Patent Publication Number: US-2023148954-A1

Title: System And Method For Mapping An Airway Obstruction

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application claims the benefit of, and priority to, U.S. Provisional Patent Application Ser. No. 63/002,790, filed on Mar. 31, 2020, titled “System and Method for Mapping an Airway Obstruction,” and the benefit of, and priority to, U.S. Provisional Patent Application Ser. No. 62/704,944, filed on Jun. 3, 2020, titled “System and Method for Mapping an Airway Obstruction,” each of which is hereby incorporated by reference herein in its entirety. 
    
    
     TECHNICAL FIELD 
     The present disclosure relates generally to systems and methods for determining an apnea obstruction, and more particularly, to systems and methods for estimating physical and temporal characteristics of the apnea obstruction using an acoustic reflection. 
     BACKGROUND 
     Many individuals suffer from sleep-related and/or respiratory-related disorders such as, for example, Periodic Limb Movement Disorder (PLMD), Restless Leg Syndrome (RLS), Sleep-Disordered Breathing (SDB) such as Obstructive Sleep Apnea (OSA), Central Sleep Apnea (CSA) and other types of apneas such as hypopnea, hyperpnea, and hypercapnia, Cheyne-Stokes Respiration (CSR), respiratory insufficiency, Obesity Hyperventilation Syndrome (OHS), Chronic Obstructive Pulmonary Disease (COPD), Neuromuscular Disease (NMD), and chest wall disorders. These disorders are often treated using respiratory therapy systems. Obstructive Sleep Apnea (OSA) is a form of Sleep Disordered Breathing (SDB), and is characterized by events including occlusion or obstruction of the upper air passage during sleep which may result from a combination of an abnormally small upper airway and the normal loss of muscle tone in the region of the tongue, soft palate and posterior oropharyngeal wall. More generally, an apnea generally refers to the cessation of breathing caused by blockage of the air (Obstructive Sleep Apnea) or the stopping of the breathing function (Central Sleep Apnea). Mixed apneas are apnea events that are combination of both obstructive and central sleep apnea symptoms, and such events typically begin as a central apneas and end as obstructive apneas. Hypopnea is generally characterized by slow or shallow breathing caused by a narrowed airway, as opposed to a blocked airway. Cheyne-Stokes Respiration (CSR) is another form of Sleep Disordered Breathing. CSR is a disorder of a patient&#39;s respiratory controller in which there are rhythmic alternating periods of waxing and waning ventilation known as CSR cycles. CSR is characterized by repetitive de-oxygenation and re-oxygenation of the arterial blood. 
     An occlusion or obstruction may be characterized by a partial or full collapse of the upper airway during sleep. This physical site of the airway obstruction is an important consideration in understanding a patient&#39;s disease state and the optimal solution for their disorder. However, the nature of airway collapse is typically difficult to measure without dedicated or invasive hardware, especially during standard PAP therapy in a home setting. 
     One such method in existence for example is DISE (Drug-induced sedation endoscopy). This sedates a patient to simulate natural sleep and examines the airway optically though an endoscope. A classification of the airway obstruction is then made manually by an expert using the VOTE system (velum, oropharynx, tongue base and epiglottis). Other methods involve affixing pressure sensors within the upper airway or using imaging techniques such as fluoroscopy, CT, MRI and radiography. Some automated methods that analyze snoring audio or airflow signals have been reported; however, these methods are less direct, may not work during PAP therapy, and may only be able to infer the site of obstruction during partial airway collapse (hypopnea). 
     The present disclosure is directed to solving these and other problems, including problems associated with determining physical and temporal characteristics of an apnea obstruction. 
     SUMMARY 
     According to some implementations of the present disclosure, a method includes emitting an acoustic signal into an airway of a user. The method further includes detecting an acoustic reflection of the acoustic signal caused by one or more physical features within the airway of the user. The method also includes analyzing acoustic data associated with the acoustic reflection. The method also further includes characterizing and/or determining an occurrence of a physical obstruction in the airway of the user. Characterizing and/or determining the apnea obstruction is based, at least in part, on the analyzed acoustic data. The characterization is indicative of an apnea event or a hypopnea event in the user, wherein the apnea event comprises an obstructive apnea event, a central apnea event or a mixed apnea event, and further distinguishes between the occurrence of the obstructive apnea event, the central apnea event, the mixed apnea event, or the hypopnea event in the user. 
     According to other implementations of the present disclosure, a system includes an acoustic device for emitting an acoustic signal and a memory device storing machine-readable instructions. The system further includes a control system including one or more processors communicatively coupled to the acoustic device. The control system is configured to execute the machine-readable instructions to detect an acoustic reflection of the acoustic signal emitted by the acoustic device into the airway of the user. The acoustic reflection is caused by a one or more physical features within the airway of the user. Acoustic data associated with the acoustic reflection is analyzed, and an occurrence of the physical obstruction is characterized in the airway of the user. The characterization is determined based, at least in part, on the analyzed acoustic data. The characterization is indicative of an apnea event or a hypopnea event in the user, wherein the apnea event comprises an obstructive apnea event, a central apnea event or a mixed apnea event, and further distinguishes between the occurrence of the obstructive apnea event, the central apnea event, the mixed apnea event, or the hypopnea event in the user. 
     According to yet other implementations of the present disclosure, a system includes a respiratory therapy system and an acoustic device associated with the respiratory therapy system. The acoustic device is configured to emit an acoustic signal into an airway of a user. The system further includes a memory device storing machine-readable instructions and a control system including one or more processors communicatively coupled to the acoustic device. The control system is configured to execute the machine-readable instructions to supply pressurized air, via the respiratory system, to the airway of the user during a sleep session. The instructions are further executed to receive flow data associated with the pressurized air, and to determine, based on the flow data, occurrence of an apnea event or a hypopnea event. The instructions are also executed to receive an acoustic reflection based on one or more physical features within the airway of the user. The instructions are also further executed to analyze acoustic data associated with the acoustic reflection, and to characterize physical and/or temporal characteristics of the physical obstruction based, at least in part, on the analyzed acoustic data. The characterization is indicative of an apnea event or a hypopnea event in the user, wherein the apnea event comprises an obstructive apnea event, a central apnea event or a mixed apnea event, and further distinguishes between the occurrence of the obstructive apnea event, the central apnea event, the mixed apnea event, or the hypopnea event in the user. 
     The above summary is not intended to represent each embodiment or every aspect of the present disclosure. Rather, the foregoing summary merely provides an example of some of the novel aspects and features set forth herein. The above features and advantages, and other features and advantages of the present disclosure, will be readily apparent from the following detailed description of representative embodiments and modes for carrying out the present invention, when taken in connection with the accompanying drawings and the appended claims. Additional aspects of the disclosure will be apparent to those of ordinary skill in the art in view of the detailed description of various embodiments, which is made with reference to the drawings, a brief description of which is provided below. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The disclosure, and its advantages and drawings, will be better understood from the following description of exemplary embodiments together with reference to the accompanying drawings. These drawings depict only exemplary embodiments, and are therefore not to be considered as limitations on the scope of the various embodiments or claims. 
         FIG.  1    is a functional block diagram of a system for determining an occurrence of an apnea obstruction, according to some implementations of the present disclosure. 
         FIG.  2 A  is a perspective view of at least a portion of the system of  FIG.  1   , a user wearing a full face mask, and a bed partner, according to some implementations of the present disclosure. 
         FIG.  2 B  is a perspective view of at least a portion of the system of  FIG.  1   , a user wearing a nasal mask, and a bed partner, according to some implementations of the present disclosure. 
         FIG.  3 A  is a diagram that illustrates an overview of a respiratory system of a user. 
         FIG.  3 B  is a diagram that illustrates nasal and mouth airways of the user of  FIG.  3 A . 
         FIG.  4    is an exemplary cepstrum plot representing a physical obstruction within a user airway, according to some implementations of the present disclosure. 
         FIG.  5    is a side view illustration of a system for determining the physical obstruction of  FIG.  4   , according to some implementations of the present disclosure. 
         FIG.  6    is a side view illustration of a partial collapsed state of a user airway. 
         FIG.  7    is a side view illustration of a fully collapsed state of a user airway. 
         FIG.  8    is a front view illustration of a mobile phone displaying options based on a physical obstruction, according to some implementations of the present disclosure. 
         FIG.  9    is a front view illustration of a mobile phone displaying further options based on a physical obstruction, according to some implementations of the present disclosure. 
         FIG.  10    illustrates a system including a mobile phone, a respiratory device, and a mask for characterizing a physical obstruction in a user airway, according to some implementations of the present disclosure. 
         FIG.  11    illustrates a system including a mobile phone and an acoustic device for characterizing a physical obstruction in a user airway, according to some implementations of the present disclosure. 
         FIG.  12    is a display illustrating an image representative of a full throat collapse, according to some implementations of the present disclosure. 
         FIG.  13    is a display illustrating an image representative of a partial throat collapse, according to some implementations of the present disclosure. 
         FIG.  14    is a display illustrating an image representative of a time period for monitoring an apnea event, according to some implementations of the present disclosure. 
         FIG.  15    is a display illustrating an image representative of a first set of settings for characterizing a physical obstruction during an apnea event, according to some implementations of the present disclosure. 
         FIG.  16    is a display illustrating an image representative of a second set of settings for characterizing a physical obstruction during an apnea event, according to some implementations of the present disclosure. 
         FIG.  17    is a display illustrating an image representative of a physical obstruction, according to some implementations of the present disclosure. 
     
    
    
     While the invention is susceptible to various modifications and alternative forms, specific implementations have been shown by way of example in the drawings and will be described in further detail herein. It should be understood, however, that the invention is not intended to be limited to the particular forms disclosed. Rather, the invention is to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the invention as defined by the appended claims. 
     DETAILED DESCRIPTION 
     Various embodiments are described with reference to the attached figures, where like reference numerals are used throughout the figures to designate similar or equivalent elements. The figures are not drawn to scale and are provided merely to illustrate the instant invention. Several aspects of the invention are described below with reference to example applications for illustration. It should be understood that numerous specific details, relationships, and methods are set forth to provide a full understanding of the invention. One having ordinary skill in the relevant art, however, will readily recognize that the invention can be practiced without one or more of the specific details, or with other methods. In other instances, well-known structures or operations are not shown in detail to avoid obscuring the invention. The various embodiments are not limited by the illustrated ordering of acts or events, as some acts may occur in different orders and/or concurrently with other acts or events. Furthermore, not all illustrated acts or events are required to implement a methodology in accordance with the present invention. 
     Elements and limitations that are disclosed, for example, in the Abstract, Summary, and Detailed Description sections, but not explicitly set forth in the claims, should not be incorporated into the claims, singly, or collectively, by implication, inference, or otherwise. For purposes of the present detailed description, unless specifically disclaimed, the singular includes the plural and vice versa. The word “including” means “including without limitation.” Moreover, words of approximation, such as “about,” “almost,” “substantially,” “approximately,” “generally,” and the like, can be used herein to mean “at,” “near,” or “nearly at,” or “within 3-5% of,” or “within acceptable manufacturing tolerances,” or any logical combination thereof, for example. 
     Generally, the present disclosure describes a system and method in which an acoustic reflection is used to non-invasively characterize an airway obstruction. The airway obstruction may be a physical obstruction (e.g., such as a tongue base of the user) in the airway that is deemed to be an apnea obstruction because it causes and occurs during an apnea event or a hypopnea obstruction because it causes and occurs during a hypopnea event. 
     A partially or fully collapse airway presents a change in acoustic impedance that produces a reflection when provided with acoustic stimulus, i.e., an acoustic signal emitted into an airway of a user. The site and/or nature of the airway collapse is estimated by analyzing reflections that occur within the airway of the user (i.e., beyond a mouth or mask of the user). The obstruction site is characterized based, at least in part, on a distance travelled into the airway and/or signal morphology of the reflected signal. 
     In addition to determining obstructive apnea or hypopnea events, the disclosed system and method can characterize a central apnea (non-obstructive) event, and distinguish such events from obstructive apnea or hypopnea events. During a central apnea event, a user&#39;s airway remains open (i.e., is not obstructed), but the user stops breathing. Thus, there is an absence of a physical obstruction in the airway of the user. The central apnea is determined based on breathing (respiration) data indicating that he user has stopped breathing. The breathing data, which is indicative of the user&#39;s breathing pattern, is obtained, for example, from a respiratory therapy system (e.g., a positive airway pressure (PAP) system), from a motion sensor suitable to detect respiration-related movement such as a sonar or radar sensor, or from a passive acoustic sensor (e.g., a microphone). Various methods may be employed to extract a breathing signal from data generated by a passive acoustic sensor, such as a microphone. typically in the vicinity of the user. For example, the envelope of the time domain signal may be measured using an appropriately sized window. There are many envelope detection types such as, for example, root mean square (RMS), maximum, minimum, peak, Hilbert transform, etc. Additionally or alternatively, the mean, median and/or standard deviation of the time domain signal or the absolute of the time domain signal may be analyzed. Further still, the amplitude over time of one or more frequency bands of the passive acoustic signal may be measured. 
