System and method for monitoring coexisting diseases

Abstract

The system includes a respiratory treatment system, a storage device, and a control system. The respiratory treatment system includes a respiratory treatment device that supplies pressurized air and a user interface coupled to the respiratory treatment device via a conduit and directing the pressurized air to the user's airway. The storage device stores machine-readable instructions. The control system executes the machine-readable instructions to generate data associated with a user of the respiratory treatment system during a current sleep session, analyze the generated data to determine a value of a first metric associated with a sleep-disordered breathing (SDB) condition, analyze the generated data to determine a value of a second metric associated with the health condition other than the SDB condition, and perform the action based at least in part on the determined value of the second metric.

Description

Technical Field

[0001] (Cross - Reference to Related Applications) This application claims the benefit and priority of U.S. Provisional Patent Application No. 63 / 080,344, filed on September 18, 2020, the entire disclosure of which is incorporated herein by reference.

[0002] The present disclosure generally relates to systems and methods for analyzing data generated while an individual is using a respiratory therapy system, and more specifically, to systems and methods for analyzing data generated while a user is using a respiratory therapy system to determine a first metric related to sleep - disordered breathing and a second metric related to the health status of other sleep - disordered breathing.

Background Art

[0003] Many individuals suffer from sleep-related disorders and / or respiratory disorders, such as sleep-disordered breathing (SDB) which can include other types of apnea such as obstructive sleep apnea (OSA), central sleep apnea (CSA), mixed apnea, and hypopnea, as well as respiratory effort-related arousals (RERA). These individuals may also suffer from other health conditions (which can be referred to as co-morbidities) such as insomnia (e.g., difficulty initiating sleep, frequent or long awakenings after initial sleep onset, and / or early awakenings from which it is not possible to return to sleep), periodic limb movement disorder (PLMD), restless legs syndrome (RLS), Cheyne-Stokes respiration (CSR), respiratory insufficiency, obesity hypoventilation 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. It is difficult to accurately identify and monitor these other health conditions. In individuals being treated for sleep-related disorders using a respiratory therapy system, the use of the respiratory therapy system can affect the severity of these health conditions. Thus, it is advantageous to be able to analyze data related to the personal use of a respiratory therapy system to determine the identity, severity, and / or progression of other health conditions. The present disclosure relates to systems, devices, and methods that enable the identification and / or monitoring of health conditions using data related to the personal use of a respiratory therapy system. SUMMARY OF THE INVENTION

[0004] According to some implementations of the disclosure, a system for monitoring a user's sleep session includes a respiratory therapy system, a memory device, and a control system. The respiratory therapy system includes a respiratory therapy device and a user interface. The respiratory therapy device is configured to supply pressurized air. The user interface is connected to the respiratory therapy device via a conduit and is configured to engage with the user and assist in guiding the supplied pressurized air into the user's airway. The memory device stores machine-readable instructions. The control system is connected to the memory device and includes one or more processors configured to execute the machine-readable instructions to generate data related to the user during a current sleep session, analyze the generated data to determine a value of a first metric related to a sleep-disordered breathing (SDB) condition, analyze the generated data to determine a value of a second metric related to a health condition other than the SDB condition, and perform an operation based at least in part on the determined value of the second metric.

[0005] According to some implementations, the one or more processors of the control system are further configured to execute the machine-readable instructions to track the second metric over a plurality of sleep sessions, determine a value of the second metric for each of the plurality of sleep sessions, determine (i) a duration of use of the respiratory therapy system for each of the plurality of sleep sessions, (ii) one or more setpoint values of the respiratory therapy system for each of the plurality of sleep sessions, or (iii) both, and identify (i) an optimal duration of use of the respiratory therapy system, (ii) an optimal value of one or more settings of the respiratory therapy system, or (iii) both, for treating the health condition.

[0006] According to some implementations, one or more processors of the control system further execute machine-readable instructions to receive historical data related to one or more previous sleep sessions of a user, analyze the historical data to determine a value of a second metric for each of the one or more previous sleep sessions, and compare the determined value of the second metric for each of the one or more previous sleep sessions with the determined value of the second metric for the current sleep session.

[0007] According to some implementations, one or more processors of the control system further execute machine-readable instructions to determine a severity of a health state at least in part based on the determined value of the second metric for the current sleep session, generate data during one or more subsequent sleep sessions, determine a value of the second metric for each of the one or more subsequent sleep sessions, determine an updated severity of the health state at least in part based on the determined value of the second metric for each of the one or more subsequent sleep sessions, and configure to cause an operation to be performed at least in part based on the updated severity of the health state.

[0008] According to some implementations, one or more processors of the control system further execute machine-readable instructions to receive additional data related to the user upon awakening, analyze the additional data to determine a value of a third metric related to the health state, and configure to cause an operation to be performed at least in part based on the determined value of the third metric related to the health state

[0009] According to some implementations of the present disclosure, a method for monitoring a user's sleep session includes generating data related to an individual of a respiratory therapy system during a current sleep session. The method further includes analyzing the generated data to determine a value of a first metric related to a sleep disordered breathing (SDB) condition. The method further includes analyzing the generated data to determine a value of a second metric related to a health condition other than the SDB condition. The method further includes causing an operation to be performed based at least in part on the determined value of the second metric.

[0010] According to some implementations, the method further includes tracking the second metric over a plurality of sleep sessions. The method further includes determining a value of the second metric for each of the plurality of sleep sessions. The method further includes determining (i) a usage duration of the respiratory therapy system for each of the plurality of sleep sessions, (ii) one or more setpoint values for each of the plurality of sleep sessions, or (iii) both. The method further includes identifying (i) an optimal usage duration of the respiratory therapy system, (ii) an optimal value of one or more settings of the respiratory therapy system, or (iii) both for treating the health condition.

[0011] According to some implementations, the method further includes receiving historical data related to one or more previous sleep sessions of the user. The method further includes analyzing the historical data to determine a value of the second metric for each of the one or more previous sleep sessions. The method further includes comparing the determined value of the second metric for each of the one or more previous sleep sessions with the determined value of the second metric for the current sleep session.

[0012] According to some implementations, the method further includes determining a severity of a health condition based at least in part on a determined value of a second metric for a current sleep session. The method further includes generating data during one or more subsequent sleep sessions. The method further includes determining a value of the second metric for each of the one or more subsequent sleep sessions. The method further includes determining an updated severity of the health condition based at least in part on the determined value of the second metric for each of the one or more subsequent sleep sessions. The method further includes causing an action to be performed based at least in part on the updated severity of the health condition.

[0013] According to some implementations, the method further includes receiving additional data related to the user upon awakening. The method further includes analyzing the additional data to determine a value of a third metric related to the health condition. The method further includes causing an action to be further performed based at least in part on the determined value of the third metric related to the health condition.

[0014] The above summary is not intended to represent every implementation or every aspect of the present disclosure. Further features and advantages of the present disclosure will become apparent from the following detailed description and the drawings.

Brief Description of the Drawings

[0015]

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DETAILED DESCRIPTION OF THE INVENTION

[0016] The present disclosure is described with reference to the accompanying drawings, and in each figure, the same or equivalent components are given the same reference numerals. Each figure is not drawn to scale and is provided merely for the purpose of explaining the present disclosure. Some aspects of the present disclosure are described below with reference to exemplary applications for illustration.

[0017] Many individuals suffer from sleep-related disorders and / or breathing disorders, such as periodic limb movement disorder (PLMD), restless legs syndrome (RLS), sleep-disordered breathing (SDB), such as obstructive sleep apnea (OSA), central sleep apnea (CSA) and other types of apnea, respiratory effort-related arousals (RERA), Cheyne-Stokes respiration (CSR), respiratory insufficiency, obesity hypoventilation syndrome (OHS), chronic obstructive pulmonary disease (COPD), neuromuscular disease (NMD) and chest wall disorders. Obstructive sleep apnea (OSA) is a form of sleep-disordered breathing (SDB) characterized by events such as obstruction or blockage of the upper airway during sleep due to a combination of an abnormally small upper airway and a normal loss of muscle tone in the regions of the tongue, soft palate, and posterior pharyngeal wall. Central sleep apnea (CSA) is another form of sleep-disordered breathing. CSA occurs when the brain temporarily stops sending signals to the muscles that control breathing. Other types of apnea include hypopnea, hyperpnea, and hypercapnia. Hypopnea is generally characterized by slow or shallow breathing caused by narrowing of the airway rather than obstruction of the airway. Hyperpnea is generally characterized by an increase in the depth and / or rate of breathing. Hypercapnia is generally characterized by a sudden increase or excess of carbon dioxide in the bloodstream and is usually caused by inadequate breathing. Respiratory effort-related arousal (RERA) events are usually characterized by an increase in respiratory effort for more than 10 seconds leading to arousal from sleep and do not meet the criteria for apnea or hypopnea events. RERA is defined as a series of breaths characterized by an increase in respiratory effort leading to arousal from sleep but not meeting the criteria for apnea or hypopnea. These events need to meet both criteria: (1) the pattern of gradually increasing negative esophageal pressure ends with a sudden decrease in negative pressure level and arousal, and (2) the event lasts for more than 10 seconds. In some implementations, a nasal cannula / pressure transducer system is sufficient and reliable for the detection of RERA. A RERA detector can be based on the actual flow signal derived from a respiratory therapy device. For example, a flow limitation measure can be determined based on the flow signal. Subsequently, an arousal measure can be derived as a function of the flow limitation measure and a measure of the sudden increase in ventilation volume.One such method is described in International Patent Publication No. WO 2008 / 138040 and U.S. Patent Publication No. 9,358,353, both assigned to ResMed, Inc., the entire disclosures of which are incorporated herein by reference.

