Patent Publication Number: US-2019167176-A1

Title: Method and apparatus for monitoring respiratory distress based on autonomic imbalance

Description:
CLAIM OF PRIORITY 
     This application claims the benefit of priority under 35 U.S.C. § 119(e) of U.S. Provisional Patent Application Ser. No. 62/595,174, filed on Dec. 6, 2017, which is herein incorporated by reference in its entirety. 
     CROSS REFERENCE TO RELATED APPLICATIONS 
     This application is related to commonly assigned U.S. Provisional Patent Application Ser. No. 62/595,166, entitled “NON-INVASIVE SYSTEM FOR MONITORING AND TREATING RESPIRATORY DISTRESS”, filed on Dec. 6, 2017, which is incorporated by reference in their entirety. 
    
    
     TECHNICAL FIELD 
     This document relates generally to medical devices and more particularly to a system that monitors a patient for predicting, detecting, and/or treating respiratory distress. 
     BACKGROUND 
     Obstructive lung diseases, including chronic obstructive pulmonary disease (COPD) and asthma, are characterized by narrowing airways that can make fully expelling air from the lungs difficult. COPD and asthma patients can experience a significant decline in health (e.g., acute COPD exacerbations and asthma attacks), to extents that require hospitalization. Despite advances in therapeutics, the prevalence of COPD and asthma continues to grow. 
     COPD currently affects nearly 13 million people in the United States and is the third leading cause of death in the country. The overwhelming primary cause of COPD is inhalation of cigarette smoke, responsible for over 90% of COPD cases. The economic and social burden of the disease is substantial and is increasing. The annual economic burden is currently estimated to be around $32 billion in the United States alone. Conditions associated with COPD include chronic bronchitis and emphysema. Chronic bronchitis is characterized by chronic cough with sputum production. Airway inflammation, mucus hypersecretion, airway hyper-responsiveness, and eventual fibrosis of the airway walls result in significant airflow and gas exchange limitations. Emphysema is characterized by destruction of the lung parenchyma, which leads to a loss of elastic recoil and tethering that maintains airway patency. Because bronchioles are not supported by cartilage like the larger airways are, they have little intrinsic support and therefore are susceptible to collapse when destruction of tethering occurs, particularly during exhalation. 
     Asthma is similar to chronic bronchitis, though its underlying cause is often an inherent defect of airway smooth muscle or the inflammatory milieu, which makes airway smooth muscle hyperreactive. Chronic asthma can have similar airway wall thickening as in chronic bronchitis, leading to a permanent, irreversible airflow obstruction. Asthma impacts over 18 million adults in the United States. Strikingly, there are 1.6 million visits to the emergency rooms resulting from this disease in the United States annually. Asthma COPD overlap syndrome (ACOS) is a condition in which a patient has clinical features of both asthma and COPD. ACOS patients are often among the sickest and most difficult to treat. 
     The most significant contributor to the economic burden of these diseases is related to healthcare services for asthma attacks and acute exacerbations of COPD (AECOPD), mostly emergency care and inpatient health care. Despite relatively efficacious drugs that treat COPD symptoms (e.g., long-acting muscarinic antagonists, long-acting beta agonists, corticosteroids, and antibiotics), a particular segment of patients known as “frequent exacerbators” often visit the emergency rooms and hospitals with exacerbations and also have a more rapid decline in lung function, poorer quality of life, and greater mortality. Similarly, a group of severe asthmatics are amongst those who visits the emergency rooms most frequently. 
     Currently, a successful strategy for managing asthma and COPD is the action plan that follows a “traffic light model” to monitor patient conditions and respond to changes. The traffic light model uses the analogy of traffic lights to illustrate the seriousness of symptoms (with green, yellow, and red zones) and the action a patient must take in each zone. This technique can be used by patients as well as caretakers to monitor symptoms. However, this approach has its limitations. For example, the patient must be compliant and be able to recognize symptoms. 
     SUMMARY 
     An example (e.g., “Example 1”) of a system for monitoring and treating respiratory distress in a patient may include signal inputs, a signal processing circuit, and a respiratory distress analyzer. The signal inputs may be configured to receive patient condition signals indicative of autonomic balance of the patient. The signal processing circuit may be configured to process the patient condition signals and to generate patient condition parameters using the processed patient condition signals. The patient condition parameters may be indicative of the autonomic balance of the patient. The respiratory distress analyzer may be configured to determine a state of the respiratory distress using the patient condition parameters. The respiratory distress analyzer may include a parameter analysis circuit, which may be configured to analyze the autonomic balance of the patient and to determine the state of the respiratory distress using an outcome of the analysis. 
     In Example 2, the subject matter of Example 1 may optionally be configured to further include a therapy device configured to deliver one or more therapies treating the respiratory distress and a control circuit configured to control the delivery of the one or more therapies based on the state of the respiratory distress. 
     In Example 3, the subject matter of any one or any combination of Examples 1 and 2 may optionally be configured to further include a storage device configured to store the state of the respiratory distress determined over time, and may optionally be configured such that the parameter analysis circuit is configured to produce and analyze a trend of the state of the respiratory distress. 
     In Example 4, the subject matter of any one or any combination of Examples 1 to 3 may optionally be configured such that the parameter analysis circuit is configured to determine a patient condition metric being a linear or nonlinear combination of the patient condition parameters and to produce one or more respiratory distress indicators indicating the state of the respiratory distress based on the patient condition metric, and the respiratory distress analyzer further includes a notification circuit configured to present the one or more respiratory distress indicators. 
     In Example 5, the subject matter of Example 4 may optionally be configured such that the parameter analysis circuit is configured to perform at least one of prediction or detection of an exacerbation of the respiratory distress based on the patient condition metric, and the notification circuit is configured to produce an alert notifying a result of the performance of the at least one of prediction or detection of the exacerbation. 
     In Example 6, the subject matter of Example 5 may optionally be configured such that the signal processing circuit is configured to generate patient condition parameters indicative of one or more physiological markers of asthma, the parameter analysis circuit is configured to perform at least one of prediction or detection of an asthma attack, and the notification circuit is configured to produce an asthma alert notifying at least one of the asthma attack being predicted or the asthma attack being detected. 
     In Example 7, the subject matter of any one or any combination of Examples 5 and 6 may optionally be configured such that the signal processing circuit is configured to generate patient condition parameters indicative of one or more physiological markers of chronic obstructive pulmonary disease (COPD), the parameter analysis circuit is configured to perform at least one of prediction or detection of an exacerbation of COPD, and the notification circuit is configured to produce a COPD alert notifying at least one of the exacerbation of COPD being predicted or the exacerbation of COPD being detected. 
     In Example 8, the subject matter of any one or any combination of Examples 1 to 7 may optionally be configured to further include a signal processing controller and a signal processing sensor. The signal processing controller is configured to receive a processing control signal and adjust the processing of the patient condition signals based on the processing control signal. The signal processing sensor is configured to sense a physical state of the patient and to produce the processing control signal based on the physical state. 
     In Example 9, the subject matter of Example 8 may optionally be configured such that the signal processing sensor includes one or more of an activity sensor configured to sense an activity level of the patient or a sleep sensor configured to sense whether the patient is sleeping. 
     In Example 10, the subject matter of any one or any combination of Examples 1 to 9 may optionally be configured such that the signal inputs are configured to receive one or more respiratory signals indicative of respiratory cycles including inspiratory and expiratory phases and one or more cardiac signals indicative of cardiac cycles including at least ventricular depolarizations, the signal processing circuit is configured to process the one or more respiratory signals and the one or more cardiac signals and to generate one or more respiration-mediated physiological parameters of the patient condition parameters, and the parameter analysis circuit is configured to determine the state of the respiratory distress based on at least the one or more respiration-mediated physiological parameters. 
     In Example 11, the subject matter of Example 10 may optionally be configured such that the signal processing circuit is configured to generate one or more respiration sinus arrhythmia (RSA) parameters of the one or more respiration-mediated physiological parameters, the one or more RSA parameters being one or more measures of the RSA, and the parameter analysis circuit is configured to determine the state of the respiratory distress based on at least the one or more RSA parameters. 
     In Example 12, the subject matter of any one or any combination of Examples 1 to 11 may optionally be configured such that the signal inputs are configured to receive one or more blood pressure signals indicative of blood pressure, one or more cardiac signals indicative of cardiac cycles including at least ventricular depolarizations and one or more physical state signals indicative of a physical state of the patient, the signal processing circuit is configured to process the one or more blood pressure signals, the one or more cardiac signals, and the one or more physical state signals and to generate one or more baroreflex sensitivity (BRS) parameters of the patient condition parameters, the one or more BRS parameters being one or more measures of the BRS, and the parameter analysis circuit is configured to determine the state of the respiratory distress based on at least the one or more BRS parameters. 
     In Example 13, the subject matter of Example 12 may optionally be configured such that the signal processing circuit is configured to generate detect levels of physical activity or exertion of the patient from the one or more physical state signals and to generate the one or more BRS parameters each for a plurality of levels of the physical activity or exertion. 
     In Example 14, the subject matter of any one or any combination of Examples 12 and 13 may optionally be configured such that the signal processing circuit is configured to generate detect a type of posture change of the patient from the one or more physical state signals and to stratify the one or more BRS parameters by the detected type of posture change. 
     In Example 15, the subject matter of any one or any combination of Examples 12 to 14 may optionally be configured such that the signal processing circuit is configured to generate detect one or more of a magnitude or a duration of posture change of the patient from the one or more physical state signals and to stratify the one or more BRS parameters by the detected one or more of the magnitude or the duration of posture change. 
     An example (e.g., “Example 16”) of a method for monitoring and treating respiratory distress in a patient is also provided. The method may include receiving patient condition signals indicative of autonomic balance of the patient and monitoring the state of the respiratory distress automatically using a respiratory distress monitoring circuit. The monitoring may include processing the patient condition signals, generating patient condition parameters using the processed patient condition signals, the patient condition parameters indicative of the autonomic balance of the patient, analyzing the autonomic balance of the patient using the patient condition parameters, and determining the state of the respiratory distress using an outcome of the analysis. 
     In Example 17, the subject matter of Example 16 may optionally further include delivering one or more therapies treating the respiratory distress and controlling the delivery of the one or more therapies based on the state of the respiratory distress. 
     In Example 18, the subject matter of any one or any combination of Examples 16 and 17 may optionally further include determining a patient condition metric being a linear or nonlinear combination of the patient condition parameters, performing at least one of prediction or detection of an exacerbation of the respiratory distress based on the patient condition metric, and producing an alert notifying a result of the performance of the at least one of prediction and detection. 
     In Example 19, the subject matter of any one or any combination of Examples 16 to 18 may optionally further include sensing a physical state of the patient and adjusting the processing of the patient condition signals based on the sensed physical state. 
     In Example 20, the subject matter of receiving patient condition signals as found in any one or any combination of Examples 16 to 19 may optionally further include receiving one or more respiratory signals indicative of respiratory cycles including inspiratory and expiratory phases and one or more cardiac signals indicative of cardiac cycles including at least ventricular depolarizations, and the subject matter of generating the patient condition parameters as found in any one or any combination of Examples 16 to 19 may optionally further include generating one or more respiration-mediated physiological parameters of the patient condition parameters. 
     In Example 21, the subject matter of generating the one or more respiration-mediated physiological parameters as found in claim  20  may optionally further include generating one or more respiration sinus arrhythmia (RSA) parameters being one or more measures of the RSA. 
     In Example 22, the subject matter of receiving patient condition signals as found in any one or any combination of Examples 16 to 21 may optionally further include receiving one or more blood pressure signals indicative of blood pressure, one or more cardiac signals indicative of cardiac cycles including at least ventricular depolarizations, and one or more physical state signals indicative of a physical state of the patient, and the subject matter of generating the patient condition parameters as found in any one or any combination of Examples 16 to 21 may optionally further include generating one or more baroreflex sensitivity (BRS) parameters being one or more measures of the BRS. 
     In Example 23, the subject matter of generating the patient condition parameters as found in claim  22  may optionally further include one or more of: detecting levels of physical activity or exertion of the patient from the one or more physical state signals and generating the one or more BRS parameters each for a plurality of levels of the physical activity or exertion, detecting a type of posture change of the patient from the one or more physical state signals and stratifying the one or more BRS parameters by the detected type of posture change, detecting a magnitude or a duration of posture change of the patient from the one or more physical state signals and stratifying the one or more BRS parameters by the detected magnitude of posture change, or detecting a duration of posture change of the patient from the one or more physical state signals and stratifying the one or more BRS parameters by the detected duration of posture change. 
     This Summary is an overview of some of the teachings of the present application and not intended to be an exclusive or exhaustive treatment of the present subject matter. Further details about the present subject matter are found in the detailed description and appended claims. Other aspects of the disclosure will be apparent to persons skilled in the art upon reading and understanding the following detailed description and viewing the drawings that form a part thereof, each of which are not to be taken in a limiting sense. The scope of the present disclosure is defined by the appended claims and their legal equivalents. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The drawings illustrate generally, by way of example, various embodiments discussed in the present document. The drawings are for illustrative purposes only and may not be to scale. 
         FIG. 1  illustrates an embodiment of a circuit for monitoring respiratory distress of a patient. 
         FIG. 2  illustrates an embodiment of another circuit for monitoring respiratory distress of a patient. 
         FIG. 3  illustrates an embodiment of system for monitoring and treating respiratory distress, wherein the circuit of  FIG. 1  or  FIG. 2  may be used. 
         FIG. 4  illustrates an embodiment of a method for monitoring and treating respiratory distress, such as may be performed by the system of  FIG. 3 . 
         FIG. 5  illustrates an embodiment of a system for monitoring respiratory distress. 
         FIG. 6  illustrates an embodiment of a system for closed-loop therapy delivery for treating respiratory distress. 
         FIG. 7  illustrates an embodiment of a system of non-invasive monitoring devices for monitoring respiratory distress. 
         FIG. 8  illustrates an example of short-term heart rate variability (HRV) throughout a respiration cycle under healthy and diseased conditions. 
         FIG. 9  illustrates another example of short-term HRV throughout a respiration cycle under healthy and diseased conditions. 
         FIG. 10  illustrates an embodiment of a method for monitoring respiratory distress based on an RSA metric. 
         FIG. 11  illustrates an example of a method for monitoring RSA using electrocardiographic (ECG) and accelerometer signals. 
         FIG. 12  illustrates an example of RSA information acquired using the method of  FIG. 11  and allowing for predicting or detecting exacerbation of respiratory distress. 
         FIG. 13  illustrates an example of BRS under healthy and diseased conditions. 
         FIG. 14  illustrates an embodiment of a method for monitoring respiratory distress based on a BRS metric. 
         FIG. 15  illustrates an example of a method for monitoring BRS using ECG, blood pressure, and accelerometer signals. 
         FIG. 16  illustrates an example of a method for monitoring BRS using ECG, heart sound, and accelerometer signals. 
         FIG. 17  illustrates an example of BRS information acquired using the method of  FIG. 15  of  FIG. 16  and allowing for predicting or detecting exacerbation of respiratory distress. 
     