     Thus, an obstructive apnea or hypopnea event results in a pause in breathing or a reduction in ventilation due to a partial or full obstruction of the airway of the user. Respiratory effort is made on the part of the user, but airflow in the airway is impeded. In contrast, a central apnea event results in a pause in breathing without an associated respiratory effort. In other words, the brain does not send a signal to breath, but the airway remains open and unobstructed. 
     Conventionally, and in general terms, an apnea would be detected via the flow signal by determining if a pause in breathing has occurred, e.g., a period of time such as, 8 seconds, elapsing with no flow signal indicative of breathing. Similarly, hypopneas would be characterised by a predetermined reduction in airflow over a period of time, e.g., 30% reduction in flow amplitude lasting &gt;6 seconds. 
     Currently, to discriminate obstructive apnea events from central apnea events, the respiratory device employs a technique called forced oscillation (FOT) which investigates the airway patency by applying an oscillating 4 Hertz (Hz) pressure waveform of 1 cmH 2 O and determining if a response is observed in the flow. If there is a 4 Hz oscillation observed in flow then the airway is deemed open and the event would be classified as central, if no 4 Hz component is present in the flow signal then the event would be deemed obstructive. One downside of FOT is that these pressure oscillations may cause discomfort to the patient or even awaken them. 
     In accordance with the present disclosure, the acoustic data can achieve both (a) the detection of apneas/hypopneas events, and (b) discrimination of central from obstructive events. As further disclosed below, an obstruction in the airway may be characterized using acoustic data. The characterization may indicate absence of an airway obstruction indicative that a central event is occurring during periods where a breathing pause has been detected (via a flow and/or pressure signal from a respiratory therapy system, passive acoustic data generated by a passive acoustic sensor, a motion sensor such as a radar or sonar sensor, or otherwise). One benefit of this method is that it avoids the potential discomfort associated with the FOT method. 
     Furthermore, also in accordance with the present disclosure, a user&#39;s breathing is clearly present in the acoustic data associated with the acoustic reflection and a breathing signal can be extracted using a variety of time or frequency methods. Consequently, apneas are detected by applying rules as described above for flow and/or pressure signals, e.g., the absence of breathing for &gt;X seconds. Similarly, the breathing amplitude may be extracted from the acoustic data and used to determine a reduction in breathing amplitude, and hence detect hypopneas or confirm the detection of hypopneas. 
     Referring to  FIG.  1   , a system  100 , according to some implementations of the present disclosure, is illustrated. The system  100  includes at least a control system  110 , a memory device  114 , and an acoustic device, according to some implementations. The acoustic device, according to some exemplary implementations, is in the form of a microphone  140  and/or a speaker  142 . In some implementations, the acoustic device includes a passive acoustic sensor, such as microphone  140 . The passive acoustic sensor is suitable for generating passive acoustic data associated with the breathing of a user. The system  100  further includes, according some other implementations, an electronic interface  119 , one or more sensors  130  (which optionally include the microphone  140  and/or the speaker  142 ), and one or more user devices  170 . In some further implementations, the system  100  includes a respiratory therapy system  120 . 
     The control system  110  includes one or more processors  112  (hereinafter, processor  112 ). The control system  110  is generally used to control (e.g., actuate) various components of the system  100  and/or analyze data obtained and/or generated by the components of the system  100 . The processor  112  can be a general or special purpose processor or microprocessor. While one processor  112  is shown in  FIG.  1   , the control system  110  can include any suitable number of processors (e.g., one processor, two processors, five processors, ten processors, etc.) that can be in a single housing, or located remotely from each other. The control system  110  can be coupled to and/or positioned within, for example, a housing of the user device  170 , and/or within a housing of one or more of the sensors  130 . The control system  110  can be centralized (within one such housing) or decentralized (within two or more of such housings, which are physically distinct). In such implementations including two or more housings containing the control system  110 , such housings can be located proximately and/or remotely from each other. 
     The memory device  114  stores machine-readable instructions that are executable by the processor  112  of the control system  110 . The memory device  114  can be any suitable computer readable storage device or media, such as, for example, a random or serial access memory device, a hard drive, a solid state drive, a flash memory device, etc. While one memory device  114  is shown in  FIG.  1   , the system  100  can include any suitable number of memory devices  114  (e.g., one memory device, two memory devices, five memory devices, ten memory devices, etc.). The memory device  114  can be coupled to and/or positioned within a housing of the respiratory device  122 , within a housing of the user device  170 , within a housing of one or more of the sensors  130 , or a combination thereof. Like the control system  110 , the memory device  114  can be centralized (within one such housing) or decentralized (within two or more of such housings, which are physically distinct). 
     The electronic interface  119  is configured to receive data (e.g., physiological data) from the one or more sensors  130  such that the data can be stored in the memory device  114  and/or analyzed by the processor  112  of the control system  110 . The electronic interface  119  can communicate with the one or more sensors  130  using a wired connection or a wireless connection (e.g., using an RF communication protocol, a Wi-Fi communication protocol, a Bluetooth communication protocol, over a cellular network, etc.). The electronic interface  119  can include an antenna, a receiver (e.g., an RF receiver), a transmitter (e.g., an RF transmitter), a transceiver, or a combination thereof. The electronic interface  119  can also include one more processors and/or one more memory devices that are the same as, or similar to, the processor  112  and the memory device  114  described herein. In some implementations, the electronic interface  119  is coupled to or integrated in the user device  170 . In other implementations, the electronic interface  119  is coupled to or integrated (e.g., in a housing) with the control system  110  and/or the memory device  114 . 
     As noted above, in some implementations, the system  100  can include a respiratory therapy system  120 . The respiratory therapy system  120  can include a respiratory pressure therapy (RPT) device  122  (referred to herein as respiratory device  122 ), a user interface  124  (also referred to as a mask), a conduit  126  (also referred to as a tube or an air circuit), a display device  128 , a humidification tank  129 , a receptacle  180 , or a combination thereof. In some implementations, the control system  110 , the memory device  114 , the display device  128 , one or more of the sensors  130 , and the humidification tank  129  are part of the respiratory device  122 . Respiratory pressure therapy refers to the application of a supply of air to an entrance to a user&#39;s airways at a controlled target pressure that is nominally positive with respect to atmosphere throughout the user&#39;s breathing cycle (e.g., in contrast to negative pressure therapies such as the tank ventilator or cuirass). The respiratory therapy system  120  is generally used to treat individuals suffering from one or more sleep-related respiratory disorders (e.g., obstructive sleep apnea, central sleep apnea, or mixed sleep apnea). 
     The respiratory device  122  is generally used to generate pressurized air that is delivered to a user (e.g., using one or more motors that drive one or more compressors). In some implementations, the respiratory device  122  generates continuous constant air pressure that is delivered to the user. In other implementations, the respiratory device  122  generates two or more predetermined pressures (e.g., a first predetermined air pressure and a second predetermined air pressure). In still other implementations, the respiratory device  122  is configured to generate a variety of different air pressures within a predetermined range. For example, the respiratory device  122  can deliver at least about 6 cmH 2 O, at least about 10 cmH 2 O, at least about 20 cmH 2 O, between about 6 cmH 2 O and about 10 cmH 2 O, between about 7 cmH 2 O and about 12 cmH 2 O, etc. The respiratory device  122  can also deliver pressurized air at a predetermined flow rate between, for example, about −20 L/min and about 150 L/min, while maintaining a positive pressure (relative to the ambient pressure). 
     The user interface  124  engages a portion of the user&#39;s face and delivers pressurized air from the respiratory device  122  to the user&#39;s airway to aid in preventing the airway from narrowing and/or collapsing during sleep. Generally, the user interface  124  engages the user&#39;s face such that the pressurized air is delivered to the user&#39;s airway via the user&#39;s mouth, the user&#39;s nose, or both the user&#39;s mouth and nose. Together, the respiratory device  122 , the user interface  124 , and the conduit  126  form an air pathway fluidly coupled with an airway of the user. The pressurized air also increases the user&#39;s oxygen intake during sleep. 
     Depending upon the therapy to be applied, the user interface  124  may form a seal, for example, with a region or portion of the user&#39;s face, to facilitate the delivery of gas at a pressure at sufficient variance with ambient pressure to effect therapy, for example, at a positive pressure of about 10 cmH 2 O relative to ambient pressure. For other forms of therapy, such as the delivery of oxygen, the user interface may not include a seal sufficient to facilitate delivery to the airways of a supply of gas at a positive pressure of about 10 cmH 2 O. 
     As shown in  FIG.  2 A , in some implementations, the user interface  124  is a facial mask (e.g., a full face mask) that covers the nose and mouth of the user. Alternatively, as shown in  FIG.  2 B , the user interface  124  is a nasal mask that provides air to the nose of the user or a nasal pillow mask that delivers air directly to the nostrils of the user. The user interface  124  can include a plurality of straps (e.g., including hook and loop fasteners) for positioning and/or stabilizing the user interface on a portion of the user (e.g., the face) and a conformal cushion (e.g., silicone, plastic, foam, etc.) that aids in providing an air-tight seal between the user interface  124  and the user. The user interface  124  can also include one or more vents for permitting the escape of carbon dioxide and other gases exhaled by the user  210 . In other implementations, the user interface  124  includes a mouthpiece (e.g., a night guard mouthpiece molded to conform to the user&#39;s teeth, a mandibular repositioning device, etc.). 
     The conduit  126  (also referred to as an air circuit or tube) allows the flow of air between two components of a respiratory therapy system  120 , such as the respiratory device  122  and the user interface  124 . In some implementations, there can be separate limbs of the conduit for inhalation and exhalation. In other implementations, a single limb conduit is used for both inhalation and exhalation. 
     One or more of the respiratory device  122 , the user interface  124 , the conduit  126 , the display device  128 , and the humidification tank  129  can contain one or more sensors (e.g., a pressure sensor, a flow rate sensor, or more generally any of the other sensors  130  described herein). These one or more sensors can be used, for example, to measure the air pressure and/or flow rate of pressurized air supplied by the respiratory device  122 . 
     The display device  128  is generally used to display image(s) including still images, video images, or both and/or information regarding the respiratory device  122 . For example, the display device  128  can provide information regarding the status of the respiratory device  122  (e.g., whether the respiratory device  122  is on/off, the pressure of the air being delivered by the respiratory device  122 , the temperature of the air being delivered by the respiratory device  122 , etc.) and/or other information. For example, other information includes a sleep score and/or a therapy score (also referred to as a myAir™ score, such as described in WO 2016/061629, which is hereby incorporated by reference herein in its entirety), the current date/time, personal information for the user  210 , etc. In some implementations, the display device  128  acts as a human-machine interface (HMI) that includes a graphic user interface (GUI) configured to display the image(s) as an input interface. The display device  128  can be an LED display, an OLED display, an LCD display, or the like. The input interface can be, for example, a touchscreen or touch-sensitive substrate, a mouse, a keyboard, or any sensor system configured to sense inputs made by a human user interacting with the respiratory device  122 . 
     The humidification tank  129  is coupled to or integrated in the respiratory device  122 . The humidification tank  129  includes a reservoir of water that can be used to humidify the pressurized air delivered from the respiratory device  122 . The respiratory device  122  can include a heater to heat the water in the humidification tank  129  in order to humidify the pressurized air provided to the user. Additionally, in some implementations, the conduit  126  can also include a heating element (e.g., coupled to and/or imbedded in the conduit  126 ) that heats the pressurized air delivered to the user. The humidification tank  129  can be fluidly coupled to a water vapor inlet of the air pathway and deliver water vapor into the air pathway via the water vapor inlet, or can be formed in-line with the air pathway as part of the air pathway itself. 
     In some implementations, the system  100  can be used to deliver at least a portion of a substance from the receptacle  180  to the air pathway the user based at least in part on the physiological data, the sleep-related parameters, other data or information, or a combination thereof. Generally, modifying the delivery of the portion of the substance into the air pathway can include (i) initiating the delivery of the substance into the air pathway, (ii) ending the delivery of the portion of the substance into the air pathway, (iii) modifying an amount of the substance delivered into the air pathway, (iv) modifying a temporal characteristic of the delivery of the portion of the substance into the air pathway, (v) modifying a quantitative characteristic of the delivery of the portion of the substance into the air pathway, (vi) modifying any parameter associated with the delivery of the substance into the air pathway, or (vii) a combination of (i)-(vi). 