[0018] Cheyne-Stokes respiration (CSR) is another form of SDB. CSR is a disorder of a patient's respiratory controller in which alternating periodic increases and decreases in ventilation, known as the CSR cycle, occur. CSR is characterized by repeated deoxygenation and reoxygenation of arterial blood. OHS is defined as the combination of severe obesity and chronic hypercapnia during wakefulness in the absence of other clear causes of hypoventilation. Symptoms include dyspnea, morning headache, and excessive daytime sleepiness. COPD encompasses any of a group of lower airway diseases that share certain characteristics, such as an increased resistance to air movement, a prolonged expiratory phase of respiration, and a loss of normal elasticity of the lungs. NMD encompasses a number of diseases and conditions that impair muscle function either directly through intrinsic muscle pathology or indirectly through neuropathy. Thoracic wall disorders are a group of chest wall deformities that cause ineffectiveness of the connection between the respiratory muscles and the chest wall.

[0019] Many of these disorders are characterized by specific events that occur during sleep, such as snoring, apnea, hypopnea, periodic limb movement, sleep disordered breathing, choking, tachycardia, dyspnea, asthma attacks, epileptic seizures, or any combination thereof. Multiple types of data can be used to monitor the health of an individual suffering from any type of sleep-related disorder and / or respiratory disorder (or other disorder) of the above methods. However, it is generally difficult to collect accurate data without interrupting or disturbing the user's sleep or any treatment the user may be receiving during sleep. Thus, it is advantageous to utilize a treatment system that includes various sensors to generate and collect data without disturbing the user, the user's sleep, or the user's treatment.

[0020] The apnea-hypopnea index (AHI) is an index used to indicate the severity of sleep apnea during a sleep session. The AHI is determined by dividing the number of apnea events and / or hypopnea events experienced by the user during the sleep session by the total number of sleep hours in that sleep session. Such an event can be, for example, a pause in breathing that lasts for at least 10 seconds or more. An AHI of less than 5 is considered normal. An AHI of 5 or more and less than 15 is considered to indicate mild sleep apnea. An AHI of 15 or more and less than 30 is considered to indicate moderate sleep apnea. An AHI of 30 or more is considered to indicate severe sleep apnea. In children, an AHI greater than 1 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 saturation to indicate the severity of obstructive sleep apnea.

[0021] Referring to FIG. 1, a system 100 according to some implementations of the present disclosure is shown. The system 100 may be used, for example, to detect, identify, treat, and / or monitor a respiratory state (and / or other states) before any external physical symptoms of any of these states appear. The system 100 includes a control system 110, a memory device 114, an electronic interface 119, one or more sensors 130, and optionally, one or more user devices 170. In some implementations, the system 100 further includes a respiratory therapy system 120 that includes a respiratory therapy device 122.

[0022] The control system 110 includes one or more processors 112 (hereinafter, the processor 112). The control system 110 is generally used to control (e.g., operate) various components of the system 100 and / or analyze data acquired and / or generated by the components of the system 100. The processor 112 may be a general-purpose or special-purpose processor or microprocessor. Although one processor 112 is shown in FIG. 1, the control system 110 may include any suitable number of processors (e.g., one processor, two processors, five processors, ten processors, etc.) that may be present within a single housing or positioned remotely from each other. One or more steps of any of the methods described herein and / or claimed may be performed using the control system 110 (or any other control system) or a part of the control system 110 such as the processor 112 (or any other processor or part of any other control system). The control system 110 may be coupled to and / or disposed within the housing of the user device 170 and / or the housing of one or more sensors 130. The control system 110 may be centralized (within one such housing) or distributed (within two or more such physically separate housings). In such an implementation including two or more housings containing the control system 110, such housings may be positioned close to each other and / or remotely from each other.

[0023] The memory device 114 stores machine-readable instructions executable by the processor 112 of the control system 110. The memory device 114 may be any suitable computer-readable storage device or medium, such as, for example, a random access memory device or a serial access memory device, a hard drive, a solid state drive, a flash memory device, etc. Although one memory device 114 is shown in FIG. 1, the system 100 may include any suitable number of memory devices 114 (e.g., one memory device, two memory devices, five memory devices, ten memory devices, etc.). The memory device 114 may be coupled to and / or disposed within the housing of the respiratory therapy device 122 of the respiratory therapy system 120, the housing of the user device 170, the housing of one or more sensors 130, or any combination thereof. Similar to the control system 110, the memory device 114 may be centralized (within one such housing) or distributed (within two or more such housings that are physically separate).

[0024] In some implementations, the memory device 114 stores a user profile related to a user. The user profile may include, for example, demographic information related to the user, biometric information related to the user, medical information related to the user, user feedback by self-report, sleep parameters related to the user (e.g., sleep-related parameters recorded from one or more previous sleep sessions), or any combination thereof. Demographic information may include, for example, information indicating the user's age, social role, ethnicity, family history (e.g., family history of insomnia or sleep apnea), employment status, education level, socioeconomic status, or any combination thereof. Medical information may include, for example, information indicating one or more medical conditions related to the user, medication usage by the user, or both. Medical information data may further include a fall risk assessment related to the user (e.g., a fall risk score according to the Morse Fall Scale), results or scores of a Multiple Sleep Latency Test (MSLT), and / or scores or values of the Pittsburgh Sleep Quality Index (PSQI). User feedback by self-report may include information indicating a subjective sleep score by self-report (poor, average, good, etc.), a subjective stress level by self-report of the user, a subjective fatigue level by self-report of the user, a subjective health status by self-report of the user, recent life events experienced by the user, or any combination thereof.

[0025] The electronic interface 119 is configured to receive data (e.g., physiological data and / or acoustic data) from one or more sensors 130 so that the data can be stored in the memory device 114 and / or analyzed by the processor 112 of the control system 110. The electronic interface 119 can communicate with one or more sensors 130 using a wired connection or a wireless connection (e.g., RF communication protocol, WiFi communication protocol, Bluetooth® communication protocol, IR communication protocol, cellular network, other optical communication protocols, etc.). The electronic interface 119 may include an antenna, a receiver (e.g., an RF receiver), a transmitter (e.g., an RF transmitter), a transceiver, or any combination thereof. The electronic interface 119 may also include another processor and / or another memory device that is the same as or similar to the processor 112 and the memory device 114 described herein. In some implementations, the electronic interface 119 is coupled or integrated with the user device 170. In other implementations, the electronic interface 119 is coupled or (e.g., within a housing) integrated with the control system 110 and / or the memory device 114.

[0026] As described above, in some implementations, system 100 includes, as needed, a respiratory therapy system 120 (also referred to as a respiratory pressure therapy system). The respiratory therapy system 120 may include a respiratory therapy device 122 (also referred to as a respiratory pressure device), a user interface 124 (also referred to as a mask, patient interface), a conduit 126 (also referred to as a tube or air circuit), a display device 128, a humidification tank 129, or a combination thereof. In some implementations, the control system 110, the memory device 114, the display device 128, one or more sensors 130, and the humidification tank 129 are part of the respiratory therapy device 122. Respiratory pressure therapy refers to an application of supplying air to the inlet of a user's airway at a controlled target pressure that is nominally positive with respect to the atmosphere throughout the user's respiratory cycle (in contrast to, for example, negative pressure therapies such as an iron lung or chest cupping). The respiratory therapy system 120 is generally used to treat individuals suffering from one or more sleep-related breathing disorders (e.g., obstructive sleep apnea, central sleep apnea, mixed sleep apnea), other respiratory disorders such as COPD, or other disorders leading to respiratory failure, which can occur during both sleep and wakefulness.

[0027] The respiratory therapy device 122 is generally used to generate pressurized air that is delivered to the user (e.g., using one or more motors (e.g., blower motors) that drive one or more compressors). In some implementations, the respiratory therapy device 122 generates a continuous, constant air pressure that is delivered to the user. In other implementations, the respiratory therapy device 122 generates two or more predetermined pressures (e.g., a first predetermined air pressure and a second predetermined air pressure). In yet another implementation, the respiratory therapy device 122 is configured to generate a variety of different air pressures within a predetermined range. For example, the respiratory therapy device 122 is at least about 6 cmH 2 O, at least about 10 cmH 2 O, at least about 20 cmH 2 O, from about 6 cmH 2 O to about 10 cmH 2 O, between about 7 cmH 2 O and about 12 cmH2 It can be transported, for example, between O's. The respiratory therapy device 122 can also transport pressurized air at a predetermined flow rate between, for example, about -20 L / minute and about 150 L / minute while maintaining a positive pressure (relative to the ambient pressure). In some implementations, the control system 110, the memory device 114, the electronic interface 119, or any combination thereof may be coupled to the housing of the respiratory therapy device 122 and / or disposed within the housing of the respiratory therapy device 122.

[0028] The user interface 124 engages a portion of the user's face and transports pressurized air from the respiratory therapy device 122 to the user's airway, helping to prevent the airway from narrowing and / or closing during sleep. Thereby, the oxygen uptake of the user during sleep can also be increased. Depending on the therapy applied, the user interface 124 forms a seal with, for example, a region or a portion of the user's face to create a pressure that is sufficiently different from the ambient pressure, for example, about 10 cmH 2 O positive pressure can facilitate the transport of gas. In other forms of therapy, such as oxygen transport, the user interface may not include a seal sufficient to facilitate the transport of the supply gas to the airway at a positive pressure of about 10 cmH 2 O.