    
    
     DETAILED DESCRIPTION 
     In the following detailed description, reference is made to the accompanying drawings which form a part hereof, and in which is shown by way of illustration specific embodiments in which the invention may be practiced. These embodiments are described in sufficient detail to enable those skilled in the art to practice the invention, and it is to be understood that the embodiments may be combined, or that other embodiments may be utilized and that structural, logical and electrical changes may be made without departing from the spirit and scope of the present invention. References to “an”, “one”, or “various” embodiments in this disclosure are not necessarily to the same embodiment, and such references contemplate more than one embodiment. The following detailed description provides examples, and the scope of the present invention is defined by the appended claims and their legal equivalents. 
     This document discusses, among other things, systems and methods for monitoring respiratory distress, including detection and/or prediction of exacerbations of pulmonary diseases affecting airways, such as asthma and chronic obstructive pulmonary disease (COPD). Exacerbations of such diseases create significant economic burden on the healthcare system. Patients experiencing these episodes tend to deteriorate over time. Thus, there is a need for consistent and accurate means to monitor patient status and detect worsening systems prior to an episode requiring hospitalization. 
     Studies have shown variable durations of increased signs and/or symptoms leading up to exacerbations in asthma patients. However, there are noticeable trends in signs and/or symptoms on average approximately one week prior to the episode. One study found that patients (at least 16 years old, from 11 countries, structured interviews of 3415 adults) reported a mean time from the first appearance to peak of signs and symptoms of 5.1 days (range: &lt;30 minutes to &gt;2 weeks) and a mean interval from the peak of symptoms to recovery of 6.2 days. Another study found that the mean maximal decrease in the morning PEF was 16% to 20%. This decrease was gradual, from day-10 to day-3, followed by a more rapid decrease. It is believed that fast onset episodes are due to triggers such as allergens and irritants, while slow onset episodes are due to faults in management. Analysis of fatal asthma attacks showed that 80% of them had slow onset. 
     Similar to asthma patients, COPD patients generally have a slow onset of signs and/or symptoms which has been reported to be approximately 1-2 weeks in duration, with symptoms steadily increased from 2 weeks prior to exacerbation, with a sharp rise during the last week. Respiratory tract infection is one of the most common triggers for COPD exacerbation, accounting for a majority of exacerbations. 
     There is a need for effective monitoring of patients to provide a reliable indication of their conditions and monitor respiratory distress to (1) warn the patients and provide a window to administer therapy to prevent hospitalizations, and/or (2) to alert patients and appropriate medical personnel of the onset of an exacerbation to get the patient the appropriate medical care. There is a need for reducing occurrence of events such as asthma attacks and acute exacerbations of COPD (AECOPD) for these patients, as such events are the primary contributor to the economic and social burden of respiratory diseases. 
     Failure to provide the correct type and/or duration of therapy for an asthma attack or acute exacerbation of COPD can prevent recovery, delay recovery, or result in an additional asthma attack or acute exacerbation of COPD soon after the initial disease event. Hence there is a need to monitor treatment of patients during recovery from an asthma attack and acute exacerbation of COPD. In one example, there is a need to monitor patients during hospitalization and emergency room visits for asthma attack and acute exacerbation of COPD to ensure the recovery therapy is effective and to prevent premature discharge. In another example, there is a need to monitor patients during recovery away from a hospital setting (e.g., at-home, or nursing home) to ensure compliance and effectiveness of the recovery therapy. Monitoring during recovery may be different from or the same as monitoring apart from recovery. Monitoring during recovery may include more frequent data gathering, gathering of additional or different parameters, and/or more frequent reporting to a caregiver (e.g., medical professional, or at-home caregiver). 
     As a patient&#39;s signs and/or symptoms worsen preceding an exacerbation, non-invasive devices can be used to capture symptomatic and physiological changes to warn the patient of declining health and/or alert appropriate personnel in the event of an exacerbation episode. Signs associated with airway obstruction such as coughing, wheezing (lung sounds), increased respiration rate, and lung hyperinflation can be captured by monitoring the patient using a system of non-invasive sensors. In addition, measures of autonomic activity can be monitored over time to assess patient condition and send a warning for a likely exacerbation based on signals acquired and/or alert appropriate personnel in the event of an exacerbation so that the patient can promptly receive medical attention. 
     In one example, the present subject matter provides a system that includes a noninvasive or minimally invasive system for monitoring COPD and asthma patients to provide a reliable indication of their condition and detect (1) worsening signs and/or symptoms, to warn patients and provide a window to administer therapy to prevent hospitalizations, and/or (2) exacerbations, in the event of rapid onset of signs and/or symptoms, to alert appropriate medical personnel of the onset of an exacerbation to get the patient the appropriate medical care. This system can also identify changes in the patient&#39;s condition subsequent to therapy to indicate a need for adjustment or termination of the therapy. In various embodiments, the system can include one or more sensors that can indirectly or directly sense symptoms and/or physiological signals indicative of worsening condition and/or onset of an exacerbation. The system can also contain a processing unit to process incoming signals and extract appropriate signal information. The processing unit can execute an algorithm to process the incoming signals along with stored data (e.g., trend data) to assess the patient&#39;s condition. In the event of worsening signs and/or symptoms preceding or at the onset of an exacerbation, the system can notify the patient, caregiver, and/or appropriate medical personnel. 
     A biomarker of respiratory distress, such as respiration sinus arrhythmia (RSA), can be captured invasively or noninvasively through direct or indirect means to provide an indication of the patient&#39;s condition and provide a warning when the condition becomes worse. RSA is a short term measure of heart rate variability (HRV), and is a physiological indicator that may have implications for monitoring pulmonary diseases such as asthma and COPD. RSA can be used to assess cardiac autonomic function, and can represent the transfer function from respiration rate to cardiac cycle length (e.g., time intervals between successive R-waves, the R-R intervals). During inspiration, inhibitory signals decrease vagal nerve activity, resulting in increased heart rate and decreased RSA. Conversely, during expiration, increasing vagus nerve activity results in decreased HR and increased RSA. 
     Asthma and COPD are associated with impairment in the autonomic balance (coordination between the sympathetic and parasympathetic nervous systems) which can be reflected by monitoring HRV and/or RSA. This imbalance, demonstrated in COPD patients, manifests as an elevation in sympathetic activity and a withdrawal of parasympathetic activity. In studies with asthma patients, the imbalance in the autonomic nervous system results from the hyperactivity of the parasympathetic branch causing bronchial constriction. In addition, the dysfunction or hypoactivity of the sympathetic branch has been tied to the severity of asthma. These alterations in autonomic balance can be monitored for individual patients to see when RSA deviates from a baseline value, either increasing or decreasing. Since this measure is based on the respiratory signal, RSA can continuously be evaluated to provide feedback on the patient&#39;s condition that does not require the patient to be performing a specific task or at an in-office assessment. Additional respiration mediated signals could be captured as a surrogate to heart rate including blood pressure, blood flow/perfusion, heart sounds, direct neural recordings, and blood gas ( 02  and CO2) concentrations. 
     In one example, the present subject matter provides a system that can monitor respiration-mediated signals in patients with pulmonary diseases that restrict airflow, such as asthma, COPD, chronic bronchitis, and emphysema. Heart rate responses to respiration can be captured through invasive or non-invasive means to monitor the patient&#39;s condition. This system can alert the patient or caretaker of worsening conditions and/or the need for intervention. This system can also identify changes in the patient&#39;s condition subsequent to therapy to indicate a need for adjustment or termination of the therapy. In various embodiments, the system can include one or more sensors that can directly or indirectly sense a respiratory signal and another physiological signal modulated by respiration. These signals can be processed to extract period of the respiration cycle including inspiration and expiration phases. The corresponding respiratory periods of the cardiac signal can be processed to extract heart rate and inter-beat intervals (the R-R intervals). An algorithm can be executed to calculate respiration-mediated signal indices and provide a measure of the patient&#39;s condition. 
     Arterial baroreflex (also referred to as baroreceptor reflex) is important for hemodynamic stability and cardioprotection, and is a strong prognostic indicator. The carotid and aortic baroreceptors detect changes in pressure, providing negative feedback to the closed-loop system for regulating blood pressure. In a healthy person, when baroreceptor activation increases due to a blood pressure increase, efferent parasympathetic activity increases to lower blood pressure through slowing the heart rate and causing peripheral vasodilation. Baroreflex sensitivity (BRS), defined as the change in inter-beat interval (IBI) in ms/mmHg, provides an indication of the function of this closed-loop system and can be measured from standard heart rate and blood pressure monitoring techniques. 
     Asthma and COPD are associated with impairment in the autonomic balance which can be reflected by monitoring BRS, either through spontaneous measures or clinical evaluations. This imbalance, as demonstrated in COPD patients, manifests as an elevation in sympathetic activity and a withdrawal of parasympathetic activity resulting in decreased BRS. In studies with asthma patients, the imbalance in the autonomic nervous system results from the hyperactivity of the parasympathetic branch. Treatment has been shown to decrease BRS as the cardiovagal responsiveness decrease and sympathetic activity increases. These alterations in autonomic balance can be monitored for individual patients to see when BRS deviates from a baseline value, either increasing or decreasing. Since this measure is based on the respiratory signal, BRS can continuously be evaluated to provide feedback on the patient&#39;s condition that does not require the patient to be performing a specific task or at an in-office assessment. 
     In the event of an AECOPD or asthma attack, the patient has heightened sympathetic nervous system activity, which causes in increase in blood pressure and heart rate. The increased blood pressure in turn activates baroreceptors which down-regulate sympathetic outflow, restoring homeostasis. This baroreflex can increase or decrease blood pressure. In a healthy person who transitions abruptly from a supine to standing position, pooling of blood in the lower extremities causes an immediate arterial blood pressure reduction, which in turn activates baroreceptors to increase sympathetic outflow causing a blood pressure and heart rate increase, again restoring homeostasis. These are healthy compensatory responses. An attenuated baroreceptor response causes a reduced and delayed heart rate and blood pressure response to a posture change (or any physical activity that typically activate the baroreceptors). 
     Natural BRS response has variability due to respiration, physical and mental stressors, which is evident in everyday activities. These dynamics can be used as an indicator of baroreceptor function, and can be used to monitor patients with airflow limitations. The dynamic BRS response can be captured using beat-to-beat sensitivity to investigate changes in heart rate and blood pressure for each cardiac contraction. One method for measuring BRS is measuring spontaneous BRS. Spontaneous BRS can be measured through consecutive beats that are characterized by simultaneous increases or decreased in blood pressure and R-R interval. BRS is then calculated as the average of the linear regression slopes detected for each sequence over a given time interval. Examples for measuring this dynamic response include measuring through monitoring respiration and/or physical activity. 
     Similar to respiratory sinus arrhythmia and diminished HRV, diminished BRS is evident in COPD and asthma patients. The dynamic spontaneous BRS can be captured through analysis of blood pressure and heart rate during a respiratory cycle. This is because there is always spontaneous blood pressure variability (BPV) due to respiration. Respiration induces HRV by mediation of the arterial baroreflex and by direct mechanical modulation of the SA node pacemaker properties. Using inspiration and expiration, consecutive increases or decreases can be captured to calculate BRS for monitoring the patient. 
     Moment-to-moment regulation of blood pressure through the baroreflex is reduced during exercise in comparison to rest. BRS decreases during exercise because the body&#39;s operating point on the curve of heart rate against blood pressure has shifted away from the maximal sensitivity point at the center of the curve (at rest condition). The shift moves the “set point” of blood pressure to a higher level with less sensitivity to changes in blood pressure. This change in baroreflex depends on exercise intensity. As the exercise intensity increases (as the heart rate increases), the response curve changes with the lowest sensitivity at the highest exercise intensity where the subjects maintained a heart rate of 150 beat per minute (bpm). As the exercise intensity increases, the operating point progressively moves away from the center point towards the upper threshold of the curve. Pulmonary disease affecting airways such as asthma and COPD are associated with alterations in autonomic function. This dysfunction can be investigated through physical activity by monitoring the baroreflex. Exercise alone causes a decrease in BRS, and exercise compounded with airway limitation may lead to a more significant reduction in BRS. By coupling activity and BRS monitoring, the baroreflex can be evaluated at a higher operating point (due to exercise) for monitoring the patient&#39;s condition and evaluating the need for therapeutic intervention. 
     In one example, the present subject matter provides a system for ambulatory assessment of baroreceptor response. The patient&#39;s baroreceptor response to events such as respiration or activity can be used for monitoring the patient&#39;s condition related to pulmonary disease, such as asthma and COPD. Heart rate, blood pressure, respiration, and activity signals can be sensed through invasive or non-invasive means, through direct or indirect measures. This system can alert the patient or caretaker of worsening conditions or the need for intervention. This system can also identify changes in the patient&#39;s condition subsequent to therapy to indicate a need for adjustment or termination of the therapy. In various embodiments, this system can include an activity sensor, a respiration sensor, and an additional sensor for measuring baroreceptor response. The system can include a processor to process the signals produced by these sensors to analyze the spontaneous baroreceptor response during respiration as detected by the respiration sensor and/or during physical activity as detected by the activity sensor. The processor can execute an algorithm to calculate baroreceptor response indices to provide a measure of the patient&#39;s condition. 
       FIG. 1  illustrates an embodiment of a respiratory distress monitoring circuit  100 . Respiratory distress monitoring circuit  100  can include signal inputs  101 , a signal processing circuit  102 , and a medical condition analyzer  103 . In various embodiments, respiratory distress monitoring circuit  100  can be implemented as part of a system for monitoring and/or treating a patient suffering from one or more medical conditions including respiratory distress. Examples of the respiratory distress include COPD and asthma. 
     Signal inputs  101  can receive patient condition signals indicative of a state of the respiratory distress of the patient. Signal processing circuit  102  can process the patient condition signals and generate patient condition parameters using the processed patient condition signals. The patient condition parameters are indicative of the state of the respiratory distress of the patient. Respiratory distress analyzer  103  can determine the state of the respiratory distress using the patient condition parameters. Respiratory distress analyzer  103  can include parameter inputs  104  and a parameter analysis circuit  105 . Parameter inputs  104  can include a physiological marker input  106  to receive one or more physiological marker parameters of the patient condition parameters and an other parameter input  107  to receive one or more other parameters of the patient condition parameters that can be used in the determination of the state of the respiratory distress. The one or more physiological marker parameters represent one of more physiological markers for the respiratory distress and can be one or more quantitative measures of the respiratory distress. Parameter analysis circuit  105  can analyze the patient condition parameters received from signal processing circuit  102  and determine the state of the respiratory distress using an outcome of the analysis. 
     In one embodiment, the patient condition signals include signals acquired by non-invasive sensors such that respiratory distress monitoring circuit  100  can be used in a non-invasive patient monitoring and/or treatment system. In one embodiment, the patient condition signals include signals indicative of autonomic balance of the patient, and parameter analysis circuit  105  can to analyze the autonomic balance of the patient and determine the state of the respiratory distress based on a state of the autonomic balance. Examples of measures of autonomic balance include RSA and BRS. In one embodiment, parameter analysis circuit  105  can produce a patient condition metric being a linear or nonlinear function of the patient condition parameters and predict an exacerbation of the respiratory distress based on the patient condition metric. Respiratory distress analyzer  103  can produce an alert notifying the prediction of the exacerbation. 
       FIG. 2  illustrates an embodiment of an embodiment of a respiratory distress monitoring circuit  200 , which can represent an example of respiratory distress monitoring circuit  100 . Respiratory distress monitoring circuit  200  can include signal inputs  201 , a signal processing circuit  202 , a signal processing controller  213 , and a respiratory distress analyzer  203 . 
     Signal input  201  can represent an example of signal input  101  and can receive the patient condition signals indicative of the state of the respiratory distress. The patient condition signals can include one or more signals sensed by one or more sensors and indicative physiological markers of the respiratory distress and one or more other signals that can otherwise be used by respiratory distress analyzer  203  in determining the state of the respiratory distress. Signal processing circuit  202  can represent an example of signal processing circuit  102  and can process the patient condition signals received by signal inputs  201  and can generate patient condition parameters indicative of the state of the respiratory distress. The patient condition parameters can include one or more physiological marker parameters that are indicative of the physiological markers of the respiratory distress and can allow for detection and/or prediction of exacerbation. In various embodiments, a sensor or a combination of sensors can be employed to monitor symptoms and physiological markers indicative of the state of the respiratory distress. Examples of physiological markers of respiratory distress that can be signs for exacerbation include:
         (i) Respiration rate;   (ii) Lung sounds, including chest sounds that can be examined by tapping chest and using a microphone to capture the response tone;   (iii) Cough;   (iv) Wheezing;   (v) Respiration flow characteristics, such as FEV1, FEV3, FEV6, TC, FVC, MV, TLC, flow rate, volume measures, and any combination of these parameters;   (vi) Oxygen Saturation;   (vii) Central cyanosis;   (viii) Activity levels;   (ix) Sleep quality;   (x) Body temperature;   (xi) Heart rate;   (xii) Heart rate variability (HRV), including heart rate acceleration and deceleration capacity;   (xiii) Respiration sinus arrhythmia (RSA);   (xiv) Blood pressure;   (xv) Blood pressure variability;   (xvi) Baroreceptor reflex sensitivity (BRS);   (xvii) Galvanic skin response;   (xviii) Direct neural measures including neural respiratory drive index (NRDI), parasternal EMG, diaphragm EMG; and   (xix) Chemical indicators of stress and inflammation.       

