Systems and methods for forced oscillation in respiratory pressure therapy

Abstract

A computer-implemented method includes providing, via a user interface of a respiratory therapy system, a flow of air to an airway of a user. The method also includes superimposing a pattern of pseudo-random varying pressure on the flow of air. The method also includes generating, via a pressure sensor of the respiratory therapy system, pressure data associated with the flow of air. The method also includes determining, based at least in part on the pressure data, physiological data indicative of one or more airway characteristic of the user.

Description

SYSTEMS AND METHODS FOR FORCED OSCILLATION IN RESPIRATORY PRESSURE THERAPYCROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims benefit of and priority to U.S. Provisional Patent Application No. 63 / 760,550, filed on February 19, 2025, and titled SYSTEMS AND METHODS FOR FORCED OSCILLATION IN RESPIRATORY PRESSURE THERAPY, which is hereby incorporated by reference herein in its entirety.TECHNICAL FIELD

[0002] The present disclosure relates generally to systems and methods for adding a pressure oscillation to a respiratory pressure therapy air flow for detecting characteristics of a patient’s respiratory systems or a respiratory therapy system, and more particularly, to systems and methods for adding a pseudo-random noise pressure oscillation to a respiratory pressure therapy air flow.BACKGROUND

[0003] Many individuals suffer from sleep-related and / or respiratory -related disorders such as, for example, Sleep Disordered Breathing (SDB), which can include Obstructive Sleep Apnea (OSA), Central Sleep Apnea (CSA), other types of apneas such as mixed apneas and hypopneas, Respiratory Effort Related Arousals (RERAs), and snoring. In some cases, these disorders manifest, or manifest more pronouncedly, when the individual is in a particular lying / sleeping position. These individuals may also suffer from other health conditions (which may be referred to as comorbidities), such as insomnia (e.g., difficulty initiating sleep, frequent or prolonged awakenings after initially falling asleep, and / or an early awakening with an inability to return to sleep), Periodic Limb Movement Disorder (PLMD), Restless Leg Syndrome (RLS), Cheyne-Stokes Respiration (CSR), respiratory insufficiency, Obesity Hyperventilation Syndrome (OHS), Chronic Obstructive Pulmonary Disease (COPD), Neuromuscular Disease (NMD), rapid eye movement (REM) behavior disorder (also referred to as RBD), dream enactment behavior (DEB), hypertension, diabetes, stroke, and chest wall disorders.

[0004] These disorders are often treated using a respiratory therapy system (e.g., a continuous positive airway pressure (CPAP) system), which delivers pressurized air to aid in preventing the individual’s airway from narrowing or collapsing during sleep. However, some 14928-2858-6896users find such systems to be uncomfortable, difficult to use, expensive, aesthetically unappealing and / or fail to perceive the benefits associated with using the system. As a result, some users will elect not to use the respiratory therapy system or discontinue use of the respiratory therapy system absent a demonstration of the severity of their symptoms and / or an improvement in their symptoms as a result of respiratory therapy treatment, or encouragement or affirmation that the respiratory therapy system is improving their sleep quality and / or reducing the symptoms of comorbidities. The present disclosure is directed to solving these and other problems.SUMMARY

[0005] According to some implementations of the present disclosure, a computer-implemented method includes providing, via a user interface of a respiratory therapy system, a flow of air to an airway of a user. The method also includes superimposing a pattern of pseudorandom varying pressure on the flow of air. The method also includes generating, via a pressure sensor of the respiratory therapy system, pressure data associated with the flow of air. The method also includes determining, based at least in part on the pressure data, physiological data indicative of one or more airway characteristics of the user. The method additionally or alternatively includes determining, based at least in part on the pressure data, operational data indicative of one or more characteristics of the respiratory therapy system.

[0006] According to some implementations of the present disclosure, the determining the physiological data includes estimating a respiratory impedance and / or admittance of the airway as a function of frequency based on the pressure data.

[0007] According to some implementations of the present disclosure, the determining the physiological data includes determining a resistance of the airway, a reactance of the airway, or both, based at least in part on the pressure data.

[0008] According to some implementations of the present disclosure, one of the one or more airway characteristics of the user is an exacerbation factor that is indicative of a likelihood that the user will experience an exacerbation event within a predetermined amount of time.

[0009] According to some implementations of the present disclosure, the predetermined amount of time is about thirty days.

[0010] According to some implementations of the present disclosure, the exacerbation factor is determined based at least in part on an increase in variability of the pressure data between a first variability of the pressure data measured over a first period of days as compared to a second variability of the pressure data taken over a subsequent period of days.24928-2858-6896

[0011] According to some implementations of the present disclosure, the pattern of pseudorandom varying pressure has a frequency that varies in a range between about 5 Hz and 15 Hz.

[0012] According to some implementations of the present disclosure, the pattern of pseudorandom varying pressure has a peak-to-peak amplitude of about 1 cm H2O.

[0013] According to some implementations of the present disclosure, the pattern of pseudorandom varying pressure is superimposed continuously on the flow of air.

[0014] According to some implementations of the present disclosure, the pattern of pseudorandom varying pressure is superimposed on the flow of air only during predetermined time intervals.

[0015] According to some implementations of the present disclosure, the pattern of pseudorandom varying pressure is superimposed on the flow of air during predetermined conditions.

[0016] According to some implementations of the present disclosure, the pattern of pseudorandom varying pressure is superimposed on the flow of air during predetermined stages of sleep.

[0017] According to some implementations of the present disclosure, the pattern of pseudorandom varying pressure is superimposed on the flow of air during predetermined sleeping positions of the user.

[0018] According to some implementations of the present disclosure, the pattern of pseudorandom varying pressure is superimposed on the flow of air when a potential apnea event is detected.

[0019] According to some implementations of the present disclosure, the pattern of pseudorandom varying pressure is superimposed over the flow of air when the flow of air has a pressure of less than a predetermined threshold pressure.

[0020] According to some implementations of the present disclosure, the predetermined threshold pressure is about 6 cm H2O.

[0021] According to some implementations of the present disclosure, the pressure data is discarded from the determining step if the pressure data is generated while the flow of air is leaking from the respiratory therapy system at a rate greater than a predetermined leak rate.

[0022] According to some implementations of the present disclosure, the predetermined leak rate is about 0.05 L / s.

[0023] According to some implementations of the present disclosure, the method further includes generating the pressure data associated with first and second pluralities of sleep sessions. The method also includes identifying an increase in variability of an airway -response feature in the pressure data between a second variability of the airway-response feature 34928-2858-6896measured over the second plurality of sleep sessions as compared to a first variability of the airway-response feature measured over the first plurality of sleep sessions. The method also includes determining the exacerbation factor based at least in part on the increase in the variability of the second variability over the first variability. The method also includes determining the exacerbation factor to be greater than a threshold value, and indicating to the user, via a display device, that an exacerbation is predicted.

[0024] According to some implementations of the present disclosure, the first plurality of sleep sessions is a set of consecutive sleep sessions, and the second plurality of sleep sessions is a second different set of consecutive sleep sessions.

[0025] According to some implementations of the present disclosure, one of the one or more airway characteristics is indicative of a closed airway.

[0026] According to some implementations of the present disclosure, one of the one or more airway characteristics is indicative of chronic obstructive pulmonary disease (COPD).

[0027] According to some implementations of the present disclosure, one of the one or more airway characteristics is utilized for mask titration, such as to determine a user interface type suitable for the user.

[0028] According to some implementations of the present disclosure, a system includes a control system including one or more processors. The system also includes a memory device having stored thereon machine readable instructions. The control system is coupled to the memory to implement the method of any one of the above-noted implementations when the machine executable instructions in the memory are executed by at least one of the one or more processors of the control system.

[0029] According to some implementations of the present disclosure, the system further includes the respiratory therapy system.

[0030] According to some implementations of the present disclosure, the control system is included in the respiratory therapy system.

[0031] According to some implementations of the present disclosure, a system for communicating one or more indications to a user includes a control system configured to implement the method of any one of the above-noted implementations.

[0032] According to some implementations of the present disclosure, a computer program product includes instructions which, when executed by a computer, cause the computer to carry out the method of any one of the above-noted implementations.

[0033] According to some implementations of the present disclosure, the computer program product is a non-transitory computer readable medium.44928-2858-6896

[0034] According to some implementations of the present disclosure, a system includes a control system including one or more processors, and a memory device having stored thereon machine readable instructions. The control system is coupled to the memory. The system also includes a respiratory therapy system including a blower motor and a user interface providing a flow of air, via the blower motor, to an airway of a user. The control system is configured to superimpose a pattern of pseudo-random varying pressure on the flow of air. The control system is also configured to generate, via a pressure sensor of the respiratory therapy system, pressure data associated with the flow of air. The control system is further configured to determine, based at least in part on the pressure data, physiological data indicative of one or more airway characteristics of the user. The control system additionally or alternatively is configured to determine, based at least in part on the pressure data, operational data indicative of one or more characteristics of the respiratory therapy system.

[0035] According to some implementations of the present disclosure, the control system is configured to determine the physiological data based at least in part by estimating a respiratory impedance and / or admittance of the airway as a function of frequency based on the pressure data.

[0036] According to some implementations of the present disclosure, the control system is configured to determine the physiological data based at least in part by determining a resistance of the airway, a reactance of the airway, or both, based at least in part on the pressure data.

[0037] According to some implementations of the present disclosure, one of the one or more airway characteristics of the user is an exacerbation factor that is indicative of a likelihood that the user will experience an exacerbation event within a predetermined amount of time.

[0038] According to some implementations of the present disclosure, the predetermined amount of time is about thirty days.

[0039] According to some implementations of the present disclosure, the exacerbation factor is determined based at least in part on an increase in variability of the pressure data between a first variability of the pressure data measured over a first period of days as compared to a second variability of the pressure data taken over a subsequent period of days.

[0040] According to some implementations of the present disclosure, the pattern of pseudorandom varying pressure has a frequency that varies in a range between about 5 Hz and 15 Hz.

[0041] According to some implementations of the present disclosure, the pattern of pseudorandom varying pressure has a peak-to-peak amplitude of about 1 cm H2O.

[0042] According to some implementations of the present disclosure, the pattern of pseudorandom varying pressure is superimposed continuously on the flow of air.54928-2858-6896

[0043] According to some implementations of the present disclosure, the pattern of pseudorandom varying pressure is superimposed on the flow of air only during predetermined time intervals.

[0044] According to some implementations of the present disclosure, the pattern of pseudorandom varying pressure is superimposed on the flow of air during predetermined conditions.

[0045] According to some implementations of the present disclosure, the pattern of pseudorandom varying pressure is superimposed on the flow of air during predetermined stages of sleep.

[0046] According to some implementations of the present disclosure, the pattern of pseudorandom varying pressure is superimposed on the flow of air during predetermined sleeping positions of the user.

[0047] According to some implementations of the present disclosure, the pattern of pseudorandom varying pressure is superimposed on the flow of air when a potential apnea event is detected.

[0048] According to some implementations of the present disclosure, the pattern of pseudorandom varying pressure is superimposed over the flow of air when the flow of air has a pressure of less than a predetermined threshold pressure.

[0049] According to some implementations of the present disclosure, the predetermined threshold pressure is about 6 cm H2O.

[0050] According to some implementations of the present disclosure, the control system is configured to discard the pressure data in the determination of the physiological data if the pressure data is generated while the flow of air is leaking from the respiratory therapy system at a rate greater than a predetermined leak rate.

[0051] According to some implementations of the present disclosure, the predetermined leak rate is about 0.05 L / s.

[0052] According to some implementations of the present disclosure, the control system is further configured to generate the pressure data associated with first and second pluralities of sleep sessions. The control system is also configured to identify an increase in variability of an airway-response feature in the pressure data between a second variability of the airwayresponse feature measured over the second plurality of sleep sessions as compared to a first variability of the airway-response feature measured over the first plurality of sleep sessions. The control system is also configured to determine the exacerbation factor based at least in part on the increase in the variability of the second variability over the first variability. The control64928-2858-6896system is also configured to determine the exacerbation factor to be greater than a threshold value, and indicate to the user, via a display device, that an exacerbation is predicted.

[0053] According to some implementations of the present disclosure, the first plurality of sleep sessions is a set of consecutive sleep sessions, and the second plurality of sleep sessions is a second different set of consecutive sleep sessions.

[0054] According to some implementations of the present disclosure, one of the one or more airway characteristics is indicative of a closed airway.

[0055] According to some implementations of the present disclosure, one of the one or more airway characteristics is indicative of chronic obstructive pulmonary disease (COPD).

[0056] According to some implementations of the present disclosure, one of the one or more airway characteristics is utilized for mask titration, such as to determine a user interface type suitable for the user.

[0057] The above summary is not intended to represent each implementation or every aspect of the present disclosure. Additional features and benefits of the present disclosure are apparent from the detailed description and figures set forth below.BRIEF DESCRIPTION OF THE DRAWINGS

[0058] FIG. 1 is a functional block diagram of a system, according to some implementations of the present disclosure;

[0059] FIG. 2 is a perspective view of at least a portion of the system of FIG. 1, a user, and a bed partner, according to some implementations of the present disclosure;

[0060] FIG. 3A is a perspective view of a respiratory therapy device of the system of FIG.1, according to some implementations of the present disclosure;

[0061] FIG. 3B is a perspective view of the respiratory therapy device of FIG. 3A illustrating an interior of a housing, according to some implementations of the present disclosure;

[0062] FIG. 4 illustrates an exemplary timeline for a sleep session, according to some implementations of the present disclosure;

[0063] FIG. 5 illustrates an exemplary hypnogram associated with the sleep session of FIG.3, according to some implementations of the present disclosure;

[0064] FIG. 6 illustrates an exemplary sinusoidal pressure perturbation used for central apnea detection, according to some implementations of the present disclosure;74928-2858-6896

[0065] FIG. 7 illustrates an exemplary pattern of pseudo-random varying pressure applied over a flow of air to a user interface, according to some implementations of the present disclosure;

[0066] FIG. 8 illustrates a plot of pressure data over a period of days showing an increase in variability of the pressure data over a period of days, according to some implementations of the present disclosure; and

[0067] FIG. 9 is a process flow diagram for a method for applying forced oscillation in respiratory pressure therapy, according to some implementations of the present disclosure.

[0068] FIG. 10 illustrates a scatter plot of Principal Component Analysis (PC A) data for distinguishing conduit types based on their FOT characteristics, according to some implementations of the present disclosure.

