patient is sleeping. In some examples, the sensor  2412  is coupled to a non-dedicated mobile device  132  (e.g. mobile phone in one example) or other access tool in array  130  ( FIG. 3B ) to enable data transmission relative to other components of a therapy device and storage in such other components. In some examples, sensing arrangement  2410  comprises at least some of substantially the same features and attributes as non-contact sensor  384 , as previously described in association with  FIG. 11 . 
     In some examples, one sensor modality may comprise an optical sensor  410  as shown in  FIG. 12 . In some instances, optical sensor  410  may be an implantable sensor  372  and/or external sensor  374  ( FIG. 11 ). For instance, one implementation of an optical sensor  410  comprises an external optical sensor for sensing heart rate and/or oxygen saturation via pulse oximetry. In some instances, the optical sensor  410  enables measuring oxygen desaturation index (ODI). In some examples, the optical sensor  410  comprises an external sensor removably couplable on the finger of the patient. 
     In some examples, optical sensor  410  can be used to measure ambient light in the patient&#39;s sleep environment, thereby enabling an evaluation of the effectiveness of the patient&#39;s sleep hygiene and/or sleeping patterns. 
     As shown in  FIG. 12 , in some examples one sensor modality comprises EMG sensor  412 , which records and evaluates electrical activity produced by muscles, whether the muscles are activated electrically or neurologically. In some instances, the EMG sensor  412  is used to sense respiratory information  302 , such as but not limited to, respiratory rate, apnea events, hypopnea events, whether the apnea is obstructive or central in origin, etc. For instance, central apneas may show no respiratory EMG effort. 
     In some instances, the EMG sensor  412  may comprise a surface EMG sensor while, in some instances, the EMG sensor  412  may comprise an intramuscular sensor. In some instances, at least a portion of the EMG sensor  412  is implantable within the patient&#39;s body and therefore remains available for performing electromyography on a long term basis. 
     In some examples, one sensor modality may comprise ECG sensor  414  which produces an electrocardiogram (ECG) signal. In some instances, the ECG sensor  414  comprises a plurality of electrodes distributable about a chest region of the patient and from which the ECG signal is obtainable. In some instances, a dedicated ECG sensor(s)  414  is not employed, but other sensors such as an array of bio-impedance sensors  404  are employed to obtain an ECG signal. In some instances, a dedicated ECG sensor(s) is not employed but ECG information is derived from a respiratory waveform, which may be obtained via any one or several of the sensor modalities in sensor type  400  in  FIG. 12 . In some examples, ECG sensor  414  is embodied as an accelerometer  406  as previously described in association with  FIG. 12  and/or in association with at least  FIG. 13A-13D . 
     In some examples, an ECG signal obtained via ECG sensor  414  may be combined with respiratory sensing (via pressure sensor  402  or impedance sensor  404 ) to determine minute ventilation, as well as a rate and phase of respiration. 
     In some examples, the ECG signal obtained via ECG sensor  414  may be combined with cardiac output sensing (via pressure sensor  402  or impedance sensor  404 ). In one aspect, the cardiac output is the product of heart rate times stroke volume. In one aspect, a higher pressure of left ventricle (LV) contractility (as represented by dP/dt) may enable inferring higher cardiac output, and therefore the left ventricle (LV) contractility may provide a relative measure of cardiac stroke volume. In some examples, this arrangement may be implemented via placing the ECG sensor  414  in the aorta or in the left ventricle. In some examples, the cardiac output sensing enables determining arterial pulse pressure (difference between systolic and diastolic pressure readings) because the stroke volume may be proportional to the arterial pulse pressure. 
     In some examples, the ECG sensor  414  may be exploited to obtain respiratory information (e.g. at least  302  in  FIG. 9 ).  FIG. 13F  is a diagram schematically representing derivation of respiratory information from a cardiac waveform, according to one example of the present disclosure. In one aspect, diagram  2300  in  FIG. 13F  provides a juxtaposition of cardiac timing information and respiratory information. As shown in  FIG. 13F , diagram  2300  includes a raw electrocardiogram waveform  2310 , which is filtered via a high pass filter  2220  to obtain a conditioned electrocardiogram waveform  2324  and filtered via a low pass filter  2230  to obtain a respiratory waveform  2332 . Accordingly, both respiratory information  302  and cardiac information  304  ( FIG. 9 ) can be obtained via an ECG sensor  414 . In some examples, as noted elsewhere, ECG sensor  414  may be implemented, at least in part, as an accelerometer  406  ( FIG. 12 ). 
       FIG. 13G  is a diagram  2350 , according to one example of the present disclosure, further illustrating aspects of a respiratory waveform derived from an ECG waveform (e.g. ECG sensor  414 ), such as described in association with  FIG. 13F . Accordingly, diagram  2350  juxtaposes a normal ECG  2324  and respiratory waveform  2332  as in  FIG. 13F , except further juxtaposing a RR interval profile  2360  with the other waveforms  2324 ,  2332 . In one aspect, diagram  2350  demonstrates how aspects of cardiac timing, such as R-R intervals and/or P-R intervals, vary with respiration. For instance, one can observe how the R-R interval waveform  2360  increases and decreases in a pattern which generally corresponds to inspiration and exhalation, respectively. Among other uses, this information may enable identifying correlations, relationships, and/or associations between cardiac disorder parameters, cardiac health parameters, sleep parameters, and/or respiratory parameters. 
     As shown in  FIG. 12 , in some examples one sensor modality includes an ultrasonic sensor  416 . In some instances, ultrasonic sensor  416  is locatable in close proximity to an opening (e.g. nose, mouth) of the patient&#39;s upper airway and via ultrasonic signal detection and processing, may sense exhaled air to enable determining at least respiratory information  302 , sleep quality information  306 , sleep disordered breathing information  308 , and/or other information  310 . In some instances, ultrasonic sensor  416  may comprise at least some of substantially the same features and attributes as described in association with at least Arlotto et al. PCT Published Patent Application 2015-014915 published on Feb. 5, 2015. 
     In some examples, an acoustic sensor  418  shown in  FIG. 12  may be employed to sense respiratory information  302  (e.g. breathing rate, respiratory waveform, etc.), cardiac information  304  (e.g. heart rate, cardiac waveform, etc.), sleep quality information  306 , sleep disordered breathing (SDB) information  308 , and/or other information  310 , as shown in  FIG. 9 . In some examples, an acoustic sensor  418  can implement sonar detection schemes via mobile device  131 ,  132  ( FIG. 3B ) to obtain at least respiratory information  302 . For instance, the acoustic sensor  418  be part of and/or cooperate with a smartphone running an application (i.e. mobile app) designed to monitor apnea events thru sonar, such as described in “Contactless Sleep Apnea Detection on Smartphones” reported by the University of Washington in May 2015 at The 13th International Conference on Mobile Systems, Applications, and Services in Florence, Italy. 
     In some examples, other sensor  420  comprises any other type of sensor or sensor modality useful for sensing and monitoring respiratory information  302 , cardiac information  304 , sleep quality information  306 , sleep disordered breathing information  308 , and/or other information  310  ( FIG. 9 ). For instance, in some examples an “other” sensor  420  may comprise a temperature sensor for sensing the ambient temperature in the patient&#39;s sleep environment and/or a temperature of the patient before, during, and after sleep, as such temperatures may affect sleep quality or may reflect information about a respiratory condition, cardiac condition, or sleep disordered breathing. 
       FIG. 13H  is a diagram  2450  schematically representing a juxtaposition of respiratory information, cardiac information, and sleep information, according to one example of the present disclosure. In general terms, diagram  2450  can facilitate identifying a period of atrial arrhythmia potentially due to apnea based on factors, such as observation of an elevated heart rate, the atrial rate being greater than the ventricular rate, etc., both of which generally coincide with an apneic period. 