[0029] In some implementations, user interface 124 is or includes a face mask that covers the user's nose and mouth (see, e.g., FIG. 2). Alternatively, user interface 124 is or includes a nasal mask that provides air to the user's nose or a nasal pillow mask that directly transports air to the user's nostrils. User interface 124 includes a strap assembly that includes a plurality of straps (e.g., including hook-and-loop fasteners) for positioning and / or stabilizing user interface 124 or a portion of user interface 124 at a desired location on the user (e.g., the face), and an isosceles cushion (e.g., silicone, plastic, foam, etc.) that helps provide an airtight seal between user interface 124 and the user. In some implementations, user interface 124 may include a connector 127 and one or more vents 125. The one or more vents 125 can be used to allow carbon dioxide and other gases exhaled by the user to escape. In other implementations, user interface 124 includes a mouthpiece (e.g., a night guard mouthpiece shaped to fit the user's teeth, a mandibular repositioning device, etc.). In some implementations, connector 127 is separate from but connectable to user interface 124 (and / or conduit 126). Connector 127 is configured to connect user interface 124 to conduit 126 to fluidly couple them.

[0030] Via conduit 126, air can flow between two components of respiratory therapy system 120, such as respiratory therapy device 122 and user interface 124. In some implementations, the conduit may have separate branches for inhalation and exhalation. In other implementations, a single-branch conduit is used for both inhalation and exhalation. Generally, respiratory therapy system 120 forms an air path that extends between the motor of respiratory therapy device 122 and the user and / or the user's airway. Thus, the air path generally includes at least the motor of respiratory therapy device 122, user interface 124, and conduit 126.

[0031] One or more of the respiratory therapy device 122, user interface 124, conduit 126, display device 128, and humidification tank 129 may include one or more sensors (e.g., a pressure sensor, a flow sensor, or any of the other sensors 130 described more generally herein). These one or more sensors can be used to measure, for example, the air pressure and / or flow rate of the pressurized air supplied by the respiratory therapy device 122.

[0032] The display device 128 is generally used to display images and / or information including still images, moving images, or both, regarding the respiratory therapy device 122. For example, the display device 128 can provide information regarding the state of the respiratory therapy device 122 (e.g., whether the respiratory therapy device 122 is on / off, the pressure of the air being transported by the respiratory therapy device 122, the temperature of the air being transported by the respiratory therapy device 122, etc.) and / or other information (e.g., a sleep score or a therapy score (such as a myAir® score as described in International Publication No. WO 2016 / 061629 and U.S. Patent Application Publication No. 2017 / 0311879, each of which is incorporated herein by reference in its entirety), the current date / time, the user's personal information, a questionnaire to the user, etc.). In some implementations, the display device 128 functions as a human-machine interface (HMI) that includes a graphical user interface (GUI) configured to display images as an input interface. The display device 128 may be an LED display, an OLED display, an LCD display, etc. The input interface may be, for example, a touch screen or a touch sensor-based substrate, a mouse, a keyboard, or any sensor system configured to sense an input made by a human user interacting with the respiratory therapy device 122.

[0033] The humidification tank 129 is connected or integrated with the respiratory therapy device 122 and includes a reservoir of water that can humidify the pressurized air transported from the respiratory therapy device 122. The respiratory therapy device 122 may include a heater that heats the water in the humidification tank 129 to humidify the pressurized air provided to the user. Additionally, in some implementations, the conduit 126 may also include a heating element (e.g., connected and / or embedded in the conduit 126) that heats the pressurized air transported to the user. The humidification tank 129 is fluidly connected to the water vapor inlet of the air path and can transport water vapor into the air path through the water vapor inlet or can be formed in line with the air path as part of the air path itself. In other implementations, the respiratory therapy device 122 or the conduit 126 may include a dry air humidifier. The dry air humidifier can incorporate sensors that interface with other sensors located elsewhere in the system 100.

[0034] The respiratory therapy system 120 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 auto positive airway pressure (APAP) system, a bilevel or variable positive airway pressure (BPAP or VPAP) system, or any combination thereof. The CPAP system transports a predetermined air pressure (e.g., determined by a sleep physician) to the user. The APAP system automatically varies the air pressure transported to the user, for example, at least partially based on respiratory data associated with the user. The BPAP or VPAP system is configured to transport a first predetermined pressure (e.g., inspiratory positive airway pressure or IPAP) and a second predetermined pressure lower than the first predetermined pressure (e.g., expiratory positive airway pressure or EPAP).

[0035] Referring to FIG. 2, a portion of the system 100 (FIG. 1) according to some implementations is shown. The user 210 and the co-sleeper 220 of the respiratory therapy system 120 are positioned on the bed 230 and lying on the mattress 232. The user interface 124 (e.g., a full-face mask) can be worn by the user 210 during a sleep session. The user interface 124 is fluidly coupled and / or connected to the respiratory therapy device 122 via the conduit 126. Next, the respiratory therapy device 122 transports pressurized air to the user 210 via the conduit 126 and the user interface 124, and helps to raise the air pressure in the user 210's throat to prevent the airway from closing and / or narrowing during sleep. The respiratory therapy device 122 may include a display device 128 that enables the user to interact with the respiratory therapy device 122. The respiratory therapy device 122 may further include a humidification tank 129 that stores water for humidifying the pressurized air. The respiratory therapy device 122 may be disposed on the nightstand 240 that is directly adjacent to the bed 230 as shown in FIG. 2, or more generally, on any surface or structure that is substantially adjacent to the bed 230 and / or the user 210. The user can also wear the blood pressure device 180 and the activity tracker 190 while lying on the mattress 232 of the bed 230.

[0036] Referring back to FIG. 1, one or more sensors 130 of the system 100 include a pressure sensor 132, a flow sensor 134, a temperature sensor 136, a motion sensor 138, a microphone 140, a speaker 142, a radio frequency (RF) receiver 146, an RF transmitter 148, a camera 150, an infrared (IR) sensor 152, a photoplethysmogram (PPG) sensor 154, an electrocardiogram (ECG) sensor 156, an electroencephalogram (EEG) sensor 158, a capacitance sensor 160, a force sensor 162, a strain gauge sensor 164, an electromyogram (EMG) sensor 166, an oxygen sensor 168, an analyte sensor 174, a moisture sensor 176, a lidar (LiDAR) sensor 178, or any combination thereof. Generally, each of the one or more sensors 130 is configured to output sensor data received and stored by the memory device 114 or one or more other memory devices. The sensor 130 may further include an electrooculogram recording (EOG) sensor, a peripheral blood oxygen saturation (SpO 2 ) sensor, a galvanic skin response (GSR) sensor, a carbon dioxide (CO 2 ) sensor, or any combination thereof.

[0037] One or more sensors 130 are shown and described as including each of a pressure sensor 132, a flow sensor 134, a temperature sensor 136, a motion sensor 138, a microphone 140, a speaker 142, an RF receiver 146, an RF transmitter 148, a camera 150, an IR sensor 152, a PPG sensor 154, an ECG sensor 156, an EEG sensor 158, a capacitance sensor 160, a force sensor 162, a strain gauge sensor 164, an EMG sensor 166, an oxygen sensor 168, an analyte sensor 174, a moisture sensor 176, and a LiDAR sensor 178, but more generally, one or more sensors 130 may include any combination and any number of each of the sensors described and / or shown herein.

[0038] One or more sensors 130 can be used to generate physiological data, acoustic data, or both, related to, for example, a user of the respiratory therapy system 120 (e.g., user 210 of FIG. 2), the respiratory therapy system 120, both the user and the respiratory therapy system 120, or other entities, objects, activities, etc. The physiological data generated by the one or more sensors 130 can be used by the control system 110 to determine sleep-wake signals related to the user during a sleep session and one or more sleep-related parameters. The sleep-wake signals can indicate one or more sleep stages (which may be referred to as sleep states), including distinct sleep stages such as sleep, wakefulness, relaxed wakefulness, micro-awakenings, or rapid eye movement (REM) stages (which may include both typical and atypical REM stages), the first non-REM stage (often referred to as "N1"), the second non-REM stage (often referred to as "N2"), the third non-REM stage (often referred to as "N3"), or any combination thereof. Methods for determining sleep stages based on physiological data generated by one or more sensors such as sensor 130 are described, for example, in International Publication No. WO 2014 / 047310, U.S. Patent No. 10,492,720, U.S. Patent No. 10,660,563, U.S. Patent Application Publication No. 2020 / 0337634, International Publication No. WO 2017 / 132726, International Publication No. WO 2019 / 122413, U.S. Patent Application Publication No. 2021 / 0150873, International Publication No. WO 2019 / 122414, U.S. Patent Application Publication No. 2020 / 0383580, each of which is incorporated herein by reference in its entirety.

[0039] Also, the sleep-wake signal can be timestamped to indicate the time when the user goes to bed, the time when the user gets out of bed, the time when the user tries to fall asleep, and the like. For example, the sleep-wake signal can be measured by one or more sensors 130 during a sleep session at a predetermined sampling rate such as 1 sample / second, 1 sample / 30 seconds, 1 sample / minute, etc. Examples of one or more sleep-related parameters that can be determined for the user during a sleep session based at least in part on the sleep-wake signal include total bedtime, total sleep time, total wake time, sleep latency, middle wake parameter, sleep efficiency, fragmentation index, amount of sleep time, consistency of respiratory rate, sleep time, wake time, percentage of sleep disorders, number of movements, or any combination thereof.