     In various embodiments, the one or more physiological marker parameters can each indicate and/or be a measure of one or more of these physiological markers. Table 1 includes a more complete list of such physiological markers with rationale for each marker. 
     Respiratory distress analyzer  203  can represent an example of respiratory distress analyzer  103  and can analyze the patient condition parameters generated by signal processing circuit  202  and determine the state of the respiratory distress based on an outcome of the analysis. Respiratory distress analyzer  203  can include parameter inputs  204 , a parameter analysis circuit  205 , a storage device  214 , and a notification circuit  215 . Parameter inputs  204  can include a physiological marker input  206  and an other parameter input  207 . Physiological marker input  206  can receive one or more physiological marker parameters generated by signal processing circuit  202 , such as one or more parameters each indicative or being a measure of one or more physiological markers listed above (i-xix) or in Table 1. Other parameter input  107  can receive one or more other parameters that can be used in the determination of the state of the respiratory distress, including information entered by the patient and/or the user. In this document, a “user” can include a physician, other medical professional, or caregiver who attends the patient including monitoring and/or treating the patient using the present system. In some example, the “user” can also include the patient, such as when the patient is allowed to adjust certain operations of the system. 
     Parameter analysis circuit  205  can represent an example of parameter analysis circuit  105  and can determine the state of the respiratory distress based on the patient condition parameters received by parameter inputs  204 . In one embodiment, parameter analysis circuit  205  determines a patient condition metric being a linear or nonlinear combination of the patient condition parameters, and produces one or more respiratory distress indicators indicating the state of the respiratory distress based on the patient condition metric. The patient condition parameters includes at least the one or more physiological marker parameters. Storage device  214  can store the state of the respiratory distress determined by parameter analysis circuit  205  over time. In various embodiments, parameter analysis circuit  205  can produce and analyze a trend of the state of the respiratory distress using the stored states. The trend allows respiratory distress analyzer  203  to identify changes in the patient&#39;s condition including changes in the state of the respiratory distress. 
     Notification circuit  215  can present the one or more respiratory distress indicators produced by parameter analysis circuit  205  to the patient and/or the user (e.g., through a user interface of a system illustrated in one of  FIGS. 5-7  and discussed below). Notification circuit  215  can include a classification circuit  216  and an alert circuit  217 . Classification circuit  216  can stratify a risk for exacerbation of the respiratory distress for the patient. The risk can be categorized based on individual characteristics of the patient, including, for example, diet, pollen levels, allergies, activity levels, disease history, and/or sleep quality. The risk stratification can allow respiratory distress analyzer  203  to respond to worsening signs differently for a patient currently in a low risk category versus a patient currently in a high risk category for the exacerbation, for example, in determine whether and how to notify the patient and/or the user. Alert circuit  217  can produce an alert notifying a need for medical intervention based on the one or more respiratory distress indicators. In one embodiment, alert circuit  217  produces the alert based on the one or more respiratory distress indicators and the patient&#39;s risk category stratified by classification circuit  216 . Depending on the stratified risk category, alert circuit  217  can produce an alert notifying a detection of the respiratory distress and a distinct alert notifying a prediction of the respiratory distress, and/or distinct alerts notifying different risk categories. 
     Signal processing controller  213  can receive a processing control signal and adjust the processing of the patient condition signals based on the processing control signal. The processing control signal can include a signal indicative of a physical state of the patient, such as a signal indicating an activity level of the patient (e.g., sensed from the patient using an activity sensor) or a signal indicating whether the patient is sleeping (e.g., sensed from the patient using a sleep sensor). For example, the activity or sleep sensor may trigger sampling for heart rate, respiration rate, and lung sounds and processing of these signals only when the patient is sleeping or at rest. In various embodiments, signal processing controller  213  can adjust a sampling rate of signal processing circuit  202  based on the processing control signal and/or activate or deactivate signal processing circuit  202  based on the processing control signal. In various embodiments, signal processing controller  213  can also activate or deactivate other portions of respiratory distress monitoring circuit  200  and/or other portions of the present system (e.g., monitoring devices acquiring the patient condition signals) based on the processing control signal. 
       FIG. 3  illustrates an embodiment of system  320  for monitoring and treating the respiratory distress. Respiratory distress monitoring circuit  100  or  200  can be implemented in system  320 . For monitoring purposes, system  320  includes at least one or more monitoring devices  321  and a respiratory distress monitoring circuit  300 . For monitoring and therapeutic purposes, system  320  can include monitoring device(s)  321 , respiratory distress monitoring circuit  300 , a control circuit  322 , and a therapy device  323 . Monitoring device(s)  321  acquire the patient condition signals. For example, monitoring device(s)  321  can include one or more sensors to sense one or more signals related to the patient&#39;s medical condition including the state of respiratory distress and produce the one or more sensor signals of the patient condition signals. Therapy device  323  can deliver one or more therapies treating the respiratory distress, including prevention of a predicted exacerbation. Control circuit  322  can control the delivery of the one or more therapies based on the state of the respiratory distress as determined by respiratory distress monitoring circuit  300 . Examples of respiratory distress monitoring circuit  300  include medical condition monitoring circuits  100  and  200 . In addition to, or in place of, delivering the one or more therapies, system  320  can also recommend to the patient or the user actions to take based on the patient&#39;s conditions including the state of the respiratory distress. In various embodiments, system  320  is a closed-loop therapy system, with monitoring device(s)  321  sensing effects of delivery of the one or more therapies for adjusting the delivery based on the effects. 
     In one embodiment, monitoring device(s)  321 , respiratory distress monitoring circuit  300 , control circuit  322 , and therapy device  323  are integrated into a single medical device. In other embodiments, monitoring device (s)  321 , respiratory distress monitoring circuit  300 , control circuit  322 , and therapy device  323  can be implemented as two or more medical devices communicatively coupled to each other to form system  320 . These two or more devices can be any combination of implantable, wearable, handheld, and/or remote devices. 
     In various embodiments, system  320  can include an implantable medical device that includes an implantable drug pump and/or a neuromodulation device (e.g., for delivering vagus nerve stimulation, pulmonary vagal fiber block therapy, and/or superior laryngeal nerve block therapy) to be used as therapy device  323 . In various embodiments, such as when the patient is not connected to a therapy device, system  320  can send alerts or notifications to the patient and/or the user when the condition including the state of the respiratory distress is worsening and/or when medical intervention becomes necessary or recommendable. System  320  can detect and/or predict an exacerbation of the respiratory distress based on early or late stage of worsening symptoms and slow or rapid onset. For example, system  320  can send early stage warnings to the patient only and late stage warnings to the user in addition to the patient. In another example, system  320  can notify the patient in a slow onset for the patient to take action but notify the user in addition to the patient in a rapid onset. In various embodiments, system  320  can be used in combination with a medical condition management plan for the patient to follow, for example, by notifying the patient of the state and/or risk category of the respiratory distress such that the patient can adjust medication and/or daily activities accordingly. 
     In various embodiments, signal inputs  101  or  201  can receive environmental information related to the state of the respiratory distress, and parameter analysis circuit  105  or  205  can determine the state of the respiratory distress based on one or more physiological marker parameters and the received environmental information. Examples of the environmental information include time of day, time of year, GPS location, pollen levels, pollution levels, humidity levels, web information on local news, hospital admissions, and/or information on disease epidemic (e.g., flu or cold). The environmental information can be sensed using one or more sensors of monitoring device(s)  321  and/or provided by external sources. 
     In various embodiments, signal inputs  101  or  201  can receive user-input data related to the state of the respiratory distress. The user-input data can be entered by the patient and/or the user. Parameter analysis circuit  105  or  205  can determine the state of the respiratory distress based on the one or more physiological marker parameters and one or more of the received environmental information and the user-input data. Examples of the user data include a log of the patient&#39;s actual asthma attacks and/or COPD exacerbations, pharmaceutical use information, and/or allergies. In various embodiments, notification circuit  215  can provide the patient with custom recommendations based upon the user-input data. 
     In various embodiments, circuits of system  320 , including its various embodiments discussed in this document, may be implemented using a combination of hardware and software. For example, the circuits may be implemented using an application-specific circuit constructed to perform one or more particular functions or a general-purpose circuit programmed to perform such function(s). Such a general-purpose circuit includes, but is not limited to, a microprocessor or a portion thereof, a microcontroller or portions thereof, and a programmable logic circuit or a portion thereof. 
       FIG. 4  illustrates an embodiment of a method  430  for monitoring and treating respiratory distress. In one embodiment, method  430  can be performed using system  320 . 
     At  431 , patient condition parameters are received and analyzed. The patient condition parameters can include one or more physiological marker parameters each indicative or being a measure of one or more physiological markers of the respiratory distress, such as those listed above (i-xix) or in Table 1. In some embodiments, the patient condition parameters can also include other parameters useable in determining the state of the respiratory distress, such as inputs from the patient and/or the user. 
     At  432 , the state of the respiratory distress is determined based on an outcome of the analysis of the patient condition parameters. In one embodiment, a patient condition matrix is produced as a liner or nonlinear function of the patient condition parameters, and one or more indicators of the state of the respiratory distress are produced based on the patient condition metric. 
     If the state of the respiratory distress (e.g., a quantitative measure of the state) does not exceed a threshold at  433 , method  430  continues from 431 again. If the state of the respiratory distress exceeds the threshold at  433 , an alert is produced to notify the patient and/or the user, and/or one or more therapies treating the respiratory distress are delivered, at  434 . Method  430  can continue from 431 again to monitor the state of the respiratory distress including the effect of the delivery of the one or more therapies and/or other medical intervention resulting from the alert. 
       FIG. 5  illustrates an embodiment of a system  520  for monitoring respiratory distress. System  520  can represent an example of system  320  (with monitoring functions only). As illustrated in  FIG. 5 , system  520  can include monitoring devices  538 , a portable device  542 , a network  545  communicatively coupled to portable device  542  via a wired or wireless communication link  543 , and a medical facility  547  communicatively coupled to network  545 . Respiratory distress monitoring circuit  300  can be distributed in portable device  542  and/or network  545 . In various embodiments, portable device  542  can be implemented as a dedicated device or in a generic device such as a smartphone, a laptop computer, or a tablet computer. Monitoring devices  538  can include monitoring devices  321  each being an implantable or non-implantable sensor communicatively coupled to portable device  542  via a wired or wireless link. Information such as the patient condition signals, the patient condition parameters, and/or the one or more respiratory distress indicators can be received and/or produced by portable device  542  and transmitted to network  545  via communication link  543  to be stored, further analyzed, and/or inform the user. When the patient&#39;s medical condition including the state of the respiratory distress (e.g., as determined in portable device  542  or network  545 ) indicates that the patient needs medical attention, a notification will be transmitted to medical facility  547  from network  545 . 
       FIG. 6  illustrates an embodiment of a system  620  for closed-loop therapy delivery for treating respiratory distress. System  620  can represent another example of system  320 . As illustrated in  FIG. 6 , system  620  includes the components of system  520  and a therapy device  650 . Therapy device  650  can be an example of therapy device  323  and can be an implantable or non-implantable device communicatively coupled to portable device via a wired or wireless communication link. Control circuit  322  can be implemented in portable device  542  and/or therapy device  323 . In various embodiments, system  320  is implemented in system  620  as a closed-loop system for monitoring and treating at least the respiratory distress. 
     Example: Non-Invasive System 
     System  320  can be implemented as a non-invasive, minimally invasive, partially implantable, or fully implantable system. When system  320  is a non-invasive system, one or more monitoring devices  321  include one or more non-invasive monitoring devices. In various embodiments, the one or more non-invasive monitoring devices can include one or more passive monitors, one or more wearable devices, one or more mobile cellular devices, one or more adhesive patches, and/or one or more any other forms of non-invasive monitoring devices suitable for acquiring the needed patient condition signals. 
     A passive monitor (also known as in-home patient monitor, bedside monitor, remote patient monitor, passive patient monitor, passive in-home monitor, passive bedside monitor, etc.) can identify the patient and sense one or more signals from the identified patient. In various embodiments, a passive monitor can use radio or microwave signals or cameras (visible or infrared) to identify individuals and detect signals. Radio or microwave signals can be used in this manner due to differences in wave reflection time back to the emitter, allowing for respiratory rate, heart rate, and movement to be detected. Cameras can be used based on minute color changes in the skin which occurs due to blood flow. Examples of physiological markers of the respiratory distress that can be sensed using a passive monitor include respiration rate, heart rate and HRV (including frequency and time domain measures), RSA (using respiratory and cardiac signals to derive RSA metrics from the expiratory and inspiratory periods of physiological signals, for example, acquired using two different filters to derive a respiratory rate signal and a heart rate signal, or using two passive monitors), activity, movement to capture activity levels and sleep quality metrics (sleep disturbances will appear as high-amplitude spikes in the data stream), and/or blood pressure (e.g., as indicated by pulse transit time measure captured by a bedside camera). A passive monitor can include a microphone to detect respiratory or lung sounds, coughing, and/or vocal expression. A passive monitor can include an ultrasound transmitter and receiver to detect patient movement and/or patient respiration. 
     A wearable monitor (also known as wearable, healthcare wearable, wearable sensor, etc.) can be worn by the patient and sense one or more signals from the patient. Examples of wearable monitors can include wrist-worns, rings, necklaces, anklets, and sensors embedded in clothing (chest patch for example). Examples of physiological markers of the respiratory distress that can be sensed using a wearable monitor include respiration rate (e.g., measured with accelerometers, gyroscopes, photoplethysmography (PPG) sensors, and/or impedance sensors), heart rate and heart rate variability (including frequency and time domain measures, e.g., measured via biopotential, bioimpedance, and/or PPG sensors), galvanic skin response (including time and frequency domain measures), blood pressure (including measurements such as systolic and diastolic blood pressure, pulse transit time, wave amplitude, and/or volume, BRS (captured by blood pressure and heart rate signals paired with one or more of activity, posture, and/or respiration signal), chemical marker (e.g. measured by sweat analysis), activity levels, sleep quality, vocal expression analysis, lung sounds, coughing, wheezing, and or external factors including location tracking, ambient temperature, and/or ambient humidity. 
     A mobile cellular device can be worn or carried by the patient or placed near the patient and sense one or more signals from the patient. An example of the mobile cellular device includes a smartphone with a patient monitoring application installed. Mobile cellular devices that allow for intermittent sensing of the patient condition signals include, for example: microphone for vocal expression analysis, accelerometer for activity tracking, global positioning system (GPS) for location tracking and associated external factors including temperature, ambient humidity, and/or allergen levels, and/or sensors coupled to mobile devices such as ECG sensors for recording heart rate, HRV (time and frequency domain measures), respiration rate, and RSA. Mobile cellular devices can also be used for continuous sensing of the patient condition signals while the patient is sleeping if the mobile devices are placed on mattress while sleeping. Examples of the patient condition parameters generated from signals continuously sensed include heart rate and HRV (time and frequency domain measures), respiration rate, RSA, and sleep quality parameters. 
     An adhesive patch including one or more sensors can be attached to the patient and sense one or more signals from the patient. Examples of physiological markers of the respiratory distress that can be sensed using an adhesive patch include ECG, heart sounds, HRV, respiration rate, RSA, lung sounds, and/or electromyogram (EMG) for neural respiratory drive. An adhesive patch can also be made capable of communicating with the user and/or insurance company to indicate when it is being worn by the patient to ensure compliance of treatment instructions and/or requirements. 
       FIG. 7  illustrates an embodiment of a system of non-invasive monitoring devices  721  for monitoring respiratory distress. Monitoring devices  721  can be an example of monitoring devices  321  and an example of using non-invasive (non-implantable) sensors for monitoring devices  538  in system  520  or  620 . For the purpose of illustration, but not restriction,  FIG. 7  shows non-invasive monitoring devices  764 ,  765 ,  766 , and  767 . Monitoring device  764  can be wearable devices including sensors for sensing, for example, blood volume pulse, temperature, bodily sounds, chemical markers, and/or activity level. Monitoring device  765  can be a passive bed monitor including one or more sensors for sensing, for example, sleep quality, hate rate, respiratory rate, and/or HRV. Monitoring device  766  can be a passive in-home monitor including one or more radiowave sensors, ultrasound sensors, and/or cameras for sensing, for example, sleep quality, heart rate, and/or respiratory rate. Monitoring device  767  can be a bodily fluid sensor such as a saliva sensor for inflammatory markers (e.g., incorporated into a toothbrush for daily use). 
     In various embodiments, monitoring device(s)  321  can include one or more minimally invasive monitoring devices. For example, monitoring device(s)  321  can include sensors integrated with minimally invasive or borderline invasive devices such as diabetes monitor, microneedles, contact lens, tattoo, inhalable sensors, ingestible sensors, artificial limbs, and/or sensor placed in the nostril or sinus. In various embodiments, monitoring device(s)  321  can include one or more monitoring devices each integrated with one or more therapy devices such as nebulizer, respirator, continuous positive airway pressure (CPAP) machine, and/or chest compression devices. In various embodiments, non-invasive and/or minimally invasive monitoring devices, when used individually or in combination, can provide a system for the patients to track symptoms objectively over time, to identify when the patient&#39;s condition is deteriorating, and to provide information to the patient and/or the user when appropriate. These monitoring devices can also provide inputs to a closed-loop therapy system. 
     Example: Monitoring Respiration-Mediated Parameter 
     In various embodiments, the state of the respiratory distress can be monitored using one or more respiration-mediated physiological parameters, such as one or more measures of respiratory sinus arrhythmia (RSA), that are indicative of the patient&#39;s autonomic balance. The state of the respiratory distress can be measured by a degree of autonomic imbalance.  FIG. 8  illustrates an example of short-term heart rate variability (HRV) throughout a respiration cycle under healthy and diseased conditions.  FIG. 9  illustrates another example of short-term HRV throughout a respiration cycle under healthy and diseased conditions. RSA is characterized by the magnitude of HRV at different time points along the respiration cycle and/or ratios between HRV at various time points along the respiration cycle. In various embodiments, the one or more patient condition parameters and/or metrics can include one or more measures of the RSA for determine the state of the respiratory distress, such as asthma or COPD. 
     Respiratory distress monitoring circuit  100 ,  200 , or  300  can be configured to monitor the state of the respiratory distress using one or more respiration-mediated physiological parameters, such as one or more measures of RSA. Referring back to  FIG. 1 , Signal inputs  101  can receive one or more respiratory signals and one or more cardiac signals. The one or more respiratory signals are indicative of respiratory cycles including inspiratory and expiratory phases. The one or more cardiac signals are indicative of cardiac cycles including at least ventricular depolarizations (R-waves). Signal processing circuit  102  can be configured to process the one or more respiratory signals and the one or more cardiac signals and to generate one or more respiration-mediated physiological parameters indicative of the state of the respiratory distress, such as one or more RSA parameters being one or more measures of the RSA. Respiratory distress analyzer  103  can be configured to determine the state of the respiratory distress using the one or more respiration-mediated physiological parameters. Physiological marker input  106  can receive the one or more respiration-mediated physiological parameters. Parameter analysis circuit  105  can be configured to analyze the one or more respiration-mediated physiological parameters received from signal processing circuit  102  and determine the state of the respiratory distress using an outcome of the analysis. 
     Referring back to  FIG. 2 , signal inputs  201  can be configured to include a respiratory signal input to receive the one or more respiratory signals and a cardiac signal input to receive the one or more cardiac signals. Signal processing circuit  202  can be to process the one or more respiratory signals and the one or more cardiac signals and to generate the patient condition parameters indicative of the state of the respiratory distress. The patient condition parameters include the one or more respiration-mediated physiological parameters indicative of the state of the respiratory distress, such as one or more RSA parameters each being a measure of the RSA. Respiratory distress analyzer  203  can be configured to determine the state of the respiratory distress based on the one or more respiration-mediated physiological parameters. Physiological parameter input  206  can be configured to receive the one or more respiration-mediated physiological parameters. The one or more respiration-mediated physiological parameters can include, but are not limited to, one or more of the following parameters:
         (a) Absolute heart rate, HRV, or R-R interval during inspiration and expiration;   (b) Change in heart rate, HRV, or R-R interval over respiration cycle;   (c) Ratio of heart rate, HRV or R-R interval during expiration to inspiration;   (d) Each in (a)-(c) above averaged over time (e.g., an ensemble average over multiple respiratory cycles);   (e) Measure of deviation from normal respiration heart rate cycling (e.g., heart rate or R-R interval plotted as a function of respiration phase);   (f) Frequency-domain parameters of heart rate and HRV as functions of respiration; and/or   (g) Phase of the respiratory signal and corresponding cardiac signal.       