[0069] While the present disclosure is susceptible to various modifications and alternative forms, specific implementations and embodiments thereof have been shown by way of example in the drawings and will herein be described in detail. It should be understood, however, that it is not intended to limit the present disclosure to the particular forms disclosed, but on the contrary, the present disclosure is to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the present disclosure as defined by the appended claims.DETAILED DESCRIPTION

[0070] Many individuals suffer from sleep-related and / or respiratory disorders, such as Sleep Disordered Breathing (SDB) such as Obstructive Sleep Apnea (OSA), Central Sleep Apnea (CSA) and other types of apneas, Respiratory Effort Related Arousals (RERAs), snoring, Cheyne-Stokes Respiration (CSR), respiratory insufficiency, Obesity Hyperventilation Syndrome (OHS), Chronic Obstructive Pulmonary Disease (COPD), Periodic Limb Movement Disorder (PLMD), Restless Leg Syndrome (RLS), Neuromuscular Disease (NMD), and chest wall disorders.

[0071] Obstructive Sleep Apnea (OSA), a form of Sleep Disordered Breathing (SDB), is characterized by events including occlusion or obstruction of the upper air passage during sleep resulting from a combination of an abnormally small upper airway and the normal loss of muscle tone in the region of the tongue, soft palate, and posterior oropharyngeal wall. More generally, an apnea generally refers to the cessation of breathing caused by blockage of the air (Obstructive Sleep Apnea) or the stopping of the breathing function (often referred to as Central Sleep Apnea). CSA results when the brain temporarily stops sending signals to the muscles84928-2858-6896that control breathing. Typically, the individual will stop breathing for between about 15 seconds and about 30 seconds during an obstructive sleep apnea event.

[0072] Other types of apneas include hypopnea, hyperpnea, and hypercapnia. Hypopnea is generally characterized by slow or shallow breathing caused by a narrowed airway, as opposed to a blocked airway. Hyperpnea is generally characterized by an increase depth and / or rate of breathing. Hypercapnia is generally characterized by elevated or excessive carbon dioxide in the bloodstream, typically caused by inadequate respiration.

[0073] A Respiratory Effort Related Arousal (RERA) event is typically characterized by an increased respiratory effort for ten seconds or longer leading to arousal from sleep and which does not fulfill the criteria for an apnea or hypopnea event. RERAs are defined as a sequence of breaths characterized by increasing respiratory effort leading to an arousal from sleep, but which does not meet criteria for an apnea or hypopnea. These events fulfil the following criteria: (1) a pattern of progressively more negative esophageal pressure, terminated by a sudden change in pressure to a less negative level and an arousal, and (2) the event lasts ten seconds or longer. In some implementations, a Nasal Cannula / Pressure Transducer System is adequate and reliable in the detection of RERAs. A RERA detector may be based on a real flow signal derived from a respiratory therapy device. For example, a flow limitation measure may be determined based on a flow signal. A measure of arousal may then be derived as a function of the flow limitation measure and a measure of sudden increase in ventilation. One such method is described in WO 2008 / 138040 and U.S. Patent No. 9,358,353, assigned to ResMed Ltd., the disclosure of each of which is hereby incorporated by reference herein in their entireties.

[0074] Cheyne-Stokes Respiration (CSR) is another form of sleep disordered breathing. CSR is a disorder of a patient’s respiratory controller in which there are rhythmic alternating periods of waxing and waning ventilation known as CSR cycles. CSR is characterized by repetitive de-oxygenation and re-oxygenation of the arterial blood.

[0075] Obesity Hyperventilation Syndrome (OHS) is defined as the combination of severe obesity and awake chronic hypercapnia, in the absence of other known causes for hypoventilation. Symptoms include dyspnea, morning headache and excessive daytime sleepiness.

[0076] Chronic Obstructive Pulmonary Disease (COPD) encompasses any of a group of lower airway diseases that have certain characteristics in common, such as increased resistance to air movement, extended expiratory phase of respiration, and loss of the normal elasticity of the lung. Additional characteristics that can be indicative of COPD include loss of elasticity in 94928-2858-6896the airways and air sacs in the lungs (alveoli), narrowing of the airways, and thicker mucus in the airways. Such one or more characteristics of the user may be detected and / or monitored by the methods and systems described herein. COPD encompasses a group of lower airway diseases that have certain characteristics in common, such as increased resistance to air movement, extended expiratory phase of respiration, and loss of the normal elasticity of the lung.

[0077] Neuromuscular Disease (NMD) encompasses many diseases and ailments that impair the functioning of the muscles either directly via intrinsic muscle pathology, or indirectly via nerve pathology. Chest wall disorders are a group of thoracic deformities that result in inefficient coupling between the respiratory muscles and the thoracic cage.

[0078] These and other disorders are characterized by particular events (e.g., snoring, an apnea, a hypopnea, a restless leg, a sleeping disorder, choking, an increased heart rate, labored breathing, an asthma attack, an epileptic episode, a seizure, or any combination thereof) that occur when the individual is sleeping.

[0079] The Apnea-Hypopnea Index (AHI) is an index used to indicate the severity of sleep apnea during a sleep session. The AHI is calculated by dividing the number of apnea and / or hypopnea events experienced by the user during the sleep session by the total number of hours of sleep in the sleep session. The event can be, for example, a pause in breathing that lasts for at least 10 seconds. An AHI that is less than 5 is considered normal. An AHI that is greater than or equal to 5, but less than 15 is considered indicative of mild sleep apnea. An AHI that is greater than or equal to 15, but less than 30 is considered indicative of moderate sleep apnea. An AHI that is greater than or equal to 30 is considered indicative of severe sleep apnea. In children, an AHI that is greater than one is considered abnormal. Sleep apnea can be considered “controlled” when the AHI is normal, or when the AHI is normal or mild. The AHI can also be used in combination with oxygen desaturation levels to indicate the severity of Obstructive Sleep Apnea.

[0080] Referring to FIG. 1, a system 10, according to some implementations of the present disclosure, is illustrated. The system 10 includes a respiratory therapy system 100, a control system 200, one or more sensors 210, a user device 260, and an activity tracker 270.

[0081] The respiratory therapy system 100 includes a respiratory pressure therapy (RPT) device 110 (referred to herein as respiratory therapy device 110), a user interface 120 (also referred to as a mask or a patient interface), a conduit 140 (also referred to as a tube or an air circuit), a display device 150, and a humidifier 160. Respiratory pressure therapy refers to the application of a supply of air to an entrance to a user’s airways at a controlled target pressure 104928-2858-6896that is nominally positive with respect to atmosphere throughout the user’s breathing cycle (e.g., in contrast to negative pressure therapies such as the tank ventilator or cuirass). The respiratory therapy system 100 is generally used to treat individuals suffering from one or more sleep-related respiratory disorders (e.g., obstructive sleep apnea, central sleep apnea, or mixed sleep apnea).

[0082] The respiratory therapy system 100 can be used, for example, as a ventilator or as a positive airway pressure (PAP) system, such as a continuous positive airway pressure (CPAP) system, an automatic positive airway pressure system (APAP), a bi-level or variable positive airway pressure system (BPAP or VPAP), or any combination thereof. The CPAP system delivers a predetermined air pressure (e.g., determined by a sleep physician) to the user. The APAP system automatically varies the air pressure delivered to the user based on, for example, respiration data associated with the user. The BPAP or VPAP system is configured to deliver a first predetermined pressure (e.g., an inspiratory positive airway pressure or IPAP) and a second predetermined pressure (e.g., an expiratory positive airway pressure or EPAP) that is lower than the first predetermined pressure.

[0083] As shown in FIG. 2, the respiratory therapy system 100 can be used to treat user 20. In this example, the user 20 of the respiratory therapy system 100 and a bed partner 30 are located in a bed 40 and are laying on a mattress 42. The user interface 120 can be worn by the user 20 during a sleep session. The respiratory therapy system 100 generally aids in increasing the air pressure in the throat of the user 20 to aid in preventing the airway from closing and / or narrowing during sleep. The respiratory therapy device 110 can be positioned on a nightstand 44 that is directly adjacent to the bed 40 as shown in FIG. 2, or more generally, on any surface or structure that is generally adjacent to the bed 40 and / or the user 20.

[0084] The respiratory therapy device 110 is generally used to generate pressurized air that is delivered to a user (e.g., using one or more motors that drive one or more compressors). In some implementations, the respiratory therapy device 110 generates continuous constant air pressure that is delivered to the user. In other implementations, the respiratory therapy device 110 generates two or more predetermined pressures (e.g., a first predetermined air pressure and a second predetermined air pressure). In still other implementations, the respiratory therapy device 110 generates a variety of different air pressures within a predetermined range. For example, the respiratory therapy device 110 can deliver at least about 6 cmE O, at least about 10 cmE O, at least about 20 cmEEO, between about 6 cmEEO and about 10 cmEEO, between about 7 cmEEO and about 12 cmE O, etc. The respiratory therapy device 110 can also deliver114928-2858-6896pressurized air at a predetermined flow rate between, for example, about -20 L / min and about 150 L / min, while maintaining a positive pressure (relative to the ambient pressure).

[0085] The respiratory therapy device 110 includes a housing 112, a blower motor 114, an air inlet 116, and an air outlet 118 (FIG. 1). Referring to FIGS. 3A and 3B, the blower motor 114 is at least partially disposed or integrated within the housing 112. The blower motor 114 draws air from outside the housing 112 (e.g., atmosphere) via the air inlet 116 and causes pressurized air to flow through the humidifier 160, and through the air outlet 118. In some implementations, the air inlet 116 and / or the air outlet 118 include a cover that is moveable between a closed position and an open position (e.g., to prevent or inhibit air from flowing through the air inlet 116 or the air outlet 118). As shown in FIGS. 3A and 3B, the housing 112 can include a vent 113 to allow air to pass through the housing 112 to the air inlet 116. As described below, the conduit 140 is coupled to the air outlet 118 of the respiratory therapy device 110.

[0086] Referring back to FIG. 1, the user interface 120 engages a portion of the user’s face and delivers pressurized air from the respiratory therapy device 110 to the user’s airway to aid in preventing the airway from narrowing and / or collapsing during sleep. This may also increase the user’s oxygen intake during sleep. Generally, the user interface 120 engages the user’s face such that the pressurized air is delivered to the user’s airway via the user’s mouth, the user’s nose, or both the user’s mouth and nose. Together, the respiratory therapy device 110, the user interface 120, and the conduit 140 form an air pathway fluidly coupled with an airway of the user. The pressurized air also increases the user’s oxygen intake during sleep. Depending upon the therapy to be applied, the user interface 120 may form a seal, for example, with a region or portion of the user’s face, to facilitate the delivery of gas at a pressure at sufficient variance with ambient pressure to effect therapy, for example, at a positive pressure of about 10 cm H2O relative to ambient pressure. For other forms of therapy, such as the delivery of oxygen, the user interface may not include a seal sufficient to facilitate delivery to the airways of a supply of gas at a positive pressure of about 10 cmFLO.

[0087] The user interface 120 can include, for example, a cushion 122, a frame 124, a headgear 126, connector 128, and one or more vents 130. The cushion 122 and the frame 124 define a volume of space around the mouth and / or nose of the user. When the respiratory therapy system 100 is in use, this volume space receives pressurized air (e.g., from the respiratory therapy device 110 via the conduit 140) for passage into the airway(s) of the user. The headgear 126 is generally used to aid in positioning and / or stabilizing the user interface 120 on a portion of the user (e.g., the face), and along with the cushion 122 (which, for example,124928-2858-6896can comprise silicone, plastic, foam, etc.) aids in providing a substantially air-tight seal between the user interface 120 and the user 20. In some implementations the headgear 126 includes one or more straps (e.g., including hook and loop fasteners). The connector 128 is generally used to couple (e.g., connect and fluidly couple) the conduit 140 to the cushion 122 and / or frame 124. Alternatively, the conduit 140 can be directly coupled to the cushion 122 and / or frame 124 without the connector 128. The vent 130 can be used for permitting the escape of carbon dioxide and other gases exhaled by the user 20. The user interface 120 generally can include any suitable number of vents (e.g., one, two, five, ten, etc.).

[0088] As shown in FIG. 2, in some implementations, the user interface 120 is a facial mask (e.g., a full face mask) that covers at least a portion of the nose and mouth of the user 20. Alternatively, the user interface 120 can be a nasal mask that provides air to the nose of the user or a nasal pillow mask that delivers air directly to the nostrils of the user 20. In other implementations, the user interface 120 includes a mouthpiece (e.g., a night guard mouthpiece molded to conform to the teeth of the user, a mandibular repositioning device, etc.).

[0089] Referring back to FIG. 1, the conduit 140 (also referred to as an air circuit or tube) allows the flow of air between components of the respiratory therapy system 100, such as between the respiratory therapy device 110 and the user interface 120. In some implementations, there can be separate limbs of the conduit for inhalation and exhalation. In other implementations, a single limb conduit is used for both inhalation and exhalation.

[0090] While the respiratory therapy system 100 has been described herein as including each of the respiratory therapy device 110, the user interface 120, the conduit 140, the display device 150, and the humidifier 160, more or fewer components can be included in a respiratory therapy system according to implementations of the present disclosure. For example, a first alternative respiratory therapy system includes the respiratory therapy device 110, the user interface 120, and the conduit 140. As another example, a second alternative system includes the respiratory therapy device 110, the user interface 120, and the conduit 140, and the display device 150. Thus, various respiratory therapy systems can be formed using any portion or portions of the components shown and described herein and / or in combination with one or more other components.

[0091] The control system 200 includes one or more processors 202 (hereinafter, processor 202). The control system 200 is generally used to control (e.g., actuate) the various components of the system 10 and / or analyze data obtained and / or generated by the components of the system 10. The processor 202 can be a general or special purpose processor or microprocessor. While one processor 202 is illustrated in FIG. 1, the control system 200 can include any number 134928-2858-6896of processors (e.g., one processor, two processors, five processors, ten processors, etc.) that can be in a single housing, or located remotely from each other. The control system 200 (or any other control system) or a portion of the control system 200 such as the processor 202 (or any other processor(s) or portion(s) of any other control system), can be used to carry out one or more steps of any of the methods described and / or claimed herein. The control system 200 can be coupled to and / or positioned within, for example, a housing of the user device 260, a portion (e.g., the respiratory therapy device 110) of the respiratory therapy system 100, and / or within a housing of one or more of the sensors 210. The control system 200 can be centralized (within one such housing) or decentralized (within two or more of such housings, which are physically distinct). In such implementations including two or more housings containing the control system 200, the housings can be located proximately and / or remotely from each other.

[0092] The memory device 204 stores machine-readable instructions that are executable by the processor 202 of the control system 200. The memory device 204 can be any suitable computer readable storage device or media, such as, for example, a random or serial access memory device, a hard drive, a solid state drive, a flash memory device, etc. While one memory device 204 is shown in FIG. 1, the system 10 can include any suitable number of memory devices 204 (e.g., one memory device, two memory devices, five memory devices, ten memory devices, etc.). The memory device 204 can be coupled to and / or positioned within a housing of a respiratory therapy device 110 of the respiratory therapy system 100, within a housing of the user device 260, within a housing of one or more of the sensors 210, or any combination thereof. Like the control system 200, the memory device 204 can be centralized (within one such housing) or decentralized (within two or more of such housings, which are physically distinct).