[0040] Also, using the physiological data and / or acoustic data generated by one or more sensors 130, a respiratory signal related to the user during a sleep session can be determined. The respiratory signal generally indicates the respiration of the user during the sleep session. The respiratory signal can indicate, for example, respiratory rate, respiratory rate variation, inspiration amplitude, expiration amplitude, inspiration / expiration amplitude ratio, inspiration / expiration duration ratio, number of events per hour, pattern of events, pressure setting of the respiratory therapy device 122, or any combination thereof. Events may include snoring, apnea, central apnea, obstructive apnea, mixed apnea, hypopnea, RERA, flow limitation (e.g., an event resulting in no increase in flow despite an increase in negative intrathoracic pressure indicating increased effort), mask leak (e.g., from the user interface 124), restless legs, sleep disorder, asphyxia, increased heart rate, heart rate variation, dyspnea, asthma attack, epileptic seizure, seizure, fever, cough, sneeze, snoring, wheezing, presence of an illness such as a cold or influenza, increased stress level, etc. Events can be detected by any means known in the art, such as those described in U.S. Patent No. 5,245,995, U.S. Patent No. 6,502,572, International Publication No. 2018 / 050913, and International Publication No. 2020 / 104465, each of which is hereby incorporated by reference in its entirety.

[0041] The pressure sensor 132 outputs pressure data that can be stored in the memory device 114 and / or analyzed by the processor 112 of the control system 110. In some implementations, the pressure sensor 132 is a pneumatic sensor (e.g., a barometric pressure sensor) that generates sensor data indicative of the respiration (e.g., inhalation and / or exhalation) of a user of the respiratory therapy system 120 and / or the ambient pressure. In such implementations, the pressure sensor 132 can be coupled or integrated with the respiratory therapy device 122. The pressure sensor 132 can be, for example, a volume sensor, an electromagnetic sensor, an inductive sensor, a resistive sensor, a piezoelectric sensor, a strain gauge sensor, an optical sensor, a potentiometric sensor, or any combination thereof. In one example, the pressure sensor 132 can be used to determine a user's blood pressure.

[0042] The flow sensor 134 outputs flow data that can be stored in the memory device 114 and / or analyzed by the processor 112 of the control system 110. In some implementations, the flow sensor 134 is used to determine the air flow from the respiratory therapy device 122, the air flow through the conduit 126, the air flow through the user interface 124, or any combination thereof. In such implementations, the flow sensor 134 can be coupled or integrated with the respiratory therapy device 122, the user interface 124, or the conduit 126. The flow sensor 134 can be, for example, a mass flow sensor such as a rotary flow meter (e.g., a Hall effect flow meter), 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.

[0043] The temperature sensor 136 outputs temperature data that can be stored in the memory device 114 and / or analyzed by the processor 112 of the control system 110. In some implementations, the temperature sensor 136 generates temperature data indicative of the core body temperature of the user, the skin temperature of the user 210, the temperature of the air flowing from and / or through the conduit 126 from the respiratory therapy device 122, the temperature within the user interface 124, the ambient temperature, or any combination thereof. The temperature sensor 136 may be, for example, a thermocouple sensor, a thermistor sensor, a silicon bandgap temperature sensor or a semiconductor-based sensor, a resistance temperature detector, or any combination thereof.

[0044] The motion sensor 138 outputs motion data that can be stored in the memory device 114 and / or analyzed by the processor 112 of the control system 110. The motion sensor 138 can be used to detect the movement of the user during a sleep session and / or the movement of any of the components of the respiratory therapy system 120, such as the respiratory therapy device 122, the user interface 124, or the conduit 126. The motion sensor 138 may include one or more inertial sensors, such as, for example, an accelerometer, a gyroscope, and a magnetometer. The motion sensor 138 can be used to detect movement or acceleration associated with arterial pulsations, such as pulsations in or around the user's face and proximal pulsations of the user interface 124, and is configured to detect features such as the shape, velocity, amplitude, or volume of the pulsations. In some implementations, the motion sensor 138 alternatively or additionally generates one or more signals representative of the body movement of the user, and from these signals, a signal representative of the sleep state of the user can be obtained, for example, via the respiratory movement of the user.

[0045] Microphone 140 outputs acoustic data that can be stored in the memory device 114 and / or analyzed by the processor 112 of the control system 110. As described in more detail herein, the acoustic data generated by the microphone 140 can be reproduced as one or more sounds (e.g., sounds from the user) during a sleep session to determine one or more sleep-related parameters (e.g., using the control system 110). As described in more detail herein, also, the acoustic data from the microphone 140 can be used to identify events experienced by the user during a sleep session (e.g., using the control system 110). In other implementations, the acoustic data from the microphone 140 represents noise associated with the respiratory therapy system 120. In some implementations, by including a plurality of microphones (e.g., an array of two or more microphones and / or microphones using beamforming) in the system 100, the audio data generated by each of the plurality of microphones can be used to identify the audio data generated by other microphones among the plurality of microphones. The microphone 140 may be connected or integrated with the respiratory therapy system 120 (or the system 100) generally in any configuration. For example, the microphone 140 may be disposed inside the respiratory therapy device 122, the user interface 124, the conduit 126, or other components. The microphone 140 may also be disposed adjacent to or connected to the outside of the respiratory therapy device 122, the outside of the user interface 124, the outside of the conduit 126, or the outside of any other component. The microphone 140 may also be a component of the user device 170 (e.g., the microphone 140 is a microphone of a smartphone). The microphone 140 may be integrated with the user interface 124, the conduit 126, the respiratory therapy device 122, or any combination thereof. Generally, the microphone 140 may be positioned at any point within or adjacent to the air path of the respiratory therapy system 120, including at least the motor of the respiratory therapy device 122, the user interface 124, and the conduit 126. Thus, the air path is also referred to as the acoustic path.

[0046] Speaker 142 outputs sound waves audible to the user. In one or more implementations, the sound waves may or may not be audible to the user of system 100 (e.g., ultrasonic). Speaker 142 can be used, for example, as an alarm clock or to play a warning or message to the user (e.g., in response to an event). In some implementations, speaker 142 can be used to communicate acoustic data generated by microphone 140 to the user. Speaker 142 can be coupled or integrated with respiratory therapy device 122, user interface 124, conduit 126, or user device 170.

[0047] The microphone 140 and the speaker 142 can be used as independent devices. In some implementations, the microphone 140 and the speaker 142 can be combined with an acoustic sensor 141 (e.g., a SONAR sensor), as described in, for example, International Publication No. WO 2018 / 050913 and International Publication No. WO 2020 / 104465, which are hereby incorporated by reference in their entirety. In such an implementation, the speaker 142 generates or emits sound waves at a predetermined interval and / or a predetermined frequency, and the microphone 140 detects the reflection of the sound waves emitted from the speaker 142. The sound waves generated or emitted by the speaker 142 have a frequency inaudible to the human ear (e.g., less than 20 Hz or greater than about 18 kHz) so as not to disturb the sleep of the user or the user's bed partner (e.g., the bed partner 220 in FIG. 2). The control system 110 can determine one or more sleep-related parameters described herein, such as the position of the user, and / or a respiration signal, respiratory rate, inspiratory amplitude, expiratory amplitude, inspiratory-to-expiratory ratio, number of events per hour, pattern of events, sleep stage, pressure setting of the respiratory therapy device 122, mouth leak condition, or any combination thereof, based at least in part on data from the microphone 140 and / or the speaker 142. In this context, the SONAR sensor may be understood to be involved in active acoustic sensing, such as by generating / transmitting an ultrasonic or low-frequency ultrasonic sensing signal (e.g., within a frequency range of about 17 - 23 kHz, 18 - 22 kHz, or 17 - 18 kHz) through the air. Such a system can be considered in relation to International Publication No. WO 2018 / 050913 and International Publication No. WO 2020 / 104465 described above. In some implementations, the speaker 142 is a bone conduction speaker. In some implementations, the one or more sensors 130 include (i) a first microphone that is the same as or similar to the microphone 140 and is integrated with the acoustic sensor 141, and (ii) a second microphone that is the same as or similar to the microphone 140 but is independent and separate from the first microphone integrated with the acoustic sensor 141.

[0048] The RF transmitter 148 generates and / or radiates 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 signal, short-wave signal, etc.). The RF receiver 146 detects the reflection of the radio waves radiated from the RF transmitter 148, and by analyzing this data by the control system 110, it is possible to determine the user's position and / or one or more sleep-related parameters described herein. Also, for wireless communication between the control system 110, the respiratory therapy devices 122, one or more sensors 130, the user device 170, or any combination thereof, an RF receiver (either the RF receiver 146 and the RF transmitter 148, or another RF pair) can be used. The RF receiver 146 and the RF transmitter 148 are shown as separate and independent elements in FIG. 1, but in some implementations, the RF receiver 146 and the RF transmitter 148 are combined as part of an RF sensor 147 (e.g., a RADAR sensor). In some such implementations, the RF sensor 147 includes a control circuit. The specific form of RF communication may be WiFi, Bluetooth®, etc.

[0049] In some implementations, the RF sensor 147 is part of a mesh system. An example of a mesh system may include a WiFi mesh system that may include mesh nodes, mesh routers, and mesh gateways, each of which may be mobile / movable or fixed. In such an implementation, the WiFi mesh system includes a WiFi router and / or a WiFi controller, and one or more satellites (e.g., access points), each of which includes an RF sensor that is the same as or similar to the RF sensor 147. The WiFi router and the satellites continuously communicate with each other using WiFi signals. Using the WiFi mesh system, motion data can be generated based at least in part on changes in the WiFi signal (e.g., differences in received signal strength) between the router and the satellites due to the movement of an object or person partially obstructing the signal. This motion data may indicate motion, respiration, heart rate, walking, falling, behavior, etc., or any combination thereof.