     Parameter analysis circuit  205  can be configured to determine the state of the respiratory distress based on at least the one or more respiration-mediated physiological parameters. In one embodiment, parameter analysis circuit  205  can be configured to produce a respiration-mediated signal metric including a linear or nonlinear combination of the respiration-mediated physiological parameters and to produce the one or more respiratory distress indicators indicating the state of the respiratory distress based on the respiration-mediated signal metric. In various embodiments, respiratory distress monitoring circuit  300  can be configured to monitor the state of the respiratory distress using the one or more respiration-mediated physiological parameters in combination with any one or more other physiological marker parameters, and/or other parameters related to the respiratory disorder, that are discussed in this document. 
     Referring back to  FIG. 3 , when respiratory distress monitoring circuit  300  is configured to monitor the state of the respiratory distress using the one or more respiration-mediated physiological parameters, such as the one or more measures of RSA, monitoring device(s)  321  can include one or more sensors that can be configured to sense the one or more respiratory signals and the one or more cardiac signals. In various embodiments, monitoring device(s)  321  can include an implantable, injectable, non-invasive, wearable, or passive monitoring device, or a combination of any of these devices, including one or more sensors to acquire, for example, a signal corresponding to respiration to indicate period of inspiration and expiration and a signal corresponding to heart rate to evaluate changes in R-R intervals during periods of the respiratory cycle identified by the respiration signal. These signals allow parameter analysis circuit  205  to produce a metric of respiration-mediated physiological signal. In various embodiments, the one or more respiration signals can be acquired directly or indirectly using, for example, one or more of the following:
         (a) Respiration sensor (e.g., patient-contacting sensor such as chest and abdominal movement sensor, acoustic sensor, airflow sensor, muscle strain sensor, and/or impedance sensor, and/or non-contacting sensor such as radio or microwave based tissue movement sensor, optical sensor, acoustic sensor, camera, and/or accelerometer or gyroscope);   (b) ECG sensor (for deriving periods of inspiration and expiration from ECG, which is modulated by respiratory activity);   (c) Heart sound sensor (for deriving periods of inspiration and expiration from a heart sound signal that is modulated by respiratory activity); and/or   (d) Blood pressure sensor (e.g., PPG sensor, blood pressure cuff, and/or invasive blood pressure sensor).
 