     In some examples, diagram  2450  is displayable as part of a clinician user interface, such as interface  1000  ( FIG. 4A ). In some examples, diagram  2450  includes a respiratory waveform  2020  and stimulation profile  2025  like that in  FIG. 13A , as well as a heart rate profile  2460 , a V-A association waveform  2470 , and a sleep position profile  2030  like that in  FIG. 13A . As shown in  FIGS. 13A and 13H , the heart rate profile  2460  includes a first portion  2462  and a second portion  2463 . The first portion  2462  represents a baseline heart rate while the second portion  2463  represents heart rate variability. In the example shown in  FIG. 13A , the second portion  2463  includes peaks  2464 ,  2466  (e.g. elevated heart rate) and valley  2467 . 
     As further shown in  FIG. 13H , the V-A association waveform  2470  includes a first baseline portion  2472  and a second portion  2473  exhibiting variability occurring in synch with the respiratory irregularity (i.e. irregular breathing)  2022 . Meanwhile, the sleep position profile  2030  indicates that the respiratory irregularities  2023 ,  2024 ,  2029 , elevated heart rate  2464 ,  2466 , and increased values of the V-A association  2474 ,  2476  correspond to a supine sleep position  2034 . 
     In one aspect, the V-A association waveform represents a ratio between the ventricular and atrial rate. This ratio is normally 1:1, and any deviation of 1:n (n&gt;1) indicates an atrial arrhythmia, or n:1 (n&gt;1) indicating a ventricular arrhythmia. 
     As shown in  FIG. 13H , a strong correlation is present between peaks of the V-A association  2474 ,  2476 , an elevated heart rate  2464 ,  2466 , and irregular respiratory cycles (e.g.  2023 ,  2029 ). 
       FIG. 13I  is a diagram  2500  schematically representing an overnight patient report  2510  including at least cardiac information, respiratory information, and sleep information, according to one example of the present disclosure. As shown in diagram  2500  of  FIG. 13I , in some examples the overnight patient report  2510  includes cardiac parameter portion  2520 , respiratory parameter portion  2530 , and Upper Airway Stimulation therapy parameter portion  2560 . 
     In some examples, the cardiac parameter portion  2520  displays information regarding an average heart rate and any arrhythmias, such as a potential instance of atrial fibrillation (AF)  2522  during an apnea episode at a particular time. In some examples, the respiratory parameter portion  2530  monitors values of various measured respiratory parameters, such as but not limited to, respiratory rate, apnea index (e.g. AHI) in supine and non-supine positions, sleeping position durations, and oxygen saturation. 
     In some examples, therapy parameter portion  2560  includes a total duration of therapy for that night and an average amplitude of stimulation. 
     In some examples, diagram  2500  is displayable and interactively engageable as a user interface (e.g.  140  in  FIG. 3C ). For instance, in some examples certain parameters, such as sleep position (within respiratory parameter portion  2530 ) are implemented at hot links, such that engagement of the link causes a graph of a stored signal (e.g. sleep position profile  2030  in  FIG. 13H ) to appear on the display exhibiting the diagram  2500 . In some examples, a stimulation profile  2025  ( FIG. 13H ) is displayable in diagram  2500  upon “clicking” on the average amplitude parameter. 
     In some examples, diagram  2500  can be displayed and engaged as part of a clinician user interface  1000  ( FIG. 4A ) while in some examples, diagram  2500  can be displayed and engaged as part of a patient user interface ( FIGS. 5A-5B ). Moreover, as noted elsewhere, portions of diagram  2500  as a user interface can be combined in various combinations with user interface portions represented in at least  FIGS. 4A-5B  and/or  FIGS. 13A-13H . 
       FIG. 14A  is a block diagram schematically representing a sensor modality array  440 , according to one example of the present disclosure. In some examples, sensor modality array  440  provides additional modes of sensing in addition to those described in association with at least  FIGS. 11-13I . In some instances, the modalities  442 ,  444 ,  446  complement and/or implement at least one of the types of sensors described in association with at least  FIGS. 11-13I . 
     Via the different sensor modalities  442 ,  444 ,  446 , at least cardiac information and/or respiratory information may be determined. 
     In some examples, one sensor modality  440  comprises a ballistocardiogram sensor  442  to determine at least cardiac-related information. In some instances, the ballistocardiogram sensor  442  may be implemented via at least accelerometer sensor  406 , acoustic sensor  418 , and/or radiofrequency sensor  408  in  FIG. 12 . In at least some instances, a ballistocardiogram may be understood as the measurement of the recoil forces of the body in reaction to cardiac ejection of blood into the vasculature. 
     In some examples, one sensor modality  440  comprises a seismocardiogram sensor  444  to determine at least cardiac-related information. In some instances, the seismocardiogram sensor  444  may be implemented via at least accelerometer sensor  406  acting in at least a vibratory/motion detecting mode. In some instances, the seismocardiogram sensor  444  may be implemented via a radiofrequency sensor  408 . In at least some instances, a seismocardiogram may be understood as representing the local vibrations of the chest wall in response to the heartbeat. 
     In some examples, one sensor modality  440  comprises a phonocardiogram sensor  446 . In some examples, a phonocardiogram sensor  446  may be implemented in a manner substantially similar as described in association with at least  FIGS. 13C-13D . 
       FIG. 14B  is a block diagram schematically representing a sensor profile manager  450 , according to one example of the present disclosure. In some examples, sensor profile manager  450  forms part of and/or cooperates with therapy device and/or monitoring resource ( 110 ,  60  in  FIG. 1 ). As shown in  FIG. 14B , sensor profile manager  450  includes a first sensor profile function  452  and second sensor profile function  454 . In some examples, the first sensor profile function  452  includes and/or monitors those sensors already associated with a therapy device and/or monitoring resource. Meanwhile, in some examples, the second sensor profile function  454  acts to receive sensor information from at least one commercially available sensor device or sensor array. The second sensor profile function  454  enables at least some of the sensors of the commercially available sensor device/array to supplement and/or replace sensors associated with the first sensor profile function  452 . 
     In some examples, the second sensor profile function  454  includes an array of pre-programmed sensor profiles. In some example, each array of pre-programmed sensor profile corresponds to a different commercially available sensor device/array. For instance, one array can correspond to one wearable sensor array (e.g.  380  in  FIG. 11 ) having at least some of substantially the same features and attributes as a wearable sensor array available under the trademark FitBit®. For instance, one array can correspond to one external sensor array (e.g.  374 ,  382  in  FIG. 11 ) having at least some of substantially the same features and attributes as a sensor array available under the trademark Beddit®. In some examples, such commercially available sensor device/arrays may correspond to and/or include some features corresponding to one of the access tools  131 - 135  ( FIG. 3B ), such as non-dedicated mobile device  132 . 
     In some examples, such commercially available sensor device/arrays can communicate securely with a therapy device (e.g.  70  in  FIG. 1 ) and/or monitoring resource (e.g.  60  in  FIGS. 1, 3A ) to ensure reliable, safe operation of the therapy device and/or monitoring resource. In some examples, such secure communication is enabled and facilitated via one of the access tools  131 - 135 , which establishes the secure communication channel. For instance, the commercially available sensor device/array may communicate directly with such “secure communication” device to establish a communication pathway between the commercially available sensor device/array and a therapy (e.g.  70  in  FIG. 1 ) and/or monitoring resource (e.g.  60  in  FIGS. 1, 3A ). In some examples, the sensor profile manager  450  enables a therapy device and/or monitor to automatically recognize and implement a commercially available sensor device/array upon establishing a secure communication channel therebetween. 