[0093] In some implementations, the memory device 204 stores a user profile associated with the user. The user profile can include, for example, demographic information associated with the user, biometric information associated with the user, medical information associated with the user, self-reported user feedback, sleep parameters associated with the user (e.g., sleep-related parameters recorded from one or more earlier sleep sessions), or any combination thereof. The demographic information can include, for example, information indicative of an age of the user, a gender of the user, a race of the user, a geographic location of the user, a relationship status, a family history of insomnia or sleep apnea, an employment status of the user, an educational status of the user, a socioeconomic status of the user, or any combination thereof. The medical information can include, for example, information indicative of one or more medical conditions associated with the user, medication usage by the user, or both. The 144928-2858-6896medical information data can further include a multiple sleep latency test (MSLT) result or score and / or a Pittsburgh Sleep Quality Index (PSQI) score or value. The self-reported user feedback can include information indicative of a self-reported subjective sleep score (e.g., poor, average, excellent), a self-reported subjective stress level of the user, a self-reported subjective fatigue level of the user, a self-reported subjective health status of the user, a recent life event experienced by the user, or any combination thereof.

[0094] As described herein, the processor 202 and / or memory device 204 can receive data (e.g., physiological data and / or audio data) from the one or more sensors 210 such that the data for storage in the memory device 204 and / or for analysis by the processor 202. The processor 202 and / or memory device 204 can communicate with the one or more sensors 210 using a wired connection or a wireless connection (e.g., using an RF communication protocol, a Wi-Fi communication protocol, a Bluetooth communication protocol, over a cellular network, etc.). In some implementations, the system 10 can include an antenna, a receiver (e.g., an RF receiver), a transmitter (e.g., an RF transmitter), a transceiver, or any combination thereof. Such components can be coupled to or integrated in a housing of the control system 200 (e.g., in the same housing as the processor 202 and / or memory device 204), or the user device 260.

[0095] Referring to back to FIG. 1, the one or more sensors 210 include a pressure sensor 212, a flow rate sensor 214, temperature sensor 216, a motion sensor 218, a microphone 220, a speaker 222, a radio-frequency (RF) receiver 226, a RF transmitter 228, a camera 232, an infrared sensor 234, a photopl ethy smogram (PPG) sensor 236, an electrocardiogram (ECG) sensor 238, an electroencephalography (EEG) sensor 240, a capacitive sensor 242, a force sensor 244, a strain gauge sensor 246, an electromyography (EMG) sensor 248, an oxygen sensor 250, an analyte sensor 252, a moisture sensor 254, a LiDAR sensor 256, or any combination thereof. Generally, each of the one or more sensors 210 are configured to output sensor data that is received and stored in the memory device 204 or one or more other memory devices.

[0096] While the one or more sensors 210 are shown and described as including each of the pressure sensor 212, the flow rate sensor 214, the temperature sensor 216, the motion sensor 218, the microphone 220, the speaker 222, the RF receiver 226, the RF transmitter 228, the camera 232, the infrared sensor 234, the photoplethysmogram (PPG) sensor 236, the electrocardiogram (ECG) sensor 238, the electroencephalography (EEG) sensor 240, the capacitive sensor 242, the force sensor 244, the strain gauge sensor 246, the electromyography (EMG) sensor 248, the oxygen sensor 250, the analyte sensor 252, the moisture sensor 254,154928-2858-6896and the LiDAR sensor 256, more generally, the one or more sensors 210 can include any combination and any number of each of the sensors described and / or shown herein.

[0097] As described herein, the system 10 generally can be used to generate physiological data associated with a user (e.g., a user of the respiratory therapy system 100) during a sleep session. The physiological data can be analyzed to generate one or more sleep-related parameters, which can include any parameter, measurement, etc. related to the user during the sleep session. The one or more sleep-related parameters that can be determined for the user 20 during the sleep session include, for example, an Apnea-Hypopnea Index (AHI) score, a sleep score, a flow signal, a respiration signal, a respiration rate, an inspiration amplitude, an expiration amplitude, an inspiration-expiration ratio, a number of events per hour, a pattern of events, a stage, pressure settings of the respiratory therapy device 110, a heart rate, a heart rate variability, movement of the user 20, temperature, EEG activity, EMG activity, arousal, snoring, choking, coughing, whistling, wheezing, or any combination thereof.

[0098] The one or more sensors 210 can be used to generate, for example, physiological data, audio data, or both. Physiological data generated by one or more of the sensors 210 can be used by the control system 200 to determine a sleep-wake signal associated with the user 20 (FIG. 2) during the sleep session and one or more sleep-related parameters. The sleep-wake signal can be indicative of one or more sleep states, including wakefulness, relaxed wakefulness, micro-awakenings, or distinct sleep stages such as, for example, a rapid eye movement (REM) stage, a first non-REM stage (often referred to as “Nl”), a second non-REM stage (often referred to as “N2”), a third non-REM stage (often referred to as “N3”), or any combination thereof. Methods for determining sleep states and / or sleep stages from physiological data generated by one or more sensors, such as the one or more sensors 210, are described in, for example, WO 2014 / 047310, U.S. Patent Pub. No. 2014 / 0088373, WO 2017 / 132726, WO 2019 / 122413, WO 2019 / 122414, and U.S. Patent Pub. No. 2020 / 0383580 each of which is hereby incorporated by reference herein in its entirety.

[0099] In some implementations, the sleep-wake signal described herein can be timestamped to indicate a time that the user enters the bed, a time that the user exits the bed, a time that the user attempts to fall asleep, etc. The sleep-wake signal can be measured by the one or more sensors 210 during the sleep session at a predetermined sampling rate, such as, for example, one sample per second, one sample per 30 seconds, one sample per minute, etc. In some implementations, the sleep-wake signal can also be indicative of a respiration signal, a respiration rate, an inspiration amplitude, an expiration amplitude, an inspiration-expiration ratio, a number of events per hour, a pattern of events, pressure settings of the respiratory 164928-2858-6896therapy device 110, or any combination thereof during the sleep session. The event(s) can include snoring, apneas, central apneas, obstructive apneas, mixed apneas, hypopneas, a mask leak (e.g., from the user interface 120), a restless leg, a sleeping disorder, choking, an increased heart rate, labored breathing, an asthma attack, an epileptic episode, a seizure, or any combination thereof. The one or more sleep-related parameters that can be determined for the user during the sleep session based on the sleep-wake signal include, for example, a total time in bed, a total sleep time, a sleep onset latency, a wake-after-sleep-onset parameter, a sleep efficiency, a fragmentation index, or any combination thereof. As described in further detail herein, the physiological data and / or the sleep-related parameters can be analyzed to determine one or more sleep-related scores.

[0100] Physiological data and / or audio data generated by the one or more sensors 210 can also be used to determine a respiration signal associated with a user during a sleep session. The respiration signal is generally indicative of respiration or breathing of the user during the sleep session. The respiration signal can be indicative of and / or analyzed to determine (e.g., using the control system 200) one or more sleep-related parameters, such as, for example, a respiration rate, a respiration rate variability, an inspiration amplitude, an expiration amplitude, an inspiration-expiration ratio, an occurrence of one or more events, a number of events per hour, a pattern of events, a sleep state, a sleet stage, an apnea-hypopnea index (AHI), pressure settings of the respiratory therapy device 110, or any combination thereof. The one or more events can include snoring, apneas, central apneas, obstructive apneas, mixed apneas, hypopneas, a mask leak (e.g., from the user interface 120), a cough, a restless leg, a sleeping disorder, choking, an increased heart rate, labored breathing, an asthma attack, an epileptic episode, a seizure, increased blood pressure, or any combination thereof. Many of the described sleep-related parameters are physiological parameters, although some of the sleep-related parameters can be considered to be non-physiological parameters. Other types of physiological and / or non-physiological parameters can also be determined, either from the data from the one or more sensors 210, or from other types of data.

[0101] The pressure sensor 212 outputs pressure data that can be stored in the memory device 204 and / or analyzed by the processor 202 of the control system 200. In some implementations, the pressure sensor 212 is an air pressure sensor (e.g., barometric pressure sensor) that generates sensor data indicative of the respiration (e.g., inhaling and / or exhaling) of the user of the respiratory therapy system 100 and / or ambient pressure. In such implementations, the pressure sensor 212 can be coupled to or integrated in the respiratory therapy device 110. The pressure sensor 212 can be, for example, a capacitive sensor, an 174928-2858-6896electromagnetic sensor, a piezoelectric sensor, a strain-gauge sensor, an optical sensor, a potentiometric sensor, or any combination thereof.

[0102] The flow rate sensor 214 outputs flow rate data that can be stored in the memory device 204 and / or analyzed by the processor 202 of the control system 200. Examples of flow rate sensors (such as, for example, the flow rate sensor 214) are described in International Publication No. WO 2012 / 012835 and U.S. Patent No. 10,328,219, both of which are hereby incorporated by reference herein in their entireties. In some implementations, the flow rate sensor 214 is used to determine an air flow rate from the respiratory therapy device 110, an air flow rate through the conduit 140, an air flow rate through the user interface 120, or any combination thereof. In such implementations, the flow rate sensor 214 can be coupled to or integrated in the respiratory therapy device 110, the user interface 120, or the conduit 140. The flow rate sensor 214 can be a mass flow rate sensor such as, for example, a rotary flow meter (e.g., Hall effect flow meters), a turbine flow meter, an orifice flow meter, an ultrasonic flow meter, a hot wire sensor, a vortex sensor, a membrane sensor, or any combination thereof. In some implementations, the flow rate sensor 214 is configured to measure a vent flow (e.g., intentional “leak”), an unintentional leak (e.g., mouth leak and / or mask leak), a patient flow (e.g., air into and / or out of lungs), or any combination thereof. In some implementations, the flow rate data can be analyzed to determine cardiogenic oscillations of the user. In some examples, the pressure sensor 212 can be used to determine a blood pressure of a user.

[0103] The temperature sensor 216 outputs temperature data that can be stored in the memory device 204 and / or analyzed by the processor 202 of the control system 200. In some implementations, the temperature sensor 216 generates temperatures data indicative of a core body temperature of the user 20 (FIG. 2), a skin temperature of the user 20, a temperature of the air flowing from the respiratory therapy device 110 and / or through the conduit 140, a temperature in the user interface 120, an ambient temperature, or any combination thereof. The temperature sensor 216 can be, for example, a thermocouple sensor, a thermistor sensor, a silicon band gap temperature sensor or semiconductor-based sensor, a resistance temperature detector, or any combination thereof.

[0104] The motion sensor 218 outputs motion data that can be stored in the memory device 204 and / or analyzed by the processor 202 of the control system 200. The motion sensor 218 can be used to detect movement of the user 20 during the sleep session, and / or detect movement of any of the components of the respiratory therapy system 100, such as the respiratory therapy device 110, the user interface 120, or the conduit 140. The motion sensor 218 can include one or more inertial sensors, such as accelerometers, gyroscopes, and magnetometers. In some 184928-2858-6896implementations, the motion sensor 218 alternatively or additionally generates one or more signals representing bodily movement of the user, from which may be obtained a signal representing a sleep state of the user; for example, via a respiratory movement of the user. In some implementations, the motion data from the motion sensor 218 can be used in conjunction with additional data from another one of the sensors 210 to determine the sleep state of the user.

[0105] The microphone 220 outputs sound and / or audio data that can be stored in the memory device 204 and / or analyzed by the processor 202 of the control system 200. The audio data generated by the microphone 220 is reproducible as one or more sound(s) during a sleep session (e.g., sounds from the user 20). The audio data form the microphone 220 can also be used to identify (e.g., using the control system 200) an event experienced by the user during the sleep session, as described in further detail herein. The microphone 220 can be coupled to or integrated in the respiratory therapy device 110, the user interface 120, the conduit 140, or the user device 260. In some implementations, the system 10 includes a plurality of microphones (e.g., two or more microphones and / or an array of microphones with beamforming) such that sound data generated by each of the plurality of microphones can be used to discriminate the sound data generated by another of the plurality of microphones.

[0106] The speaker 222 outputs sound waves that are audible to a user of the system 10 (e.g., the user 20 of FIG. 2). The speaker 222 can be used, for example, as an alarm clock or to play an alert or message to the user 20 (e.g., in response to an event). In some implementations, the speaker 222 can be used to communicate the audio data generated by the microphone 220 to the user. The speaker 222 can be coupled to or integrated in the respiratory therapy device 110, the user interface 120, the conduit 140, or the user device 260.

[0107] The microphone 220 and the speaker 222 can be used as separate devices. In some implementations, the microphone 220 and the speaker 222 can be combined into an acoustic sensor 224 (e.g., a SONAR sensor), as described in, for example, WO 2018 / 050913, WO 2020 / 104465, U.S. Pat. App. Pub. No. 2022 / 0007965, each of which is hereby incorporated by reference herein in its entirety. In such implementations, the speaker 222 generates or emits sound waves at a predetermined interval and the microphone 220 detects the reflections of the emitted sound waves from the speaker 222. The sound waves generated or emitted by the speaker 222 have a frequency that is not audible to the human ear (e.g., below 20 Hz or above around 18 kHz) so as not to disturb the sleep of the user 20 or the bed partner 30 (FIG. 2). Based at least in part on the data from the microphone 220 and / or the speaker 222, the control system 200 can determine a location of the user 20 (FIG. 2) and / or one or more of the sleep- 194928-2858-6896related parameters described in herein such as, for example, a respiration signal, a respiration rate, an inspiration amplitude, an expiration amplitude, an inspiration-expiration ratio, a number of events per hour, a pattern of events, a sleep state, a sleep stage, pressure settings of the respiratory therapy device 110, or any combination thereof. In such a context, a sonar sensor may be understood to concern an active acoustic sensing, such as by generating and / or transmitting ultrasound and / or low frequency ultrasound sensing signals (e.g., in a frequency range of about 17-23 kHz, 18-22 kHz, or 17-18 kHz, for example), through the air.

[0108] In some implementations, the sensors 210 include (i) a first microphone that is the same as, or similar to, the microphone 220, and is integrated in the acoustic sensor 224 and (ii) a second microphone that is the same as, or similar to, the microphone 220, but is separate and distinct from the first microphone that is integrated in the acoustic sensor 224.

[0109] The RF transmitter 228 generates and / or emits radio waves having a predetermined frequency and / or a predetermined amplitude (e.g., within a high frequency band, within a low frequency band, long wave signals, short wave signals, etc.). The RF receiver 226 detects the reflections of the radio waves emitted from the RF transmitter 228, and this data can be analyzed by the control system 200 to determine a location of the user and / or one or more of the sleep-related parameters described herein. An RF receiver (either the RF receiver 226 and the RF transmitter 228 or another RF pair) can also be used for wireless communication between the control system 200, the respiratory therapy device 110, the one or more sensors 210, the user device 260, or any combination thereof. While the RF receiver 226 and RF transmitter 228 are shown as being separate and distinct elements in FIG. 1, in some implementations, the RF receiver 226 and RF transmitter 228 are combined as a part of an RF sensor 230 (e.g., a RADAR sensor). In some such implementations, the RF sensor 230 includes a control circuit. The format of the RF communication can be Wi-Fi, Bluetooth, or the like.