[0050] The camera 150 outputs image data that can be reproduced as one or more images (e.g., still images, moving images, thermal images, or combinations thereof) that can be stored in the memory device 114. The image data from the camera 150 can be used by the control system 110 to determine one or more of the sleep-related parameters described herein. For example, the image data from the camera 150 can be used to identify the user's position, determine the time when the user goes to bed on the user's bed (such as the bed 230 in FIG. 2), and determine the time when the user gets out of the bed 230. The camera 150 can also be used to track eye movement, pupil dilation (when one or both of the user's eyes are open), blink rate, or any changes during REM sleep. The camera 150 can also be used to track the position of the user that may affect the duration and / or severity of the onset of apnea symptoms in a user suffering from positional obstructive sleep apnea.

[0051] IR sensor 152 outputs infrared image data that can be reproduced as one or more infrared images (e.g., still images, moving images, or both) that can be stored in the memory device 114. The infrared data from the IR sensor 152 can be used to determine one or more sleep-related parameters during a sleep session, including the user's temperature and / or the user's movement. The IR sensor 152 can also be used in combination with the camera 150 when measuring the presence, position, and / or movement of the user. The IR sensor 152 can detect infrared light having a wavelength between, for example, about 700 nm and about 1 mm, while the camera 150 can detect visible light having a wavelength between about 380 nm and about 740 nm.

[0052] The IR sensor 152 outputs infrared image data that can be reproduced as one or more infrared images (e.g., still images, moving images, or both) that can be stored in the memory device 114. Using the infrared data from the IR sensor 152, one or more sleep-related parameters during a sleep session can be determined, including the user's temperature and / or the user's movement. Also, the IR sensor 152 can be used in combination with the camera 150 when measuring the user's presence, position, and / or movement. The IR sensor 152 can detect infrared light having a wavelength between, for example, about 700 nm and about 1 mm, while the camera 150 can detect visible light having a wavelength between about 380 nm and about 740 nm.

[0053] The PPG sensor 154 outputs physiological data related to the user that can be used to determine one or more sleep-related parameters. Examples of those sleep-related parameters include heart rate, heart rate pattern, heart rate variability, cardiac cycle, respiratory rate, inspiratory amplitude, expiratory amplitude, inspiratory / expiratory ratio, estimated blood pressure parameters, or any combination thereof. The PPG sensor 154 can be worn by the user, embedded in the clothing and / or fabric worn by the user, embedded in and / or connected to the user interface 124 and / or its associated headgear (e.g., straps, etc.).

[0054] The ECG sensor 156 outputs physiological data related to the electrical activity of the user's heart. In some implementations, the ECG sensor 156 includes one or more electrodes disposed on a part of or around the user during a sleep session. The physiological data from the ECG sensor 156 can be used, for example, to determine one or more of the sleep-related parameters described herein.

[0055] The EEG sensor 158 outputs physiological data related to the electrical activity of the user's brain. In some implementations, the EEG sensor 158 includes one or more electrodes disposed on or around the user's scalp during a sleep session. Using the physiological data from the EEG sensor 158, for example, the sleep stage of the user at any given time during a sleep session can be determined. In some implementations, the EEG sensor 158 can be integrated into the user interface 124 and / or its associated headgear (e.g., straps, etc.).

[0056] The capacitance sensor 160, the force sensor 162, and the strain gauge sensor 164 output data that can be stored in the memory device 114 and used by the control system 110 to determine one or more of the sleep-related parameters described herein. The EMG sensor 166 outputs physiological data related to the electrical activity generated by one or more muscles. The oxygen sensor 168 outputs oxygen data indicating the oxygen concentration of a gas (e.g., within the conduit 126 or in the user interface 124). The oxygen sensor 168 can be, for example, an ultrasonic oxygen sensor, an electro-chemical oxygen sensor, a chemical oxygen sensor, an optical oxygen sensor, or any combination thereof. In some implementations, the one or more sensors 130 also include a galvanic skin response (GSR) sensor, a blood flow sensor, a respiration sensor, a pulse sensor, a blood pressure sensor, a oximetry sensor, or any combination thereof.

[0057] The analyte sensor 174 can be used to detect the presence of an analyte in the user's breath. The data output by the analyte sensor 174 can be stored in the memory device 114 and used by the control system 110 to determine the identity and concentration of any analyte in the user's breath. In some implementations, the analyte sensor 174 is positioned near the user's mouth to detect analytes in the breath exhaled from the user's mouth. For example, if the user interface 124 is a face mask that covers the user's nose and mouth, the analyte sensor 174 can be placed inside the face mask to monitor the user's mouth breathing. In other implementations, when the user interface 124 is a nasal mask or nasal pillow mask, the analyte sensor 174 can be positioned near the user's nose to detect analytes in the breath exhaled from the user's nose. In still other implementations, when the user interface 124 is a nasal mask or nasal pillow mask, the analyte sensor 174 can be positioned near the user's mouth. In this implementation, the analyte sensor 174 can be used to detect whether air is inadvertently leaking from the user's mouth. In some implementations, the analyte sensor 174 is a volatile organic compound (VOC) sensor that can be used to detect carbon-based chemical substances or carbon-based compounds such as carbon dioxide. In some implementations, the analyte sensor 174 can also be used to detect whether the user is breathing through their nose or mouth. For example, if the presence of an analyte is detected based on data output by the analyte sensor 174 positioned near the user's mouth or (in implementations where the user interface 124 is a face mask) inside the face mask, the control system 110 can use this data as an indication that the user is breathing through their mouth.

[0058] The moisture sensor 176 outputs data that is stored in the memory device 114 and can be used by the control system 110. The moisture sensor 176 can be used to detect moisture in various areas surrounding the user (e.g., inside the conduit 126 or the user interface 124, near the user's face, near the connection of the conduit 126 and the user interface 124, near the connection of the conduit 126 and the respiratory therapy device 122, etc.). Thus, in some implementations, the moisture sensor 176 can be coupled or integrated within the user interface 124 or the conduit 126 to monitor the humidity of the pressurized air from the respiratory therapy device 122. In other implementations, the moisture sensor 176 is placed near any area where it is necessary to monitor the moisture level. The moisture sensor 176 can also be used to monitor the ambient environment surrounding the user, such as the humidity of the air in the user's bedroom. The moisture sensor 176 can also be used to track the user's biological reaction to environmental changes.

[0059] To sense depth, one or more LiDAR sensors 178 can be used. Such optical sensors (e.g., laser sensors) can be used to detect objects and create a three-dimensional (3D) map of the surrounding environment such as a living space. LiDAR generally utilizes a pulsed laser to measure the time of flight. LiDAR is also referred to as 3D laser scanning. In one example of use of such sensors, a stationary device having a LiDAR sensor 178 or a mobile device (such as a smartphone) can measure and map areas more than 5 meters away from the sensor. LiDAR data can be fused with, for example, point cloud data estimated by an electromagnetic RADAR sensor. The LiDAR sensor 178 can also automatically create a geofence for the RADAR system by using artificial intelligence (AI) to detect and classify features in a space that may cause problems for the RADAR system, such as a glass window (which may be highly reflective to RADAR). Also, LiDAR can be used to estimate, for example, a person's height as well as changes in height when a person is sitting or has fallen. LiDAR can be used to form a 3D mesh representation of the environment. In a further application, for solid surfaces through which radio waves pass (e.g., radio-transparent materials), LiDAR enables the classification of different types of obstacles because it reflects off such surfaces.

[0060] Although shown independently in FIG. 1, any combination of one or more sensors 130 can be integrated and / or coupled to any one or more components of a system 100 including a respiratory therapy device 122, a user interface 124, a conduit 126, a humidification tank 129, a control system 110, a user device 170, or any combination thereof. For example, an acoustic sensor 141 and / or an RF sensor 147 can be integrated and / or coupled to the user device 170. In such an implementation, the user device 170 can be considered a secondary device that generates additional or secondary data used by a system 100 (e.g., a control system 110) according to some aspects of the present disclosure. In some implementations, a pressure sensor 132 and / or a flow sensor 134 are integrated and / or coupled to the respiratory therapy device 122. In some implementations, at least one of the one or more sensors 130 is not coupled to the respiratory therapy device 122, the control system 110, or the user device 170, and is placed substantially adjacent to the user during a sleep session (e.g., placed on or in contact with a part of the user, worn by the user, coupled to or placed on a nightstand, coupled to a mattress, coupled to a ceiling, etc.). More generally, the one or more sensors 130 can be placed at any suitable location relative to the user such that they can generate physiological data related to the user and / or a co-sleeper 220 during one or more sleep sessions.

[0061] Data from one or more sensors 130 can be analyzed to determine one or more sleep-related parameters that may include a respiration signal, respiratory rate, respiratory pattern, inspiration amplitude, expiration amplitude, inspiration / expiration ratio, occurrence of one or more events, number of events per hour, event pattern, average event duration, range of event durations, ratio of different numbers of events, sleep stage, apnea-hypopnea index (AHI), or any combination thereof. The one or more events may include snoring, apnea, central apnea, obstructive apnea, mixed apnea, hypopnea, intentional user interface leak, unintentional user interface leak, mouth leak, cough, lower limb restlessness, sleep disorder, choking, increased heart rate, dyspnea, asthma attack, epileptic seizure, seizure, increased blood pressure, hyperventilation, or any combination thereof. Many of these sleep-related parameters are physiological parameters, although some of the sleep-related parameters are considered non-physiological parameters. Other types of physiological and non-physiological parameters can also be determined based on either data from one or more sensors 130 or other types of data.