     In some examples, the second sensor profile function  454  enables the therapy device and/or monitoring resource to seamlessly integrate and/or leverage the commercially available sensor device/arrays with the sensors associated with the first sensor profile function  452 . The sensors associated with the first sensor profile function  452  may be on board sensors (e.g. accelerometer  406  on/in pulse generator (IPG)), implantable sensors, or external sensors in the manner described in association with at least  FIGS. 9-12 . 
     In some examples, second sensor profile function  454  is configured to integrate the use of sensors in access tools  131 - 135  ( FIG. 3B ) separately from a commercially available sensor device/array or in complementary association with a commercially available sensor device/array. In some examples, one such access tool in array  130  ( FIG. 3B ) comprises a non-dedicated mobile device  132 , such as a smart phone, tablet, phablet, etc. 
     In some examples, second sensor profile function  454  includes a custom parameter  450  by which a custom sensor profile function can be built to receive sensor information from a customized sensor device/array. 
     In some examples, the sensor profile manager  450  can be updated to include changes to a sensor(s) in the first sensor profile function  452  and/or second sensor profile function  454 . For instance, a sensor profile associated with a new commercially available sensor device/array can be uploaded to become part of the second sensor profile function  454 . 
       FIG. 15A  is a block diagram schematically representing cardiac condition array  500 , according to one example of the present disclosure. As shown in  FIG. 15A , in some examples, cardiac condition array  500  comprises premature beats condition  502 , supraventricular condition  504 , ventricular condition  506 , bradyarrhythmia condition  508 , chronotropic incompetence  509 , hypertension  510 , heart failure  511 , and/or other condition  512 . Meanwhile, combination condition  514  comprises a combination of at least two of the conditions  502 - 512  of array  500 . 
     In some examples, any one of the conditions in array  500  may be sensed and/or monitored as cardiac information  304  ( FIG. 9 ), via sensor  370  (FIG.  11 ), via one of the sensor modalities represented in the sensor type array  400  ( FIG. 12 ), and/or other mechanisms available to a clinician. 
     In some examples, the supraventricular condition  504  includes, but is not limited to, atrial fibrillation, atrial flutter, and/or paroxysmal supraventricular tachycardia. In one sense, atrial fibrillation is associated with rapid, irregular, and/or unsynchronized contraction of the muscle fibers of the atrium of the patient&#39;s heart. In one sense, atrial fibrillation is identifiable by disorganized electrical impulses (sometimes originating in the roots of the pulmonary veins) overcoming the normal electrical pulses coming from the sinoatrial node. This phenomenon may lead to irregular conduction of impulses from the atria to the ventricles, such that the contraction and relaxation of the atria are out of synch with the ventricles of the heart. 
     In some examples, atrial fibrillation is recognizable via observing a standard deviation of Atrial-Atrial timings. In a normally functioning heart, the Atrial-to-Atrial timings are very tightly coupled. However, if one observes a large spread in Atrial to Atrial timings, this pattern may indicate atrial fibrillation. In one context, such as viewing a cardiac waveform (e.g. ECG), atrial fibrillation is associated with a large number of small P waves for a single QRS complex, such that the cardiac waveform exhibits a near absence of distinct P waves in the cardiac waveform. 
     In some examples, one can use the cardiac information  304  to observe a Ventricular-to-Atrial Beat Ratio, which is 1:1 in a normally function heart. However, if the V-A Beat Ratio is 1:n, wherein n&gt;1 for a consistent period of time, then these values likely indicate atrial fibrillation. 
     In some examples, the ventricular condition  506  includes ventricular arrhythmias such as, but is not limited to, ventricular fibrillation and/or ventricular tachycardia. In some examples, if the above-referenced V-A Beat Ratio is 1:n, wherein n&lt;1, this Beat Ratio may indicate a ventricular arrhythmia. In some examples, ventricular arrhythmia may be identified via a high ventricular rate. 
     In at least some examples, the bradyarrhythmia condition  508  includes, but is not limited to, the heart rate being abnormally slow. In some examples, the threshold for bradyarrhythmia is defined as a heart rate of 60 beats per minute or less. The bradyarrhythmia condition  508  may be caused by conditions such as sinusbradycardia, sinoatrial block and/or atrioventricular block. 
     In at least some examples, chronotropic incompetence condition  509  corresponds to the inability of the heart to increase its rare commensurate with increased activity or demand, such as a steady or falling heart rate coincident with an elevated or increasing respiratory rate. 
     In some examples, other condition  152  includes other cardiac conditions, which may or may not be formally recognized as negative cardiac conditions or cardiac disorders but for which treatment may be desirable. 
     In some examples, combination condition  514  represents the existence of and/or combined effect of multiple cardiac conditions. 
       FIG. 15B  is a block diagram schematically representing a condition determination portion  530 , according to one example of the present disclosure. The condition determination portion  530  includes a heart rate parameter  532 , a cardiac timing parameter  534 , other parameter  536 , and a combination parameter  538 . In some instances, as noted in association with at least  FIG. 15A , the behavior of heart rate alone may be indicative of some cardiac conditions while in some instances, heart rate information paired with other respiratory, cardiac, sleep information may be indicative of some cardiac conditions. Similarly, as noted in association with at least  FIG. 15A , cardiac timing alone may be indicative of some cardiac conditions while in some instances, cardiac timing information paired with other respiratory, cardiac, sleep information may be indicative of some cardiac conditions. 
     It will be understood that in some examples, cardiac timing refers to observing a pattern of behavior of the operation of different portions of the heart or of behavioral aspects of the heart as the heart attempts to repeat the cardiac cycle. For instance, observing atrial-atrial timing is one form of cardiac timing that may be indicative of atrial fibrillation. Similarly, in one instance, observing a ventricular-to-atrial beat ratio is one form of cardiac timing which may be indicative of atrial fibrillation or ventricular arrhythmia, depending on the value of the ratio. In some examples, such relationships are identifiable and displayable via various tables, graphs, and/or user interfaces, as illustrated in at least some of  FIGS. 4A-5B  and  FIGS. 13A-13I . 
       FIG. 15C  is a block diagram schematically representing a determination engine  570 , according to one example of the present disclosure. In some examples, determination engine  570  comprises at least some of substantially the same features and attributes as monitoring resource  60  as previously described in association with at least  FIG. 3A . In some examples, at least some aspects of determination engine  570  may form part of monitoring resource  60  ( FIG. 3A ). In some examples, the determination engine  570  includes cardiac condition parameter  572 , notification function  574 , notification criteria  576 , variances parameter  578 , threshold parameter  580 , responsive parameter  582 , and non-responsive parameter  584 . 
     In some examples, the determination engine  570  determines a wide variety of physiologic information regarding the patient. In some examples, this determined information may include cardiac information such as positive cardiac conditions (e.g. cardiac health conditions) and/or negative cardiac conditions (e.g. cardiac disorders), either of which are represented by cardiac condition parameter  572  in  FIG. 15C . In some examples, this determined information may also include sleep quality information and/or sleep disordered breathing-related information. 
     In some examples, the determination engine  570  is dedicated to determining cardiac information such as positive and/or cardiac conditions. Meanwhile, in some examples, the determination engine is dedicated to tracking solely negative cardiac conditions. 
     In some examples, a cardiac disorder parameter represents a plurality of cardiac disorders and determination engine  570  (of the monitoring resource  60 ) may differentiate between a first class of the respective cardiac disorders and a second class of the respective cardiac disorders. The first class of respective cardiac disorders may correspond to a negative cardiac condition present before and after obstructive sleep apnea treatment via the system through the monitoring period. The second class of respective cardiac disorders corresponds to a presence of a negative cardiac condition present before obstructive sleep apnea treatment via the system through the monitoring period and a substantial decrease (e.g. diminishing, subsiding) in the negative cardiac condition after obstructive sleep apnea treatment via the system through the monitoring period. 