The one or more cardiac signals (or surrogate respiration-mediated signals) can be acquired using, for example, one or more of the following:
   (a) ECG sensor (ECG is modulated by respiratory activity);   (b) Heart sound sensor (acoustic vibrations from the cardiac cycle are modulated by respiratory activity);   (c) Blood pressure or flow sensor (e.g., PPG sensor, blood pressure cuff, and/or invasive blood pressure sensor);   (d) Blood gas concentration sensor (e.g., pulse oximeter and/or invasive blood gas sensor; and/or   (e) Nerve sensor for direct neural recordings (e.g., surface electrodes and/or invasive nerve recording sensors).
 
In various embodiments, monitoring device(s)  321  can include one or more sensors in one or more remote devices coupled to respiratory distress monitoring circuit  300  via one or more wireless or wired communication links. Such one or more remote devices can include, but are not limited to, one or more of the following:
   (a) Invasive or noninvasive device for processing sensor input data;   (b) Personal device for alerts and notification on pain levels; and/or   (c) Invasive or noninvasive devices used as part of a closed-loop system, including a closed-loop systems where the patient closes the loop by himself/herself.       

       FIG. 10  illustrates an embodiment of a method  1070  for monitoring respiratory distress based on a patient condition metric derived from respiratory and cardiac signals, such as an RSA metric. Method  1070  can be performed using system  320 , which can be implemented in system  520  or  620 . 
     At  1071 , patient condition signals are received. The patient condition signals include a respiratory signal indicative of inspiration and expiration times and a cardiac signal indicative of R-R interval (i.e., as referred to as cardiac cycle length or ventricular rate interval, measure as the time interval between consecutive R-waves). At  1072 , the patient condition metric (e.g., the RSA metric) is determined using the respiratory and cardiac signals. At  1073 , the state of the respiratory distress is determined based the patient condition metric such as the RSA metric. If the state of the respiratory distress (e.g., a quantitative measure of the state) does not exceed a threshold at  1074 , method  1070  continues from  1071  again. If the state of the respiratory distress exceeds the threshold at  1074 , an alert is produced to notify the patient and/or the user, and/or one or more therapies treating the respiratory distress are delivered, at  1075 . Method  1070  can continue from  1071  again to monitor the state of the respiratory distress including the effect of the delivery of the one or more therapies and/or other medical intervention resulting from the alert. 
       FIG. 11  illustrates an embodiment of a method for monitoring RSA using ECG and accelerometer signals. In one embodiment, the ECG and accelerometer signals are sensed using a chest patch attached to the chest of the patient. In another embodiment, the ECG and accelerometer signals are sensed using an implantable cardiac monitor (ICM) that is placed within the patient. Mean heart rates are calculated using R-waves detected from the ECG signal separately for inspiration and expiration periods detected from the accelerometer signal. An RSA index (representing an autonomic measure) is calculated as a ratio of the mean heart rate during inspiration to the mean heart rate during expiration.  FIG. 12  illustrates an example of RSA index plotted against time for healthy and diseased (with the respiratory distress. An exacerbation of the respiratory distress is detected or predicted when the RSA index falls below a threshold (dash line). In various embodiments, different thresholds can be used for detection and prediction. An alert can be produced to notify the patient and/or the user that exacerbation of the respiratory distress is detected. A distinct alert can be produced to notify the patient and/or the user that exacerbation of the respiratory distress is predicted. 
     Example: Monitoring Brs 
     In various embodiments, the state of the respiratory distress can be monitored using one or more respiration-mediated physiological parameters, such as one or more measures of baroreflex sensitivity (BRS) that are indicative of the patient&#39;s autonomic balance. The state of the respiratory distress can be measured by a degree of autonomic imbalance.  FIG. 13  illustrates an example of BRS under healthy and diseased conditions. With abnormal autonomic activity associated with the respiratory distress, the change in heart rate in response to change in blood pressure is attenuated, resulting in decreased BRS. This attenuation in BRS can lead to impaired sympathetic inhibition, elevated blood pressure, and worsening of the respiratory distress. In various embodiments, the one or more patient condition parameters and/or metrics can include one or more measures of the BRS for determine the state of the respiratory distress, such as asthma or COPD. 
     Respiratory distress monitoring circuit  100 ,  200 , or  300  can be configured to monitor the state of the respiratory distress using one or more BRS parameters being one or more measures of BRS. Referring back to  FIG. 1 , Signal inputs  101  can receive one or more blood pressure signals, one or more cardiac signals, and one or more physical state signals. The one or more respiratory signals are indicative of respiratory cycles including inspiratory and expiratory phases. The one or more cardiac signals are indicative of cardiac cycles including at least ventricular depolarizations (R-waves). The one or more physical state signals each indicate a physical state or change in the physical state of the patent that affects the patient&#39;s BRS. Signal processing circuit  102  can be configured to process the one or more blood pressure signals, the one or more cardiac signals, and the one or more physical state signals and to generate the one or more BRS parameters. Respiratory distress analyzer  103  can be configured to determine the state of the respiratory distress using the one or more BRS parameters. Physiological marker input  106  can receive the one or more BRS parameters. Parameter analysis circuit  105  can be configured to analyze the one or more BRS parameters received from signal processing circuit  102  and determine the state of the respiratory distress using an outcome of the analysis. 
     Referring back to  FIG. 2 , signal inputs  201  can be configured to include a blood pressure input to receive the one or more blood pressure signals, a cardiac signal input to receive the one or more cardiac signals, and a physical state input to receive the one or more physical state signals. Signal processing circuit  202  can be configured to process the one or more blood pressure signals, the one or more cardiac signals, and the one or more physical state input signals and to generate the patient condition parameters indicative of the state of the respiratory distress. The patient condition parameters include the one or more BRS parameters and one or more physical state parameters. Respiratory distress analyzer  203  can be configured to determine the state of the respiratory distress based on the one or more BRS parameters and the one or more physical state parameters. The one or more physical state parameters indicate one or more physical states of the patient that affects the patient&#39;s BRS and allow the one or more BRS parameters to be expressed as functions of the one or more physical states. Physiological parameter input  206  can be configured to receive the one or more BRS parameters and the one or more physical state parameters. The one or more BRS parameters can include, but are not limited to, one or more of the following parameters:
         (a) BRS (which can vary based on minimum blood pressure and heart rate thresholds for minimum change, and can vary based on the minimum number of beats in a sequence);   (b) Range of BRS;   (c) Coherence or correlation measures;   (d) Delay or latency;   (e) Recovery times;   (f) Baroreceptor characterization—sigmoid curve and morphology;   (g) Change in cardiac measure (e.g., captured as a slope of change or as a time interval for the parameter to reach a certain percentage of the peak change);   (h) Change in blood pressure measure (e.g., captured as a slope of change or as a time interval for the parameter to reach a certain percentage of the peak change); and/or   (i) Other measure(s) of baroreceptor response.       