     Modifying the temporal characteristic of the delivery of the portion of the substance into the air pathway can include changing the rate at which the substance is delivered, starting and/or finishing at different times, continuing for different time periods, changing the time distribution or characteristics of the delivery, changing the amount distribution independently of the time distribution, etc. The independent time and amount variation ensures that, apart from varying the frequency of the release of the substance, one can vary the amount of substance released each time. In this manner, a number of different combination of release frequencies and release amounts (e.g., higher frequency but lower release amount, higher frequency and higher amount, lower frequency and higher amount, lower frequency and lower amount, etc.) can be achieved. Other modifications to the delivery of the portion of the substance into the air pathway can also be utilized. 
     The respiratory therapy system  120  can be used, for example, as a ventilator or a PAP system, such as, for example, (i) a continuous positive airway pressure (CPAP) system, (ii) an automatic positive airway pressure system (APAP), (iii) a bi-level or variable positive airway pressure system (B PAP or VPAP), or (iv) or a combination thereof. The CPAP system delivers a predetermined air pressure (e.g., determined by a sleep physician) to the user. The APAP system automatically varies the air pressure delivered to the user based on, for example, respiration data associated with the user. The BPAP or VPAP system is configured to deliver a first predetermined pressure (e.g., an inspiratory positive airway pressure or IPAP) and a second predetermined pressure (e.g., an expiratory positive airway pressure or EPAP) that is lower than the first predetermined pressure. 
     Still referring to  FIG.  1   , the one or more sensors  130  of the system  100  include a pressure sensor  132 , a flow rate sensor  134 , temperature sensor  136 , a motion sensor  138 , the microphone  140 , the speaker  142 , a radio-frequency (RF) receiver  146 , a RF transmitter  148 , a camera  150 , an infrared sensor  152 , a photoplethysmogram (PPG) sensor  154 , an electrocardiogram (ECG) sensor  156 , an electroencephalography (EEG) sensor  158 , a capacitive sensor  160 , a force sensor  162 , a strain gauge sensor  164 , an electromyography (EMG) sensor  166 , an oxygen sensor  168 , an analyte sensor  174 , a moisture sensor  176 , or a combination thereof. Generally, each of the one or more sensors  130  are configured to output sensor data that is received and stored in the memory device  114  or one or more other memory devices. 
     While the one or more sensors  130  are shown and described as including each of the pressure sensor  132 , the flow rate sensor  134 , the temperature sensor  136 , the motion sensor  138 , the microphone  140 , the speaker  142 , the RF receiver  146 , the RF transmitter  148 , the camera  150 , the infrared sensor  152 , the photoplethysmogram (PPG) sensor  154 , the electrocardiogram (ECG) sensor  156 , the electroencephalography (EEG) sensor  158 , the capacitive sensor  160 , the force sensor  162 , the strain gauge sensor  164 , the electromyography (EMG) sensor  166 , the oxygen sensor  168 , the analyte sensor  174 , and the moisture sensor  176  more generally, the one or more sensors  130  can include a combination and any number of each of the sensors described and/or shown herein. 
     As described herein, the system  100  generally can be used to generate physiological data associated with a user (e.g., a user of the respiratory therapy system  120  shown in  FIGS.  2 A- 2 B ) during a sleep session. The physiological data can be analyzed to generate one or more sleep-related parameters, which can include any parameter, measurement, etc. related to the user during the sleep session. The one or more sleep-related parameters that can be determined for the user  210  during the sleep session include, for example, an Apnea-Hypopnea Index (AHI) score, a sleep score, a flow signal, a respiration signal, a respiration rate, an inspiration amplitude, an expiration amplitude, an inspiration-expiration ratio, a number of events per hour, a pattern of events, a stage, pressure settings of the respiratory device  122 , a heart rate, a heart rate variability, movement of the user  210 , temperature, EEG activity, EMG activity, arousal, snoring, choking, coughing, whistling, wheezing, or a combination thereof. 
     In some implementations, the physiological data generated by one or more of the sensors  130  can be used by the control system  110  to determine a sleep-wake signal associated with the user  210  during the sleep session and one or more sleep-related parameters. The sleep-wake signal can be indicative of one or more sleep states, including wakefulness, relaxed wakefulness, micro-awakenings, and/or sleep stages, including a rapid eye movement (REM) stage, a first non-REM stage (often referred to as “N1”), a second non-REM stage (often referred to as “N2”), a third non-REM stage (often referred to as “N3”), or a combination thereof. Methods for determining sleep states and/or sleep stages from physiological data generated by one or more of the sensors, such as sensors  130 , are described in, for example, WO 2014/047310, US 2014/0088373, WO 2017/132726, WO 2019/122413, and WO 2019/122414, each of which is hereby incorporated by reference herein in its entirety. 
     The sleep-wake signal can also be timestamped to determine a time that the user enters the bed, a time that the user exits the bed, a time that the user attempts to fall asleep, etc. The sleep-wake signal can be measured by the one or more sensors  130  during the sleep session at a predetermined sampling rate, such as, for example, one sample per second, one sample per 30 seconds, one sample per minute, etc. In some implementations, the sleep-wake signal can also be indicative of a respiration signal, a respiration rate, an inspiration amplitude, an expiration amplitude, an inspiration-expiration ratio, a number of events per hour, a pattern of events, pressure settings of the respiratory device  122 , or a combination thereof during the sleep session. The event(s) can include snoring, apneas, central apneas, obstructive apneas, mixed apneas, hypopneas, a mask leak (e.g., from the user interface  124 ), a restless leg, a sleeping disorder, choking, an increased heart rate, labored breathing, an asthma attack, an epileptic event, a seizure, or a combination thereof. The one or more sleep-related parameters that can be determined for the user during the sleep session based on the sleep-wake signal include, for example, a total time in bed, a total sleep time, a sleep onset latency, a wake-after-sleep-onset parameter, a sleep efficiency, a fragmentation index, or a combination thereof. 
     Generally, the sleep session includes any point in time after the user  210  has laid or sat down in the bed  230  (or another area or object on which they intend to sleep), and has turned on the respiratory device  122  and donned the user interface  124 . The sleep session can thus include time periods (i) when the user  210  is using the CPAP system but before the user  210  attempts to fall asleep (for example when the user  210  lays in the bed  230  reading a book); (ii) when the user  210  begins trying to fall asleep but is still awake; (iii) when the user  210  is in a light sleep (also referred to as stage 1 and stage 2 of non-rapid eye movement (NREM) sleep); (iv) when the user  210  is in a deep sleep (also referred to as slow-wave sleep, SWS, or stage 3 of NREM sleep); (v) when the user  210  is in rapid eye movement (REM) sleep; (vi) when the user  210  is periodically awake between light sleep, deep sleep, or REM sleep; or (vii) when the user  210  wakes up and does not fall back asleep. 
     The sleep session is generally defined as ending once the user  210  removes the user interface  124 , turns off the respiratory device  122 , and gets out of bed  230 . In some implementations, the sleep session can include additional periods of time, or can be limited to only some of the above-disclosed time periods. For example, the sleep session can be defined to encompass a period of time beginning when the respiratory device  122  begins supplying the pressurized air to the airway or the user  210 , ending when the respiratory device  122  stops supplying the pressurized air to the airway of the user  210 , and including some or all of the time points in between, when the user  210  is asleep or awake. 
     The pressure sensor  132  outputs pressure data that can be stored in the memory device  114  and/or analyzed by the processor  112  of the control system  110 . In some implementations, the pressure sensor  132  is an air pressure sensor (e.g., barometric pressure sensor) that generates sensor data indicative of the respiration (e.g., inhaling and/or exhaling) of the user of the respiratory therapy system  120  and/or ambient pressure. In such implementations, the pressure sensor  132  can be coupled to or integrated in the respiratory device  122 . The pressure sensor  132  can be, for example, a capacitive sensor, an electromagnetic sensor, a piezoelectric sensor, a strain-gauge sensor, an optical sensor, a potentiometric sensor, or a combination thereof. 
     The flow rate sensor  134  outputs flow rate data that can be stored in the memory device  114  and/or analyzed by the processor  112  of the control system  110 . In some implementations, the flow rate sensor  134  is used to determine an air flow rate from the respiratory device  122 , an air flow rate through the conduit  126 , an air flow rate through the user interface  124 , or a combination thereof. In such implementations, the flow rate sensor  134  can be coupled to or integrated in the respiratory device  122 , the user interface  124 , or the conduit  126 . The flow rate sensor  134  can be a mass flow rate sensor such as, for example, a rotary flow meter (e.g., Hall effect flow meters), a turbine flow meter, an orifice flow meter, an ultrasonic flow meter, a hot wire sensor, a vortex sensor, a membrane sensor, or a combination thereof. 
     The temperature sensor  136  outputs temperature data that can be stored in the memory device  114  and/or analyzed by the processor  112  of the control system  110 . In some implementations, the temperature sensor  136  generates temperatures data indicative of a core body temperature of the user  210  ( FIGS.  2 A- 2 B ), a skin temperature of the user  210 , a temperature of the air flowing from the respiratory device  122  and/or through the conduit  126 , a temperature in the user interface  124 , an ambient temperature, or a combination thereof. The temperature sensor  136  can be, for example, a thermocouple sensor, a thermistor sensor, a silicon band gap temperature sensor or semiconductor-based sensor, a resistance temperature detector, or a combination thereof. 
     The motion sensor  138  outputs motion data that can be stored in the memory device  114  and/or analyzed by the processor  112  of the control system  110 . The motion sensor  138  can be used to detect movement of the user  210  during the sleep session, and/or detect movement of any of the components of the respiratory therapy system  120 , such as the respiratory device  122 , the user interface  124 , or the conduit  126 . The motion sensor  138  can include one or more inertial sensors, such as accelerometers, gyroscopes, and magnetometers. In some implementations, the motion sensor  138  alternatively or additionally generates one or more signals representing bodily movement of the user, from which may be obtained a signal representing a sleep state of the user; for example, via a respiratory movement of the user. In some implementations, the motion data from the motion sensor  138  can be used in conjunction with additional data from another sensor  130  to determine the sleep state of the user. 
     The microphone  140  outputs sound data that can be stored in the memory device  114  and/or analyzed by the processor  112  of the control system  110 . The microphone  140  can be used to record sound(s) during a sleep session (e.g., sounds from the user  210 ) to determine (e.g., using the control system  110 ) one or more sleep-related parameters, as described in further detail herein. The microphone  140  can be coupled to or integrated in the respiratory device  122 , the user interface  124 , the conduit  126 , or the user device  170 . In some implementations, the system  100  includes a plurality of microphones (e.g., two or more microphones and/or an array of microphones with beamforming) such that sound data generated by each of the plurality of microphones can be used to discriminate the sound data generated by another of the plurality of microphones. 
     The speaker  142  outputs sound waves that are audible to a user of the system  100  (e.g., the user  210  of  FIGS.  2 A- 2 B ). The speaker  142  can be used, for example, as an alarm clock or to play an alert or message to the user  210  (e.g., in response to an event). The speaker  142  can be coupled to or integrated in the respiratory device  122 , the user interface  124 , the conduit  126 , or the external device  170 . 
     The microphone  140  and the speaker  142  can be used as separate devices. In some implementations, the microphone  140  and the speaker  142  can be combined into an acoustic sensor  141  (e.g., a SONAR sensor), as described in, for example, WO 2018/050913 and WO 2020/104465, which is hereby incorporated by reference herein in its entirety. In such implementations, the speaker  142  generates or emits sound waves at a predetermined interval and the microphone  140  detects the reflections of the emitted sound waves from the speaker  142 . The sound waves generated or emitted by the speaker  142  have a frequency that is not audible to the human ear (e.g., below 20 Hz or above around 18 kHz) so as not to disturb the sleep of the user  210  or the bed partner  220  ( FIGS.  2 A- 2 B ). Based at least in part on the data from the microphone  140  and/or the speaker  142 , the control system  110  can determine a location of the user  210  ( FIGS.  2 A- 2 B ) and/or one or more of the sleep-related parameters (e.g., a mouth leak status) described in herein such as, for example, a respiration signal, a respiration rate, an inspiration amplitude, an expiration amplitude, an inspiration-expiration ratio, a number of events per hour, a pattern of events, a sleep state, a sleep stage, pressure settings of the respiratory device  122 , or any combination thereof. In this context, a sonar sensor may be understood to include an active acoustic sensing, such as by generating/transmitting ultrasound or low frequency ultrasound sensing signals (e.g., in a frequency range of about 17-23 kHz, 18-22 kHz, or 17-18 kHz, for example), through the air. Such a system may be considered in relation to WO2018/050913 and WO 2020/104465 mentioned above. 