[0110] In some implementations, the RF sensor 230 is a part of a mesh system. One example of a mesh system is a Wi-Fi mesh system, which can include mesh nodes, mesh router(s), and mesh gateway(s), each of which can be mobile / movable or fixed. In such implementations, the Wi-Fi mesh system includes a Wi-Fi router and / or a Wi-Fi controller and one or more satellites (e.g., access points), each of which include an RF sensor that the is the same as, or similar to, the RF sensor 230. The Wi-Fi router and satellites continuously communicate with one another using Wi-Fi signals. The Wi-Fi mesh system can be used to generate motion data based on changes in the Wi-Fi signals (e.g., differences in received signal strength) between the router and the satellite(s) due to an object or person moving partially204928-2858-6896obstructing the signals. The motion data can be indicative of motion, breathing, heart rate, gait, falls, behavior, etc., or any combination thereof.[oni] The camera 232 outputs image data reproducible as one or more images (e.g., still images, video images, thermal images, or any combination thereof) that can be stored in the memory device 204. The image data from the camera 232 can be used by the control system 200 to determine one or more of the sleep-related parameters described herein, such as, for example, one or more events (e.g., periodic limb movement or restless leg syndrome), a respiration signal, a respiration rate, an inspiration amplitude, an expiration amplitude, an inspiration-expiration ratio, a number of events per hour, a pattern of events, a sleep state, a sleep stage, or any combination thereof. Further, the image data from the camera 232 can be used to, for example, identify a location of the user, to determine chest movement of the user (FIG. 2), to determine air flow of the mouth and / or nose of the user, to determine a time when the user enters the bed (FIG. 2), and to determine a time when the user exits the bed. In some implementations, the camera 232 includes a wide angle lens or a fish eye lens.

[0112] The infrared (IR) sensor 234 outputs infrared image data reproducible as one or more infrared images (e.g., still images, video images, or both) that can be stored in the memory device 204. The infrared data from the IR sensor 234 can be used to determine one or more sleep-related parameters during a sleep session, including a temperature of the user 20 and / or movement of the user 20. The IR sensor 234 can also be used in conjunction with the camera 232 when measuring the presence, location, and / or movement of the user 20. The IR sensor 234 can detect infrared light having a wavelength between about 700 nm and about 1 mm, for example, while the camera 232 can detect visible light having a wavelength between about 380 nm and about 740 nm.

[0113] The PPG sensor 236 outputs physiological data associated with the user 20 (FIG.2) that can be used to determine one or more sleep-related parameters, such as, for example, a heart rate, a heart rate variability, a cardiac cycle, respiration rate, an inspiration amplitude, an expiration amplitude, an inspiration-expiration ratio, estimated blood pressure parameter(s), or any combination thereof. The PPG sensor 236 can be worn by the user 20, embedded in clothing and / or fabric that is worn by the user 20, embedded in and / or coupled to the user interface 120 and / or its associated headgear (e.g., straps, etc.), etc.

[0114] The ECG sensor 238 outputs physiological data associated with electrical activity of the heart of the user 20. In some implementations, the ECG sensor 238 includes one or more electrodes that are positioned on or around a portion of the user 20 during the sleep session.214928-2858-6896The physiological data from the ECG sensor 238 can be used, for example, to determine one or more of the sleep-related parameters described herein.

[0115] The EEG sensor 240 outputs physiological data associated with electrical activity of the brain of the user 20. In some implementations, the EEG sensor 240 includes one or more electrodes that are positioned on or around the scalp of the user 20 during the sleep session. The physiological data from the EEG sensor 240 can be used, for example, to determine a sleep state and / or a sleep stage of the user 20 at any given time during the sleep session. In some implementations, the EEG sensor 240 can be integrated in the user interface 120 and / or the associated headgear (e.g., straps, etc.).

[0116] The capacitive sensor 242, the force sensor 244, and the strain gauge sensor 246 output data that can be stored in the memory device 204 and used / analyzed by the control system 200 to determine, for example, one or more of the sleep-related parameters described herein. The EMG sensor 248 outputs physiological data associated with electrical activity produced by one or more muscles. The oxygen sensor 250 outputs oxygen data indicative of an oxygen concentration of gas (e.g., in the conduit 140 or at the user interface 120). The oxygen sensor 250 can be, for example, an ultrasonic oxygen sensor, an electrical oxygen sensor, a chemical oxygen sensor, an optical oxygen sensor, a pulse oximeter (e.g., SpCh sensor), or any combination thereof.

[0117] The analyte sensor 252 can be used to detect the presence of an analyte in the exhaled breath of the user 20. The data output by the analyte sensor 252 can be stored in the memory device 204 and used by the control system 200 to determine the identity and concentration of any analytes in the breath of the user. In some implementations, the analyte sensor 174 is positioned near a mouth of the user to detect analytes in breath exhaled from the user’s mouth. For example, when the user interface 120 is a facial mask that covers the nose and mouth of the user, the analyte sensor 252 can be positioned within the facial mask to monitor the user’s mouth breathing. In other implementations, such as when the user interface 120 is a nasal mask or a nasal pillow mask, the analyte sensor 252 can be positioned near the nose of the user to detect analytes in breath exhaled through the user’s nose. In still other implementations, the analyte sensor 252 can be positioned near the user’s mouth when the user interface 120 is a nasal mask or a nasal pillow mask. In this implementation, the analyte sensor 252 can be used to detect whether any air is inadvertently leaking from the user’s mouth and / or the user interface 120. In some implementations, the analyte sensor 252 is a volatile organic compound (VOC) sensor that can be used to detect carbon-based chemicals or compounds. In some implementations, the analyte sensor 174 can also be used to detect whether the user is 224928-2858-6896breathing through their nose or mouth. For example, if the data output by an analyte sensor 252 positioned near the mouth of the user or within the facial mask (e.g., in implementations where the user interface 120 is a facial mask) detects the presence of an analyte, the control system 200 can use this data as an indication that the user is breathing through their mouth.

[0118] The moisture sensor 254 outputs data that can be stored in the memory device 204 and used by the control system 200. The moisture sensor 254 can be used to detect moisture in various areas surrounding the user (e.g., inside the conduit 140 or the user interface 120, near the user’s face, near the connection between the conduit 140 and the user interface 120, near the connection between the conduit 140 and the respiratory therapy device 110, etc.). Thus, in some implementations, the moisture sensor 254 can be coupled to or integrated in the user interface 120 or in the conduit 140 to monitor the humidity of the pressurized air from the respiratory therapy device 110. In other implementations, the moisture sensor 254 is placed near any area where moisture levels need to be monitored. The moisture sensor 254 can also be used to monitor the humidity of the ambient environment surrounding the user, for example, the air inside the bedroom.

[0119] The Light Detection and Ranging (LiDAR) sensor 256 can be used for depth sensing. This type of optical sensor (e.g., laser sensor) can be used to detect objects and build three dimensional (3D) maps of the surroundings, such as of a living space. LiDAR can generally utilize a pulsed laser to make time of flight measurements. LiDAR is also referred to as 3D laser scanning. In an example of use of such a sensor, a fixed or mobile device (such as a smartphone) having a LiDAR sensor 256 can measure and map an area extending 5 meters or more away from the sensor. The LiDAR data can be fused with point cloud data estimated by an electromagnetic RADAR sensor, for example. The LiDAR sensor(s) 256 can also use artificial intelligence (Al) to automatically geofence RADAR systems by detecting and classifying features in a space that might cause issues for RADAR systems, such a glass windows (which can be highly reflective to RADAR). LiDAR can also be used to provide an estimate of the height of a person, as well as changes in height when the person sits down, or falls down, for example. LiDAR may be used to form a 3D mesh representation of an environment. In a further use, for solid surfaces through which radio waves pass (e.g., radio-translucent materials), the LiDAR may reflect off such surfaces, thus allowing a classification of different type of obstacles.

[0120] In some implementations, the one or more sensors 210 also include a galvanic skin response (GSR) sensor, a blood flow sensor, a respiration sensor, a pulse sensor, a sphygmomanometer sensor, an oximetry sensor, a sonar sensor, a RADAR sensor, a blood 234928-2858-6896glucose sensor, a color sensor, a pH sensor, an air quality sensor, a tilt sensor, a rain sensor, a soil moisture sensor, a water flow sensor, an alcohol sensor, or any combination thereof.

[0121] While shown separately in FIG. 1, any combination of the one or more sensors 210 can be integrated in and / or coupled to any one or more of the components of the system 100, including the respiratory therapy device 110, the user interface 120, the conduit 140, the humidifier 160, the control system 200, the user device 260, the activity tracker 270, or any combination thereof. For example, the microphone 220 and the speaker 222 can be integrated in and / or coupled to the user device 260 and the pressure sensor 212 and / or flow rate sensor 214 are integrated in and / or coupled to the respiratory therapy device 110. In some implementations, at least one of the one or more sensors 210 is not coupled to the respiratory therapy device 110, the control system 200, or the user device 260, and is positioned generally adjacent to the user 20 during the sleep session (e.g., positioned on or in contact with a portion of the user 20, worn by the user 20, coupled to or positioned on the nightstand, coupled to the mattress, coupled to the ceiling, etc.).

[0122] One or more of the respiratory therapy device 110, the user interface 120, the conduit 140, the display device 150, and the humidifier 160 can contain one or more sensors (e.g., a pressure sensor, a flow rate sensor, or more generally any of the other sensors 210 described herein). These one or more sensors can be used, for example, to measure the air pressure and / or flow rate of pressurized air supplied by the respiratory therapy device 110.

[0123] The data from the one or more sensors 210 can be analyzed (e.g., by the control system 200) to determine one or more sleep-related parameters, which can include a respiration signal, a respiration rate, a respiration pattern, an inspiration amplitude, an expiration amplitude, an inspiration-expiration ratio, an occurrence of one or more events, a number of events per hour, a pattern of events, a sleep state, an apnea-hypopnea index (AHI), or any combination thereof. The one or more events can include snoring, apneas, central apneas, obstructive apneas, mixed apneas, hypopneas, a mask leak, a cough, a restless leg, a sleeping disorder, choking, an increased heart rate, labored breathing, an asthma attack, an epileptic episode, a seizure, increased blood pressure, or any combination thereof. Many of these sleep-related parameters are physiological parameters, although some of the sleep-related parameters can be considered to be non-physiological parameters. Other types of physiological and non-physiological parameters can also be determined, either from the data from the one or more sensors 210, or from other types of data.

[0124] The user device 260 (FIG. 1) includes a display device 262. The user device 260 can be, for example, a mobile device such as a smart phone, a tablet, a gaming console, a smart 244928-2858-6896watch, a laptop, or the like. Alternatively, the user device 260 can be an external sensing system, a television (e.g., a smart television) or another smart home device (e.g., a smart speaker(s) such as Google Home, Amazon Echo, Alexa etc.). In some implementations, the user device is a wearable device (e.g., a smart watch). The display device 262 is generally used to display image(s) including still images, video images, or both. In some implementations, the display device 262 acts as a human-machine interface (HMI) that includes a graphic user interface (GUI) configured to display the image(s) and an input interface. The display device 262 can be an LED display, an OLED display, an LCD display, or the like. The input interface can be, for example, a touchscreen or touch-sensitive substrate, a mouse, a keyboard, or any sensor system configured to sense inputs made by a human user interacting with the user device 260. In some implementations, one or more user devices can be used by and / or included in the system 10.

[0125] In some implementations, the system 100 also includes an activity tracker 270. The activity tracker 270 is generally used to aid in generating physiological data associated with the user. The activity tracker 270 can include one or more of the sensors 210 described herein, such as, for example, the motion sensor 218 (e.g., one or more accelerometers and / or gyroscopes), the PPG sensor 236, and / or the ECG sensor 238. The physiological data from the activity tracker 270 can be used to determine, for example, a number of steps, a distance traveled, a number of steps climbed, a duration of physical activity, a type of physical activity, an intensity of physical activity, time spent standing, a respiration rate, an average respiration rate, a resting respiration rate, a maximum he respiration art rate, a respiration rate variability, a heart rate, an average heart rate, a resting heart rate, a maximum heart rate, a heart rate variability, a number of calories burned, blood oxygen saturation, electrodermal activity (also known as skin conductance or galvanic skin response), or any combination thereof. In some implementations, the activity tracker 270 is coupled (e.g., electronically or physically) to the user device 260.

[0126] In some implementations, the activity tracker 270 is a wearable device that can be worn by the user, such as a smartwatch, a wristband, a ring, or a patch. For example, referring to FIG. 2, the activity tracker 270 is worn on a wrist of the user 20. The activity tracker 270 can also be coupled to or integrated with a garment or clothing that is worn by the user. Alternatively still, the activity tracker 270 can also be coupled to or integrated in (e.g., within the same housing) the user device 260. More generally, the activity tracker 270 can be communicatively coupled with, or physically integrated in (e.g., within a housing), the control254928-2858-6896system 200, the memory device 204, the respiratory therapy system 100, and / or the user device 260.

[0127] In some implementations, the system 100 also includes a blood pressure device 280. The blood pressure device 280 is generally used to aid in generating cardiovascular data for determining one or more blood pressure measurements associated with the user 20. The blood pressure device 280 can include at least one of the one or more sensors 210 to measure, for example, a systolic blood pressure component and / or a diastolic blood pressure component.

[0128] In some implementations, the blood pressure device 280 is a sphygmomanometer including an inflatable cuff that can be worn by the user 20 and a pressure sensor (e.g., the pressure sensor 212 described herein). For example, in the example of FIG. 2, the blood pressure device 280 can be worn on an upper arm of the user 20. In such implementations where the blood pressure device 280 is a sphygmomanometer, the blood pressure device 280 also includes a pump (e.g., a manually operated bulb) for inflating the cuff. In some implementations, the blood pressure device 280 is coupled to the respiratory therapy device 110 of the respiratory therapy system 100, which in turn delivers pressurized air to inflate the cuff. More generally, the blood pressure device 280 can be communicatively coupled with, and / or physically integrated in (e.g., within a housing), the control system 200, the memory device 204, the respiratory therapy system 100, the user device 260, and / or the activity tracker 270.