     The one or more physical state parameters can include, but are not limited to, one or more of the following parameters:
         (a) Respiratory cycle timing (timing of inspiration and expiration phases);   (b) Level of physical activity or exertion (e.g., indicated by the activity and respiration sensors, classified as mild, moderate, or vigorous activity)(baroreceptor response can be characterized over a continuum of levels of physical activity or exertion indicated by signals, such as activity, respiration, and/or biochemical markers, for example, by vector magnitude units (in g) over a period of time, caloric expenditure, distance traveled, or other activity or exertion measures, or a combination of these parameters);   (c) Type of posture change (e.g., indicated by the posture sensor, classified as laying to sitting, laying to standing, sitting to standing, etc.); and/or   (d) Magnitude of posture change (angle) and/or time duration of posture change (seconds, or degrees/second).
 
The one or more BRS parameters can be stratified by values of such one or more physical state parameters.
       

     Parameter analysis circuit  205  can be configured to determine the state of the respiratory distress based on at least the one or more BRS parameters and the one or more physical state parameters. In one embodiment, parameter analysis circuit  205  can be configured to produce a BRS metric including a linear or nonlinear combination of the one or more BRS parameters as stratified by the one or more physical state parameters and to produce the one or more respiratory distress indicators indicating the state of the respiratory distress based on the BRS metric. In various embodiments, respiratory distress monitoring circuit  300  can be configured to monitor the state of the respiratory distress using the one or more BRS parameters and the one or more physical state parameters in combination with any one or more other physiological marker parameters, and/or other parameters related to the respiratory disorder, that are discussed in this document. 
     Referring back to  FIG. 3 , when respiratory distress monitoring circuit  300  is configured to monitor the state of the respiratory distress using the one or more BRS parameters and the one or more physical state parameters, monitoring device(s)  321  can include one or more sensors that can be configured to sense the one or more blood pressure signals, the one or more cardiac signals, and the one or more physical state signals. In various embodiments, monitoring device(s)  321  can include an implantable, injectable, non-invasive, wearable, or passive monitoring device, or a combination of any of these devices, including one or more sensors to acquire, for example, one or more signals indicative of baroreceptor response and one or more signals indicative of activity level, respiration, and/or posture of the patient. In various embodiments, the one or more sensors can include, but are not limited to, one or more of the following:
         (a) A sensor to directly or indirectly sense a blood pressure signal (e.g., a pressure sensor to sense the blood pressure directly through invasive or noninvasive means; a heart sound sensor to sense the second heard sound (S2), a sensor to sense pulse transit time, and/or a sensor to sense a blood volume pulse waveform;   (b) A sensor to directly or indirectly sense a cardiac signal (e.g., ECG allowing for measuring heart rate or R-R interval and/or HRV, including time and/or frequency domain measures of the HRV, and/or a heart sound signal allowing for detection of the first heart sound (S1)); and/or   (c) One or more sensors to sense the physical state of the patient (e.g., activity, posture, respiration rate, and heart rate):
           (i) An activity sensor including one or more of accelerometers, gyroscopes, electromyography (EMG) sensors, GPS, or other sensors indicating physical activity;   (ii) A posture sensor including one or more of accelerometers, gyroscopes, passive motion capture, or other sensors indicating postures; and/or   (iii) A sensor to directly or indirectly sense the respiration rate and/or the heart rate, such as one or more of:
               (1) Respiration sensor (e.g., patient-contacting sensor such as chest and abdominal movement sensor, acoustic sensor, airflow sensor, muscle strain sensor, and/or impedance sensor, and/or non-contacting sensor such as radio or microwave based tissue movement sensor, optical sensor, acoustic sensor, camera, and/or accelerometer or gyroscope);   (2) ECG sensor (for deriving periods of inspiration and expiration from ECG, which is modulated by respiratory activity);   (3) Heart sound sensor (for deriving periods of inspiration and expiration from a heart sound signal that is modulated by respiratory activity); and/or   (4) Blood pressure sensor (e.g., PPG sensor, blood pressure cuff, and/or invasive blood pressure sensor).
 
In various embodiments, monitoring device(s)  321  can include one or more sensors in one or more remote devices coupled to respiratory distress monitoring circuit  300  via one or more wireless or wired communication links. Such one or more remote devices can include, but are not limited to, one or more of the following:
   
               
           (a) Invasive or noninvasive device for processing sensor input data;   (b) Personal device for alerts and notification on pain levels; and/or   (c) Invasive or noninvasive devices used as part of a closed-loop system, including a closed-loop systems where the patient closes the loop by himself/herself.       

       FIG. 14  illustrates an embodiment of a method  1480  for monitoring respiratory distress based on a BRS metric. Method  1480  can be performed using system  320 , which can be implemented in system  520  or  620 . 
     At  1481 , patient condition signals are received. The patient condition signals include a respiratory signal indicative of inspiration and expiration times, a cardiac signal indicative of R-R interval, and a blood pressure signal indicative systolic blood pressure. At  1482 , the BRS metric is determined using the respiratory, cardiac, and blood pressure signals. At  1483 , the state of the respiratory distress is determined based the BRS metric. If the state of the respiratory distress (e.g., a quantitative measure of the state) does not exceed a threshold at  1484 , method  1480  continues from  1481  again. If the state of the respiratory distress exceeds the threshold at  1484 , an alert is produced to notify the patient and/or the user, and/or one or more therapies treating the respiratory distress are delivered, at  1485 . Method  1480  can continue from  1481  again to monitor the state of the respiratory distress including the effect of the delivery of the one or more therapies and/or other medical intervention resulting from the alert. 
       FIG. 15  illustrates an example of a method for monitoring BRS using ECG, blood pressure, and accelerometer signals sensed by a chest patch and a wrist-worn device on the patient.  FIG. 16  illustrates an example of a method for monitoring BRS using ECG, heart sound, and accelerometer signals sensed by an ICM. The accelerometer signal is used as a respiratory signal indicative of inspiration and expiration phases uses as the patient&#39;s physical state. A BRS index (representing an autonomic measure) is calculated as a slope of a curve being the R-R interval against the systolic blood pressure (as indicated by the heart sounds). This represents one of various techniques to quantify spontaneous BRS, and allows for “up” and “down” sequences, which are controlled by different mechanisms, to be evaluated separately. Other techniques include spectral methods that look at the power of the blood pressure and heart rate signals in certain frequency ranges as well as their ratios.  FIG. 17  illustrates an example of BRS index plotted against time for healthy and diseased (with the respiratory distress). An exacerbation of the respiratory distress is detected or predicted when the BRS index falls below a threshold (dash line). In various embodiments, different thresholds can be used for detection and prediction. An alert can be produced to notify the patient and/or the user that exacerbation of the respiratory distress is detected. A distinct alert can be produced to notify the patient and/or the user that exacerbation of the respiratory distress is predicted. 
     It is to be understood that the above detailed description is intended to be illustrative, and not restrictive. Other embodiments will be apparent to those of skill in the art upon reading and understanding the above description. The scope of the invention should, therefore, be determined with reference to the appended claims, along with the full scope of equivalents to which such claims are entitled. 
     
       
         
           
               
             
               
                 TABLE 1 
               
             
            
               
                   
               
               
                 Physiological markers for the respiratory distress. 
               
            
           
           
               
               
               
               
            
               
                   
                 Physiology/ 
                 Response to 
                 Exemplary Method of 
               
               
                 Measurement 
                 Additional Notes 
                 Acute Exacerbation 
                 Measurement/Devices 
               
               
                   
               
               
                 Respiratory Rate 
                 Dyspnea. Increased 
                 Increase 
                 Impedance measure, 
               
               
                   
                 respiratory rate and/or 
                   
                 accelerometer, EMG, 
               
               
                   
                 decreased tidal volume 
                   
                 radio/micro waves, 
               
               
                   
                   
                   
                 optical-based sensor, 
               
               
                   
                   
                   
                 acoustic-based 
               
               
                   
                   
                   
                 sensor, camera, ECG 
               
               
                 Heart Rate 
                 Increased heart rate. 
                 Increase 
                 ECG, heart sounds, 
               
               
                   
                 Overall increase in 
                   
                 accelerometer, 
               
               
                   
                 sympathetic activation and 
                   
                 radio/micro waves, 
               
               
                   
                 a decrease in 
                   
                 optical-based sensor, 
               
               
                   
                 parasympathetic 
                   
                 acoustic-based 
               
               
                   
                 activation 
                   
                 sensor, camera 
               
               
                 Cough 
                 Increased occurrence of 
                 Increase 
                 Microphone, 
               
               
                   
                 coughing leading up to 
                   
                 accelerometer 
               
               
                   
                 exacerbation 
               
               
                 Wheezing 
                 More frequent wheezing 
                 Increase 
                 Microphone, 
               
               
                   
                   
                   
                 accelerometer 
               
               
                 Oxygen Saturation 
                 Blood O2 saturation 
                 Decrease 
                 Pulse oximeter; 
               
               
                   
                   
                   
                 Smartphone app 
               
               
                 Central Cyanosis 
                 Blood O2 saturation 
                 Increase 
                 Pulse oximeter; 
               
               
                   
                   
                   
                 Smartphone app 
               
               
                 Altered 
                 Balance and posture can 
                 Variable 
                 Accelerometer 
               
               
                 Consciousness 
                 be altered due to the onset 
               
               
                   
                 (gradual or sudden) of an 
               
               
                   
                 exacerbation 
               
               
                 Activity levels 
                 Decrease in activity levels 
                 Risk factor/ 
                 Accelerometer 
               
               
                   
                 due to labored breathing 
                 Trigger 
               
               
                 Sleep quality 
                 Poor sleep quality, more 
                 Decrease 
                 Accelerometer, 
               
               
                   
                 often awakening, sleeping 
                   
                 gyroscope, ECG, 
               
               
                   
                 in an upright position 
                   
                 radio/micro waves, 
               
               
                   
                   
                   
                 optical-based sensor, 
               
               
                   
                   
                   
                 acoustic-based 
               
               
                   
                   
                   
                 sensor, camera, GSR 
               
               
                 Posture/Chest 
                 Poorer posture overall, 
                 Variable 
                 Accelerometer, 
               
               
                 posture 
                 chest inflation alters chest 
                   
                 gyroscope (posture) 
               
               
                   
                 posture 
               
               
                 Balance 
                 Reduced balance, 
                 Decrease 
                 Accelerometer, 
               
               
                   
                 coordination 
                   
                 gyroscope (posture) 
               
               
                 Gait 
                 Altered gait pattern 
                 Variable 
                 Accelerometer 
               
               
                 Vocal 
                 Patients with acute, severe 
                 Turbulent, 
                 Microphone 
               
               
                 Expression 
                 asthma appear seriously 
                 altered 
               
               
                   
                 dyspneic at rest, are 
               
               
                   
                 unable to talk with 
               
               
                   
                 sentences or phrases 
               
               
                 Inflammation 
                 Increase in inflammatory 
                 Increase 
                 Chemical sensor 
               
               
                   
                 markers (blood, saliva, 
               
               
                   
                 breath, sputum, etc.) 
               