     The RF transmitter  148  generates and/or emits radio waves having a predetermined frequency and/or a predetermined amplitude (e.g., within a high frequency band, within a low frequency band, long wave signals, short wave signals, etc.). The RF receiver  146  detects the reflections of the radio waves emitted from the RF transmitter  148 , and this data can be analyzed by the control system  110  to determine a location of the user  210  ( FIGS.  2 A- 2 B ) and/or one or more of the respiratory- and/or sleep-related parameters described herein. An RF receiver (either the RF receiver  146  and the RF transmitter  148  or another RF pair) can also be used for wireless communication between the control system  110 , the respiratory device  122 , the one or more sensors  130 , the user device  170 , or a combination thereof. While the RF receiver  146  and RF transmitter  148  are shown as being separate and distinct elements in  FIG.  1   , in some implementations, the RF receiver  146  and RF transmitter  148  are combined as a part of an RF sensor  147  (e.g., a RADAR sensor). In some such implementations, the RF sensor  147  includes a control circuit. The specific format of the RF communication could be Wi-Fi, Bluetooth, etc. 
     In some implementations, the RF sensor  147  is a part of a mesh system. One example of a mesh system is a Wi-Fi mesh system, which can include mesh nodes, mesh router(s), and mesh gateway(s), each of which can be mobile/movable or fixed. In such implementations, the Wi-Fi mesh system includes a Wi-Fi router and/or a Wi-Fi controller and one or more satellites (e.g., access points), each of which include an RF sensor that the is the same as, or similar to, the RF sensor  147 . The Wi-Fi router and satellites continuously communicate with one another using Wi-Fi signals. The Wi-Fi mesh system can be used to generate motion data based on changes in the Wi-Fi signals (e.g., differences in received signal strength) between the router and the satellite(s) due to an object or person moving partially obstructing the signals. The motion data can be indicative of motion, breathing, heart rate, gait, falls, behavior, etc., or a combination thereof. 
     The camera  150  outputs image data reproducible as one or more images (e.g., still images, video images, thermal images, or a combination thereof) that can be stored in the memory device  114 . The image data from the camera  150  can be used by the control system  110  to determine one or more of the sleep-related parameters described herein, such as, for example, one or more events (e.g., periodic limb movement or restless leg syndrome), a respiration signal, a respiration rate, an inspiration amplitude, an expiration amplitude, an inspiration-expiration ratio, a number of events per hour, a pattern of events, a sleep state, a sleep stage, or a combination thereof. Further, the image data from the camera  150  can be used to, for example, identify a location of the user, to determine chest movement of the user  210 , to determine air flow of the mouth and/or nose of the user  210 , to determine a time when the user  210  enters the bed  230 , and to determine a time when the user  210  exits the bed  230 . 
     The infrared (IR) sensor  152  outputs infrared image data reproducible as one or more infrared images (e.g., still images, video images, or both) that can be stored in the memory device  114 . The infrared data from the IR sensor  152  can be used to determine one or more sleep-related parameters during a sleep session, including a temperature of the user  210  and/or movement of the user  210 . The IR sensor  152  can also be used in conjunction with the camera  150  when measuring the presence, location, and/or movement of the user  210 . The IR sensor  152  can detect infrared light having a wavelength between about 700 nm and about 1 mm, for example, while the camera  150  can detect visible light having a wavelength between about 380 nm and about 740 nm. In another example, the measurement is estimated by monitoring the acoustic reflection for a time period sufficient to capture time of flight to two or more sites of the physical obstruction, the physical obstruction having multiple sites extending through the airway of the user. In accordance with a specific example, the monitoring includes receiving and analyzing a reflection for a time period. 
     The PPG sensor  154  outputs physiological data associated with the user  210  ( FIGS.  2 A- 2 B ) that can be used to determine one or more sleep-related parameters, such as, for example, a heart rate, a heart rate variability, a cardiac cycle, respiration rate, an inspiration amplitude, an expiration amplitude, an inspiration-expiration ratio, estimated blood pressure parameter(s), or a combination thereof. The PPG sensor  154  can be worn by the user  210 , embedded in clothing and/or fabric that is worn by the user  210 , embedded in and/or coupled to the user interface  124  and/or its associated headgear (e.g., straps, etc.), etc. 
     The ECG sensor  156  outputs physiological data associated with electrical activity of the heart of the user  210  ( FIGS.  2 A- 2 B ). In some implementations, the ECG sensor  156  includes one or more electrodes that are positioned on or around a portion of the user  210  during the sleep session. The physiological data from the ECG sensor  156  can be used, for example, to determine one or more of the sleep-related parameters described herein. 
     The EEG sensor  158  outputs physiological data associated with electrical activity of the brain of the user  210 . In some implementations, the EEG sensor  158  includes one or more electrodes that are positioned on or around the scalp of the user  210  during the sleep session. The physiological data from the EEG sensor  158  can be used, for example, to determine a sleep state of the user  210  at any given time during the sleep session. In some implementations, the EEG sensor  158  can be integrated in the user interface  124  and/or the associated headgear (e.g., straps, etc.). 
     The capacitive sensor  160 , the force sensor  162 , and the strain gauge sensor  164  output data that can be stored in the memory device  114  and used by the control system  110  to determine one or more of the sleep-related parameters described herein. The EMG sensor  166  outputs physiological data associated with electrical activity produced by one or more muscles. The oxygen sensor  168  outputs oxygen data indicative of an oxygen concentration of gas (e.g., in the conduit  126  or at the user interface  124 ). The oxygen sensor  168  can be, for example, an ultrasonic oxygen sensor, an electrical oxygen sensor, a chemical oxygen sensor, an optical oxygen sensor, or a combination thereof. 
     The analyte sensor  174  can be used to detect the presence of an analyte in the exhaled breath of the user  210 . The data output by the analyte sensor  174  can be stored in the memory device  114  and used by the control system  110  to determine the identity and concentration of any analytes in the user  210 &#39;s breath. In some implementations, the analyte sensor  174  is positioned near a mouth of the user  210  to detect analytes in breath exhaled from the user  210 &#39;s mouth. For example, when the user interface  124  is a facial mask that covers the nose and mouth of the user  210 , the analyte sensor  174  can be positioned within the facial mask to monitor the user  210 &#39;s mouth breathing. In other implementations, such as when the user interface  124  is a nasal mask or a nasal pillow mask, the analyte sensor  174  can be positioned near the nose of the user  210  to detect analytes in breath exhaled through the user  210 &#39;s nose. In still other implementations, the analyte sensor  174  can be positioned near the user  210 &#39;s mouth when the user interface  124  is a nasal mask or a nasal pillow mask. In this implementation, the analyte sensor  174  can be used to detect whether any air is inadvertently leaking from the user  210 &#39;s mouth. In some implementations, the analyte sensor  174  is a volatile organic compound (VOC) sensor that can be used to detect carbon-based chemicals or compounds. In some implementations, the analyte sensor  174  can also be used to detect whether the user  210  is breathing through their nose or mouth. For example, if the data output by an analyte sensor  174  positioned near the mouth of the user  210  or within the facial mask (in implementations where the user interface  124  is a facial mask) detects the presence of an analyte, the processor  112  can use this data as an indication that the user  210  is breathing through their mouth. 
     The moisture sensor  176  outputs data that can be stored in the memory device  114  and used by the control system  110 . The moisture sensor  176  can be used to detect moisture in various areas surrounding the user (e.g., inside the conduit  126  or the user interface  124 , near the user  210 &#39;s face, near the connection between the conduit  126  and the user interface  124 , near the connection between the conduit  126  and the respiratory device  122 , etc.). Thus, in some implementations, the moisture sensor  176  can be positioned in the user interface  124  or in the conduit  126  to monitor the humidity of the pressurized air from the respiratory device  122 . In other implementations, the moisture sensor  176  is placed near any area where moisture levels need to be monitored. The moisture sensor  176  can also be used to monitor the humidity of the ambient environment surrounding the user  210 , for example the air inside the user  210 &#39;s bedroom. 
     In some implementations, the one or more sensors  130  also include a galvanic skin response (GSR) sensor, a blood flow sensor, a respiration sensor, a pulse sensor, a sphygmomanometer sensor, an oximetry sensor, a sonar sensor, a RADAR sensor, a blood glucose sensor, a color sensor, a pH sensor, an air quality sensor, a tilt sensor, a LIDAR sensor, a rain sensor, a soil moisture sensor, a water flow sensor, an alcohol sensor, or a combination thereof. 
     While shown separately in  FIG.  1   , a combination of the one or more sensors  130  can be integrated in and/or coupled to any one or more of the components of the system  100 , including the respiratory device  122 , the user interface  124 , the conduit  126 , the humidification tank  129 , the control system  110 , the user device  170 , or a combination thereof. For example, the acoustic sensor  141  and/or the RF sensor  147  can be integrated in and/or coupled to the user device  170 . In such implementations, the user device  170  can be considered a secondary device that generates additional or secondary data for use by the system  100  (e.g., the control system  110 ) according to some aspects of the present disclosure. In some implementations, at least one of the one or more sensors  130  is not coupled to the respiratory device  122 , the control system  110 , or the user device  170 , and is positioned generally adjacent to the user  210  during the sleep session (e.g., positioned on or in contact with a portion of the user  210 , worn by the user  210 , coupled to or positioned on the nightstand, coupled to the mattress, coupled to the ceiling, etc.). 
     The data from the one or more sensors  130  can be analyzed to determine one or more sleep-related parameters, which can include a respiration signal, a respiration rate, a respiration pattern, an inspiration amplitude, an expiration amplitude, an inspiration-expiration ratio, an occurrence of one or more events, a number of events per hour, a pattern of events, a sleep state, an apnea-hypopnea index (AHI), or any combination thereof. The one or more events can include snoring, apneas, central apneas, obstructive apneas, mixed apneas, hypopneas, a mask leak, a cough, a restless leg, a sleeping disorder, choking, an increased heart rate, labored breathing, an asthma attack, an epileptic event, a seizure, increased blood pressure, or any combination thereof. Many of these sleep-related parameters are physiological parameters, although some of the sleep-related parameters can be considered to be non-physiological parameters. Other types of physiological and non-physiological parameters can also be determined, either from the data from the one or more sensors  130 , or from other types of data. 
     The user device  170  ( FIG.  1   ) includes a display device  128 . The user device  170  can be, for example, a mobile device such as a smart phone, a tablet, a gaming console, a smart watch, a laptop, or the like. Alternatively, the user device  170  can be an external sensing system, a television (e.g., a smart television) or another smart home device (e.g., a smart speaker(s) such as Google Home, Amazon Echo, Alexa etc.). In some implementations, the user device is a wearable device (e.g., a smart watch). The display device  172  is generally used to display image(s) including still images, video images, or both. In some implementations, the display device  172  acts as a human-machine interface (HMI) that includes a graphic user interface (GUI) configured to display the image(s) and an input interface. The display device  172  can be an LED display, an OLED display, an LCD display, or the like. The input interface can be, for example, a touchscreen or touch-sensitive substrate, a mouse, a keyboard, or any sensor system configured to sense inputs made by a human user interacting with the user device  170 . In some implementations, one or more user devices can be used by and/or included in the system  100 . 
     While the control system  110  and the memory device  114  are described and shown in  FIG.  1    as being a separate and distinct component of the system  100 , in some implementations, the control system  110  and/or the memory device  114  are integrated in the user device  170  and/or the respiratory device  122 . Alternatively, in some implementations, the control system  110  or a portion thereof (e.g., the processor  112 ) can be located in a cloud (e.g., integrated in a server, integrated in an Internet of Things (IoT) device, connected to the cloud, be subject to edge cloud processing, etc.), located in one or more servers (e.g., remote servers, local servers, etc., or a combination thereof. 
     While system  100  is shown as including all of the components described above, more or fewer components can be included in a system for implementing the present disclosure. For example, a first alternative system includes the control system  110 , the memory device  114 , and at least one of the one or more sensors  130 . As another example, a second alternative system includes the control system  110 , the memory device  114 , at least one of the one or more sensors  130 , and the user device  170 . As yet another example, a third alternative system includes the control system  110 , the memory device  114 , the respiratory therapy system  120 , at least one of the one or more sensors  130 , and the user device  170 . Thus, various systems for implementing the present disclosure can be formed using any portion or portions of the components shown and described herein and/or in combination with one or more other components. 