     Because at least some cardiac conditions are determined based on more fundamental physiologic information, such as heart rate (e.g.  532  in  FIG. 15B ) or cardiac timing (e.g.  534  in  FIG. 15B ), the information determined via determination engine  570  includes the heart rate parameter  532 , cardiac timing parameter  534 , and/or other physiologic information parameter  536  as shown in  FIG. 15B . 
     The notification function  574  can deliver a notification taking the form of an notification to the user or clinician, which is communicated via text (e.g. SMS), email, audible notification, pop-up window, etc., in some form of user interface (e.g. user interface  140  in  FIG. 3C ) accessible by the patient and/or clinician. In some instances, such user interface may be accessed via one of the access tools  131 - 135  previously described in association with at least  FIG. 3B , and which may include a patient programmer, clinician programmer, computer, tablet, smart phone, phablet, etc. Such devices may or may not be dedicated for use with the determination engine  570 , monitoring system and/or therapy system associated with the patient. 
     In some examples, the notification criteria  576  provides a criteria to be met before the determination engine  570  implements a notification via notification function  574 . In some examples, the notification criteria  576  is selectively adjustable by the clinician as to what conditions or information is used and/or as to which values (e.g. quantity, amplitude, frequency, duration, etc.) of a particular parameter are used to form the notification criteria  576 . In some examples, the notification criteria  576  corresponds to at least some of the aspects of a diagnosis criteria used to diagnose a particular cardiac condition. In some examples, the notification criteria  576  is separate from, and independent of, such diagnosis criteria. 
     In some examples, notification function  574  acts to implement a notification to a patient or clinician regarding the identification of a cardiac condition. In some examples, the notification function  574  is limited to providing notifications upon the notification criteria  576  being met. 
     In some examples, variances function  578  determines the extent to which a particular parameter exhibits variances from expected behaviors or patterns. For instance, when determining heart rate parameter  532  ( FIG. 15B ), determining variances or variability in the heart rate may indicate a negative cardiac condition while a stable heart rate may indicate a positive cardiac condition or successful treatment of a negative cardiac condition or successful treatment of a sleep disordered breathing condition. 
     In some examples, threshold function  580  is used to set a threshold at which sensed physiologic information is deemed to correspond to a particular cardiac condition. However, in some examples, several types of physiologic information are involved in determining a cardiac condition, such that meeting a threshold for just one sensed physiologic information may not result in determination of a cardiac condition. 
     In some examples, the notification threshold may be automatically determined from baseline data, which is developed upon determining the threshold at which a physician typically takes action or responds to the notification. In some examples, the notification threshold is selected by the clinician in advance. 
     In some examples, the determination engine  570  includes a responsive parameter  582  to facilitate determination of any cardiac conditions which may be responsive (negatively or positively) to treatment of sleep disordered breathing during or after the monitoring period (e.g.  124  in  FIG. 3A ). In some instances, the responsive parameter  582  may facilitate a clinician in determining which, if any, cardiac conditions were alleviated as a beneficial consequence of treatment of sleep disordered breathing such that one may forego direct treatment of the cardiac condition. 
     In some examples, the determination engine  570  includes a non-responsive parameter  584  to facilitate determination of any cardiac condition which may be non-responsive to treatment of sleep disordered breathing during or after the monitoring period (e.g.  124  in  FIG. 3A ). In some instances, the non-responsive parameter  584  may facilitate a clinician in determining which, if any, cardiac conditions may be alleviated via treatment directly regarding the cardiac condition because the particular cardiac condition was not alleviated as a consequence of treatment of sleep disordered breathing. In this way, treatment of sleep disordered breathing along with the coincident monitoring of cardiac conditions may help eliminate variables in determining which therapies to which the cardiac condition may be responsive. 
       FIG. 16A  is a block diagram schematically representing a therapy device  650 , according to one example of the present disclosure. In some examples, therapy device  650  includes at least some of substantially the same features and attributes as system  50  in  FIG. 1  and the various examples as previously described in association with  FIGS. 1-15C . 
     As shown in  FIG. 16A , therapy device  650  includes non-cardiac pulse generator  652 , sensor  654 , stimulation element  660 , and a monitoring resource  664  to determine cardiac-related information  667  according to a monitoring period  668 . In some examples, the cardiac-related information  667  may comprise at least the cardiac condition information  500  in  FIG. 15A . 
     In some examples, non-cardiac pulse generator  652  comprises at least some of substantially the same features as the pulse generators previously described in association with at least  FIGS. 6A, 6C, 7 . In some examples, sensor  654  comprises at least some of substantially the same features as the sensor(s)  370 ,  400  as previously described in association with at least  FIGS. 11-12 . 
     In some examples, stimulation element  660  of device  650  comprises at least some of substantially the same features as the stimulation element(s) as previously described in association with at least  FIGS. 6A and 7  such that stimulation element is operated according to a treatment period  662 . In some examples, stimulation element  660  is operated to stimulate upper-airway-related body tissue (e.g.  180  in  FIG. 6B ) to restore upper airway patency. 
     In some examples, device  650  monitors a cardiac condition  664  according to a monitoring period  668 , in a manner at least consistent with the monitoring of cardiac conditions, as previously described in association with at least  FIGS. 1-15C . 
     In some examples, device  650  may further include a therapy manager (e.g.  110  in  FIG. 3A ) and/or control portion  880  having manager  885  ( FIG. 23 ). In such examples, the manager and/or control portion may coordinate stimulation via the stimulation element  660  according to the treatment period  662  (also  112  in  FIG. 3A ) and/or may monitor cardiac conditions  670  according to the monitoring period  668  (also  124  in  FIG. 3A ). 
       FIG. 16B  is a diagram schematically representing a stimulation system  670 , according to an example of the present disclosure. As illustrated in  FIG. 16B , in some examples system  670  comprises an implantable pulse generator (IPG)  675 , capable of being surgically positioned within a pectoral region of a patient  671 , and a stimulation lead  674  electrically coupled with the IPG  675 . In some examples, pulse generator  675  comprises at least some of substantially the same features and attributes as the pulse generator  200 , as previously described in association with at least  FIG. 6A, 6C, 7  and the various examples described throughout the present disclosure. 
     The lead  672  includes a stimulation element  676  (e.g. electrode portion, such a cuff electrode) and extends from the IPG  675  so that the stimulation element  690  is positioned in contact with a desired nerve  673  to stimulate nerve  673  for restoring upper airway patency. In some examples, the desired nerve comprises a hypoglossal nerve. In some examples, stimulation element  676  comprises at least some of substantially the same features and attributes as the stimulation element  174 ,  216 , as previously described in association with at least  FIGS. 6A and 7 , and the various examples described throughout the present disclosure. In some instances, a body of the stimulation lead  674  may sometimes be referred to as being interposed between, and extending between the IPG  675  and the stimulation element  676 . 
     One implantable stimulation system in which lead  672  may be utilized, for example, is described in U.S. Pat. No. 6,572,543 to Christopherson et al., and which is incorporated herein by reference in its entirety. In one example, device  670  comprises includes at least one sensor portion  680  (electrically coupled to the IPG  675  and extending from the IPG  675  via lead  677 ) positioned in the patient  671  for sensing respiratory effort, such as respiratory pressure. 