               
                 Accessory 
                 Rapid, shallow breathing 
                 Variable 
                 EMG 
               
               
                 Muscle Activity 
                 changes abdominal/thoracic 
               
               
                   
                 muscle activity 
               
               
                 Physical Stress 
                 Factors into patient&#39;s 
                 Risk factor/ 
                 Subjective input 
               
               
                   
                 overall health and 
                 Trigger 
               
               
                   
                 susceptibility to infection 
               
               
                   
                 or other triggering event 
               
               
                 Mental Stress 
                 Factors into patient&#39;s 
                 Risk factor/ 
                 Subjective input 
               
               
                   
                 overall health and 
                 Trigger 
               
               
                   
                 susceptibility to infection 
               
               
                   
                 or other triggering event 
               
               
                 Menstrual Cycle 
                 Factors into patient&#39;s 
                 Risk factor/ 
                 Subjective input 
               
               
                   
                 overall health and 
                 Trigger 
               
               
                   
                 susceptibility to infection 
               
               
                   
                 or other triggering event 
               
               
                 Time-of- 
                 Factors into patient&#39;s 
                 Risk factor/ 
                 Automatically 
               
               
                 day/year 
                 overall health and 
                 Trigger 
                 Synced (GPS, mobile 
               
               
                   
                 susceptibility to infection 
                   
                 device) 
               
               
                   
                 or other triggering event 
               
               
                 Mucus 
                 Elevated mucus 
                 Increase 
                 Impedance measure 
               
               
                 production 
                 production blocks arieays, 
               
               
                   
                 restricting airflow and 
               
               
                   
                 difficulty breathing 
               
               
                 Airway smooth 
                 Airways constrict making 
                 Increase 
                 Impedance measure, 
               
               
                 muscle contraction 
                 it difficult to breath 
                   
                 EMG 
               
               
                 Autonomic Function 
               
               
                 Heart Rate 
                 Decrease in heart rate 
                 Decrease 
                 ECG, heart sounds, 
               
               
                 Variability 
                 variability due to the 
                   
                 accelerometer, 
               
               
                   
                 imbalance in the 
                   
                 radio/micro waves, 
               
               
                   
                 autonomic nervous 
                   
                 optical-based sensor. 
               
               
                   
                 system, with sympathetic 
                   
                 acoustic-based 
               
               
                   
                 system dominating. 
                   
                 sensor, camera 
               
               
                 Respiration 
                 Essentially the transfer 
                 Decrease 
                 ECG, heart sounds, 
               
               
                 Sinus 
                 function from respiration 
                   
                 accelerometer, 
               
               
                 Arrhythmia 
                 rate to R-R intervals. 
                   
                 radio/micro waves, 
               
               
                   
                 Another way to assess 
                   
                 optical-based sensor, 
               
               
                   
                 cardiac autonomic 
                   
                 acoustic-based 
               
               
                   
                 function. RSA which 
                   
                 sensor, camera 
               
               
                   
                 decreases in the presence 
               
               
                   
                 of increased sympathetic 
               
               
                   
                 activity/decreased 
               
               
                   
                 parasympathetic activity. 
               
               
                 Heart Rate 
                 Need premature 
                 Decrease 
                 ECG, heart sounds 
               
               
                 Turbulence 
                 ventricular complexes to 
               
               
                   
                 be occurring to quantify 
               
               
                   
                 HRT 
               
               
                 Baroreceptor 
                 Measured after injection 
                 Decrease 
                 ECG, PPG, heart 
               
               
                 Reflex 
                 of phenylephrine 
                   
                 sounds 
               
               
                 Sensitivity 
                 Also a spontaneous 
               
               
                   
                 measures that can be 
               
               
                   
                 acquired continuously 
               
               
                   
                 using respiration as a way 
               
               
                   
                 to alter autonomic balance 
               
               
                 Heart Rate 
                 Sympathetic autonomic 
                 Increase 
                 ECG, heart sounds, 
               
               
                 Acceleration 
                 nerves act to quicken the 
                   
                 accelerometer 
               
               
                 Capacity 
                 heart and strengthen the 
               
               
                   
                 acceleration capacity. 
               
               
                   
                 During AECOPD, airflow 
               
               
                   
                 obstruction aggravates 
               
               
                   
                 autonomic function, 
               
               
                   
                 resulting in an imbalance 
               
               
                   
                 in the system - increase in 
               
               
                   
                 sympathetic activity 
               
               
                 Heart Rate 
                 Vagal nerve slows the 
                 Decrease 
                 ECG, heart sounds, 
               
               
                 Deceleration 
                 heart rate and enhances 
                   
                 accelerometer 
               
               
                 Capacity 
                 the heart rate deceleration 
               
               
                   
                 capacity. During 
               
               
                   
                 AECOPD, airflow 
               
               
                   
                 obstruction aggravates 
               
               
                   
                 autonomic function, 
               
               
                   
                 resulting in an imbalance 
               
               
                   
                 in the system - decrease in 
               
               
                   
                 parasympathetic (vagal) 
               
               
                   
                 activity. 
               
               
                 Galvanic Skin 
                 Acute stress, anxiety 
                 Increase 
                 Electrodes on the 
               
               
                 Response/ 
                 caused by an exacerbation 
                   
                 hand 
               
               
                 Electrodermal 
                 results in increased 
               
               
                 activity 
                 sympathetic activity 
               
               
                   
                 which causes sweat glands 
               
               
                   
                 to fill up and skin 
               
               
                   
                 conductance increases 
               
               
                   
                 creating skin conductance 
               
               
                   
                 fluctuations. 
               
               
                 Blood Pressure 
                 increase in blood pressure 
                 Increase 
                 PPG, S2, Pulse 
               
               
                   
                 due to increased 
                   
                 amplitude 
               
               
                   
                 sympathetic nervous 
               
               
                   
                 system activity and 
               
               
                   
                 resulting vasoconstruction 
               
               
                 Blood Flow 
                 Diaphragmatic blood flow 
                 Decrease 
                 PPG, S2, Pulse 
               
               
                   
                 reduces during acute 
                   
                 amplitude 
               
               
                   
                 episodes. In the case of 
               
               
                   
                 persistence of the severe 
               
               
                   
                 asthma attack, ventilatory 
               
               
                   
                 muscles cannot sustain 
               
               
                   
                 adequate tidal volumes 
               
               
                   
                 and respiratory failure 
               
               
                   
                 ensues. 
               
               
                 Perfusion 
                 Diaphragmatic perfusion 
                 Decrease 
                 PPG 
               
               
                   
                 reduces during acute 
               
               
                   
                 episodes. In the case of 
               
               
                   
                 persistence of the severe 
               
               
                   
                 asthma attack, ventilatory 
               
               
                   
                 muscles cannot sustain 
               
               
                   
                 adequate tidal volumes 
               
               
                   
                 and respiratory failure 
               
               
                   
                 ensues. 
               
               
                 Skin 
                   
                 Variable 
                 Thermometer 
               
               
                 Temperature 
               
               
                 Body 
                 Bacterial or viral 
                 Increase 
                 Thermometer 
               
               
                 Temperature 
                 infection, may cause your 
               
               
                   
                 body temperature to rise 
               
               
                 Pupil Diameter 
                 Dilation of the pupil is 
                 Increase 
                 Camera 
               
               
                   
                 indicative of sympathetic 
               
               
                   
                 activation 
               
               
                 Electrooc- 
                 Correlates to autonomic 
                 Variable 
                 Electrodes 
               
               
                 ulography 
                 tone - variable 
               
               
                   
                 relationship depending on 
               
               
                   
                 time/frequency domain 
               
               
                   
                 analysis performed 
               
               
                 Pulse Transit 
                 Increased sympathetic 
                 Decrease 
                 PPG 
               
               
                 Time &amp; Pulse 
                 activity constricts 
               
               
                 Wave Amplitude 
                 vasculature causing transit 
               
               
                 (Alternative 
                 time and wave amplitude 
               
               
                 measure for BP) 
                 to decrease 
               
               
                 Normalized 
                 Sympathetic tone causes 
                 Decrease 
                 PPG 
               
               
                 Pulse Volume 
                 vascular construction. 
               
               
                 (NPV) 
                 NPV can be derived from 
               
               
                   
                 finger tip PPG and also 
               
               
                   
                 from the bottom of the ear 
               
               
                   
                 canal. NPV is an indirect 
               
               
                   
                 measures of autonomic 
               
               
                   
                 tone 
               
               
                 Forced Expiratory 
               
               
                 Volume 
               
               
                 FEV1 
                 forcibly exhaled air in 1 
                 Decreased 
                 Thoracic impedance, 
               
               
                   
                 second; mainly reflects 
                 (decreases 
                 accelerometers, flow 
               
               
                   
                 larger airways obstruction 
                 with stage/severity) 
                 sensors, ECG 
               
               
                 FEV1/FVC 
                 fixed ratio &lt;70% defines 
                 Decrease 
                 Thoracic impedance, 
               
               
                   
                 airflow limitations. FVC = 
                   
                 accelerometers, flow 
               
               
                   
                 forced vital capacity 
                   
                 sensors, ECG 
               
               
                 TLC 
                 Total lung capacity is the 
                 Increase 
                 Thoracic impedance, 
               
               
                   
                 greatest volume of gas in 
                   
                 accelerometers, flow 
               
               
                   
                 the lungs after maximal 
                   
                 sensors, ECG 
               
               
                   
                 voluntary inspiration. 
               
               
                   
                 Increase in TLC in COPD 
               
               
                   
                 usually reflects lung 
               
               
                   
                 compliance due to 
               
               
                   
                 emphysema, as thoracic 
               
               
                   
                 compliance decreases 
               
               
                 FRC 
                 Functional residual 
                 Increase 
                 Thoracic impedance, 
               
               
                   
                 capacity is the lung 
                   
                 accelerometers, flow 
               
               
                   
                 volume at the end of quiet 
                   
                 sensors, ECG 
               
               
                   
                 expiration during tidal 
               
               
                   
                 breathing. Increased in 
               
               
                   
                 COPD patients. 
               
               
                 FEV3 
                 later fraction of forced 
                 Decrease 
                 Thoracic impedance, 
               
               
                   
                 exhalation better reflects 
                   
                 accelerometers, flow 
               
               
                   
                 smaller airway 
                   
                 sensors, ECG 
               
               
                   
                 contributions and may be 
               
               
                   
                 a more sensitive measure 
               
               
                   
                 to diagnose early airway 
               
               
                   
                 obstruction in COPD 
               
               
                 FEV3/FEV6 
                 ratio of later fraction 
                 Decrease 
                 Thoracic impedance, 
               
               
                   
                 measures of forced 
                   
                 accelerometers, flow 
               
               
                   
                 exhalation to represent the 
                   
                 sensors, ECG 
               
               
                   
                 small airways. Ratio less 
               
               
                   
                 than the lower limit of 
               
               
                   
                 normal as the sole 
               
               
                   
                 abnormality identifies a 
               
               
                   
                 distinct population with 
               
               
                   
                 evidence of small airways 
               
               
                   
                 disease 
               
               
                   
                 advantage of spirometric 
               
               
                   
                 ratios is that they have 
               
               
                   
                 less variability than do 
               
               
                   
                 timed forced expirations 
               
               
                 Lung 
                 Absolute lung volume is 
                 TLC, FRC, &amp;RV 
                 Thoracic impedance, 
               
               
                 hyperinflation: 
                 evaluated by measuring 
                 all &gt;= 120-130% 
                 accelerometers, flow 
               
               
                 TLC, FRC, RV 
                 the increase in total lung 
                   
                 sensors, ECG 
               
               
                   
                 capacity (TLC), functional 
               
               
                   
                 residual capacity (FRC), 
               
               
                   
                 residual volume (RV), and 
               
               
                   
                 decrease in inspiratory 
               
               
                   
                 capacity (IC). 
               