[0129] In other implementations, the blood pressure device 280 is an ambulatory blood pressure monitor communicatively coupled to the respiratory therapy system 100. An ambulatory blood pressure monitor includes a portable recording device attached to a belt or strap worn by the user 20 and an inflatable cuff attached to the portable recording device and worn around an arm of the user 20. The ambulatory blood pressure monitor is configured to measure blood pressure between about every fifteen minutes to about thirty minutes over a 24-hour or a 48-hour period. The ambulatory blood pressure monitor may measure heart rate of the user 20 at the same time. These multiple readings are averaged over the 24-hour period. The ambulatory blood pressure monitor determines any changes in the measured blood pressure and heart rate of the user 20, as well as any distribution and / or trending patterns of the blood pressure and heart rate data during a sleeping period and an awakened period of the user 20. The measured data and statistics may then be communicated to the respiratory therapy system 100.

[0130] The blood pressure device 280 maybe positioned external to the respiratory therapy system 100, coupled directly or indirectly to the user interface 120, coupled directly or 264928-2858-6896indirectly to a headgear associated with the user interface 120, or inflatably coupled to or about a portion of the user 20. The blood pressure device 280 is generally used to aid in generating physiological data for determining one or more blood pressure measurements associated with a user, for example, a systolic blood pressure component and / or a diastolic blood pressure component. In some implementations, the blood pressure device 280 is a sphygmomanometer including an inflatable cuff that can be worn by a user and a pressure sensor (e.g., the pressure sensor 212 described herein).

[0131] In some implementations, the blood pressure device 280 is an invasive device which can continuously monitor arterial blood pressure of the user 20 and take an arterial blood sample on demand for analyzing gas of the arterial blood. In some other implementations, the blood pressure device 280 is a continuous blood pressure monitor, using a radio frequency sensor and capable of measuring blood pressure of the user 20 once very few seconds (e.g., every 3 seconds, every 5 seconds, every 7 seconds, etc.) The radio frequency sensor may use continuous wave, frequency-modulated continuous wave (FMCW with ramp chirp, triangle, sinewave), other schemes such as PSK, FSK etc., pulsed continuous wave, and / or spread in ultra-wideband ranges (which may include spreading, PRN codes, or impulse systems).

[0132] While the control system 200 and the memory device 204 are described and shown in FIG. 1 as being a separate and distinct component of the system 100, in some implementations, the control system 200 and / or the memory device 204 are integrated in the user device 260 and / or the respiratory therapy device 110. Alternatively, in some implementations, the control system 200 or a portion thereof (e.g., the processor 202) can be located in a cloud (e.g., integrated in a server, integrated in an Internet of Things (loT) device, connected to the cloud, be subj ect to edge cloud processing, etc.), located in one or more servers (e.g., remote servers, local servers, etc., or any combination thereof.

[0133] While system 100 is shown as including all of the components described above, more or fewer components can be included in a system according to implementations of the present disclosure. For example, a first alternative system includes the control system 200, the memory device 204, and at least one of the one or more sensors 210 and does not include the respiratory therapy system 100. As another example, a second alternative system includes the control system 200, the memory device 204, at least one of the one or more sensors 210, and the user device 260. As yet another example, a third alternative system includes the control system 200, the memory device 204, the respiratory therapy system 100, at least one of the one or more sensors 210, and the user device 260. Thus, various systems can be formed using any274928-2858-6896portion or portions of the components shown and described herein and / or in combination with one or more other components.

[0134] As used herein, a sleep session can be defined in multiple ways. For example, a sleep session can be defined by an initial start time and an end time. In some implementations, a sleep session is a duration where the user is asleep, that is, the sleep session has a start time and an end time, and during the sleep session, the user does not wake until the end time. That is, any period of the user being awake is not included in a sleep session. From this first definition of sleep session, if the user wakes ups and falls asleep multiple times in the same night, each of the sleep intervals separated by an awake interval is a sleep session.

[0135] Alternatively, in some implementations, a sleep session has a start time and an end time, and during the sleep session, the user can wake up, without the sleep session ending, so long as a continuous duration that the user is awake is below an awake duration threshold. The awake duration threshold can be defined as a percentage of a sleep session. The awake duration threshold can be, for example, about twenty percent of the sleep session, about fifteen percent of the sleep session duration, about ten percent of the sleep session duration, about five percent of the sleep session duration, about two percent of the sleep session duration, etc., or any other threshold percentage. In some implementations, the awake duration threshold is defined as a fixed amount of time, such as, for example, about one hour, about thirty minutes, about fifteen minutes, about ten minutes, about five minutes, about two minutes, etc., or any other amount of time.

[0136] In some implementations, a sleep session is defined as the entire time between the time in the evening at which the user first entered the bed, and the time the next morning when user last left the bed. Put another way, a sleep session can be defined as a period of time that begins on a first date (e.g., Monday, January 6, 2020) at a first time (e.g., 10:00 PM), that can be referred to as the current evening, when the user first enters a bed with the intention of going to sleep (e.g., not if the user intends to first watch television or play with a smart phone before going to sleep, etc.), and ends on a second date (e.g., Tuesday, January 7, 2020) at a second time (e.g., 7:00 AM), that can be referred to as the next morning, when the user first exits the bed with the intention of not going back to sleep that next morning.

[0137] In some implementations, the user can manually define the beginning of a sleep session and / or manually terminate a sleep session. For example, the user can select (e.g., by clicking or tapping) one or more user-selectable element that is displayed on the display device 262 of the user device 260 (FIG. 1) to manually initiate or terminate the sleep session.284928-2858-6896

[0138] Generally, the sleep session includes any point in time after the user 20 has laid or sat down in the bed 40 (or another area or object on which they intend to sleep), and has turned on the respiratory therapy device 110 and donned the user interface 120. The sleep session can thus include time periods (i) when the user 20 is using the respiratory therapy system 100, but before the user 20 attempts to fall asleep (for example when the user 20 lays in the bed 40 reading a book); (ii) when the user 20 begins trying to fall asleep but is still awake; (iii) when the user 20 is in a light sleep (also referred to as stage 1 and stage 2 of non-rapid eye movement (NREM) sleep); (iv) when the user 20 is in a deep sleep (also referred to as slow-wave sleep, SWS, or stage 3 of NREM sleep); (v) when the user 20 is in rapid eye movement (REM) sleep; (vi) when the user 20 is periodically awake between light sleep, deep sleep, or REM sleep; or (vii) when the user 20 wakes up and does not fall back asleep.

[0139] The sleep session is generally defined as ending once the user 20 removes the user interface 120, turns off the respiratory therapy device 110, and gets out of bed 40. In some implementations, the sleep session can include additional periods of time, or can be limited to only some of the above-disclosed time periods. For example, the sleep session can be defined to encompass a period of time beginning when the respiratory therapy device 110 begins supplying the pressurized air to the airway or the user 20, ending when the respiratory therapy device 110 stops supplying the pressurized air to the airway of the user 20, and including some or all of the time points in between, when the user 20 is asleep or awake.

[0140] Referring to the timeline 300 in FIG. 4, the enter bed time tbed is associated with the time that the user initially enters the bed (e.g., bed 40 in FIG. 2) prior to falling asleep (e.g., when the user lies down or sits in the bed). The enter bed time tbed can be identified based on a bed threshold duration to distinguish between times when the user enters the bed for sleep and when the user enters the bed for other reasons (e.g., to watch TV). For example, the bed threshold duration can be at least about 10 minutes, at least about 20 minutes, at least about 30 minutes, at least about 45 minutes, at least about 1 hour, at least about 2 hours, etc. While the enter bed time tbed is described herein in reference to a bed, more generally, the enter time tbed can refer to the time the user initially enters any location for sleeping (e.g., a couch, a chair, a sleeping bag, etc.).

[0141] The go-to-sleep time (GTS) is associated with the time that the user initially attempts to fall asleep after entering the bed (tbed). For example, after entering the bed, the user may engage in one or more activities to wind down prior to trying to sleep (e.g., reading, watching TV, listening to music, using the user device 260, etc.). The initial sleep time (tsieep)294928-2858-6896is the time that the user initially falls asleep. For example, the initial sleep time (tsieep) can be the time that the user initially enters the first non-REM sleep stage.

[0142] The wake-up time twake is the time associated with the time when the user wakes up without going back to sleep (e.g., as opposed to the user waking up in the middle of the night and going back to sleep). The user may experience one of more unconscious microawakenings (e.g., microawakenings MAi and MA2) having a short duration (e.g., 5 seconds, 10 seconds, 30 seconds, 1 minute, etc.) after initially falling asleep. In contrast to the wake-up time twake, the user goes back to sleep after each of the microawakenings MAi and MA2. Similarly, the user may have one or more conscious awakenings (e.g., awakening Ai) after initially falling asleep (e.g., getting up to go to the bathroom, attending to children or pets, sleep walking, etc.). However, the user goes back to sleep after the awakening Ai. Thus, the wake-up time twake can be defined, for example, based on a wake threshold duration (e.g., the user is awake for at least 15 minutes, at least 20 minutes, at least 30 minutes, at least 1 hour, etc.).

[0143] Similarly, the rising time trise is associated with the time when the user exits the bed and stays out of the bed with the intent to end the sleep session (e.g., as opposed to the user getting up during the night to go to the bathroom, to attend to children or pets, sleep walking, etc.). In other words, the rising time trise is the time when the user last leaves the bed without returning to the bed until a next sleep session (e.g., the following evening). Thus, the rising time trise can be defined, for example, based on a rise threshold duration (e.g., the user has left the bed for at least 15 minutes, at least 20 minutes, at least 30 minutes, at least 1 hour, etc.). The enter bed time tbed time for a second, subsequent sleep session can also be defined based on a rise threshold duration (e.g., the user has left the bed for at least 4 hours, at least 6 hours, at least 8 hours, at least 12 hours, etc.).

[0144] As described above, the user may wake up and get out of bed one more times during the night between the initial tbed and the final trise. In some implementations, the final wake-up time twake and / or the final rising time trise that are identified or determined based on a predetermined threshold duration of time subsequent to an event (e.g., falling asleep or leaving the bed). Such a threshold duration can be customized for the user. For a standard user which goes to bed in the evening, then wakes up and goes out of bed in the morning any period (between the user waking up (twake) or raising up (trise), and the user either going to bed (tbed), going to sleep (tors) or falling asleep (tsieep) of between about 12 and about 18 hours can be used. For users that spend longer periods of time in bed, shorter threshold periods may be used (e.g., between about 8 hours and about 14 hours). The threshold period may be initially selected and / or later adjusted based on the system monitoring the user’s sleep behavior.304928-2858-6896

[0145] The total time in bed (TIB) is the duration of time between the time enter bed time tbed and the rising time ise. The total sleep time (TST) is associated with the duration between the initial sleep time and the wake-up time, excluding any conscious or unconscious awakenings and / or micro-awakenings therebetween. Generally, the total sleep time (TST) will be shorter than the total time in bed (TIB) (e.g., one minute short, ten minutes shorter, one hour shorter, etc.). For example, referring to the timeline 300 of FIG. 4, the total sleep time (TST) spans between the initial sleep time tsieep and the wake-up time twake, but excludes the duration of the first micro-awakening MAi, the second micro-awakening MA2, and the awakening Ai. As shown, in this example, the total sleep time (TST) is shorter than the total time in bed (TIB).

[0146] In some implementations, the total sleep time (TST) can be defined as a persistent total sleep time (PTST). In such implementations, the persistent total sleep time excludes a predetermined initial portion or period of the first non-REM stage (e.g., light sleep stage). For example, the predetermined initial portion can be between about 30 seconds and about 20 minutes, between about 1 minute and about 10 minutes, between about 3 minutes and about 5 minutes, etc. The persistent total sleep time is a measure of sustained sleep, and smooths the sleep-wake hypnogram. For example, when the user is initially falling asleep, the user may be in the first non-REM stage for a very short time (e.g., about 30 seconds), then back into the wakefulness stage for a short period (e.g., one minute), and then goes back to the first non-REM stage. In this example, the persistent total sleep time excludes the first instance (e.g., about 30 seconds) of the first non-REM stage.

[0147] In some implementations, the sleep session is defined as starting at the enter bed time (tbed) and ending at the rising time (trise), i.e., the sleep session is defined as the total time in bed (TIB). In some implementations, a sleep session is defined as starting at the initial sleep time (tsieep) and ending at the wake-up time (twake). In some implementations, the sleep session is defined as the total sleep time (TST). In some implementations, a sleep session is defined as starting at the go-to-sleep time (tors) and ending at the wake-up time (twake). In some implementations, a sleep session is defined as starting at the go-to-sleep time (tors) and ending at the rising time (Vise). In some implementations, a sleep session is defined as starting at the enter bed time (tbed) and ending at the wake-up time (twake). In some implementations, a sleep session is defined as starting at the initial sleep time (tsieep) and ending at the rising time (Vise).

[0148] Referring to FIG. 5, an exemplary hypnogram 400 corresponding to the timeline 300 (FIG. 4), according to some implementations, is illustrated. As shown, the hypnogram 400 includes a sleep-wake signal 401, a wakefulness stage axis 410, a REM stage axis 420, a light sleep stage axis 430, and a deep sleep stage axis 440. The intersection between the sleep-wake 314928-2858-6896signal 401 and one of the axes 410-440 is indicative of the sleep stage at any given time during the sleep session.

[0149] The sleep-wake signal 401 can be generated based on physiological data associated with the user (e.g., generated by one or more of the sensors 210 described herein). The sleepwake signal can be indicative of one or more sleep states, including wakefulness, relaxed wakefulness, microawakenings, a REM stage, a first non-REM stage, a second non-REM stage, a third non-REM stage, or any combination thereof. In some implementations, one or more of the first non-REM stage, the second non-REM stage, and the third non-REM stage can be grouped together and categorized as a light sleep stage or a deep sleep stage. For example, the light sleep stage can include the first non-REM stage, and the deep sleep stage can include the second non-REM stage and the third non-REM stage. While the hypnogram 400 is shown in FIG. 5 as including the light sleep stage axis 430 and the deep sleep stage axis 440, in some implementations, the hypnogram 400 can include an axis for each of the first non-REM stage, the second non-REM stage, and the third non-REM stage. In other implementations, the sleepwake signal can also be indicative of a respiration signal, a respiration rate, an inspiration amplitude, an expiration amplitude, an inspiration-expiration ratio, a number of events per hour, a pattern of events, or any combination thereof. Information describing the sleep-wake signal can be stored in the memory device 204.

[0150] The hypnogram 400 can be used to determine one or more sleep-related parameters, such as, for example, a sleep onset latency (SOL), wake-after-sleep onset (WASO), a sleep efficiency (SE), a sleep fragmentation index, sleep blocks, or any combination thereof.