     Generally, a user who is prescribed usage of a respiratory system will tend to experience higher quality sleep and less fatigue during the day after using the respiratory therapy system  120  during the sleep compared to not using the respiratory therapy system  120  (especially when the user suffers from sleep apnea or other sleep related disorders). However, many users do not conform to their prescribed usage because the user interface  124  is uncomfortable or cumbersome, or due to other side effects (e.g., dry mouth, dry lips, dry throat, discomfort, etc.). Users are more likely to fail to use the respiratory therapy system  120  as prescribed (or discontinue usage altogether) if they fail to perceive that they are experiencing any benefits (e.g., less fatigue during the day). 
     However, the side effects and/or the lack of improvement in sleep quality may be due to mouth leak rather than a lack of efficacy to the treatment. Thus, it is advantageous to characterize or determine a physical obstruction in the airway of the user and communicate information regarding the obstruction to the user or a third party to aid the user in obtaining higher quality sleep and/or improved treatment of sleep disordered breathing (SDB) involving apnea and/or hypopnea events, so that the user does not discontinue or reduce their usage of the respiratory therapy system  120  due to a perceived lack of benefit(s). 
     Referring generally to  FIGS.  2 A- 2 B , a portion of the system  100  ( FIG.  1   ), according to some implementations, is illustrated. A user  210  of the respiratory therapy system  120  and a bed partner  220  are located in a bed  230  and are laying on a mattress  232 . The user interface  124  (e.g., a full facial mask in  FIG.  2 A  or a nasal mask in  FIG.  2 B ) can be worn by the user  210  during a sleep session. The user interface  124  is fluidly coupled and/or connected to the respiratory device  122  via the conduit  126 . In turn, the respiratory device  122  delivers pressurized air to the user  210  via the conduit  126  and the user interface  124  to increase the air pressure in the throat of the user  210  to aid in preventing the airway from closing and/or narrowing during sleep. The respiratory device  122  can be positioned on a nightstand  240  that is directly adjacent to the bed  230  as shown in  FIG.  2 A , or more generally, on any surface or structure that is generally adjacent to the bed  230  and/or the user  210 . 
     In some implementations, the control system  110 , the memory  114 , any of the one or more sensors  130 , or a combination thereof can be located on and/or in any surface and/or structure that is generally adjacent to the bed  230  and/or the user  210 . For example, in some implementations, at least one of the one or more sensors  130  can be located at a first position  255 A on and/or in one or more components of the respiratory therapy system  120  adjacent to the bed  230  and/or the user  210 . The one or more sensors  130  can be coupled to the respiratory therapy system  120 , the user interface  124 , the conduit  126 , the display device  128 , the humidification tank  129 , or a combination thereof. 
     Alternatively or additionally, at least one of the one or more sensors  130  can be located at a second position  255 B on and/or in the bed  230  (e.g., the one or more sensors  130  are coupled to and/or integrated in the bed  230 ). Further, alternatively or additionally, at least one of the one or more sensors  130  can be located at a third position  255 C on and/or in the mattress  232  that is adjacent to the bed  230  and/or the user  210  (e.g., the one or more sensors  130  are coupled to and/or integrated in the mattress  232 ). Alternatively or additionally, at least one of the one or more sensors  130  can be located at a fourth position  255 D on and/or in a pillow that is generally adjacent to the bed  230  and/or the user  210 . 
     Alternatively or additionally, at least one of the one or more sensors  130  can be located at a fifth position  255 E on and/or in the nightstand  240  that is generally adjacent to the bed  230  and/or the user  210 . Alternatively or additionally, at least one of the one or more sensors  130  can be located at a sixth position  255 F such that the at least one of the one or more sensors  130  are coupled to and/or positioned on the user  215  (e.g., the one or more sensors  130  are embedded in or coupled to fabric, clothing  212 , and/or a smart device  270  worn by the user  210 ). More generally, at least one of the one or more sensors  130  can be positioned at any suitable location relative to the user  210  such that the one or more sensors  130  can generate sensor data associated with the user  210 . 
     In some implementations, a primary sensor, such as the microphone  140 , is configured to generate acoustic data associated with the user  210  during a sleep session. For example, one or more microphones (the same as, or similar to, the microphone  140  of  FIG.  1   ) can be integrated in and/or coupled to (i) a circuit board of the respiratory device  122 , (ii) the conduit  126 , (iii) a connector between components of the respiratory therapy system  120 , (iv) the user interface  124 , (v) a headgear (e.g., straps) associated with the user interface, or (vi) a combination thereof. 
     Additionally or alternatively, one or more microphones (the same as, or similar to, the microphone  140  of  FIG.  1   ) can be integrated in and/or coupled to a co-located smart device, such as the user device  170 , a TV, a watch (e.g., a mechanical watch or the smart device  270 ), a pendant, the mattress  232 , the bed  230 , beddings positioned on the bed  230 , the pillow, a speaker (e.g., the speaker  142  of  FIG.  1   ), a radio, a tablet, a waterless humidifier, or a combination thereof. 
     Additionally or alternatively, in some implementations, one or more microphones (the same as, or similar to, the microphone  140  of  FIG.  1   ) can be remote from the system  100  ( FIG.  1   ) and/or the user  210  ( FIGS.  2 A- 2 B ), so long as there is an air passage allowing acoustic signals to travel to the one or more microphones. For example, the one or more microphones can be in a different room from the room containing the system  100 . Based at least in part on an analysis of the acoustic data, a physical obstruction in the airway of a user can be characterize and/or determined. 
     Referring to  FIGS.  3 A and  3 B , an overview shows an airway  309  of a respiratory system  312  of a user  310  (e.g., a patient). The airway  309  includes, more specifically, a nasal airway  311  and a mouth airway  313 . The user  310  generally includes a nasal cavity, an oral cavity, a larynx, vocal folds, an esophagus, a trachea, a bronchus, lungs, alveolar sacs, a heart, and a diaphragm. More generally, the user  310  has a throat  320 , which includes a region(s) of the respiratory system  312  of the user  310  generally in the neck area of the user  310 . The diaphragm of the user  310  is a sheet of muscle that extends across the bottom of the rib cage of the user  310 . The diaphragm generally separates the thoracic cavity  330  of the user  310 , which contains the heart, lungs, and ribs, from the abdominal cavity  340  of the user  310 . As the diaphragm contracts, the volume of the thoracic cavity  330  increases and air is drawn into the lungs. A hypoglossal nerve  318  is generally involved in controlling movement of the tongue  316  and includes a plurality of nerve branches distributed to the extrinsic and intrinsic muscles of the tongue  316 . 
     Referring more specifically to  FIG.  3 B , the airway  309  of the user  310  includes the nasal cavity, nasal bone, lateral nasal cartilage, greater alar cartilage, nostrils (one shown), a lip superior, a lip inferior, the larynx, a hard palate, a soft palate, an oropharynx, a tongue, an epiglottis, the vocal folds, the esophagus, and the trachea. The respiratory system  312  of the user  310  facilitates gas exchange. The nose  350  and mouth  360  of the user  310  form the entrance, respectively, to the airway  309  of the user  310 . As best shown in  FIG.  3 A , the airway  309  include a series of branching tubes, which become narrower, shorter, and more numerous as they penetrate deeper into the lungs of the user  310 . The prime function of the lungs is gas exchange, allowing oxygen to move from the inhaled air into the venous blood and carbon dioxide to move in the opposite direction. The trachea divides into right and left main bronchi, which further divide eventually into terminal bronchioles. The bronchi make up conducting airways, and do not take part in gas exchange. Further divisions of the airway  309  leads to the respiratory bronchioles, and eventually to the alveoli. The alveolated region of the lungs is where the gas exchange takes place, and is referred to as the respiratory zone. 
     A range of respiratory disorders exist that can impact the user  310 . Certain disorders are characterized by particular events (e.g., apneas, hypopneas, hyperpneas, or any combination thereof). Examples of sleep-related and/or respiratory disorders include Periodic Limb Movement Disorder (PLMD), Restless Leg Syndrome (RLS), Sleep-Disordered Breathing (SDB), Obstructive Sleep Apnea (OSA), Cheyne-Stokes Respiration (CSR), respiratory insufficiency, Obesity Hyperventilation Syndrome (OHS), Chronic Obstructive Pulmonary Disease (COPD), Neuromuscular Disease (NMD), and chest wall disorders. 
     Obstructive Sleep Apnea (OSA), which is a form of Sleep Disordered Breathing (SDB), is characterized by events including occlusion or obstruction of the upper air passage during sleep resulting from a combination of an abnormally small upper airway  309  and the normal loss of muscle tone in the region of the tongue, soft palate and posterior oropharyngeal wall. The condition causes the affected patient to stop breathing for periods typically of 30 to 120 seconds in duration, sometimes 200 to 300 times per night. OSA often causes excessive daytime somnolence, and it may cause cardiovascular disease and brain damage. The syndrome is a common disorder, particularly in middle aged overweight males, although a person affected may have no awareness of the problem. 
     Cheyne-Stokes Respiration (CSR) is another form of sleep disordered breathing. CSR is a disorder of a patient&#39;s respiratory controller in which there are rhythmic alternating periods of waxing and waning ventilation known as CSR cycles. CSR is characterized by repetitive de-oxygenation and re-oxygenation of the arterial blood. It is possible that CSR is harmful because of the repetitive hypoxia. In some users, CSR is associated with repetitive arousal from sleep, which causes severe sleep disruption, increased sympathetic activity, and increased afterload. 
     Respiratory failure is an umbrella term for respiratory disorders in which the lungs are unable to inspire sufficient oxygen or exhale sufficient CO 2  to meet the user&#39;s needs. Respiratory failure may encompass some or all of the following disorders. A user with respiratory insufficiency (a form of respiratory failure) may experience abnormal shortness of breath on exercise. 
     Obesity Hyperventilation Syndrome (OHS) is defined as the combination of severe obesity and awake chronic hypercapnia, in the absence of other known causes for hypoventilation. Symptoms include dyspnea, morning headache and excessive daytime sleepiness. 
     Chronic Obstructive Pulmonary Disease (COPD) encompasses any of a group of lower airway diseases that have certain characteristics in common, such as increased resistance to air movement, extended expiratory phase of respiration, and loss of the normal elasticity of the lung. Examples of COPD are emphysema and chronic bronchitis. COPD is caused by chronic tobacco smoking (primary risk factor), occupational exposures, air pollution and genetic factors. Symptoms include: dyspnea on exertion, chronic cough and sputum production. 
     Neuromuscular Disease (NMD) encompasses many diseases and ailments that impair the functioning of the muscles either directly via intrinsic muscle pathology, or indirectly via nerve pathology. Some users suffering from NMD are characterized by progressive muscular impairment leading to loss of ambulation, being wheelchair-bound, swallowing difficulties, respiratory muscle weakness and, eventually, death from respiratory failure. Neuromuscular disorders can be divided into rapidly progressive and slowly progressive: (i) rapidly progressive disorders: characterized by muscle impairment that worsens over months and results in death within a few years (e.g. amyotrophic lateral sclerosis (ALS) and Duchenne muscular dystrophy (DMD) in teenagers); (ii) variable or slowly progressive disorders: characterized by muscle impairment that worsens over years and only mildly reduces life expectancy (e.g. limb girdle, Facioscapulohumeral and myotonic muscular dystrophy). Symptoms of respiratory failure in NMD include: increasing generalized weakness, dysphagia, dyspnea on exertion and at rest, fatigue, sleepiness, morning headache, and difficulties with concentration and mood changes. 
     Chest wall disorders are a group of thoracic deformities that result in inefficient coupling between the respiratory muscles and the thoracic cage. The disorders are usually characterized by a restrictive defect and share the potential of long term hypercapnic respiratory failure. Scoliosis and/or kyphoscoliosis may cause severe respiratory failure. Symptoms of respiratory failure include: dyspnea on exertion, peripheral edema, orthopnea, repeated chest infections, morning headaches, fatigue, poor sleep quality and loss of appetite. 
     These other disorders are characterized by particular events (e.g., snoring, an apnea, a hypopnea, a restless leg, a sleeping disorder, choking, an increased heart rate, labored breathing, an asthma attack, an epileptic event, a seizure, or any combination thereof) that occur when the individual is sleeping. While these other sleep-related disorders may have similar symptoms as insomnia, distinguishing these other sleep-related disorders from insomnia is useful for tailoring an effective treatment plan distinguishing characteristics that may call for different treatments. For example, fatigue is generally a feature of insomnia, whereas excessive daytime sleepiness is a characteristic feature of other disorders (e.g., PLMD) and reflects a physiological propensity to fall asleep unintentionally. 