               
                   
                 Lung hyperinflation exists 
               
               
                   
                 when TLC, FRC, and 
               
               
                   
                 RV &gt;= 120-130% of the 
               
               
                   
                 predicted volume 
               
               
                 Tidal Volume 
                 increase in displaced air 
                 Increase 
                 Thoracic impedance, 
               
               
                 (VT) 
                 between inspiration and 
                   
                 accelerometers, flow 
               
               
                   
                 expiration. Short rapid 
                   
                 sensors, ECG 
               
               
                   
                 breathing 
               
               
                 Peak expiratory 
                 Maximum speed of 
                 Decrease 
                 Thoracic impedance, 
               
               
                 flow (PEF) 
                 expiration decreases as the 
                   
                 accelerometers, flow 
               
               
                   
                 airways become 
                   
                 sensors, ECG 
               
               
                   
                 blocked/constricted 
               
               
                 Forced 
                 Amount of air that can be 
                 Decrease 
                 Thoracic impedance, 
               
               
                 expiratory 
                 exhaled decreases. 
                   
                 accelerometers, flow 
               
               
                 volume (FEV) 
                 Expiration becomes 
                   
                 sensors, ECG 
               
               
                   
                 slower and more difficult 
               
               
                 Inspiration/ 
                 Normal is 1:2 at rest, 1:1 
                 Decrease 
                 Thoracic impedance, 
               
               
                 expiration 
                 during exercise. Ratio 
                   
                 accelerometers, flow 
               
               
                 ratio (IER) 
                 decrease with an 
                   
                 sensors, ECG 
               
               
                   
                 increasing expiration 
               
               
                   
                 period due to difficulty 
               
               
                   
                 breathing, expelling air 
               
               
                   
                 from the lungs 
               
               
                 Minute volume 
                 Total volume of gas 
                 Increase 
                 Thoracic impedance, 
               
               
                 (MV) 
                 inhaled or exhaled in 1 
                   
                 accelerometers, flow 
               
               
                   
                 minute. Rapid breathing 
                   
                 sensors, ECG 
               
               
                   
                 during exacerbation 
               
               
                 Forced vital 
                 Amount of air (total 
                 Decrease 
                 Thoracic impedance, 
               
               
                 capacity (FVC) 
                 amount of air) exhaled 
                   
                 accelerometers, flow 
               
               
                   
                 during the FEV test. 
                   
                 sensors, ECG 
               
               
                 End Expiratory 
                 Corresponds to FRC in the 
                 Increase 
                 Thoracic impedance, 
               
               
                 volume (EEV)/ 
                 presence of positive end 
                   
                 accelerometers, flow 
               
               
                 ΔrEEV 
                 expiration pressure. 
                   
                 sensors, ECG 
               
               
                   
                 ΔrEEV is used if short 
               
               
                   
                 term filtering is used. 
               
               
                 HII 
                 Hyperinflation causes the 
                 Increase 
                 Thoracic impedance, 
               
               
                 (hyperinflation 
                 patient to operate on the 
                   
                 accelerometers, flow 
               
               
                 index) 
                 relatively flat portion of 
                   
                 sensors, ECG 
               
               
                   
                 the chest wall-lung 
               
               
                   
                 compliance curve leading 
               
               
                   
                 to a rapid shallow 
               
               
                   
                 breathing pattern 
               
               
                 Airway Resistance 
               
               
                 Impulse 
                 Pressure oscillations are 
                 Variable 
                 Impulse oscillometry 
               
               
                 Oscillometry 
                 applied at the mouth to 
                   
                 system (IOS) 
               
               
                 (IOS) 
                 measure pulmonary 
               
               
                   
                 resistance and reactance. 
               
               
                   
                 Noninvasive, rapid 
               
               
                   
                 technique requiring only 
               
               
                   
                 passive cooperation by the 
               
               
                   
                 patient. 
               
               
                 Neural Measures 
               
               
                 Neural 
                 Calculated as the product 
                 Increase 
                 EMG device 
               
               
                 Respiratory 
                 of the second intercostal 
               
               
                 Drive Index 
                 space parasternal 
               
               
                 (NRDI) 
                 electromyography activity 
               
               
                   
                 normalized to the peak 
               
               
                   
                 EMG activity during a 
               
               
                   
                 maximum inspiratory sniff 
               
               
                   
                 manoeuver. 
               
               
                   
                 Parasternal EMG 
               
               
                   
                 (EMGpara) signals 
               
               
                   
                 recorded from surface 
               
               
                   
                 electrodes have a direct 
               
               
                   
                 relationship with 
               
               
                   
                 respiratory muscle load 
               
               
                   
                 and have been shown to 
               
               
                   
                 respond to acute change. 
               
               
                   
                 Differentiates between 
               
               
                   
                 “improvers” and 
               
               
                   
                 “deteriorators” in the 
               
               
                   
                 hospital, and was also a 
               
               
                   
                 predictor of hospital 
               
               
                   
                 readmittance. 
               
               
                 Diaphragmatic 
                 Known mechanisms of 
                 Variable 
               
               
                 Changes due to 
                 compromised diaphragmatic 
               
               
                 Hyperinflation 
                 function secondary to 
               
               
                 * Not currently 
                 hyperinflation: 
               
               
                 measured but 
                 worsening of the length- 
               
               
                 secondary 
                 tension relationship 
               
               
                 effects due to 
                 decrease in the zone of 
               
               
                 hyperinflation 
                 apposition 
               
               
                 could be 
                 decrease in the curvature 
               
               
                 captured 
                 change in the mechanical 
               
               
                   
                 arrangement of costal and 
               
               
                   
                 crural components increase 
               
               
                   
                 in the elastic recoil of 
               
               
                   
                 the thoracic cage 
               
               
                 Exhaled Breath 
               
               
                 Exhaled Breath 
                 Exhaled breath temperature 
                 Increase --&gt; 
                 eNose, SpiroNose 
               
               
                 Temperature 
                 can be an indication of airway 
                 AECOPD 
                 (breathcloud.org), 
               
               
                   
                 inflammation. Peak 
                 Decrease --&gt; 
                 chemical sensor 
               
               
                   
                 exhaled breath in patients 
                 stable COPD 
               
               
                   
                 with exacerbations 
               
               
                   
                 increased and dropped 
               
               
                   
                 down with recovery. 
               
               
                   
                 Patients with stable COPD 
               
               
                   
                 had decreased peak EBT 
               
               
                   
                 in comparison to controls 
               
               
                   
                 (non smokers and 
               
               
                   
                 smokers) 
               
               
                 Fractional 
                 During inflammation, 
                 Increase 
                 eNose, chemical 
               
               
                 exhaled nitric 
                 larger amount of NO are 
                   
                 sensor 
               
               
                 oxide (FeNO) 
                 produced for prolonged 
               
               
                   
                 periods. NO concentrations 
               
               
                   
                 are known to be higher in 
               
               
                   
                 disease such as asthma and 
               
               
                   
                 COPD. 
               
               
                 pH - expired 
                 Acidification (decrease in 
                 Decrease 
                 eNose, chemical 
               
               
                 breath 
                 pH) could be a maker of 
                   
                 sensor 
               
               
                   
                 airway inflammation and 
               
               
                   
                 disease severity. pH is 
               
               
                   
                 reduced during acute 
               
               
                   
                 exacerbations 
               
               
                 O2 - expired 
                 Rapid shallow breathing, 
                 Increase 
                 eNose, chemical 
               
               
                 breath 
                 increases respiratory O2 
                   
                 sensor 
               
               
                   
                 concentrations. Reduces 
               
               
                   
                 CO2 concentrations 
               
               
                 CO2 - expired 
                 In early stages of acute 
                 Decrease 
                 eNose, chemical 
               
               
                 breath 
                 exacerbations, patients 
                   
                 sensor 
               
               
                   
                 have respiratory alkalosis 
               
               
                 Volatile Organic 
                 Electronic noses that can 
               
               
                 Compounds 
                 pick up VOCs to assess 
               
               
                 (VOCs) 
                 profile and classify 
               
               
                   
                 patients 
               
               
                   
                 Inflammatory related and 
               
               
                   
                 detectable in exhaled 
               
               
                   
                 breath 
               
               
                   
                 13 VOCs: Isoprene, C16 
                 Predictive 
                 eNose, chemical 
               
               
                   
                 hydrocarbon, 4,7- 
                 Profile 
                 sensor 
               
               
                   
                 Dimethyl-undecane, 2,6- 
               
               
                   
                 Dimethyl-heptane, 4- 
               
               
                   
                 Methyl-octane, 
               
               
                   
                 Hexadecane, 3,7- 
               
               
                   
                 Dimethyl 1,3,6-octane, 
               
               
                   
                 2,4,6-Trimethyl-decane, 
               
               
                   
                 Hexanal, Benzonitrile, 
               
               
                   
                 Octadecane, Undecane, 
               
               
                   
                 Terpineol 
               
               
                 Chemical Markers 
               
               
                 Nitric Oxide 
                 During inflammation, 
                 Increase 
                 Chemical Sensor 
               
               
                   
                 larger amount of NO are 
               
               
                   
                 produced for prolonged 
               
               
                   
                 periods. NO concentrations 
               
               
                   
                 are known to be higher 
               
               
                   
                 in disease such as asthma 
               
               
                   
                 and COPD. 
               
               
                 CRP 
                 Nonspecific marker of 
                 Increase 
                 Chemical Sensor 
               
               
                   
                 inflammation, has an 
               
               
                   
                 inverse relation with lung 
               
               
                   
                 function and probably 
               
               
                   
                 reflects disease severity. 
               
               
                   
                 CRP levels rise during 
               
               
                   
                 exacerbations particularly 
               
               
                   
                 when there is an increased 
               
               
                   
                 neutrophilic influx due to 
               
               
                   
                 abacterial cause. Also, a 
               
               
                   
                 raised CRP in stable state 
               
               
                   
                 predicts recurrent 
               
               
                   
                 exacerbations either due 
               
               
                   
                 to a failure to completely 
               
               
                   
                 resolve the first episode or 
               
               
                   
                 an underlying airway 
               
               
                   
                 colonization that 
               
               
                   
                 predisposes to further 
               
               
                   
                 episodes 
               
               
                 External Index 
               
               
                 Air Temperature 
                 Cold temperatures 
                 Risk factor/Trigger 
                 Integrated weather 
               
               
                   
                 increase risk of 
                   
                 application to sync 
               
               
                   
                 exacerbation 
                   
                 with devices to 
               
               
                 Air 
                 Increase in known 
                 Risk factor/Trigger 
                 shown when someone 
               
               
                 Contaminants 
                 allergens for patients 
                 Increase in particulate 
                 is more at risk 
               
               
                   
                 increases risk of an 
                 count/size --&gt; 
                 Ambient air sensor 
               
               
                   
                 exacerbation 
                 increase in AECOPD 
               
               
                   
                 Real-time monitoring of 
               
               
                   
                 personal air pollution 
               
               
                   
                 exposure 
               
               
                   
                 Can monitor number of 
               
               
                   
                 particulates and their size- 
               
               
                   
                 can inform predictions of 
               
               
                   
                 acute exacerbations or 
               
               
                   
                 have more long-term 
               
               
                   
                 monitoring benefits 
               
               
                 Humidity 
                 cold dry air is a trigger for 
                 Risk factor/Trigger 
               
               
                   
                 asthma attacked 
               
               
                 Altitude 
                 Fewer exacerbations occur 
                 Risk factor/Trigger 
               
               
                   
                 at high altitudes 
               
               
                 Air Pressure 
                 Fewer exacerbations occur 
                 Risk factor/Trigger 
               
               
                   
                 at low pressure