     In some examples, the sensor portion  680  detects respiratory effort including respiratory patterns (e.g., inspiration, expiration, respiratory pause, etc.). In some examples, this respiratory information is employed to trigger activation of stimulation element  676  to stimulate a target nerve  673 . Accordingly, in some examples, the IPG  675  receives sensor waveforms (e.g. one form of respiratory information) from the respiratory sensor portion  680 , thereby enabling the IPG  675  to deliver electrical stimulation according to a therapeutic treatment regimen in accordance with examples of the present disclosure. In some examples, the respiratory information is used to apply the stimulation synchronously with inspiration or synchronized relative to another aspect of the respiratory cycle. In some examples, this arrangement may sometimes be referred to as closed-loop stimulation. In some examples, the respiratory sensor portion  680  is powered by the IPG  675 . 
     In some examples, stimulation may be applied without synchronization relative to a portion of the respiratory cycle, and therefore may sometimes be referred to as open-loop stimulation or therapy. 
     In some examples, sensor portion  680  comprises at least some of substantially the same features and attributes as sensor(s)  370  and  400 , as previously described in association with at least  FIGS. 11-12  and the various examples described throughout the present disclosure. 
     Accordingly, in some examples, the sensor portion  680  comprises a pressure sensor, such as pressure sensor  402  ( FIG. 12 ). In one example, the pressure sensor in this example detects pressure in the thorax of the patient. In other examples, the sensed pressure can be a combination of thoracic pressure and cardiac pressure (e.g., blood flow). With this configuration, a controller associated with IPG  675  is configured to analyze this pressure sensing information to detect the respiratory patterns of the patient. 
     In some other examples, the respiratory sensor portion comprises a bio-impedance sensor or an array of bio-impedance sensors and can be located in regions other than the pectoral region. In one aspect, such an impedance sensor is configured to sense a bio-impedance signal or pattern whereby the control unit evaluates respiratory patterns within the bio-impedance signal. For bio-impedance sensing, in one example, electric current will be injected through an electrode portion within the body and an electrically conductive portion of a housing (i.e. case, can, etc.) of the IPG  675  with the voltage being sensed between two spaced apart stimulation electrode portions (such as stimulation element  676 ), or also between one of the stimulation electrode portions and the electrically conductive portion of the case of IPG  675  to compute the impedance. 
     In some examples, system  670  comprises other sensors (instead of sensor portion  680 ) or additional sensors (in addition to sensor portion  680 ) to obtain physiologic data associated with respiratory functions. For instance, as shown in  FIG. 16B , in some examples system  670  may include various electrode portions  682 ,  683 ,  684  distributed about the chest area for measuring a trans-thoracic bio-impedance signal, an electrocardiogram (ECG) signal, or other respiratory-associated signals, other cardiac signals, etc. 
     In some examples, the various electrode portions  682 ,  683 ,  684  or even a single lead is used to measure trans-thoracic electrical bio-impedance in combination with obtaining a far field ECG to filter/blank cardiac artifacts from the bio-impedance signal. In some examples, the trans-thoracic bio-impedance signal may be used to determine cardiac output and respiratory output (e.g. minute ventilation). For instance, the thoracic bio-impedance may provide a relative measure of respiratory output and stroke volume, and thereby provide a custom ventilation parameter, which in turn may be used in a self-developing correlation vector (as later described in association with at least  FIG. 22 ) to monitor changes over time (such as during the monitoring period  124  in  FIG. 3A ). 
     In some examples, the sensing and stimulation system for treating sleep disordered breathing (such as but not limited to obstructive sleep apnea) is a totally implantable system which provides therapeutic solutions for patients diagnosed with obstructive sleep apnea. In other examples, one or more components of the system are not implanted in a body of the patient, as was previously noted for the examples of external components  204  of non-cardiac pulse generator  200  in association with  FIG. 6C . A few non-limiting examples of such non-implanted components include external sensors (respiration, impedance, etc.), an external processing unit, or an external power source. Of course, it is further understood that, in some examples, the implanted portion(s) of the system provides a communication pathway to enable transmission of data and/or controls signals both to and from the implanted portions of the system relative to the external portions of the system. The communication pathway includes a radiofrequency (RF) telemetry link or other wireless communication protocols. 
     Whether partially implantable or totally implantable, the system is designed to stimulate an upper-airway-patency-related nerve during some portion of the repeating respiratory cycle to thereby prevent obstructions or occlusions in the upper airway during sleep. 
     In some examples, among other potential functions, the pulse generator  675  includes a sensing engine  690 , stimulation engine  692 , and a therapy manager  694  and control portion  696 , as shown in  FIG. 16C . In some examples, the control portion  696  comprises at least some of substantially the same features and attributes as control portion  880  as described in association with  FIG. 23 . 
     In some examples, the pulse generator  675  includes a monitoring resource having at least some of substantially the same features and attributes as monitoring resource  60  as previously described in association with at least  FIGS. 1 ,  3 A and other monitoring resources described throughout the examples of the present disclosure. 
     Via an array of parameters, the sensing engine  690  receives and determines signals from various physiologic sensors (such as a pressure sensor, blood oxygenation sensor, acoustic sensor, electrocardiogram (ECG) sensor, or impedance sensor as described in association with at least  FIGS. 11-12 ) in order to determine a respiratory state of a patient, whether or not the patient is asleep or awake, and other respiratory-associated indicators, etc. Such respiratory detection may be received from either a single sensor or any multiple of sensors, or combination of various physiologic sensors which may provide a more reliable and accurate signal. In one example, sensing engine  690  receives signals from sensor portion  680  and/or sensors  682 ,  683 ,  684  in  FIG. 16B , or any of the sensor(s) as previously described in association with at least  FIGS. 11-12 . 
     In some examples, sensing engine  690  cooperates with, is in communication with, and/or forms part of a monitoring resource (e.g. at least  60  in  FIG. 3A ;  570  in  FIG. 15 ) to receive, determine, and/or monitor at least the parameters, information and conditions, as previously described in association with at least  FIGS. 1-15C . 
     In some examples, among other functions the therapy manager  694  of pulse generator  675  acts to synthesize respiratory information, to determine suitable stimulation parameters (via stimulation engine  692 ) based on that respiratory information, and to direct electrical stimulation to the target nerve. In some examples, therapy manager  694  may comprise at least some of substantially the same features and attributes of control portion  880  and/or may cooperate with control portion  880  in  FIG. 23 . 
       FIG. 17A  is a block diagram schematically representing a monitoring resource  700 , according to one example of the present disclosure. As shown in  FIG. 17A , monitoring resource  700  includes sensor  702  and monitoring engine  704 . 
     In some examples, sensor  702  includes at least some of substantially the same features as the sensors previously described in association with at least  FIGS. 11-12  and with  FIGS. 13A-15C . Accordingly, the sensor(s)  702  may be internal (e.g. implanted within the patient) or external to the patient, or a combination of both internal and external. When external, the sensors may be wearable by the patient, removably securable to the patient, or part of the patient&#39;s environment. 
     In some examples, the monitoring engine  704  monitors sleep parameter  706  and cardiac parameter  708  regarding the patient. In some examples, the cardiac parameter  708  includes at least some of substantially the same features and attributes as cardiac parameters ( 62  in  FIG. 1, 3A ;  64  in  FIG. 2A ;  66  in  FIG. 2B ;  304  in  FIG. 9 ) and cardiac information ( FIGS. 13-15 ) as disclosed throughout the present disclosure. 
     In some examples, monitoring resource  700  is separate from, and independent of, a therapy device but may communicate with a therapy device, such as (but not limited to) one of the therapy devices described in at least some examples of the present disclosure. In some examples, monitoring resource  700  forms part of, or cooperates with, a therapy device, such as one of the therapy devices described in at least some examples of the present disclosure. 
     In some examples, monitoring resource  700  forms part of and/or cooperates with a therapy manager ( 694  in  FIG. 16C ) while in some examples, monitoring resource  700  is separate from, and independent of, such managers but may communicate with such managers. 