     One or more of the disorders and associated symptoms discussed above can be better treated if the cause is related to a physical obstruction within the airway  309 . Determining characteristics, such as the location and size, of the physical obstruction are extremely beneficial in recommending and/or improving treatment of the affected person. 
     Referring to  FIG.  4   , a cepstrum illustrates an acoustic signal  400  with a varying acoustic amplitude along a Y-axis as the acoustic signal travel along an X-axis. The acoustic signal  400  has an acoustic amplitude that varies based generally on any encountered physical obstructions. The cepstrum is obtained via one or more signal processing methods. 
     The traveled path along the X-axis includes a pre-airway portion X 1  that refers to a distance of the acoustic signal  400  up to and including a mask location M 1  of a user  511  (illustrated in  FIG.  5   ). At the mask location M 1 , the acoustic signal  400  has a first amplitude Y 1  that is indicative at least in part of a first acoustic reflection  402 , which is caused by a mask at the mask location M 1 . The distance between a point of origin O of sound emission and the mask location M 1  is indicated as a first distance X 1 . The mask location M 1  ends at a mouth location M 2 . 
     Then, after encountering the mouth location M 2 , the acoustic signal  400  further travels into the airway  309  of the user a second distance X 2  and encounters a physical obstruction P. At the physical obstruction P, the acoustic signal has a second amplitude Y 2  that is indicative at least in part of a second acoustic reflection  404  (also referred to as “the acoustic reflection  404 ”), which is caused by the physical obstruction P. The physical obstruction P extends into the airway  309  a third distance X 3  after the second distance X 2 . Thus, the cepstrum identifies a distance (e.g., the first distance X 1 , the second distance X 2 , and or the third distance X 3 ) that is associated with the first and second acoustic reflections  402 ,  404 , the distance showing the location of the physical obstruction P within the airway  309  of the user  511  (illustrated in  FIG.  5   ). 
     The mask, according to some examples, is in the form of the user interface  124  described above and illustrated in  FIG.  2 A . For example, the mask is a facial mask that is positioned to cover the nose and mouth of the user. When the mask is placed at the mouth location M 2 , the cepstrum identifies the second distance X 2 , which is the distance between the mask and the physical obstruction P. 
     The first acoustic reflection  402  has a specific signal morphology that is different than the signal morphology of the second acoustic reflection  404 . For example, the first amplitude Y 1  is greater than the second amplitude Y 2 . In another example, the shape of the first acoustic reflection  402  is different than the shape of the second acoustic reflection  404 . According to some examples, the signal morphology includes an acoustic shape of an acoustic wave. 
     According to some implementations of the present disclosure, one or more of the physical characteristics of the physical obstruction P are estimated based on the signal morphology of one or more of the first acoustic reflection  402  and the second acoustic reflection  404 . 
     Referring to  FIG.  5   , a system  500  includes an acoustic device  502  for generating the acoustic signal  400 . The acoustic device  502  includes a sound generator  504  and a sound detector  506 . In some implementations, the sound detector  506  may act as a passive acoustic sensor suitable for suitable for generating passive acoustic data associated with the breathing of a user. 
     According to some implementations of the present disclosure, the sound generator  504  is in the form of a speaker or a motor. For example, the speaker is the speaker  142  described above and illustrated in  FIG.  1   . In another example, the motor is included in the respiratory device  122  described above and illustrated in  FIGS.  1  and  2 A . Thus, for example the acoustic signal is emitted by either or both of a speaker or motor  502 . 
     According to some implementations of the present disclosure, at least one of the sound generator  504  and the sound detector  506  is not necessarily included in the acoustic device  502 , but is generally included anywhere in the system  500 . According to some implementations, the system  500  is generally similar or identical to the system  100  that is described above and is illustrated in  FIG.  1   , and includes one or more components of the system  100 . 
     According to some implementations of the present disclosure, the speaker  502  generates the acoustic signal  400  in the form of a sound, the sound being one or more of a standard sound (e.g., an original, unmodified sound from an off-the-shelf sound application), a custom sound, an inaudible frequency, a white noise sound, a broad band impulse, a continuous sinusoidal waveform, a square waveform, a sawtooth waveform, and a frequency modulated sinusoid (e.g., chirp). According to some other implementations, the acoustic signal  400  is in the form of one or more of an audible sound, an ultrasonic sound, a white noise signal, a Gaussian white noise signal, a brown noise signal, a pink noise signal, a wide-band signal, a narrow-band signal, or combinations thereof. 
     According to some implementations of the present disclosure, the sound detector  506  is in the form of a microphone. For example, the microphone is the microphone  140  described above and illustrated in  FIG.  1   . 
     The system  500  can further include a memory device  514  for storing machine-readable instructions. According to some implementations, the memory device  514  is in the form of the memory device  114  described above and illustrated in  FIG.  1   . 
     The system  500  can further include a control system  510  having one or more processors  512 . The processors  512  are communicatively coupled to the acoustic device  502 . According to some implementations of the present disclosure, the control system  510  is in the form of the control system  110  described above and illustrated in  FIG.  1   . According to some implementations of the present disclosure, the processors  512  are in the form of the processors  112  described above and illustrate in  FIG.  1   . 
     The control system  510  is configured to execute the machine-readable instructions stored in the memory device  514  to detect the acoustic reflection  404  of the acoustic signal  400  emitted by the acoustic device  502  into the airway  309  of a user  511 . The acoustic reflection  404  is caused by the physical obstruction P within the airway  309  of the user  511 . 
     According to some implementations, the system  500  optionally includes a motion sensor  516  for detecting a position of the user  511 . According to some implementations of the present disclosure, the motion sensor  516  is in the form of the motion sensor  138  described above and illustrated in  FIG.  1   . The motion sensor  516  includes, optionally, one or more of a camera, a bed sensor, an accelerometer, a sonar sensor, a radio frequency (RF) sensor such as an impulse-radio ultra wideband (IR-UWB) sensor, and/or any other sensors described above in reference to the motion sensor  138 , acoustic sensor  141  or RF sensor  147 . 
     The motion sensor  516  detects a user position, including a user location and/or a user orientation. For example, the user location identifies the user position relative to the system  500  and/or relative to a bed (such as the bed  230  described above and illustrated in  FIG.  2 A ). In another example, the user location identifies the user orientation relative to the system  500  and/or relative to the bed. 
     Acoustic data associated with the acoustic reflection  404  is analyzed, and an occurrence of the physical obstruction P in the airway  309  of the user  511  is characterized. The characterization of the occurrence of the physical obstruction P is based at least in part on the analyzed acoustic data. According to some implementations of the present disclosure, the characterization is based solely on the analyzed acoustic data. That is, in some implementations, the methods and systems disclosed herein are capable of characterizing an apnea event based solely on the analyzed acoustic data. The physical obstruction P is indicative of an apnea event or a hypopnea event in the user  511 . Analysis of acoustic reflections to detect obstructions in a conduit of a respiratory system is disclosed in WO 2010/091462, which is hereby incorporated by reference herein in its entirety. Similar analysis may be employed in the present case to characterize a physical obstruction in the airway of an individual user, wherein the physical obstruction is an obstructive apnea, a central apnea or a hypopnea. 
     According to some implementations of the present disclosure, the acoustic data includes a physical measurement of at least one of a mouth area, a nose area, and a throat area of the user  511  (which are more clearly illustrated in  FIGS.  3 A and  3 B ). 
     The characterization of the occurrence of the physical obstruction P includes, for example, determining a site location of the physical obstruction P within the airway  309  of the user  511 . According to the illustration of  FIG.  5   , the physical obstruction P is after the second distance X 2  from the point of entry of the acoustic signal into the user&#39;s airway  309 , e.g., the mouth  360  of the user  511 . The characterization further includes, according to other examples, determining the site size, site shape, and/or any other physical characteristics of the physical obstruction P. 
     According to some implementations of the present disclosure, one or more of the physical characteristics of the physical obstruction P are estimated based on a change in acoustic impedance, as indicated by the analyzed acoustic data. According to some implementations of the present disclosure, one or more of the physical characteristics of the physical obstruction P are estimated based on analyzing the acoustic reflection  404  that occurs from the physical obstruction P within the airway  309  of the user  511 . 
     According to some implementations of the present disclosure, one or more of the physical characteristics of the physical obstruction P are estimated based on an analysis of (a) one or more acoustic waves reflecting at the mouth location M 2  (before an entry into the airway  309  of the user  511 ), and (b) one or more acoustic waves reflecting at the physical obstruction P (after the entry into the airway  309  of the user  511 ). According to some other implementations of the present disclosure, one or more of the physical characteristics of the physical obstruction P are estimated based on the second distance X 2  that acoustic waves travel into the airway  309  of the user  511  past the mouth location M 2 . According to some examples, the entry point into the airway  309  is a mouth area (i.e., at the mouth location M 2 ) or a nose area of the user  511 . 
     According to some implementations of the present disclosure, one or more of the physical characteristics of the physical obstruction P are estimated based on one or more characteristics of the user  511 , such as age, sex, weight, mouth size, neck size, or type of mask currently used by the user  511 . 
     According to some implementations of the present disclosure, the acoustic signal  400  is emitted at specific monitoring times after detecting that an apnea event or a hypopnea event is occurring, e.g., after detecting using a respiratory device that an apnea event or a hypopnea event is occurring. For example, the specific monitoring times are selected to be at intervals of 0.1 seconds for a duration of at least 4 seconds. 
     According to some implementations of the present disclosure, the acoustic signal  400  is emitted intermittently. For example, the acoustic signal  400  is emitted every 0.01 seconds to every 10 seconds of a predetermined time period. According to one example, the predetermined time period is between 10:00 pm and 6:00 am, during a typical sleeping period of the user  511 . 
     According to some implementations of the present disclosure, the acoustic signal  400  is emitted continuously. For example, the acoustic signal  400  is emitted continuously during a predetermined time period. According to one example, the predetermined time period is between 10:00 pm and 6:00 am, during a typical sleeping period of the user  511 . 
     According to some implementations of the present disclosure, the acoustic reflection  404  is monitored for a time period sufficient to capture time of flight to two or more sites of the physical obstruction P. For example, the physical obstruction P has multiple sites that include a first site S 1  and a second site S 2  extending through the airway  309  of the user  511 . In other words, the physical obstruction P is not necessarily restricted to a single point in the airway  309 , but optionally extends along the airway  309 . 
     According to some implementations of the present disclosure, a minimum time period for monitoring the acoustic reflection  404  is at least about 0.015 seconds from the time when the acoustic signal  400  is emitted. According to other implementations of the present disclosure, the minimum time period is at least about 4 seconds from the time when the acoustic signal  400  is emitted. 
     According to some implementations of the present disclosure, the acoustic data is sampled during an entire apnea event or hypopnea event. For example, a typical apnea event is defined as cessation of breathing for a minimum of 10 seconds. However, the apnea event may last between about 10 seconds and about 30 seconds. In some cases, the apnea event may be over a minute. Accordingly, the acoustic data, assuming that monitoring of the physical obstruction P starts at the onset of the apnea event, is sampled for a minimum of 10 seconds, until the apnea event ends. According to an exemplary implementation of the present disclosure, a duration of the physical obstruction P is determined by monitoring and determining the onset and the end of the physical obstruction P. 
     According to some implementations of the present disclosure, the occurrence of the physical obstruction P is characterized within about 0.25 seconds from the onset of the physical obstruction. Thus, according to this example, the characterization of the physical obstruction P (e.g., location and size) is achieved very quickly. 
     According to some implementations of the present disclosure, the occurrence of the physical obstruction P is characterized after approximately 4-6 seconds from the start of the apnea event or the hypopnea event. The respective event is optionally determined independently with a different system, such as the respiratory therapy system  120  described above in reference to  FIGS.  1 ,  2 A, and  2 B . For example, the respiratory therapy system  120  is a PAP system, a CPAP system, a BPAP system, a VPAP system, or a combination thereof. 
     According to some implementations of the present disclosure, the system  500  includes the respiratory therapy system  120  (illustrated in  FIGS.  1 ,  2 A, and  2 B ) in the form of the PAP system  120  for treating obstructive sleep apnea. Optionally, the PAP system  120  is capable of independently determining an occurrence of an apnea event or hypopnea event, as discussed above in reference to  FIGS.  1 ,  2 A, and  2 B . 
     According to some implementations, as discussed above and illustrates in  FIGS.  1 ,  2 A , and  2 B, the PAP system  120  includes a respiratory device  122  that delivers pressurized air to a user. Flow data and/or pressure data associated with the pressurized air is used to independently determine the occurrence of the apnea event or hypopnea event. The PAP system  120  includes at least one of an external pressure sensor and/or an internal pressure sensor for outputting the flow data and/or the pressure data. The external pressure sensor and/or the internal pressure are optionally in the form of the pressure sensor  132  and/or the flow rate sensor  134  (described above and illustrated in  FIG.  1   ). 