[0062] The user device 170 includes a display device 172. The user device 170 may be a mobile device such as, for example, a smartphone, a tablet, a laptop, a game console, a smartwatch, etc. Alternatively, the user device 170 may be an external sensing system, a television (e.g., a smart TV), or another smart home device (e.g., a smart speaker such as Google Home™, Google Nest™, Amazon Echo™, Amazon Echo Show™, an Alexa™ enabled device, etc.). In some implementations, the user device 170 is a wearable device (e.g., a smartwatch). The display device 172 is generally used to display an image including still images, moving images, or both. In some implementations, the display device 172 functions as a human-machine interface (HMI) including a graphical user interface (GUI) configured to display an image and an input interface. The display device 172 may be an LED display, an OLED display, an LCD display, etc. The input interface may be, for example, a touch screen or a touch-sensing substrate, a mouse, a keyboard, or any sensor system configured to detect an input made by a human user interacting with the user device 170. In some implementations, one or more user devices 170 may be used by and / or included in the system 100.

[0063] The blood pressure device 180 is generally used to help generate physiological data for determining one or more blood pressure measurements related to a user. The blood pressure device 180 may include, for example, at least one of the one or more sensors 130 to measure a systolic blood pressure component and / or a diastolic blood pressure component.

[0064] In some implementations, the blood pressure device 180 is a blood pressure meter that includes an inflatable cuff that can be worn by a user and a pressure sensor (e.g., the pressure sensor 132 described herein). For example, as shown in the example of FIG. 2, the blood pressure device 180 can be worn on a user's upper arm. In such an implementation where the blood pressure device 180 is a blood pressure meter, the blood pressure device 180 also includes a pump (e.g., a manually operated valve) to inflate the cuff. In some implementations, the blood pressure device 180 is connected to the respiratory therapy device 122 of the respiratory therapy system 120, and the respiratory therapy device 122 then transports pressurized air to inflate the cuff. More generally, the blood pressure device 180 can be communicatively coupled to and / or physically integrated with (e.g., within a housing) the control system 110, the memory device 114, the respiratory therapy system 120, the user device 170, and / or the activity tracker 190.

[0065] The activity tracker 190 is generally used to help generate physiological data for determining activity measurements related to a user. Activity measurements can include, for example, the number of steps, distance traveled, number of steps climbed, duration of physical activity, type of physical activity, intensity of physical activity, time spent standing, respiratory rate, average respiratory rate, resting respiratory rate, maximum respiratory rate, respiratory rate variability, heart rate, average heart rate, resting heart rate, maximum heart rate, heart rate variability, calorie consumption, blood oxygen saturation, skin electrical activity (also known as skin conductance or galvanic skin response), or any combination thereof. The activity tracker 190 can include one or more sensors 130 described herein, such as, for example, a motion sensor 138 (e.g., one or more accelerometers and / or gyroscopes), a PPG sensor 154, and / or an ECG sensor 156.

[0066] In some implementations, the activity tracker 190 is a wearable device that can be worn by a user, such as a smartwatch, a wristband, a ring, or a patch. For example, referring to FIG. 2, the activity tracker 190 is worn on the user's wrist. Additionally, the activity tracker 190 can be coupled or integrated with clothing or apparel worn by the user. Alternatively, the activity tracker 190 can also be coupled or (e.g., within the same housing) integrated with the user device 170. More generally, the activity tracker 190 can be communicatively coupled to, or physically integrated (e.g., within a housing) with, the control system 110, the memory device 114, the respiratory therapy system 120, the user device 170, and / or the blood pressure device 180.

[0067] The control system 110 and the memory device 114 are described and shown in FIG. 1 as separate and distinct components of the system 100. However, in some implementations, the control system 110 and / or the memory device 114 are integrated with the user device 170 and / or the respiratory therapy device 122. Alternatively, in some implementations, the control system 110 or a portion thereof (e.g., the processor 112) is located in the cloud (e.g., integrated with a server, integrated with an Internet of Things (IoT) device, connected to the cloud, receiving edge cloud processing, etc.) and can be located on one or more servers (e.g., a remote server, a local server, etc., or any combination thereof).

[0068] System 100 is shown as including all of the above components, but according to implementations of the present disclosure, a system that a user uses to identify a user interface may include more or fewer components. For example, a first alternative system includes at least one of control system 110, memory devices 114, and one or more sensors 130. As another example, a second alternative system includes at least one of control system 110, memory devices 114, one or more sensors 130, and user device 170. As yet another example, a third alternative system includes control system 110, memory devices 114, respiratory therapy system 120, at least one of one or more sensors 130, and user device 170. As a further example, a fourth alternative system includes control system 110, memory devices 114, respiratory therapy system 120, at least one of one or more sensors 130, user device 170, blood pressure device 180, and / or activity tracker 190. Thus, various systems for changing pressure settings can be formed using any portion of the components shown and described herein and / or in combination with one or more other components.

[0069] Referring again to FIG. 2, in some implementations, control system 110, memory devices 114, any one or combination of one or more sensors 130 may be positioned on and / or within any surface and / or structure that is substantially adjacent to bed 230 and / or user 210. For example, in some implementations, at least one of one or more sensors 130 may be positioned at a first position on and / or within one or more components of respiratory therapy system 120 that are adjacent to bed 230 and / or user 210. One or more sensors 130 may be coupled to respiratory therapy system 120, user interface 124, conduit 126, display device 128, humidification tank 129, or a combination thereof.

[0070] Alternatively or additionally, at least one of the one or more sensors 130 may be positioned at a second position on and / or within the bed 230 (e.g., the one or more sensors 130 are coupled to and / or integrated with the bed 230). Further alternatively or additionally, at least one of the one or more sensors 130 may be positioned at a third position on and / or within the mattress 232 adjacent to the bed 230 and / or the user 210 (e.g., the one or more sensors 130 are coupled to and / or integrated with the mattress 232). Alternatively or additionally, at least one of the one or more sensors 130 may be positioned at a fourth position on and / or within a pillow substantially adjacent to the bed 230 and / or the user 210.

[0071] Alternatively or additionally, at least one of the one or more sensors 130 may be positioned at a fifth position on and / or within the nightstand 240 substantially adjacent to the bed 230 and / or the user 210. Alternatively or additionally, at least one of the one or more sensors 130 may be positioned at a sixth position such that it is coupled to and / or disposed on the user 210 (e.g., the one or more sensors 130 are embedded in or coupled to a fabric, clothing, and / or smart device worn by the user 210). More generally, at least one of the one or more sensors 130 can be positioned at any suitable position relative to the user 210 so as to be able to generate sensor data related to the user 210.

[0072] In some implementations, a primary sensor, such as microphone 140, is configured to generate acoustic data related to user 210 during a sleep session. For example, one or more microphones (identical or similar to microphone 140 of FIG. 1) may be integrated and / or coupled to (i) a circuit board of respiratory therapy device 122, (ii) conduit 126, (iii) a connector between components of respiratory therapy system 120, (iv) user interface 124, (v) a headgear (e.g., a strap) associated with the user interface, or (vi) a combination thereof.

[0073] In some implementations, in addition to the primary sensor, one or more secondary sensors can be used to generate additional data. In some such implementations, the one or more secondary sensors include a microphone (e.g., microphone 140 of system 100), a flow sensor (e.g., flow sensor 134 of system 100), a pressure sensor (e.g., pressure sensor 132 of system 100), a temperature sensor (e.g., temperature sensor 136 of system 100), a camera (e.g., camera 150 of system 100), a vane anemometer (VAF), a hot wire anemometer (MAF), a cold wire sensor, a laminar flow sensor, an ultrasonic sensor, an inertial sensor, or a combination thereof.

[0074] Alternatively or additionally, one or more microphones (identical or similar to the microphone 140 of FIG. 1) may be integrated with and / or coupled to collocated smart devices such as the user device 170, a TV, a watch (e.g., a mechanical watch worn by the user or another smart device), a pendant, a mattress 232, a bed 230, bedding disposed on the bed 230, a pillow, a speaker (e.g., the speaker 142 of FIG. 1), a radio, a tablet device, a dry air humidifier, or combinations thereof. A collocated smart device may be any smart device within range of detecting sound emitted by the user, the respiratory therapy system 120, and / or any part of the system 100. In some implementations, a collocated smart device is a smart device in the same room as the user during a sleep session.

[0075] Alternatively or additionally, in some implementations, one or more microphones (identical or similar to the microphone 140 of FIG. 1) may be remote from the system 100 (FIG. 1) and / or the user 210 (FIG. 2) if there is an air passage through which an acoustic signal can be transmitted thereto. For example, one or more microphones may be installed in a room different from the room in which the system 100 is housed.

[0076] As used herein, a sleep session can be defined in several ways, for example, based at least in part on an initial start time and an end time. In some implementations, a sleep session is the duration during which the user is asleep, i.e., the sleep session has a start time and an end time, and the user does not wake up until the end time during the sleep session. That is, the time during which the user is awake is not included in the sleep session. From this first definition of a sleep session, if the user wakes up and falls asleep multiple times in one night, each sleep period separated by those wake periods is a sleep session.

[0077] Alternatively, in some implementations, a sleep session has a start time and an end time, and during the sleep session, the user may wake up without the sleep session ending as long as the continuous duration for which the user is awake is below an awake duration threshold. The awake duration threshold can be defined as a percentage of the sleep session. The awake duration threshold can be, for example, about 20 percent of the sleep session, about 15 percent of the sleep session duration, about 10 percent of the sleep session duration, about 5 percent of the sleep session duration, about 2 percent of the sleep session duration, etc., or any other arbitrary threshold percentage. In some implementations, the awake duration threshold is defined as a fixed time such as about 1 hour, about 30 minutes, about 15 minutes, about 10 minutes, about 5 minutes, about 2 minutes, etc., or any other arbitrary amount of time.