     In some examples, monitoring resource  700  is implemented within and/or forms a standalone device. In some examples, monitoring resource  700  is incorporated within and forms an application on a mobile device (e.g.  131 ,  132  in  FIG. 3B ). In some instances, the mobile device (e.g.  131  in  FIG. 3B ) is dedicated to monitoring cardiac parameters and/or sleep disordered breathing parameters while in some instances, the mobile device (e.g.  132  in  FIG. 3B ) is a non-dedicated mobile device, such as but not limited to, a smart phone, tablet, phablet, notebook computer, etc. 
       FIG. 17B  is a block diagram schematically representing a monitoring resource  710 , according to one example of the present disclosure. As shown in FIG.  17 B, monitoring resource  710  comprises at least some of substantially same features and attributes as monitoring resource  700  in  FIG. 17A , except with sensor  712  and/or information  714  being separate from and/or independent of monitoring resource  710 , and with monitor resource  710  being in cooperation with and/or in communication with sensor  712  and/or information  714 . 
       FIG. 18A  is a block diagram schematically representing a manager  750 , according to one example of the present disclosure. As shown in  FIG. 18A , monitoring resource  750  comprises a monitoring engine  752  and an evaluation engine  758 . The determination engine  752  monitors at least an array of sleep parameters  754  and an array of cardiac parameters  756 . The evaluation engine  758  evaluates the monitored parameters  754 ,  756  looking for positive and negative correlations of the parameters relative to each other, as will be further described later in association with at least  FIG. 22 . In some examples, the sleep parameters  754  comprise sleep quality parameters, sleep disordered breathing parameters, among other sleep-related parameters. In some examples, the sleep disordered breathing parameters comprise at least obstructive sleep apnea-related parameters. In some examples, the obstructive sleep apnea-related parameters comprise various physiologic parameters associated with the presence or absence of obstructive sleep apnea. 
     In some examples, some of the cardiac parameters may comprise a cardiac disorder parameter. In some examples, the cardiac disorder parameters  756  are associated with negative cardiac conditions. However, in some examples, a cardiac disorder parameter  756  may be associated with a positive cardiac condition. In some examples, the cardiac conditions comprise various physiologic parameters associated with the presence or absence of cardiac conditions. 
       FIG. 18B  is table identifying at least some sleep/sleep quality parameters, at least some cardiac conditions/parameters, and other parameters, according to one example of the present disclosure. Any one of the sleep/sleep quality parameters, the cardiac condition/parameters, the other parameters, and/or the pulmonary parameters can be correlated with each other as driven by the actual physiologic behavior of the patient. 
     It will be understood that in some examples, via analytic tools, the various sleep quality parameters and cardiac parameters may be organized manually or automatically (via self-development) into other formats, matrices, grids, and/or multi-dimensional forms, which reflect the functional or correlational relationship among the respective sleep and cardiac parameters. At least some examples are provided throughout the Figures, including but not limited to, at least  FIGS. 4A-5B and 13A-13I . 
     In some examples, each sleep/sleep quality parameter is compared relative to a first criteria for that respective sleep/sleep quality parameter, and in some examples, each cardiac condition/parameter is compared relative to a second criteria for that particular cardiac condition/parameter. 
     In some examples, via evaluation engine  758  monitoring resource  750  ( FIG. 18A ) automatically determines uniquely for each patient any positive sleep quality parameters characterized by their improvement with SDB treatment associated with the monitoring period and any negative sleep quality parameters characterized by their deterioration with SDB treatment associated with the monitoring period. 
     In some examples, via evaluation engine  758  (of monitoring resource  750 ) automatically determines uniquely for each patient any cardiac disorder parameters characterized by their decrease with SDB treatment associated with the monitoring period and any cardiac disorder parameters characterized by their persistence despite SDB treatment associated with the monitoring period. 
     In some examples, the evaluation engine  758  (of monitoring resource  750 ) determines a correlation of positive sleep quality parameters and decreased cardiac disorder parameters. In some examples, the evaluation engine  758  determines a correlation of negative sleep quality parameters and persistent cardiac disorder parameters. 
     In some examples, the evaluation engine  758  (of monitoring resource  750 ) determines a correlation of positive sleep quality parameters and persistent cardiac disorder parameters. In some examples, the evaluation engine determines a correlation of negative sleep quality parameters and improved cardiac disorder parameters. 
     In some examples, the first criteria set includes a separate criteria/threshold for each different sleep quality parameter and the second criteria/set includes a separate criteria/threshold for each different cardiac disorder parameter. 
     In some examples, one sleep quality parameter includes determining a duration and quantity of non-REM and REM sleep stages, as well as a total duration of sleep. In some examples, an accelerometer (e.g. accelerometer  406  in  FIG. 12 ) is used to determine a duration of sleep and/or soundness of sleep via sensing body motion, body activity, body position(s), and/or body posture. 
     In some examples, at least some patient data which was determined during or after a monitoring period can be displayed via a graph  760  such as shown in  FIG. 18C , according to one example of the present disclosure. As shown in  FIG. 18C , in some examples graph  760  displays information about at least one sleep disordered breathing (SDB) parameter  761 A, at least one cardiac parameter  761 B, and at least one other parameter  761 C (e.g. pulmonary). In one instance, the at least one SDB parameter  761 A comprises an apnea-hypopnea index (AHI) while the at least one cardiac parameter  761 B comprises an average heart rate variability (HRV). It will be understood that in at least some examples, the apnea-hypopnea index (AHI) may correspond to a quantity of apneas over a time period. In one instance, the other parameter  761 C comprises a respiratory rate. It will be understood that many other parameters from each of the respective categories of sleep disordered breathing, cardiac, and other/pulmonary can be selected for display and comparison on graph  760  instead of or in addition to those shown in the example of  FIG. 18C . 
     In some examples, graph  760  displays the respective parameters  761 A,  761 B,  761 C as box-and-whisker plots as shown in  FIG. 18C  in which a box (e.g.  762 A,  762 B,  762 C) graphically demonstrates a range of primary values for each respective parameter  761 A- 761 C and the whiskers (e.g.  763 A,  763 B,  763 C) extending from each end of the respective box identify a number of data points outside the primary range. By aligning the box-and-whisker plots for the parameters relative to each other, one can then correlate when both the SDB parameter  761 A and the cardiac parameter  761 B are both out of the primary range (e.g. box) at the same time. Stated differently, one can observe where the respective whiskers overlap. Moreover, once any such correlation is identified, further filtering can be applied to other parameters (such as those at least some of the parameters listed in Table of  FIG. 18B ) to observe behavior of other parameters during those times and potentially identify further correlation(s) between those “other” parameters and the SDB parameter  761 A and cardiac parameter  761 B. It will be understood in some examples, the sleep quality parameter  902  of an apnea-hypopnea index (AHI) (e.g. number of apnea events per unit of time) may be substituted by a total quantity of obstructive sleep apnea events, a severity of apnea event(s), etc. In some examples, the patient data shown in graph  760  of  FIG. 18C  is obtained during or after a monitoring period (e.g.  124  in  FIG. 3A ). In some instances, the monitoring period may be relatively long term, such as but not limited to, one year such as might occur upon a patient having a one year check-up with a clinician. In such instances, the values represented in the box-and-whisker plots would correspond to one year of data, such that any correlations derivable from the plots may demonstrate long term trends regarding a patient&#39;s cardiac conditions (positive or negative) over that time period relative to the patient&#39;s SDB therapy over that same time period. 
     With further reference to  FIG. 18A , in some examples monitoring resource  750  comprises a portion of a therapy device  765 , as shown in  FIG. 19 , in which therapy device  765  includes non-cardiac stimulator circuitry  767 , which can take the forms previously described in association with at least  FIGS. 6A, 6C, 7, 10A-10B , and  16 A- 16 B, or other forms The non-cardiac stimulator circuitry  767  is configurable to stimulate upper-airway-patency related body tissues, such as nerves, muscles, etc. 