     The PAP system  120 , as incorporated in the system  500 , optionally includes a user interface similar to or identical to the user interface  124  described above and illustrates in  FIGS.  1 ,  2 A, and  2 B  for positioning on the user  511 . The user interface includes, a nasal mask, a nasal pillow mask, and/or a full-face mask. 
     According to some implementations in which the system  500  includes the respiratory therapy system  120  (illustrated in  FIGS.  1 ,  2 A, and  2 B ), the control system  500  is configured to supply pressurized air via the respiratory therapy system  120  to the airway  309  of the user  511  during a sleep session. The control system  500  receives flow data associated with the pressurized air (e.g., via the flow rate sensor  134  illustrated in  FIG.  1   ), and determines, based on the flow data, an occurrence of an apnea event or a hypopnea event. The control system  500  emits the acoustic signal  400 , via the acoustic device  502 , into the airway  309  of the user  511  during the apnea event or the hypopnea event. The acoustic reflection  404  is received based on the physical obstruction P within the airway  309  of the user  511 . Acoustic data associated with the acoustic reflection  404  is analyzed, and physical characteristics of the physical obstruction P are characterized based, at least in part, on the analyzed acoustic data. 
     According to some implementations of the present disclosure, the acoustic reflection may be processed to decouple reflections in space and/or time by estimating a cepstrum signal. This can be achieved by processing the acoustic reflection in a series of stages: calculate the frequency spectrum of the acoustic reflection via a fast Fourier transform; take the natural logarithm of the absolute magnitude of this frequency spectrum; and perform the inverse Fourier transform and calculate the real part of the signal to produce a cepstrum. This process may be repeated over multiple time intervals and an average cepstrum may be estimated. The resulting cepstrum signal transforms the acoustic response into the quefrency domain which is analogous to separating reflections in time and/or distance. The cepstrum signal could then be further processed to characterize the physical obstruction using one or more statistical analysis, pattern recognition algorithms, and/or machine learning or deep learning techniques. 
     According to some implementations of the present disclosure, the acoustic data includes a plurality of audio samples. According to some further implementations of the present disclosure, the multiple audio samples are averaged to provide an average result of the acoustic reflection  404 . The audio samples correspond to a plurality of acoustic reflections received over a period of time, such as a predetermined period of time. 
     According to some implementations of the present disclosure, the control system  510  is further configured to identify a location of a body organ of the user  511 , e.g., a lung. Thus, instead of or in addition to identifying the physical obstruction P, the control system  510  provides an additional medical benefit of identifying other body organs. By identifying a lung(s) and the location thereof based on the reflected acoustic signal, one can better estimate the location of a physical obstruction in the airway. 
     Referring to  FIG.  6   , according to some implementations of the present disclosure, a determination is made that the airway  309  is in a partially collapsed state PC. The partially collapsed state PC indicates that a hypopnea event is occurring. Acoustic data shows that the acoustic reflection  404  is represented having a first shape  600  with a first amplitude  602 . 
     Referring to  FIG.  7   , according to some implementations of the present disclosure, a determination is made that the airway  309  is in a fully collapsed state FC. The fully collapsed state FC indicates that a hypopnea event is occurring. Acoustic data shows that the acoustic reflection  404  is represented having a second shape  700  with a second amplitude  702 , which are different than the first shape  600  and the first amplitude  602  of the acoustic data associated with the hypopnea event described above and illustrated in  FIG.  6   . 
     The first amplitude  602  or the second amplitude  702  generally refer to features in a cepstrum signal, such as peaks or troughs. For purposes of analysis, any segment of a signal that deviates above or below zero and its amplitude are of relevance. For example, segment features of relevance include a segment, height, a segment width for any given peak, an area under a curve (and whether it is +ve or −ve). In reference to a physical obstruction, a +ve bump in the cepstrum signal is likely to take on a different shape that is indicative of the physical obstruction. 
     For example, occlusion of a conduit and signatures in the acoustic data includes baseline acoustic signatures that are determined from acoustic data for a headgear conduit under normal non-occluded operation during a therapy session, along with varying degrees of occlusion, and varying locations of occlusion within regions prone for occlusion for a headgear conduit. The baseline acoustic signatures can then be compared to actual acoustic signatures (e.g., cepstrums) to identify anomalies in a headgear conduit and determine that the anomaly is due to an occlusion. In some implementations, an estimate of the degree of occlusion in the headgear conduit can be estimated, such as assigning a percentage of blockage, or partial blockage, or full blockage. Baseline data for comparison to operational acoustic signatures can be stored in a digital library of baseline acoustic signatures and/or developed through training algorithms for acoustic data for a particular respiratory therapy system or from a collection of respiratory therapy systems. 
     In some implementations, it is contemplated that a determined signature can be analyzed by a machine learning algorithm having learned patterns associated with occluded and/or non-occluded headgear conduits during operation of a therapy session, along with learned patterns with varying degrees of occlusion, and varying locations of occlusion within regions prone for occlusion in a headgear conduit. 
     In some implementations, a method of detecting an occlusion is based on determining if an identified anomaly in a determined acoustic signature is an occlusion by inputting analyzed acoustic data into a machine learning model. In one or more implementations, the machine learning model can be supervised or unsupervised. In one or more implementations, the machine learning model can be a neural network. In one or more implementations, the neural network can be a deep neural network or a shallow neural network. In one or more implementations, the deep neural network can be a convolutional neural network. According to some aspects of the method, the deep neural network includes one or more convolutional layers and one or more max pooling layers. 
     In some implementations, one or more acoustic signatures can be fed into, for example, a machine learning model (e.g., linear regression, logistic regression, nearest neighbor, decision trees, PCA, naive Bayes classifier, and k-means clustering, random forest, etc.) and return an identification of an anomaly in a headgear conduit (e.g., occluded, non-occluded, degree of occlusion). In one or more implementations, determining if an identified anomaly in a determined acoustic signature relates to an occlusion of a headgear conduit can be based, at least in part, on one or more determined acoustic signatures matching one or more known signatures of headgear conduits of a headgear interface with a known degree of occlusion (e.g., fully occluded, partially occluded, non-occluded). The determination that an identified anomaly relates to an occlusion can be determined based on a matching determined signatures with known signatures. 
     In some implementations, it is contemplated that various features or signatures derived from generated acoustic data for an acoustic signal can be determined, such as a peak height or peak heights for a signal over time. Changes between a peak height for a non-occluded headgear conduit and a peak height for an occluded headgear conduit (or varying degrees of occlusion) can be analyzed. The analysis can then identify anomalies in a headgear conduit and determine that the anomaly is due to an occlusion. For example, if a peak height is above a certain threshold value, the analysis would determine that the anomaly is an occlusion in the headgear conduit. If the peak height is below a certain value, the analysis would determine the anomaly is not an occlusion in the headgear conduit. 
     The present technology contemplates that in response to determining an identified anomaly from a determined acoustic signature relates to an occlusion, information related to a detected occlusion in a headgear conduit is stored for analysis by a body position algorithm module and/or a body movement algorithm module associated with the respiratory therapy system. In some implementations, the information related to a detected occlusion in one or more headgear conduits can be inputted into a body position algorithm module and/or a body movement algorithm module associated with the respiratory therapy system. An occlusion of a headgear conduit would be expected to indicate that a user&#39;s head/body is in a side position/orientation. 
     In some implementations, an acoustic signature in the context of a cepstrum is contemplated to include a certain pattern appearing in the cepstrum. In determining is an anomaly (e.g., an occlusion) exists in a headgear conduit, the analysis can include looking at one cepstrum or many cepstrums (e.g., combined). A cepstrum can include multiple features that together can be indicative of a blocked headgear conduit. Cepstrum features of interest can include the location, the amplitude, and the shape of various bumps, peaks, and troughs in the cepstrum plot. For a conduit occlusion, a change in the cepstrum in a region on interest of the headgear conduit (e.g., regions  450   a ,  450   b  from  FIG.  4   ) is typically characterized by a positive peak in the cepstrum plot corresponding to the point of occlusion (i.e. narrowing) of the headgear conduit. However, the conduit occlusion may additionally or alternatively be characterized by one or more negative peaks in the cepstrum plot corresponding to the points of widening of the acoustic pathway in the vicinity of the point of occlusion of the headgear conduit. Other features of interest can include the peak height (amplitude), the peak prominence, the skewness, the kurtosis, areas above/below lines of intersection, location or amplitude of fiducial points, frequency content, the number of peaks, standard deviation, and the distance between peaks or any combination of the above. 
     In some implementations, processing or preprocessing can also be completed on the audio data, such as filtering or first or second derivatives or integrals. For example, analysis of generated acoustic data can include calculating a derivative of the cepstrum to determine a rate of change of the cepstrum signal. The derivative of the cepstrum can provide additional information that is used to detect conduit occlusions. The derivative of the cepstrum also allows the identification of alternate features from the same input. 
     Although the present disclosure describes the determination of an acoustic signature in the context of a cepstrum for the detection of conduit occlusions, it is contemplated that acoustic signatures may be detected by similar operations in one or more spectrums, time domain, auto-correlation, discrete cosine transform, cross-correlation, Mel-Frequency cepstral coefficients, linear predictive coding, wavelet decomposition, cepstrum, power cepstrum, and/or complex cepstrum. 
     In some implementations, the acoustic signal can be emitted into the tube connected to the user interface via an audio transducer, such as a speaker. Alternatively, or in addition, the acoustic signal can be emitted into the tube via a motor of the respiratory therapy device connected to the tube. For example, the motor of the respiratory therapy device can emit sound from the rotation of its fan blades as it generates air flow and forces air through the tube. The emitted sound can be the acoustic signal, or at least part of the acoustic signal. 
     In some implementations, the acoustic signal can be a sound audible to a human (e.g., an average human with average human hearing). A sound is audible to a human when the sound has an amplitude that is loud enough for detection by the human and when it has a frequency within the frequency range of human hearing, which is generally about 20 Hz to about 20,000 Hz. In some implementations, the acoustic signal can be a sound that is inaudible to a human (e.g., an average human with average human hearing). A sound is inaudible when the sound has an amplitude that is less than the lowest perceptible amplitude of a human. For example, in one or more implementations, the acoustic signal can be an ultrasonic sound that has a frequency that is higher than the highest perceptible frequency for human hearing. 
     In one or more implementations, the generated acoustic data can be generated from a plurality of acoustic reflections of a plurality of acoustic signals. The acoustic signals can be the same acoustic signal that is repeatedly emitted, or can be different acoustic signals that are emitted together, forming a single acoustic signal, or can be emitted separately. When there are multiple acoustic signals for multiple acoustic reflections, the generated acoustic data can be an average of the plurality of acoustic reflections of the plurality of the acoustic signals. 
     In some implementations, the generated acoustic data can include a primary reflection within the acoustic reflection of the acoustic signal. Additionally, or in the alternative, the generated acoustic data can include a secondary reflection within the acoustic reflection of the acoustic signal. Additionally, or in the alternative, the generated acoustic data can include a tertiary reflection within the acoustic reflection of the acoustic signal. The generated acoustic data can be analyzed from the primary, secondary, or tertiary reflections. For example, the acoustic signatures used to detect or identify a conduit occlusion can relate to the primary reflection within the generated acoustic data. Alternatively, the acoustic signatures used to detect or identify a conduit occlusion can relate to a reflection other than the primary reflection within the generated acoustic data, such as a secondary reflection, a tertiary reflection, etc. Alternatively, the acoustic signatures used to detect or identify a conduit occlusion can include combinations of reflections, such as the first and second reflections; the first and third reflections; the first, second and third reflections; the second and third reflections, etc. 
     In some implementations, the magnitudes of a cepstrum corresponding to a primary reflection may be larger than the magnitudes of a cepstrum corresponding to a secondary reflection. However, the shapes of the plots for the primary and secondary reflections may be similar. In one or more implementations, acoustic signatures in a secondary reflection can be used to confirm acoustic signatures in a primary reflection with respect to detecting a conduit occlusion of the user interface. In one or more implementations, the presence/absence of additional reflections (e.g., a secondary reflection and/or tertiary reflection) can be used as an acoustic signature for detecting a conduit occlusion of the user interface. In one or more implementations, the ratio of the secondary reflection (and/or tertiary reflection) with respect to the primary reflection can be used as an acoustic signature for detecting an occlusion of the user interface. 