[0078] In some implementations, a sleep session is defined as the total time from the time the user first goes to bed at night to the time the user last gets out of bed the next morning. In other words, a sleep session can be defined as starting at a first time (e.g., 10:00 PM) on a first date (e.g., Monday, January 6, 2020), referred to as the current night, when the user first goes to bed with the intention of sleeping (e.g., not when the user first watches TV or fiddles with a smartphone before going to bed), and ending at a second time (e.g., 7:00 AM) on a second date (e.g., Tuesday, January 7, 2020), referred to as the next morning, when the user first wakes up with the intention of not going back to sleep again.

[0079] In some implementations, the user can manually define the start of a sleep session and / or manually end the sleep session. For example, the user can select (e.g., by clicking or tapping) one or more user-selectable elements displayed on the display device 172 of the user device 170 (FIG. 1) to manually start or end the sleep session.

[0080] Referring to FIG. 3, an exemplary timeline 300 of a sleep session is shown. The timeline 300 includes a time of going to bed (t bed ), a time of falling asleep (t GTS ), an initial sleep time (t sleep ), a first micro-arousal MA 1 , a second micro-arousal MA 2 , an arousal A, a wake-up time (t wake ), and a wake-up time (t rise ).

[0081] The time of going to bed t bed is associated with the time when the user first goes to bed (e.g., the bed 230 in FIG. 2) before falling asleep (e.g., the user lies down or sits on the bed). The time of going to bed t bed can be identified at least partially based on a time-in-bed threshold duration to distinguish between the time when the user goes to bed to sleep and the time when the user goes to bed for other reasons (e.g., to watch TV). For example, the time-in-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. Although the time of going to bed t bed is described in relation to a bed herein, more generally, the time of going to bed t bed can represent the time when the user first takes a place (e.g., a sofa, a chair, a sleeping bag, etc.) to sleep.

[0082] The time of falling asleep (GTS) is associated with the time when the user first tries to fall asleep after going to bed (t bed ). For example, after going to bed, the user can engage in one or more activities (e.g., reading, watching TV, listening to music, using the user device 170, etc.) to relax before trying to fall asleep. The initial sleep time (t sleep ) is the time when the user first falls asleep. For example, the initial sleep time (t sleep ) can be the time when the user first enters the first non-REM sleep stage.

[0083] The wake-up time t wakeis a time associated with the time when the user wakes up without falling back asleep (as opposed to, for example, the user waking up in the middle of the night and then returning to sleep). After initially falling asleep, the user may experience one or more unconscious micro-awakenings (e.g., micro-awakenings MA 1 and MA 2 ) having a short duration (e.g., 5 seconds, 10 seconds, 30 seconds, 1 minute, etc.). The user does not wake up at wake-up time t wake but instead falls back asleep after each of the micro-awakenings MA 1 and MA 2 . Similarly, after initially falling asleep, the user may have one or more conscious awakenings (e.g., awakening A) (e.g., getting up to go to the toilet, taking care of a child or pet, sleepwalking, etc.). However, the user falls back asleep after awakening A. Therefore, the wake-up time t wake may be defined based at least in part on a wake-up threshold duration (e.g., the user has been awake for 15 minutes or more, 20 minutes or more, 30 minutes or more, 1 hour or more, etc.).

[0084] Similarly, the wake-up time t rise is associated with the time when the user leaves the bed and is absent from the bed for the purpose of ending the sleep session (as opposed to, for example, the user getting up to go to the toilet at night, taking care of a child or pet, sleepwalking, etc.). In other words, the wake-up time t rise is the time when the user last left the bed without returning to the bed until the next sleep session (e.g., the next night). Therefore, the wake-up time t rise may be defined based at least in part on a wake-up threshold duration (e.g., the user has been away from the bed for 15 minutes or more, 20 minutes or more, 30 minutes or more, 1 hour or more, etc.). The time of going to bed t bed for the second and subsequent sleep sessions may also be defined based at least in part on a wake-up threshold duration (e.g., the user has been away from the bed for 4 hours or more, 6 hours or more, 8 hours or more, 12 hours or more, etc.).

[0085] As described above, the user is the first tbed from the last t rise until the last t, there may be a possibility of waking up more than once at night and getting out of bed. In some implementations, the last waking time t wake and / or the last getting-up time t rise is specified or determined at least in part based on a predetermined threshold duration of time following an event (e.g., falling asleep or leaving the bed). Such a threshold duration may be customized for the user. For a standard user who goes to bed at night and wakes up in the morning and gets out of bed, any time between about 12 hours to about 18 hours (from when the user wakes up (t wake ) or gets up (t rise ) until the user goes to bed (t bed ), falls asleep (t GTS ) or sleeps (t sleep )) can be used. For users who spend a long time in bed, a shorter threshold time (e.g., about 8 hours to about 14 hours) may be used. The threshold period may be initially selected and / or later adjusted at least in part based on a system that monitors the user's sleep behavior.

[0086] Total in-bed time (TIB) is the duration from the time of going to bed t bed to the time of getting up t rise . Total sleep time (TST) is associated with the duration from the initial sleep time to the waking time, excluding the duration of conscious or unconscious awakenings and / or micro-awakenings during that time. Generally, total sleep time (TST) is shorter than total in-bed time (TIB) (e.g., 1 minute shorter, 10 minutes shorter, 1 hour shorter, etc.). For example, referring to the timeline 300 in FIG. 3, total sleep time (TST) spans between the initial sleep time t sleep and the waking time t wake , but the durations of the first micro-awakening MA 1 , the second micro-awakening MA 2 , and the awakening A are excluded. As shown, in this example, total sleep time (TST) is shorter than total in-bed time (TIB).

[0087] In some implementations, the total sleep time (TST) can be defined as the total persistent sleep time (PTST). In such implementations, the total persistent sleep time excludes a predetermined initial portion or initial period of the first non-REM stage (e.g., the light sleep stage). For example, this predetermined initial portion can be from about 30 seconds to about 20 minutes, from about 1 minute to about 10 minutes, from about 3 minutes to about 5 minutes, etc. The total persistent sleep time is a measurement of continuous sleep and smooths the sleep-wake sleep profile. For example, when the user first falls asleep, the user enters the first non-REM stage in a very short time (e.g., about 30 seconds), and then, after returning to the wake stage in a short time (e.g., 1 minute), may return to the first non-REM stage. In this example, the total persistent sleep time excludes the first instance of the first non-REM stage (e.g., about 30 seconds).

[0088] In some implementations, the sleep session starts at the time of going to bed (t bed ) and ends at the time of waking up (t rise ), that is, the sleep session is defined as the total in-bed time (TIB). In some implementations, the sleep session starts at the initial sleep time (t sleep ) and ends at the waking time (t wake ). In some implementations, the sleep session is defined as the total sleep time (TST). In some implementations, the sleep session starts at the time of falling asleep (t GTS ) and ends at the waking time (t wake ). In some implementations, the sleep session starts at the time of falling asleep (t GTS ) and ends at the time of waking up (t rise ). In some implementations, the sleep session starts at the time of going to bed (t bed ) and ends at the waking time (t wake ). In some implementations, the sleep session starts at the initial sleep time (t sleep ) and ends at the time of waking up (t rise ).

[0089] Referring to FIG. 4, an exemplary sleep progression diagram 400 corresponding to the timeline 300 (FIG. 3) according to some implementations is shown. As illustrated, the sleep progression diagram 400 includes a sleep-wake signal 401, a wake stage axis 410, a REM stage axis 420, a light sleep stage axis 430, and a deep sleep stage axis 440. The intersection of the sleep-wake signal 401 and one of the axes 410-440 indicates the sleep stage at any given time during the sleep session.

[0090] The sleep / wake signal 401 can be generated based at least in part on physiological data related to the user (e.g., generated by one or more of the sensors 130 described herein). The sleep-wake signal can indicate one or more sleep stages including a wake state, a relaxed wake state, a micro-awakening, 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 and classified as a light sleep stage or a deep sleep stage. For example, the light sleep stage may include the first non-REM stage, and the deep sleep stage may include the second non-REM stage and the third non-REM stage. Although the sleep progression diagram 400 includes a light sleep stage axis 430 and a deep sleep stage axis 440 in FIG. 4, in some implementations, the sleep progression diagram 400 can include axes for each of the first non-REM stage, the second non-REM stage, and the third non-REM stage. In other implementations, the sleep-wake signal can indicate a respiratory signal, a respiratory rate, an inhalation amplitude, an exhalation amplitude, an inhalation / exhalation amplitude ratio, an inhalation / exhalation duration ratio, the number of events per hour, an event pattern, or any combination thereof. The information described about the sleep-wake signal can be stored in the memory device 114.

[0091] The sleep progression diagram 400 can be used to determine one or more sleep-related parameters such as, for example, sleep onset latency (SOL), wake after sleep onset (WASO), sleep efficiency (SE), a sleep fragmentation index, a sleep block, or any combination thereof.