     In some examples, the non-cardiac stimulator circuitry  767  may comprise a transvenously implantable stimulation element operably couplable relative to an external pulse generator. In some examples, such non-cardiac stimulator circuitry may comprise a percutaneously implantable stimulation element wirelessly operably couplable relative to an external pulse generator. In either case, when coupled together in this manner, power, data, and/or control may be transferred wirelessly between the implantable stimulation element and the external pulse generator. 
     In either the transvenous or percutaneous modality, in some such examples, some components associated with pulse generation and/or control may be implantable in proximity to or co-located with the implantable stimulation element. 
     In some examples, monitoring resource  750  is separate from, and independent of, a therapy device (e.g.  765  in  FIG. 19 ) but may communicate with or cooperate with such therapy devices. 
     In some examples, therapy device  765  includes a wireless communication link  768  ( FIG. 20 ) for receiving and/or obtaining the information (e.g.  300  in  FIG. 9  and  FIGS. 13A-14B ) for determining via determination engine  752  ( FIG. 18A ). 
     In some examples, therapy device  765  includes or is in communication with a sensor  769  ( FIG. 21 ) for receiving and/or obtaining the information (e.g.  300  in  FIG. 9  and  FIGS. 13A-14B ) for determining via determination engine  752  ( FIG. 18A ). In some examples, sensor  769  comprises at least some of substantially the same features and attributes as the sensor(s) as previously described in association with at least  FIGS. 11-12 . 
       FIG. 22  is block diagram schematically representing an evaluation engine  770 , according to one example of the present disclosure. In some examples, evaluation engine  770  serves as evaluation engine  758  in the examples of  FIGS. 18A-19 . As shown in  FIG. 20 , in some examples evaluation engine  758  comprises a correlation function  772 , correlation criteria  790 , notification criteria  792 , and patient compliance parameter  794 . 
     Correlation function  772  operates to identify and determine correlations among different determined parameters, such as but not limited to, sleep quality parameters  754  and cardiac disorder parameters  756  as provided in determination engine  752  of  FIG. 18A . In some examples, pulmonary parameters and/or other parameters are determined and correlated along with the sleep quality and cardiac parameters  754 ,  756 . In some examples, correlation function  772  operates via an automatic mode  774  in which such correlations are automatically determined via statistical analysis and/or predetermined correlation metrics, such as correlation criteria  790 . In some examples, correlation function  772  operates via a manual mode  776  in which such correlations are identified manually. 
     Notification criteria  792  enables setting a criteria which is to be met before a notification (e.g.  574  in  FIG. 15C ) is made to a clinician regarding any identified correlation. In some examples, notification criteria  792  comprises at least some of substantially the same features and attributes as notification criteria  576  as previously described in association with at least  FIG. 15C . 
     Patient compliance parameter  794  enables determining the extent to which a patient has been compliant with a therapy for treating sleep disordered breathing, thereby equipping a clinician or evaluator to weigh this patient compliance as a factor when evaluating any notification regarding a correlation identified via correlation function  772 . In some instances, the patient compliance parameter  794  may be expressed as a usage parameter, which may form part of a self-developing correlation vector regarding combinations of positive parameters (i.e. those contributing to efficacious therapy) or a self-developing correlation vector regarding combinations of negative parameters (i.e. those contributing to a lack of efficacious therapy). 
     In some examples, evaluation engine  770  includes an array  779  of evaluative operators, such as but not limited to, positive parameter  780 , negative parameter  781 , increase parameter  782 , decrease parameter  783 , persistence parameter  784 , subside parameter  785 , and threshold parameter  786  for identifying associated values of determined parameters ( 754 ,  756  in  FIG. 18A ) and/or identifying associated correlations among the determined parameters. In some instances, an increase in a positive parameter sometimes may be referred to as an improvement while in other instances, a decrease (or subsiding) in a negative parameter sometimes may be referred to as an improvement. 
     In some examples, during or after a monitoring period, the evaluation engine  770  may automatically identify associations and/or correlations between sleep quality parameters  754  and cardiac disorder parameters  756  ( FIG. 18A ). In this way, the evaluation engine  770  enables automatic development of a correlation vector for a particular patient during or after a monitoring period, wherein the correlation vector reflects some relationship among sleep quality parameters  754  and cardiac disorder parameters  756  such that treatment of sleep disordered breathing may result in a decrease (e.g.  783  in  FIG. 22 ) in, or subsiding (e.g.  785  in  FIG. 22 ) of, an existing cardiac disorder or may result in the persistence (e.g.  784  in  FIG. 22 ) of a cardiac disorder despite treatment of sleep disordered breathing. In some instances, a decrease or subsiding for a positive parameter may be referred to a deterioration. In some instances, in an increase in a negative parameter sometimes may be referred to as a deterioration. 
     For instance, in some examples, during or after a monitoring period, the evaluation engine  770  may identify that a cardiac condition such as atrial fibrillation persists despite treatment for sleep disordered breathing. Upon confirmation that the sleep disordered breathing treatment was effective, it may then be determined that the atrial fibrillation may have causes unrelated to the sleep disordered breathing previously exhibited by the patient. A clinician may then recommend other therapeutic steps to alleviate the cardiac disorder (e.g. atrial fibrillation), such as drug therapy, surgery, ablation, electrically stimulating a portion of the heart (e.g. pacing, defibrillation, etc.), and/or non-hypoglossal nerve stimulation such as stimulating the vagus nerve. 
     Alternatively, during or after a monitoring period, the evaluation engine  770  may identify that a cardiac condition, such as atrial fibrillation subsides or decreases during or after treatment for sleep disordered breathing. Upon confirmation that the sleep disordered breathing treatment was effective, it may then be determined that the sleep disordered breathing previously exhibited by the patient was at least partially responsible for the previously exhibited atrial fibrillation. 
     In some examples, a correlation between a patient compliance/usage parameter  794  of the SDB therapy device and cardiac parameters may allow for a single variable to indicate the efficacy of the SDB treatment. A low number could signal that re-programming of the SDB therapy device is recommended to improve SDB therapy efficacy or that referral to a cardiac health specialist is appropriate. In this way, leading indicators to treat cardiac health (in the specific context of SDB therapy) may help the long term health of patients. 
     In some examples, one correlation vector comprises an atrial fibrillation burden parameter vs. patient compliance for SDB therapy vs. SDB efficacy. This correlation vector may be useful to notify clinicians in taking early action regarding either the SDB therapy and/or the atrial fibrillation behavior. For instance, if the atrial fibrillation burden persists (e.g. persistence parameter  784  in  FIG. 22 ) even with a high value of SDB efficacy and a high value of SDB therapy patient compliance, this correlation may be an indication of a structural cardiac issue and that the patient may benefit from an interventional cardiac procedure such as, but not limited to, ablation to treat the atrial fibrillation behavior. In this way, the correlation vector helps to identify cardiac parameters that are not positively responsive to SDB therapy. 
     In some examples, the atrial fibrillation burden can be quantified in at least two ways. For instance, the atrial fibrillation burden can be quantified via RR interval variability (where R refers to the R in a QRS complex of a cardiac waveform) or via atrial-atrial (AA) timing vs ventricle-ventricle (VV) timing. 