[0151] The sleep onset latency (SOL) is defined as the time between the go-to-sleep time (terrs) and the initial sleep time (tsieep). In other words, the sleep onset latency is indicative of the time that it took the user to actually fall asleep after initially attempting to fall asleep. In some implementations, the sleep onset latency is defined as a persistent sleep onset latency (PSOL). The persistent sleep onset latency differs from the sleep onset latency in that the persistent sleep onset latency is defined as the duration time between the go-to-sleep time and a predetermined amount of sustained sleep. In some implementations, the predetermined amount of sustained sleep can include, for example, at least 10 minutes of sleep within the second non-REM stage, the third non-REM stage, and / or the REM stage with no more than 2 minutes of wakefulness, the first non-REM stage, and / or movement therebetween. In other words, the persistent sleep onset latency requires up to, for example, 8 minutes of sustained sleep within the second non-REM stage, the third non-REM stage, and / or the REM stage. In other implementations, the predetermined amount of sustained sleep can include at least 10324928-2858-6896minutes of sleep within the first non-REM stage, the second non-REM stage, the third non-REM stage, and / or the REM stage subsequent to the initial sleep time. In such implementations, the predetermined amount of sustained sleep can exclude any microawakenings (e.g., a ten second micro-awakening does not restart the 10-minute period).

[0152] The wake-after-sleep onset (WASO) is associated with the total duration of time that the user is awake between the initial sleep time and the wake-up time. Thus, the wake-after-sleep onset includes short and micro-awakenings during the sleep session (e.g., the microawakenings MAi and MA2 shown in FIG. 4), whether conscious or unconscious. In some implementations, the wake-after-sleep onset (WASO) is defined as a persistent wake-after-sleep onset (PWASO) that only includes the total durations of awakenings having a predetermined length (e.g., greater than 10 seconds, greater than 30 seconds, greater than 60 seconds, greater than about 5 minutes, greater than about 10 minutes, etc.)

[0153] The sleep efficiency (SE) is determined as a ratio of the total time in bed (TIB) and the total sleep time (TST). For example, if the total time in bed is 8 hours and the total sleep time is 7.5 hours, the sleep efficiency for that sleep session is 93.75%. The sleep efficiency is indicative of the sleep hygiene of the user. For example, if the user enters the bed and spends time engaged in other activities (e.g., watching TV) before sleep, the sleep efficiency will be reduced (e.g., the user is penalized). In some implementations, the sleep efficiency (SE) can be calculated based on the total time in bed (TIB) and the total time that the user is attempting to sleep. In such implementations, the total time that the user is attempting to sleep is defined as the duration between the go-to-sleep (GTS) time and the rising time described herein. For example, if the total sleep time is 8 hours (e.g., between 11 PM and 7 AM), the go-to-sleep time is 10:45 PM, and the rising time is 7: 15 AM, in such implementations, the sleep efficiency parameter is calculated as about 94%.

[0154] The fragmentation index is determined based at least in part on the number of awakenings during the sleep session. For example, if the user had two micro-awakenings (e.g., micro-awakening MAi and micro-awakening MA2 shown in FIG. 5), the fragmentation index can be expressed as 2. In some implementations, the fragmentation index is scaled between a predetermined range of integers (e.g., between 0 and 10).

[0155] The sleep blocks are associated with a transition between any stage of sleep (e.g., the first non-REM stage, the second non-REM stage, the third non-REM stage, and / or the REM) and the wakefulness stage. The sleep blocks can be calculated at a resolution of, for example, 30 seconds.334928-2858-6896

[0156] In some implementations, the systems and methods described herein can include generating or analyzing a hypnogram including a sleep-wake signal to determine or identify the enter bed time (tbed), the go-to-sleep time (tors), the initial sleep time (tsieep), one or more first micro-awakenings (e.g., MAi and MA2), the wake-up time (twake), the rising time (Vise), or any combination thereof based at least in part on the sleep-wake signal of a hypnogram.

[0157] In other implementations, one or more of the sensors 210 can be used to determine or identify the enter bed time (tbed), the go-to-sleep time (tors), the initial sleep time (tsieep), one or more first micro-awakenings (e.g., MAi and MA2), the wake-up time (twake), the rising time (trise), or any combination thereof, which in turn define the sleep session. For example, the enter bed time tbed can be determined based on, for example, data generated by the motion sensor 218, the microphone 220, the camera 232, or any combination thereof. The go-to-sleep time can be determined based on, for example, data from the motion sensor 218 (e.g., data indicative of no movement by the user), data from the camera 232 (e.g., data indicative of no movement by the user and / or that the user has turned off the lights) data from the microphone 220 (e.g., data indicative of the user turning off a TV), data from the user device 260 (e.g., data indicative of the user no longer using the user device 260), data from the pressure sensor 212 and / or the flow rate sensor 214 (e.g., data indicative of the user turning on the respiratory therapy device 110, data indicative of the user donning the user interface 120, etc.), or any combination thereof.

[0158] In some implementations, the respiratory therapy system 100 utilizes Central Apnea Detection (CAD) to detect whether an airway is open or closed during an apnea. When an apnea is detected, the CAD algorithm causes the blower motor 114 to force a pressure oscillation that causes a pressure and flow oscillation at the user interface 120. For example, referring to FIG. 6, the blower motor 114 forces a sinusoidal pressure perturbation 500 over the nominal pressure being delivered to the user interface 120. The sinusoidal pressure perturbation 500 can have a peak-to-peak amplitude of about 1 cm H2O and a frequency of about 4.167 Hz. The sinusoidal pressure perturbation 500 is applied, the resulting pressure oscillations are measured, for example by a pressure sensor 212, and a corresponding flow oscillation may be measured (e.g., by a flow sensor and / or derived from blower motor characteristics and pressure drop across a known restriction), and the results are used to calculate the admittance of a patient’s airway. The admittance of the patient’s airway is used to determine whether the patient’s airway is open or closed. If the patient’s airway is determined to be open, the nominal pressure being delivered to the user interface 120 is kept unchanged as prescribed. However, if the patient’s airway is determined to be closed, the level 344928-2858-6896of pressure delivered to the user interface 120 is increased (e.g., increased by a controlled amount and / or in accordance with an auto-titration algorithm to mitigate airway obstruction) to open the patient’s airway or to mitigate airway obstruction and prevent the airway from collapsing again.

[0159] The CAD is only executed on detection of a potential apnea event. In implementations, the CAD starts after an apnea has been detected, typically less than 10 seconds after detection. The CAD continues until a new breath is detected. If a breath is detected before the CAD starts, then the CAD is not started. CAD uses Forced Oscillation Technique (FOT) which involves superimposing a small pressure oscillation (typically 1-2 cm H2O, at ~4-5 (e.g., 4.167) Hz) on the delivered pressure. The response of the airway to these oscillations provides insight into upper airway status, e.g., high impedance (less oscillatory response) indicates upper airway is closed, whereas low impedance (more oscillatory response) indicates upper airway is open. In some implementations, impedance or admittance over a moving time window (e.g., 1-10 seconds) may be estimated using frequency-domain techniques (e.g., FFT / cross-spectral estimation), and optionally a signal-quality metric (e.g., magnitude-squared coherence) may be applied to accept or reject such an estimate.

[0160] While the CAD signal is useful for detecting an airway closure, other pressure signals can be superimposed over the nominal pressure delivered to the user interface 120 for monitoring or measuring airway characteristics of the patient, including characteristics of the patient’s lungs. Such pressure signals can include a mono-frequency signal (typical of Forced Oscillation Technique (FOT), such as used in CAD), pseudo-random noise signal (resulting in a pseudo-random varying pressure), and / or an impulse signal (resulting in air pressure pulses). In implementations, a pattern of pseudo-random varying pressure is superimposed on the nominal pressure and flow of delivered air at the user interface 120. For example, referring to FIG. 7, the pattern of pseudo-random varying pressure 505 is superimposed over the nominal pressure being delivered to the user interface 120. The pseudo-random varying pressure 505 may be band-limited (e.g., to about 5-32 Hz, optionally about 5-15 Hz) and may be generated as (i) filtered noise, (ii) a multi-sine with randomized phases, (iii) a maximum-length sequence (MLS), (iv) a stored pseudo-random sequence, or (v) any combination thereof. In implementations, the pseudo-random varying pressure 505 has a peak-to-peak amplitude of about 1 cm H2O. As such, the pseudo-random varying pressure 505 is configured to be sufficiently small to be tolerated by the user while still providing a measurable airway response. In implementations, the pseudo-random varying pressure 505 has a frequency that varies in a range between about 5 Hz and 32 Hz, optionally between about 5 Hz and 15 Hz. In contrast to 354928-2858-6896a mono-frequency signal, a pseudo-random varying pressure signal allows for spatial characterisation due to the range of frequencies comprised in the signal, and may be employed to locate and / or characterise obstruction point(s) in a user’s airway, and which may be used to better tailor treatment types (e.g. PAP, surgery, etc.), to the user, better phenotype sleep condition (e.g., OSA) of the user, better detection of positional OSA (POSA), etc. The pseudorandom varying pressure signal, comprising a range of frequencies, also allows for characterisation of lung health parameters, as described further herein, since the higher frequencies can penetrate further into the airways to the lungs.

[0161] The pseudo-random varying pressure 505 is applied and the resulting pressure oscillations are measured, for example by the pressure sensor 212. In some implementations, flow is also measured and one or more airway-response parameters is computed as a function of frequency, including impedance Z(f) (which = R(f) + jX(f)), admittance Y(f) (which = 1 / Z(f)), resistance R(f) (which = Re{Z(f)}), reactance X(f) (which = Im{Z(f)}), or derived quantities such as R5 and X5 (resistance and reactance at about 5 Hz), area of reactance (AX), and / or resonant frequency. Pressure data associated with the flow of air through the patient’s airway, including the patient’s lungs, and / or physiological data derived therefrom, is thus generated. Based at least in part on this generated pressure data, physiological data indicative of one or more airway characteristics of the patient, including characteristics of the patient’s lungs, are determined. In some implementations, the measured pressure / flow is corrected for known contributions from the patient interface and conduit (e.g., mask and tube impedance) using calibration data, a stored model, and / or a conduit classification method described herein.

[0162] If the respiratory therapy system 100 has leaks, often the leaks can skew or introduce errors into the pressure data. For example, leaks can typically occur from the user interface 120, including from the cushion 122 and / or the frame 124, such as at an interface of the user’s face and the user interface 120. In some implementations, the leak is estimated (e.g., based on the difference between blower flow and patient flow, or a leak model) and either (i) the impedance / admittance estimate for leak is compensated for, and / or (ii) time segments having leak above a threshold are excluded from the computation. Therefore, any pressure data generated while the flow of air is leaking from the respiratory therapy system at a rate greater than a predetermined leak rate is discarded and not used for determining physiological data indicative of the one or more airway characteristics of the user. For example, the predetermined leak rate for discarding the pressure data is about 0.05 L / s.

[0163] In implementations, the pattern of pseudo-random varying pressure 505 is superimposed continuously on the flow of air at the user interface 120. In other 364928-2858-6896implementations, the pattern of pseudo-random varying pressure 505 is superimposed on the flow of air only during predetermined time intervals or during predetermined conditions. For example, the pattern of pseudo-random varying pressure 505 is superimposed on the flow of air during predetermined stages of sleep and / or during predetermined sleeping positions of the user. This can allow characterization of the user’s airways while user is in a certain sleep stage, such as a deep sleep stage when the user is likely to be aroused by the delivered pressure comprising the pseudo-random varying pressure signal, or in a certain sleep position, such as a supine position in which the user may have a higher / lower propensity to experience apnea events. In some implementations, the pattern of pseudo-random varying pressure 505 is superimposed on the flow of air when periods of relatively stable breathing and / or stable pressure are detected thus excluding, for example, transitions such as arousal s, mask-on events, or large pressure ramps. In further implementations, the pattern of pseudo-random varying pressure 505 is superimposed on the flow of air when apnea is detected. This can allow characterization of the user’s airways while the apnea occurs, such as discrimination of open versus closed airway, but using a pressure oscillation signal that results in more comfortable delivery of pressurized air to a user that is sleeping.

[0164] In another implementation, the pattern of pseudo-random varying pressure 505 is superimposed on the flow of air when the flow of air has a pressure of less than a predetermined threshold pressure, for example, when the predetermined threshold pressure is between about 4 cm H2O and about 8 cm H2O, optionally when the predetermined threshold pressure is about 6 cm H2O. This can allow characterization of the user’s airways at more comfortable pressures for the user. Such a predetermined threshold pressure also ensures that the pseudo-random varying pressure 505 is superimposed at a pressure that is sufficiently distant from minimum / maximum pressure specifications of the respiratory device motor at which the final pressure signal might be “clipped” by the pressure system control over the motor, and thus alter or otherwise impact the pseudo-random varying pressure signal. In addition, such a predetermined threshold pressure can help avoid airflow turbulences through the tube and / or mask that may be caused by higher pressures, thus resulting in a pressure (and flow) signal with less noise, from which better quality FOT parameters may be determined. Moreover, vent flow and, thus, leak are lower at lower pressures, thus reducing impact of these confounding factors.

[0165] The pressure data is analyzed to generate parameters that are an indication of the response of the patient’s airway, optionally including the patient’s lungs, to the flow of air in association with the pseudo-random varying pressure 505. In implementations, the pressure 374928-2858-6896data is analyzed to determine an impedance of the patient’s airway, which can be expressed as a function of the resistance and the reactance of the airways. In implementations, the pressure data is analyzed to determine a resistance and / or a reactance of the airway in response to the flow of air, or impedance of the airway, which is a function of resistance and reactance. Airway characteristics that can be indicated by the pressure data, such as by the physiological data determined therefrom, include an open or (partially) closed airway, and mechanical properties of the respiratory system indicative of chronic obstructive pulmonary disease (COPD) and / or values of those properties indicative of exacerbation of COPD, for example. Such mechanical properties include airway resistance (R), which measures the opposition to airflow due to airway narrowing, obstruction, or collapse, comprising key parameters such as total resistance (wherein resistance at 5 Hz (R5) represents the total airway resistance, while resistance at 20 Hz (R20) reflects central airway resistance), and heterogeneity (i.e., R5-R20, wherein the greater the difference between R5 and R20 suggests increased small airway obstruction). Increased values measured over time for R5 and R5-R20 can indicate worsening small airway obstruction due to inflammation, mucus plugging, or bronchoconstriction.