     The respiratory therapy system analyzes the generated acoustic data to correlate the occlusion or non-occlusion features of the headgear conduit to one or more signatures within the generated acoustic data. The one or more signatures can include, for example, a maximum magnitude of a transform, a minimum magnitude of a transform, a standard deviation of a transform, a skewness of a transform, a kurtosis of a transform, a median of an absolute value of a transform, a sum of the absolute value of a transform, a sum of a positive area of a transform, a sum of a negative area of a transform, a fundamental frequency, an energy corresponding to the fundamental frequency, a mean energy, at least one resonant frequency of a combination of the conduit and the user interface, a change in the at least one resonant frequency, a number of peaks within a range, a peak prominence, distance between peaks, or a combination thereof. In one or more implementations, one or more of the signatures can be changes in one or more of the above aspects over time. The changes can be during a series of iterations of comparing determined acoustic signatures with baseline acoustic signatures, such as consecutive iterations over the course of a predetermined time period (e.g., ten seconds, twenty seconds, thirty seconds, one minute, a few minutes). 
     In some implementations, analyzing the generated acoustic data includes generating a spectrum of the generated acoustic data, such as calculating a transform (e.g., Discrete Fourier Transform DFT, or Discrete Cosine Transform DCT) of the generated acoustic data. Thereafter, analyzing the generated acoustic data can further include calculating a logarithm of the spectrum. Thereafter, analyzing the generated acoustic data can include calculating a cepstrum of the logarithm spectrum, such as calculating an inverse transform (e.g., iDFT or iDCT) of the logarithm spectrum. The calculated cepstrum can then be analyzed for one or more acoustic signatures that can be used to identify anomalies in a headgear conduit and determine if the identified anomaly relates to an occlusion. 
     In some implementations, the analysis of the generated acoustic data does not require analyzing all of the generated acoustic data. For example, analyzing the generated acoustic data can include selecting a segment of the above-described spectrum. After selecting the segment, the analysis can further include calculating a direct transform (e.g., DFT or DCT) of the segment of the spectrum. From the direct transform, the one or more features of the user interface can be correlated to one or more signatures within the Fourier transform of the segment to identify the category of the user interface. Alternatively, analyzing the generated acoustic data can include selecting a segment of the log spectrum of the generated acoustic data described above. From the log spectrum, the analysis can include calculating a transform of the log spectrum, such as a Fourier transform of the log spectrum. Alternatively, the analysis can include calculating an inverse transform of the log spectrum, or an inverse transform of the transform of the log spectrum. 
     In some implementations, a segment of the generated acoustic signal can be selected for analysis by trimming the generated acoustic data based on where within the generated acoustic data the one or more signatures that indicate where occlusions in the headgear user interface are generally located. This is generally based on a known location of the microphone and a known length of tube, at the end of which the headgear user interface and headgear conduits are located. 
     In some implementations, the acoustic signatures used to identify an occlusion in a headgear user interface can relate to the primary reflection within the generated acoustic data. Alternatively, the signatures used to categorize the user interface can relate to a reflection other than the primary reflection within the generated acoustic data, such as a secondary reflection, a tertiary reflection, etc. Alternatively, the signatures used to categorize the user interface can include combinations of reflections, such as the first and second reflections; the first and third reflections; the first, second and third reflections; the second and third reflections, etc. 
     According to some implementations, the determination of an occlusion in a headgear user interface can include the one or more signatures of the cepstrum, e.g., one or more peaks or troughs in the cepstrum, having a maximum magnitude larger than a threshold. 
     In some implementations, calculating the cepstrum can be achieved by processing the acoustic reflection in a series of stages: calculate the frequency spectrum of the acoustic reflection via a fast Fourier transform; take the natural logarithm of the absolute magnitude of this frequency spectrum; and perform the inverse Fourier transform and calculate the real part of the signal to produce a cepstrum. This process may be repeated over multiple time intervals and an average cepstrum may be estimated. The resulting cepstrum signal transforms the acoustic reflection into the quefrency domain which is analogous to separating reflections in time and/or distance. In one or more implementations, the cepstrum can be calculated using a fast Fourier transform of 4096 samples of the acoustic reflection with, optionally, 50% overlap of the samples. These 4096 samples represent about 0.2 seconds of generated acoustic data. 
     In some implementations, the analyzing of the generated acoustic data can include calculating a derivative of the cepstrum to determine a rate of change of the cepstrum signal. The derivative of the cepstrum can provide additional information that is determine if an identified anomaly relates to an occlusion. 
     In some implementations, the respiratory therapy system analyzing the generated acoustic data can include normalizing the generated acoustic data. Normalizing the generated acoustic data can account for confounding conditions. The confounding conditions can be, for example, microphone gain, breathing amplitude, therapy pressure, varying tube length, curled or stretched tube, different therapy pressures, ambient temperature, or a combination thereof. 
     Referring to  FIG.  8   , a mobile phone  800  implements one or more of the components and functions discussed above in reference to the system  500  illustrated in  FIG.  5   . The mobile phone  800  includes a display  802  on which a recommendation  804  is provided, in response to determining the occurrence of and/or characterizing the physical obstruction P (illustrated in  FIG.  5   ). For example, the recommendation  804  includes identifying a user as a candidate for corrective surgery. In another example, the recommendation  804  includes identifying a user as a candidate for alternative therapy. According to yet another example, the recommendation  804  identifies therapy settings and recommended changes to the therapy settings. According to yet another example, the recommendation is based on a user position, as determined by the motion sensor  516  (described above and illustrated in  FIG.  5   ). According to yet another example, the recommendation  804  includes suggesting a sleep position for the user. 
     According to some implementations of the present disclosure, the recommendation  804  is further based on a user&#39;s medical history, in addition to physical characteristics of the physical obstruction P. For example, the recommendation  804  is further based on previous physical obstructions within the airway of the user. 
     According to some implementations of the present disclosure, the recommendation  804  includes identifying one or more masks suitable for use in a respiratory system (such as the PAP respiratory therapy system  120  described above in reference to  FIGS.  1 ,  2 A, and  2     b ). The mask includes, for example, a nasal mask, a nasal pillow mask, and a full-face mask. 
     Referring to  FIG.  9   , a mobile phone  900  implements one or more of the components and functions discussed above in reference to the system  500  illustrated in  FIG.  5   . The mobile phone  900  includes a display  902  on which various options are displayed, in response to determining the occurrence of the physical obstruction P (illustrated in  FIG.  5   ). For example, the display  902  shows instructions for adjusting settings  904  of a therapy for treatment of apnea or hypopnea in real time. The settings  804  are automatically changed or are manually changed by a user or other person. In another example, the display  902  shows instructions for personalized coaching  906 . Optionally, in addition to or instead of the display  902 , the instructions are provided in audio form via a speaker  942  (which is similar or identical to the speaker  142  described above and illustrated in  FIG.  1   ). The personalized coaching instructions  906  include, for example, changes, such as lifestyle changes, directed to obesity, sleeping position, or sleep hygiene. By “sleep hygiene,” it is meant one or more of a recommended time to go to bed and/or to go to sleep, a recommended time to get out of bed and/or to wake, optimal lighting and/or temperature conditions of the sleep environment, etc. 
     According to some implementations of the present disclosure, the display  902  further displays instructions for estimating one or more physical characteristics of the physical obstruction P (illustrated in  FIG.  5   ). For example, the physical characteristics include at least one of a size, shape, or location of the physical obstruction P. 
     Referring to  FIG.  10   , a system  1000  is configured to determine both (a) the occurrence of an apnea event or hypopnea event and (b) characteristics of the physical obstruction P. According to some implementations, the system  1000  includes one or more components of the system  100  described above and illustrated in  FIG.  1   . The system  1000  includes a control system  1010 , which may be in the form of a mobile phone  1010 , or other electronic device, and which includes a display  1028 . The mobile phone  1010  is communicatively coupled to a respiratory device  1022 , a speaker  1004 , and a microphone  1040 . The respiratory device  1022  is similar or identical to the respiratory device  122  described above and illustrated in  FIGS.  1 ,  2 A, and  2 B . The speaker  1004  and the microphone  1040  are fixed to a mask  1024 , which is attached to mouth and nasal area of the user  511 . According to one specific example, the speaker  1004  and the microphone  1040  are connected to a CPAP system (described above and illustrated in  FIG.  1   ). 
     The respiratory device  1022  supplies pressurized air  1011  to the airway  309  of the user  511  during a sleep session. Flow data associated with the pressurized air  1011  is received and, based on the flow data, the system  1000  determines that an apnea event or a hypopnea event is occurring. 
     The speaker  1004  emits the acoustic signal  400  through the mask  1024 , past the mouth M of the user  511 , and into the airway  309  of the user  511 . The acoustic signal  400  hits the physical obstruction P and reflects back in the form of the acoustic reflection  404 . The acoustic reflection  404  is detected by the microphone  1040 . Acoustic data  1009  associated with the acoustic reflection  404  is analyzed by the mobile phone  1010 . In response to the analysis, the system  1000  the display  1028  shows an apnea indicia  1003  warning that an apnea occurrence has been determined (e.g., “WARNING! APNEA OCCURRENCE”). The display  1028  also shows an obstruction indicia  1005  indicating that the physical obstruction P has been characterized (e.g., “PHYSICAL OBSTRUCTION DETERMINED”). Optionally, assuming that the user  511  is asleep during the apnea occurrence, the system  1000  displays the apnea indicia  1003  and/or the obstruction indicia  1005  at a later time (e.g., the following morning) to the user  511  and/or a third party. 
     Referring to  FIG.  11   , a system  1100  is configured to determine characteristic of the physical obstruction P when an n apnea event or a hypopnea event is occurring. According to some implementations, the system  1100  includes one or more components described above and illustrated in  FIG.  1   . The system  1100  includes a control system  1110 , which may be in the form of a mobile phone  1110 . The mobile phone  1110  is communicatively coupled with an acoustic device  1102 . The acoustic device  1102  is separate from or integrated with the mobile phone  1110 . The acoustic device includes a speaker  1104  and a microphone  1140 . Optionally, the speaker  1104  and the microphone  1140  are standard components of the mobile phone  1110  or are added as one or more after-market components. Optionally, if the acoustic device  1102  is integrated with the mobile phone  1110 , a mounting bracket is configured for maintaining the mobile phone  1110  near the mouth of the user while the user is sleeping. 
     The mobile phone  1110  is configured to cause the speaker  1104  to emit the acoustic signal  400  into the airway  309  of the user  511 . The acoustic signal  400  bounces off the physical obstruction O and reflects back in the form of the acoustic reflection  404 . The microphone  1140  detects the acoustic reflection  404 , and acoustic data  1109  associated with the acoustic reflection  404  is transmitted to and analyzed by the mobile phone  1110 . A display  1128  of the mobile phone  1110  displays an indicia  1103  that the physical obstruction is being estimated. 
     Referring to  FIGS.  12 - 17   , various representations are displayed on a display  1228  of a mobile phone  1210 , which is configured to function in accordance with one or more embodiments described above. According to alternative embodiments, in accordance with the one or more embodiments described above, the mobile phone  1210  is a tablet, a computer screen, or a CPAP display. According to one exemplary embodiment, general recommendations are displayed to the user by detailed results are displayed to the doctor. 
     According one example, the mobile phone  1201  includes one or more components described above in reference to system  100  (illustrated in  FIG.  1   ) and system  500  (illustrated in  FIG.  5   ). In  FIG.  12   , the display  1228  illustrates a full throat collapse occurring during an apnea event. In  FIG.  13   , the display  1228  illustrates a partial throat collapses occurring during a hypopnea event. In  FIG.  14   , the display  1228  illustrates elapsed time since an apnea occurrence has started. In  FIG.  15   , the display  1228  illustrates a first set of settings, such as supplying pressurized air, receiving pressure data or flow data, and/or analyzing an apnea event for occurrence and/or cause. In  FIG.  16   , the display  1128  illustrates a second set of settings, such as emitting acoustic signal, receiving sound reflection data, and/or analyzing the size of the physical obstruction. In  FIG.  17   , the display  1128  illustrates an image or presentation of the physical obstruction P based on the analyzed sound reflection data. 
     One or more elements or aspects or steps, or any portion(s) thereof, from one or more of any of claims  1 - 95  below can be combined with one or more elements or aspects or steps, or any portion(s) thereof, from one or more of any of the other claims  1 - 95  or combinations thereof, to form one or more additional implementations and/or claims of the present disclosure. 
     While the present disclosure has been described with reference to one or more particular embodiments or implementations, those skilled in the art will recognize that many changes may be made thereto without departing from the spirit and scope of the present disclosure. Each of these implementations and obvious variations thereof is contemplated as falling within the spirit and scope of the present disclosure. It is also contemplated that additional implementations according to aspects of the present disclosure may combine any number of features from any of the implementations described herein.