     It will be understood that the self-developing correlation vector of sleep quality parameters  754  and cardiac disorder parameters  756  may develop associations and/or correlations between respective parameters  754  and  756  which are unique for a particular patient and not necessarily exhibited by a larger patient population as a whole. This arrangement may lead to unique treatment options for a particular patient. Moreover, in some instances, any correlation data which is self-developed for each patient may be aggregated with self-developed correlation data from other patients to enable determining correlations (or a lack of correlation) among at least some sleep quality parameters  754  (which includes, but it is not limited to, sleep disordered breathing parameters) and at least some cardiac disorder parameters  756  which are common among a group of patients. 
       FIG. 23  is a block diagram schematically representing a control portion  880 , according to one example of the present disclosure. In some examples, control portion  880  includes a controller  882  and a memory  884 . In some examples, control portion  880  provides one example implementation of a control portion forming a part of, or implementing, any one of managers, monitoring resource, determination engines, and/or therapy devices/systems, as represented throughout the present disclosure in association with  FIGS. 1-22 . 
     In general terms, controller  882  of control portion  880  comprises at least one processor  883  and associated memories. The controller  882  is electrically couplable to, and in communication with, memory  884  to generate control signals to direct operation of at least some components of the systems, devices, components, monitoring resource, managers, functions, parameters, and/or engines described throughout the present disclosure. In some examples, these generated control signals include, but are not limited to, employing engine  885  stored in memory  884  to manage therapy for a patient, provide sleep monitoring, and/or provide cardiac monitoring, in the manner described in at least some examples of the present disclosure. It will be further understood that control portion  880  (or another control portion) may also be employed to operate general functions of the various therapy devices/systems, access tools  131 - 135  ( FIG. 3B ) described throughout the present disclosure. 
     In response to or based upon commands received via a user interface (e.g. user interface  140  in  FIG. 3C ) and/or via machine readable instructions, controller  882  generates control signals to implement therapy implementation, monitoring, management, sleep monitoring, and/or cardiac monitoring in accordance with at least some of the previously described examples of the present disclosure. In some examples, controller  882  is embodied in a general purpose computing device while in other examples, controller  882  is embodied in a monitoring resource generally or incorporated into or associated with at least some of the related components described throughout the present disclosure. 
     For purposes of this application, in reference to the controller  882 , the term “processor” shall mean a presently developed or future developed processor (or processing resource(s)) that executes sequences of machine readable instructions contained in a memory. In some examples, execution of the sequences of machine readable instructions, such as those provided via memory  884  of control portion  880  cause the processor to perform actions, such as operating controller  882  to implement therapy, sleep monitoring, and/or cardiac monitoring, as generally described in (or consistent with) at least some examples of the present disclosure. The machine readable instructions may be loaded in a random access memory (RAM) for execution by the processor from their stored location in a read only memory (ROM), a mass storage device, or some other persistent storage (e.g., non-transitory tangible medium or non-volatile tangible medium), as represented by memory  884 . In some examples, memory  884  comprises a computer readable tangible medium providing non-volatile storage of the machine readable instructions executable by a process of controller  882 . In other examples, hard wired circuitry may be used in place of or in combination with machine readable instructions to implement the functions described. For example, controller  882  may be embodied as part of at least one application-specific integrated circuit (ASIC). In at least some examples, the controller  882  is not limited to any specific combination of hardware circuitry and machine readable instructions, nor limited to any particular source for the machine readable instructions executed by the controller  482 . 
       FIG. 24A  is a block diagram schematically representing instructions  3502 , according to one example of the present disclosure. 
     With regard to the instructions  3502  ( FIG. 24A ),  3504  ( FIG. 24B ) and the instructions  3600  ( FIG. 25 ),  3650  ( FIG. 26 ),  3660  ( FIG. 27 ),  3670  ( FIG. 28 ),  3700  ( FIG. 29 ), in some examples, any or all of the instructions  3500 ,  3600 ,  3650 ,  3660 ,  3670 ,  3700  may be implemented via at least some of substantially the same systems, devices, functions, parameters, engines, monitoring resource, modules, managers, elements, components, instructions, etc. as previously described in association with at least  FIGS. 1-23 . In some examples, any or all of the respective instructions may be implemented via at least some systems, devices, functions, parameters, engines, monitoring resource, modules, managers, elements, components, instructions, etc. other than those previously described in association with at least  FIGS. 1-23 . Moreover, the respective instructions represented in association with at least  FIGS. 24-29  may be combined with other instructions associated with the various systems, devices, functions, parameters, engines, monitoring resource, modules, managers, elements, components, etc. as previously described in association with at least  FIGS. 1-23 . 
     In addition, regarding the instructions  3502  ( FIG. 24A ),  3504  ( FIG. 24B ) and the instructions  3600  ( FIG. 25 ),  3650  ( FIG. 26 ),  3660  ( FIG. 27 ),  3670  ( FIG. 28 ),  3700  ( FIG. 29 ), in some examples, any or all of the instructions  3502 ,  3504 ,  3600 ,  3650 ,  3660 ,  3670 ,  3700  may be implemented as a method via at least some of substantially the same systems, devices, functions, parameters, engines, monitoring resource, modules, managers, elements, components, instructions, features, attributes, etc. as previously described in association with at least  FIGS. 1-23 . 
     As shown in  FIG. 24A , at  3502  the instructions  3500  comprise monitoring at least one sleep parameter and at least one cardiac parameter. 
       FIG. 24B  is a block diagram schematically representing instructions  3504 , according to one example of the present disclosure. As shown in  FIG. 24B , at  3504 , the instructions comprise performing the monitoring based on at least sensed physiologic-related information. In some examples, instructions  3504  are implemented to complement instructions  3502 . 
       FIG. 25  is a flow diagram schematically representing instructions  3600 , according to one example of the present disclosure. As shown in  FIG. 25 , at  3602  the instructions  3600  comprise directing treatment of obstructive sleep apnea by stimulating the airway-patency-related body tissue via stimulator circuitry. At  3604 , the instructions  3600  comprise monitoring, via at least one sensor, at least one sleep parameter and at least one cardiac parameter. 
       FIG. 26  is a block diagram schematically representing instructions  3650 , according to one example of the present disclosure. Instructions  3650  comprise determining any positive sleep parameters characterized by their improvement with OSA treatment and any negative sleep parameters characterized by their deterioration with OSA treatment. 
       FIG. 27  is a block diagram schematically representing instructions  3660 , according to one example of the present disclosure. Instructions  3660  comprise determining cardiac parameters characterized by their decrease with OSA treatment and any cardiac parameters characterized by their persistence and/or increase despite OSA treatment. 
       FIG. 28  is a block diagram schematically representing instructions  3670 , according to one example of the present disclosure. Instructions  3670  comprise determining a first correlation of positive sleep parameters and decreased cardiac parameters and a second correlation of negative sleep parameters relative to persistent and/or increased cardiac disorder parameters. 
     In some examples, instructions  3650  ( FIG. 26 ), instructions  3660  ( FIG. 27 ), and instructions  3670  ( FIG. 28 ) are implemented together in a complementary manner. 
       FIG. 29  is a block diagram schematically representing instructions  3700 , according to one example of the present disclosure. Instructions  3700  comprise displaying at least one sleep parameter and/or at least one cardiac parameter. In some examples, instructions  3700  ( FIG. 29 ) are implemented in a complementary manner with the instructions  3502  ( FIG. 24A ), instructions  3504  ( FIG. 24B ), instructions  3600  ( FIG. 25 ), instructions  3650  ( FIG. 26 ), instructions  3660  ( FIG. 27 ), and/or instructions  3670  ( FIG. 28 ), whether in the form of a method or otherwise as noted above. 
     Although specific examples have been illustrated and described herein, it will be appreciated by those of ordinary skill in the art that a variety of alternate and/or equivalent implementations may be substituted for the specific examples shown and described without departing from the scope of the present disclosure. This application is intended to cover any adaptations or variations of the specific examples discussed herein.