[0166] Mechanical properties can also include airway reactance (X), which measures the elasticity and inertial properties of the lung, which are primarily influenced by peripheral airways, comprising key parameters such as reactance at 5 Hz (X5, reflecting lung compliance and airway closure), and area under the reactance curve (AX, which integrates the total burden of abnormal airway mechanics). More negative X5 overtime indicates increasing lung stiffness and peripheral airway dysfunction, whereas increased AX indicates worsening airway closure and trapping. Mechanical properties can also include airway resonant frequency, which measures the frequency at which reactance is zero, representing the balance between airway compliance and inertia. Increased resonant frequency over times can reflect worsening airway obstruction and lung compliance issues. Mechanical properties can also include impedance (Z), which measures the total mechanical load to breathing, combining resistance and reactance. Increased impedance overtime can indicate worsening airflow limitation and lung dysfunction. Coherence (y), which measures the reliability of the FOT measurement, may also be estimated to assess signal quality. Lower coherence, often due to increased breathing variability or changes in breathing effort, may indicate reduced measurement reliability. Coherence can help determine whether a result is valid, ensuring that measurements accurately reflect lung mechanics rather than being influenced by artifacts, patient movement, or irregular breathing. When coherence is low, results may need to be interpreted with caution or repeated to confirm accuracy.384928-2858-6896

[0167] The pressure data, or physiological data derived therefrom, is also useful for user interface titration, that is, determination of a user interface type suitable for a user based on their physical (e.g., airway) characteristics. In such implementations, one of the physical characteristics indicated by pressure data may be nasal resistance, which can be caused by nasal narrowing or obstruction. This may be determined based on pressure data, and physiological data derived therefrom, determined during periods when a user is breathing through their nose and / or wearing a nasal or nasal pillows mask. High nasal resistance, relative to a predetermined baseline (such as a baseline personalized to the user, or based on a baseline determined from a population of users), can be indicative of mouth breathing or mouth leak if the user wears a nasal mask. A determination of high nasal resistance can, for example, be used to recommend a full face mask for the user. As such, in implementations, the computed airway-response parameters (e.g., impedance features) may be used as inputs to a selection algorithm for a mask type and / or size, and / or may be used to confirm a quality of fit.

[0168] In implementations, one of the airway characteristics indicated by pressure data is an exacerbation factor that is an indication of a likelihood that the patient will experience an exacerbation event within a predetermined time. For example, referring to FIG. 8, in implementations, the exacerbation factor is determined based at least in part on an increase in variability of the pressure data. In some implementations, the “pressure data” used for exacerbation detection comprises one or more airway-response parameters derived from the excitation response (e.g., R5 and / or X5), rather than raw pressure alone. FIG. 8 shows a plot of forced oscillation technique (FOT) parameters presented versus time measured in days, and including a data point for each day. The FOT parameters shown are resistance and reactance of the patient’s airway, including the lungs. The increase in variability of the FOT parameters is measured between a first variability of the pressure data measured over a first period of days 801 as compared to a second variability of the pressure data taken over a second period of days 802.

[0169] Based on the increase in the variability of the pressure data shown in the plot of FIG. 8, there is an indication that the patient will experience an exacerbation event 804 within a predetermined time, e.g., about 30 days, from the beginning of the second period of days 802. The period of time 803 is exemplary of a period of time in which the variability of the FOT parameters is decreasing but has not yet stabilized compared to the pressure data in the window 801. In some implementations, variability is computed for a selected airway-response parameter (e.g., nightly median R5, nightly median X5, and / or a nightly composite feature)394928-2858-6896and represents a dispersion metric (e.g., standard deviation, interquartile range (IQR), or median absolute deviation (MAD)) across a plurality of sleep sessions.

[0170] Referring now to FIGS. 8 and 9, in implementations an exemplary general method 900 for applying the forced oscillation technique, such as using a pseudo-random varying pressure signal, in respiratory pressure therapy according to some implementations of the present disclosure is illustrated. One or more of steps of the method 900 can be implemented using any aspect of the system 100 (FIGS. 1-7) described herein. At step 905 in FIG. 9, pressure data associated with a first plurality of sleep sessions is generated. For example, the first plurality of sleep sessions are a set of consecutive sleep sessions, and for example the data points for the pressure data are acquired daily in respect of the consecutive sleep sessions. Pressure data for an exemplary first plurality of sleep sessions is shown in the time window 801 in FIG. 8. The variability of the pressure data for both resistance and reactance is shown to be relatively low in the first plurality of sleep sessions in the time window 801. Advantageously, monitoring over a first plurality of sleep sessions and second plurality of sleep sessions avoids outlier measurement s) of physiological parameters that might be detected for certain respiratory conditions, such as asthma and COPD, which might result in misleading data and assessment of a condition during periodic physiological examinations.

[0171] At step 910 in FIG. 9, pressure data associated with a second plurality of sleep sessions is generated. For example, the second plurality of sleep sessions are a set of consecutive sleep sessions, and may occur subsequent to the first plurality of sleep sessions, and for example the data points for the pressure data are acquired daily in respect of the consecutive sleep sessions. Pressure data for an exemplary second plurality of sleep sessions is shown in the time window 802 in FIG. 8. As such, in implementations, it may be understood that the first plurality may define a baseline window (e.g., Ni nights) and the second plurality may define a monitoring window (e.g., N2 nights) that is later in time (e.g., the immediately subsequent N2 nights), although other window definitions may be used.

[0172] At step 915, an increase in the variability of the pressure data (e.g., airway -response parameter, i.e., FOT parameter) between the variability of the pressure data measured over the second plurality of sleep sessions as compared to the variability of the pressure data measured over the first plurality of sleep sessions is identified. The variability of the pressure data for both resistance and reactance is shown to be increased in the second plurality of sleep sessions in the time window 802 of FIG. 8 as compared to the variability of the pressure data for the first plurality of sleep sessions in the time window 801 of FIG. 8.404928-2858-6896

[0173] At step 920, the exacerbation factor based at least in part on the increase in the variability of the second variability over the first variability is determined. In implementations, the variability of the data is a measure of the standard deviation of the data, which gives an indication of how scattered the data is in comparison to the mean of the data. In implementations, the exacerbation factor is a ratio of the standard deviation of the data measured in the second plurality of sleep sessions to the standard deviation of the data measured in the first plurality of sleep sessions. Other variability measures may be used (e.g., interquartile range (IQR), or median absolute deviation (MAD)), optionally normalized by a central tendency metric (e.g., mean or median). In some implementations, the exacerbation factor (EF) is defined as EF = Va / Vari, where Vari and Van are dispersion metrics for the baseline window and monitoring window, respectively, computed for the same airwayresponse feature.

[0174] At step 925, the exacerbation factor is determined to be greater than a threshold value. In implementations, the threshold value can be based on the type of exacerbation factor determined. In some implementations, the threshold is selected based on patient cohort data and / or individualized baseline, and may be adjusted based on confounders such as leak, mask type, and / or therapy pressure range. In implementations, the exacerbation factor is a ratio of the standard deviation of the data measured in the second plurality of sleep sessions to the standard deviation of the data measured in the first plurality of sleep sessions, and the threshold value can about three. In implementations, the threshold value can be in a range from about 1.5 to 10 or more. In other implementations, the exacerbation factors can comprise changes versus baseline, which can be indicated of exacerbation of conditions such as COPF. An increase in R5 (total airway resistance) by more than about 20%, and / or R5-R20 (small airway resistance) by more than about 25-30%, can suggest worsening of airway obstruction, whereas an increase of about 30-40% in R5-R20 can indicate a severe exacerbation. A more negative X5 (reactance at 5 Hz) by over about 20% and an increase in AX (reactance area) by more than about % can indicate early deterioration in lung compliance, with changes exceeding about 30-40% and about 50%, respectively, indicating severe exacerbation. An increase in resonant frequency by more than about 10-20% can suggest worsening lung function and air trapping.

[0175] At step 930, the user, and / or a physician / caregiver or other third party, is informed, such as via the display device, that an exacerbation is predicted. The indication may include an estimated time window (e.g., within about 30 days), a confidence score, and / or a recommendation to review therapy settings, clinical status, or both.414928-2858-6896

[0176] In implementations, the prediction of the exacerbation may inform a change in the settings of a respiratory therapy device. Settings may be changed manually or automatically, optionally with input or approval from a phy si ci an / car egiver or other third party. Thus, a change in the settings of a respiratory therapy device before or during an exacerbation of respiratory condition such as COPD can allow rapid treatment of symptoms and potential reduction in the severity of the exacerbation. Furthermore, monitoring of pressure data and exacerbation factors during sleep sessions can ensure that potential exacerbations are detected early, and which might otherwise be missed if monitored during periodic visits to or tests by, for example, a physician or clinic (i.e., spot check assessments). The presently disclosed method allows such spot checks but while also enabling continuous and longitudinal (i.e., intranight and inter-night) monitoring of the lung health user of respiratory therapy systems such as PAP therapy.

[0177] In other implementations, the method for applying the forced oscillation technique, such as using a pseudo-random varying pressure signal, may be applied for the detection and / or discrimination of conduit (tube) types. This method involves superimposing an oscillating pressure signal (e.g., FOT signal) on the airflow within the respiratory therapy system. Pressure data generated via a sensor(s) in the respiratory therapy system is then analyzed to determine physical data indicative of one or more characteristics of the physical elements of the respiratory therapy system, such as a conduit. Such characteristics can include one or more of impedance (magnitude and / or phase), resistance, and reactance associated with the physical element. For characterizing a conduit element of a respiratory therapy system, these characteristics are based at least in part on the length and / or diameter of the conduit, and so analysis of these characteristics may be used to detect and / or discriminate conduit types. Characteristics may be assessed at multiple frequencies (e.g., 5-50 Hz, 5-15 Hz, etc.), which makes a pseudo-random varying pressure signal particularly useful.

[0178] Principal component analysis (PCA) is an example of a useful technique to analyze these characteristics for detection and / or discrimination of conduit types. For example, referring to FIG. 10, a PCA scatter plot may visually distinguish conduit types based on their FOT characteristics, showing clustering in principal component space. This may be further enhanced by separating data based on mask type (known) connected to the conduit. For example, FIG. 10 illustrates how three different tube types (1, 2, and 3) can be distinguished for each of three given mask types (N20, P10, and P30i). By applying PCA to these components, the method can determine the type of conduit, or distinguish between types of conduits. In implementations, information relating to the type of conduit may be used to inform 424928-2858-6896settings of a respiratory therapy device, or changes to those settings based on, for example, attachment of a different conduit to the respiratory therapy device. Settings may be changed manually or automatically, optionally with input or approval from a phy si ci an / car egiver or other third party.ALTERNATIVE IMPLEMENTATION SECTION

[0179] Implementation 1. A computer-implemented method includes providing, via a user interface of a respiratory therapy system, a flow of air to an airway of a user. The method also includes superimposing a pattern of pseudo-random varying pressure on the flow of air. The method also includes generating, via a pressure sensor of the respiratory therapy system, pressure data associated with the flow of air. The method also includes determining, based at least in part on the pressure data, physiological data indicative of one or more airway characteristic of the user.

[0180] Implementation 2. The method of implementation 1, wherein the determining includes estimating a respiratory impedance and / or admittance of the airway as a function of frequency based on the pressure data.

[0181] Implementation 3. The method of implementation 1 or implementation 2, wherein the determining the physiological data comprises determining a resistance of the airway, a reactance of the airway, or both, based at least in part on the pressure data.

[0182] Implementation 4. The method of any one of implementations 1 to 3, wherein one of the one or more airway characteristics of the user is an exacerbation factor that is indicative of a likelihood that the user will experience an exacerbation event within a predetermined amount of time.

[0183] Implementation 5. The method of implementation 4, wherein the predetermined amount of time is about thirty days.

[0184] Implementation 6. The method of implementation 4 or implementation 5, wherein the exacerbation factor is determined based at least in part on an increase in variability of the pressure data between a first variability of the pressure data measured over a first period of days as compared to a second variability of the pressure data taken over a subsequent period of days.

[0185] Implementation 7. The method of any one of implementations 1 to 6, wherein the pattern of pseudo-random varying pressure has a frequency that varies in a range between about 5 Hz and 15 Hz.434928-2858-6896

[0186] Implementation 8. The method of any one of implementations 1 to 7, wherein the pattern of pseudo-random varying pressure has a peak-to-peak amplitude of about 1 cm H2O.

[0187] Implementation 9. The method of any one of implementations 1 to 8, wherein the pattern of pseudo-random varying pressure is superimposed continuously on the flow of air.

[0188] Implementation 10. The method of any one of implementations 1 to 8, wherein the pattern of pseudo-random varying pressure is superimposed on the flow of air only during predetermined time intervals.

[0189] Implementation 11. The method of any one of implementations 1 to 10, wherein the pattern of pseudo-random varying pressure is superimposed on the flow of air during predetermined conditions.

[0190] Implementation 12. The method of implementation 11, wherein the pattern of pseudo-random varying pressure is superimposed on the flow of air during predetermined stages of sleep.

[0191] Implementation 13. The method of implementation 11, wherein the pattern of pseudo-random varying pressure is superimposed on the flow of air during predetermined stages of sleep.

[0192] Implementation 14. The method of implementation 11, wherein the pattern of pseudo-random varying pressure is superimposed on the flow of air when a potential apnea event is detected.

[0193] Implementation 15. The method of implementation 11, wherein the pattern of pseudo-random varying pressure is superimposed over the flow of air when the flow of air has a pressure of less than a predetermined threshold pressure.

[0194] Implementation 16. The method of implementation 15, wherein the predetermined threshold pressure is about 6 cm H2O.

[0195] Implementation 17. The method of any one of implementations 1 to 16, wherein the pressure data is discarded from the determining step if the pressure data is generated while the flow of air is leaking from the respiratory therapy system at a rate greater than a predetermined leak rate.

[0196] Implementation 18. The method of implementation 17, wherein the predetermined leak rate is about 0.05 L / s.

[0197] Implementation 19. The method of any one of implementations 4 to 18, further including generating the pressure data associated with first and second pluralities of sleep sessions. The method also includes identifying an increase in variability of an airway -response feature in the pressure data between a second variability of the airway-response feature 444928-2858-6896measured over the second plurality of sleep sessions as compared to a first variability of the airway-response feature measured over the first plurality of sleep sessions. The method also includes determining the exacerbation factor based at least in part on the increase in the variability of the second variability over the first variability. The method also includes determining the exacerbation factor to be greater than a threshold value, and indicating to the user, via a display device, that the exacerbation event is predicted.

[0198] Implementation 20. The method of implementation 19, wherein the first plurality of sleep sessions is a set of consecutive sleep sessions, and the second plurality of sleep sessions is a second different set of consecutive sleep sessions.

[0199] Implementation 21. The method of any one of implementations 1 to 20, wherein one of the one or more airway characteristics is indicative of a closed airway.

[0200] Implementation 22. The method of any one of implementations 1 to 20, wherein one of the one or more airway characteristics is indicative of chronic obstructive pulmonary disease (COPD).

[0201] Implementation 23. The method of any one of implementations 1 to 20, wherein one of the one or more airway characteristics is utilized for mask titration to determine a user interface type suitable for the user.

[0202] Implementation 24. A system including a control system of the respiratory therapy system including one or more processors, and a memory device having stored thereon machine readable instructions, wherein the control system is coupled to the memory, and the method of any one of implementations 1 to 23 is implemented when the machine executable instructions in the memory are executed by at least one of the one or more processors of the control system.

[0203] Implementation 25. The system of implementation 24, further including a respiratory therapy system.

[0204] Implementation 26. The system of implementation 25, wherein the control system is included in the respiratory therapy system.

[0205] Implementation 27. A system for communicating one or more indications to the user, the system including a control system configured to implement the method of any one of implementations 1 to 23.

[0206] Implementation 28. A computer program product including instructions which, when executed by a computer, cause the computer to carry out the method of any one of implementations 1 to 23.

[0207] Implementation 29. The computer program product of implementation 28, wherein the computer program product is a non-transitory computer readable medium.454928-2858-6896

[0208] Implementation 30. A system including a control system including one or more processors, and a memory device having stored thereon machine readable instructions. The control system is coupled to the memory. The system also includes a respiratory therapy system including a blower motor and a user interface providing a flow of air, via the blower motor, to an airway of a user. The control system is configured to superimpose a pattern of pseudorandom varying pressure on the flow of air. The control system is also configured to generate, via a pressure sensor of the respiratory therapy system, pressure data associated with the flow of air. The control system is further configured to determine, based at least in part on the pressure data, physiological data indicative of one or more airway characteristics of the user.

[0209] Implementation 31. The system of implementation 30, wherein the control system is configured to determine the physiological data based at least in part by estimating a respiratory impedance and / or admittance of the airway as a function of frequency based on the pressure data.

[0210] Implementation 32. The system of implementation 30 or implementation 31, wherein the control system is configured to determine the physiological data based at least in part by determining a resistance of the airway, a reactance of the airway, or both, based at least in part on the pressure data.

[0211] Implementation 33. The system of any one of implementations 30 to 32, wherein one of the one or more airway characteristics of the user is an exacerbation factor that is indicative of a likelihood that the user will experience an exacerbation event within a predetermined amount of time.

[0212] Implementation 34. The system of implementation 33, wherein the predetermined amount of time is about thirty days.

[0213] Implementation 35. The system of implementation 33 or implementation 34, wherein the exacerbation factor is determined based at least in part on an increase in variability of the pressure data between a first variability of the pressure data measured over a first period of days as compared to a second variability of the pressure data taken over a subsequent period of days.

[0214] Implementation 36. The system of any one of implementations 30 to 35, wherein the pattern of pseudo-random varying pressure has a frequency that varies in a range between about 5 Hz and 15 Hz.

[0215] Implementation 37. The system of any one of implementations 30 to 36, wherein the pattern of pseudo-random varying pressure has a peak-to-peak amplitude of about 1 cm H2O.464928-2858-6896

[0216] Implementation 38. The system of any one of implementations 30 to 37, wherein the pattern of pseudo-random varying pressure is superimposed continuously on the flow of air.

[0217] Implementation 39. The system of any one of implementations 30 to 37, wherein the pattern of pseudo-random varying pressure is superimposed on the flow of air only during predetermined time intervals.

[0218] Implementation 40. The system of any one of implementations 30 to 39, wherein the pattern of pseudo-random varying pressure is superimposed on the flow of air during predetermined conditions.

[0219] Implementation 41. The system of implementation 40, wherein the pattern of pseudo-random varying pressure is superimposed on the flow of air during predetermined stages of sleep.

[0220] Implementation 42. The system of implementation 40, wherein the pattern of pseudo-random varying pressure is superimposed on the flow of air during predetermined sleeping positions of the user.

[0221] Implementation 43. The system of implementation 40, wherein the pattern of pseudo-random varying pressure is superimposed on the flow of air when a potential apnea event is detected.

[0222] Implementation 44. The system of implementation 40, wherein the pattern of pseudo-random varying pressure is superimposed over the flow of air when the flow of air has a pressure of less than a predetermined threshold pressure.

[0223] Implementation 45. The system of implementation 44, wherein the predetermined threshold pressure is about 6 cm H2O.

[0224] Implementation 46. The system of any one of implementations 30 to 45, wherein the control system is configured to discard the pressure data in the determination of the physiological data if the pressure data is generated while the flow of air is leaking from the respiratory therapy system at a rate greater than a predetermined leak rate.

[0225] Implementation 47. The system of implementation 46, wherein the predetermined leak rate is about 0.05 L / s.

[0226] Implementation 48. The system of any one of implementations 33 to 46, wherein the control system is further configured to generate the pressure data associated with first and second pluralities of sleep sessions. The control system is also configured to identify an increase in variability of an airway-response feature in the pressure data between a second variability of the airway-response feature measured over the second plurality of sleep sessions 474928-2858-6896as compared to a first variability of the airway-response feature measured over the first plurality of sleep sessions. The control system is also configured to determine the exacerbation factor based at least in part on the increase in the variability of the second variability over the first variability. The control system is also configured to determine the exacerbation factor to be greater than a threshold value, and indicate to the user, via a display device, that the exacerbation event is predicted.

[0227] Implementation 49. The system of implementation 48, wherein the first plurality of sleep sessions is a set of consecutive sleep sessions, and the second plurality of sleep sessions is a second different set of consecutive sleep sessions.

[0228] Implementation 50. The system of any one of implementations 30 to 49, wherein one of the one or more airway characteristics is indicative of a closed airway.

[0229] Implementation 51. The system of any one of implementations 30 to 49, wherein one of the one or more airway characteristics is indicative of chronic obstructive pulmonary disease (COPD).

[0230] Implementation 52. The system of any one of implementations 30 to 49, wherein one of the one or more airway characteristics is utilized for mask titration to determine a user interface type suitable for the user.

[0231] One or more elements or aspects or steps, or any portion(s) thereof, from one or more of any of the above implementations above can be combined with one or more elements or aspects or steps, or any portion(s) thereof, from one or more of any of the other above implementations or combinations thereof, to form one or more additional implementations and / or claims of the present disclosure. As used herein, “about” may be understood to represent ±20%, ±10%, ±5%, or ±1% of the indicated value.

[0232] While the present disclosure has been described with reference to one or more particular embodiments or implementations, those skilled in the art will recognize that many changes may be made thereto without departing from the spirit and scope of the present disclosure. Each of these implementations and obvious variations thereof is contemplated as falling within the spirit and scope of the present disclosure. It is also contemplated that additional implementations according to aspects of the present disclosure may combine any number of features from any of the implementations described herein.484928-2858-6896

Claims

CLAIMSWHAT IS CLAIMED IS:

1. A computer-implemented method comprising:providing, via a user interface of a respiratory therapy system, a flow of air to an airway of a user;superimposing a pattern of pseudo-random varying pressure on the flow of air; generating, via a pressure sensor of the respiratory therapy system, pressure data associated with the flow of air; anddetermining, based at least in part on the pressure data, physiological data indicative of one or more airway characteristics of the user.

2. The method of claim 1, wherein the determining the physiological data comprises estimating a respiratory impedance and / or admittance of the airway as a function of frequency based on the pressure data.

3. The method of claim 1 or 2, wherein the determining the physiological data comprises determining a resistance of the airway, a reactance of the airway, or both, based at least in part on the pressure data.

4. The method of any one of claims 1 to 3, wherein one of the one or more airway characteristics of the user is an exacerbation factor that is indicative of a likelihood that the user will experience an exacerbation event within a predetermined amount of time.

5. The method of claim 4, wherein the predetermined amount of time is about thirty days.

6. The method of claims 4 or 5, wherein the exacerbation factor is determined based at least in part on an increase in variability of the pressure data between a first variability of the pressure data measured over a first period of days as compared to a second variability of the pressure data taken over a subsequent period of days.

7. The method of any one of claims 1 to 6, wherein the pattern of pseudo-random varying pressure has a frequency that varies in a range between about 5 Hz and 15 Hz.494928-2858-68968. The method of any one of claims 1 to 7, wherein the pattern of pseudo-random varying pressure has a peak-to-peak amplitude of about 1 cm H2O.

9. The method of any one of claims 1 to 8, wherein the pattern of pseudo-random varying pressure is superimposed continuously on the flow of air.

10. The method of any one of claims 1 to 8, wherein the pattern of pseudo-random varying pressure is superimposed on the flow of air only during predetermined time intervals.

11. The method of any one of claims 1 to 10, wherein the pattern of pseudo-random varying pressure is superimposed on the flow of air during predetermined conditions.

12. The method of claim 11, wherein the pattern of pseudo-random varying pressure is superimposed on the flow of air during predetermined stages of sleep.

13. The method of claim 11, wherein the pattern of pseudo-random varying pressure is superimposed on the flow of air during predetermined sleeping positions of the user.

14. The method of claim 11, wherein the pattern of pseudo-random varying pressure is superimposed on the flow of air when a potential apnea event is detected.

15. The method of claim 11, wherein the pattern of pseudo-random varying pressure is superimposed over the flow of air when the flow of air has a pressure of less than a predetermined threshold pressure.

16. The method of claim 15, wherein the predetermined threshold pressure is about 6 cm H2O.

17. The method of any one of claims 1 to 16, wherein the pressure data is discarded from the determining step if the pressure data is generated while the flow of air is leaking from the respiratory therapy system at a rate greater than a predetermined leak rate.

18. The method of claim 17, wherein the predetermined leak rate is about 0.05 L / s.504928-2858-689619. The method of any one of claims 4 to 18, further comprising:generating the pressure data associated with first and second pluralities of sleep sessions;identifying an increase in variability of an airway-response feature in the pressure data between a second variability of the airway-response feature measured over the second plurality of sleep sessions as compared to a first variability of the airwayresponse feature measured over the first plurality of sleep sessions; determining the exacerbation factor based at least in part on the increase in the variability of the second variability over the first variability;determining the exacerbation factor to be greater than a threshold value; and indicating to the user, via a display device, that the exacerbation event is predicted.

20. The method of claim 19, wherein the first plurality of sleep sessions is a set of consecutive sleep sessions, and the second plurality of sleep sessions is a second different set of consecutive sleep sessions.

21. The method of any one of claims 1 to 20, wherein one of the one or more airway characteristics is indicative of a closed airway.

22. The method of any one of claims 1 to 20, wherein one of the one or more airway characteristics is indicative of chronic obstructive pulmonary disease (COPD).

23. The method of any one claims 1 to 20, wherein one of the one or more airway characteristics is utilized for mask titration to determine a user interface type suitable for the user.

24. A system comprising:a control system including one or more processors; anda memory device having stored thereon machine readable instructions;wherein the control system is coupled to the memory, and the method of any one of claims 1 to 23 is implemented when the machine executable instructions in the memory are executed by at least one of the one or more processors of the control system.

25. The system of claim 24, further comprising a respiratory therapy system.514928-2858-689626. The system of claim 25, wherein the control system is comprised in the respiratory therapy system.

27. A system for communicating one or more indications to a user, the system comprising a control system configured to implement the method of any one of claims 1 to 23.

28. A computer program product comprising instructions which, when executed by a computer, cause the computer to carry out the method of any one of claims 1 to 23.

29. The computer program product of claim 28, wherein the computer program product is a non-transitory computer readable medium.

30. A system comprising:a control system including one or more processors;a memory device having stored thereon machine readable instructions, wherein the control system is coupled to the memory,a respiratory therapy system, comprising:a blower motor; anda user interface providing a flow of air, via the blower motor, to an airway of a user;the control system configured to:superimpose a pattern of pseudo-random varying pressure on the flow of air; generate, via a pressure sensor of the respiratory therapy system, pressure data associated with the flow of air; anddetermine, based at least in part on the pressure data, physiological data indicative of one or more airway characteristics of the user.

31. The system of claim 30, wherein the control system is configured to determine the physiological data based at least in part by estimating a respiratory impedance and / or admittance of the airway as a function of frequency based on the pressure data.524928-2858-689632. The system of claim 30 or 31, wherein the control system is configured to determine the physiological data based at least in part by determining a resistance of the airway, a reactance of the airway, or both, based at least in part on the pressure data.

33. The system of any one of claims 30 to 32, wherein one of the one or more airway characteristics of the user is an exacerbation factor that is indicative of a likelihood that the user will experience an exacerbation event within a predetermined amount of time.

34. The system of claim 33, wherein the predetermined amount of time is about thirty days.

35. The system of claim 33 or claim 34, wherein the exacerbation factor is determined based at least in part on an increase in variability of the pressure data between a first variability of the pressure data measured over a first period of days as compared to a second variability of the pressure data taken over a subsequent period of days.

36. The system of any one of claims 30 to 35, wherein the pattern of pseudo-random varying pressure has a frequency that varies in a range between about 5 Hz and 15 Hz.

37. The system of any one of claims 30 to 36, wherein the pattern of pseudo-random varying pressure has a peak-to-peak amplitude of about 1 cm H2O.

38. The system of any one of claims 30 to 37, wherein the pattern of pseudo-random varying pressure is superimposed continuously on the flow of air.

39. The system of any one of claims 30 to 37, wherein the pattern of pseudo-random varying pressure is superimposed on the flow of air only during predetermined time intervals.

40. The system of any one of claims 30 to 39, wherein the pattern of pseudo-random varying pressure is superimposed on the flow of air during predetermined conditions.

41. The system of claim 40, wherein the pattern of pseudo-random varying pressure is superimposed on the flow of air during predetermined stages of sleep.534928-2858-689642. The system of claim 40, wherein the pattern of pseudo-random varying pressure is superimposed on the flow of air during predetermined sleeping positions of the user.

43. The system of claim 40, wherein the pattern of pseudo-random varying pressure is superimposed on the flow of air when a potential apnea event is detected.

44. The system of claim 40, wherein the pattern of pseudo-random varying pressure is superimposed over the flow of air when the flow of air has a pressure of less than a predetermined threshold pressure.

45. The system of claim 44, wherein the predetermined threshold pressure is about 6 cm H2O.

46. The system of any one of claims 30 to 45, wherein the control system is configured to discard the pressure data in the determination of the physiological data if the pressure data is generated while the flow of air is leaking from the respiratory therapy system at a rate greater than a predetermined leak rate.

47. The system of claim 46, wherein the predetermined leak rate is about 0.05 L / s.

48. The system of any one of claims 33 to 46, wherein the control system is further configured to:generate the pressure data associated with first and second pluralities of sleep sessions; identify an increase in variability of an airway-response feature in the pressure data between a second variability of the airway-response feature measured over the second plurality of sleep sessions as compared to a first variability of the airwayresponse feature measured over the first plurality of sleep sessions; determine the exacerbation factor based at least in part on the increase in the variability of the second variability over the first variability;determine the exacerbation factor to be greater than a threshold value; and indicate to the user, via a display device, that the exacerbation event is predicted.

49. The system of claim 48, wherein the first plurality of sleep sessions is a set of consecutive sleep sessions, and the second plurality of sleep sessions is a second different set of consecutive sleep sessions.544928-2858-689650. The system of any one of claims 30 to 49, wherein one of the one or more airway characteristics is indicative of a closed airway.

51. The system of any one of claims 30 to 49, wherein one of the one or more airway characteristics is indicative of chronic obstructive pulmonary disease (COPD).

52. The system of any one of claims 30 to 49, wherein one of the one or more airway characteristics is utilized for mask titration to determine a user interface type suitable for the user.554928-2858-6896

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