[0092] The sleep onset latency (SOL) is the time from the sleep onset time (t GTS ) to the initial sleep time (t sleep ). In other words, the sleep onset latency indicates the time it takes for the user to actually fall asleep after first attempting to fall asleep. In some implementations, the sleep onset latency is defined as the persistent sleep onset latency (PSOL). The persistent sleep onset latency differs from the sleep onset latency in that it is defined as the duration from the sleep onset time to a predetermined amount of persistent sleep. In some implementations, the predetermined amount of persistent sleep may include, for example, the second non-REM stage, the third non-REM stage, and / or the REM stage including an awakening state of 2 minutes or less, the first non-REM stage, and / or at least 10 minutes of sleep during the transition therebetween. In other words, the persistent sleep onset latency requires, for example, up to 8 minutes of persistent sleep in the second non-REM stage, the third non-REM stage, and / or the REM stage. In other implementations, the predetermined amount of persistent sleep may include at least 10 minutes of sleep in the first non-REM stage, the second non-REM stage, the third non-REM stage, and / or the REM stage after the initial sleep time. In such an implementation, the predetermined amount of persistent sleep may exclude any micro-awakenings (e.g., after a 10-second micro-awakening, the 10 minutes are not resumed).

[0093] The wake after sleep onset (WASO) is associated with the total duration that the user is awake between the initial sleep time and the wake time. Thus, the wake after sleep onset includes short-term micro-awakenings (e.g., the micro-awakenings MA 1 and MA 2 ) during the sleep session, whether conscious or unconscious. In some implementations, the wake after sleep onset (WASO) is defined as the persistent wake after sleep onset (PWASO) that includes only the total duration of awakenings having a predetermined length (e.g., 10 seconds or more, 30 seconds or more, 60 seconds or more, about 5 minutes or more, about 10 minutes or more, etc.).

[0094] Sleep efficiency (SE) is determined as the ratio of total in-bed time (TIB) to total sleep time (TST). For example, if the total in-bed time is 8 hours and the total sleep time is 7.5 hours, the sleep efficiency for that sleep session is 93.75%. Sleep efficiency indicates the user's sleep hygiene. For example, if the user goes to bed and spends time on other activities (e.g., watching TV) before sleep, the sleep efficiency decreases (e.g., the user is in an unfavorable state). In some implementations, the sleep efficiency (SE) can be calculated based at least in part on the total in-bed time (TIB) and the total time the user attempts to fall asleep. In such implementations, the total time the user attempts to fall asleep is defined as the duration from the lights-out (GTS) time described herein to the wake-up time. For example, in an implementation where the total sleep time is 8 hours (e.g., from 11 PM to 7 AM), the lights-out time is 10:45 PM, and the wake-up time is 7:15 AM, the sleep efficiency parameter is calculated as approximately 94%.

[0095] The fragmentation index is determined based at least in part on the number of awakenings during a sleep session. For example, if the user has two micro-awakenings (e.g., micro-awakenings MA 1 and micro-awakening MA 2 ), the fragmentation index can be represented as 2. In some implementations, the fragmentation index is scaled between integers in a predetermined range (e.g., between 0 and 10).

[0096] Sleep blocks are associated with transitions between any sleep stage (e.g., the first non-REM stage, the second non-REM stage, the third non-REM stage, and / or REM) and the wake stage. For example, sleep blocks can be calculated with a resolution of 30 seconds.

[0097] In some implementations, the systems and methods described herein generate or analyze a hypnogram that includes sleep-wake signals and, based at least in part on the sleep-wake signals of the hypnogram, determine the in-bed time (t bed ), the lights-out time (t GTS ), the initial sleep time (t sleep) one or more first micro-awakenings (e.g., MA 1 and MA 2 ), wake-up time (t wake ), get-up time (t rise ), or any combination thereof may be determined or specified.

[0098] In other implementations, one or more of the sensors 130 are used to define a sleep session by determining or specifying the time of going to bed (t bed ), the time of falling asleep (t GTS ), the initial sleep time (t sleep ), one or more first micro-awakenings (e.g., MA 1 and MA 2 ), wake-up time (t wake ), get-up time (t rise ), or any combination thereof. For example, the time of going to bed t bed can be determined at least in part based on data generated by, for example, the motion sensor 138, the microphone 140, the camera 150, or any combination thereof. For example, data from the motion sensor 138 (e.g., data indicating that the user is not moving), data from the camera 150 (e.g., data indicating that the user is not moving and / or data indicating that the user has turned off the light), data from the microphone 140 (e.g., data indicating that the user has turned off the TV), data from the user device 170 (e.g., data indicating that the user has stopped using the user device 170), data from the pressure sensor 132 and / or the flow sensor 134 (e.g., data indicating that the user has turned on the respiratory therapy device 122, data indicating that the user has worn the user interface 124, etc.) or any combination thereof, the time of falling asleep can be determined.

[0099] Referring to FIG. 5, a method 500 for monitoring a user during a sleep session while the user is using a respiratory therapy system (e.g., respiratory therapy system 12) is shown. Generally, a control system (e.g., control system 110 of system 100) is configured to perform various steps of method 600. A memory device (e.g., memory device 114 of system 100) can be used to store any type of data used in the steps of method 500 (or other methods). Also, a user suffering from SDB may have additional health conditions (also referred to as co-morbidities) associated with SDB. The use of a respiratory therapy system (e.g., as a CPAP system) can generate data that can be analyzed to identify and / or monitor these other health conditions. In some cases, using a respiratory therapy system can also treat not only SDB but also additional health conditions. By analyzing the data generated during the use of the respiratory therapy system and, optionally, additional data, various metrics related to the additional health conditions can be determined, and more effective treatment options for the additional health conditions can be determined.

[0100] Step 502 of method 500 includes generating data during a current sleep session related to a user of a respiratory therapy system. The data may be generated by any suitable source including a plurality of different sensors (e.g., sensor 130). The sensors may be disposed within the housing of a respiratory therapy device (e.g., respiratory therapy device 122) used with the respiratory therapy system or outside the housing of the respiratory therapy device. The data may also be generated by a number of different devices such as a smartwatch, activity meter, mobile phone, any number of external medical measurement devices, etc. The respiratory therapy system can include a respiratory therapy device that supplies pressurized air to a user's airway, for example, via a conduit and a user interface. The generated data can include data indicating the pressure of the pressurized air, the flow rate of the pressurized air, and other characteristics of the pressurized air. The user interface can include a full face mask, nasal mask, or other type of user interface that covers the user's mouth and nose.

[0101] Step 504 of method 500 includes analyzing the generated data to determine a value of a first metric related to sleep disordered breathing (SDB). Step 506 of method 500 includes analyzing the generated data to determine a value of a second metric related to the user's health state other than SDB. The first metric can include metrics for analyzing the user's sleep session. For example, if the respiratory therapy system is used as a CPAP system, the first metric can be related to minimizing events (e.g., apnea events) using the CPAP system.

[0102] The first metric (and in some implementations, the second metric) can include a respiration signal, respiratory rate, respiratory pattern, inspiration amplitude, expiration amplitude, inspiration / expiration ratio, occurrence of one or more events, number of events per hour, pattern of events, average duration of an event, range of event durations, ratio of different numbers of events, sleep stage, time spent in each of multiple sleep stages, sleep stage pattern, apnea hypopnea index (AHI), sleep score, treatment score, total sleep time, total bedtime, wake time, wake-up time, sleep profile, total light sleep time, total deep sleep time, total REM sleep time, total sleep time during treatment, total sleep time after treatment, number of awakenings, sleep latency, stress level, or any combination thereof.

[0103] In some implementations, the first metric is related only to SDB, and the second metric is related only to health status. In some implementations, the first metric is related to SDB and health status, and the second metric is related only to health status. In some implementations, the first metric is related only to SDB, and the second metric is related to health status and SDB. In some implementations, the first metric is related to SDB and health status. In some implementations, the second metric is related to health status and SDB.

[0104] In some implementations, the second metric is related to the presence of a cardiac condition. For example, the second metric can be related to heart failure, heart murmur, increased heart rate, heart attack, atrial fibrillation, arrhythmia, myocarditis, cardiac autonomic neuropathy, chronic inflammatory diseases such as atherosclerosis (repeated drops in blood oxygen levels at night due to SDB have been shown to be associated with systemic inflammation), etc. The second metric can include heart rate variability (a lack of decrease in heart rate variability during a sleep session indicates that the heart is stressed and not responding properly to sleep), standard deviation of the inter-beat intervals (also called SDNN and potentially related to the heart's resilience), root mean square of the successive differences between adjacent beats (also called RMSSD and potentially related to the heart's resilience), cardiac output (a decrease in cardiac output (e.g., a decrease in the amount of blood pumped per time interval) may indicate hypertension and / or heart failure), atrioventricular structure (an altered structure may indicate a risk of atrial fibrillation), tidal volume (an unstable tidal volume may indicate heart failure), apnea duration, expiratory duration, expiratory slope, envelope of the flow signal (indicating the user's stable level of breathing), respiratory rate, etc.

[0105] In some implementations, the second metric is related to the user's clinical stress level or sympathetic nerve activity. For example, the second metric can include heart rate, heart rate variability, skin conductance (e.g., skin current response), arterial pulse velocity, arterial pulse shape, arterial pulse volume, arterial pulse amplitude, or any combination thereof. Further, one or more of these different metrics can be used to quantify the user's stress level such that the second metric itself includes a quantifiable metric of the user's stress level. The second metric can be a metric of the user's stress response to various different types of apnea events. For example, the user's second metric indicates that the user experiences an increased stress level (which can be measured using heart rate, heart rate variability, skin conductance, arterial pulse characteristics, etc.) in response only to long apnea events and / or apnea events with a significantly reduced inspiratory volume. However, the second metric for different users indicates that the user experiences an increased stress level in response to short apnea events and / or apnea events with a slightly reduced inspiratory volume, as well as longer duration and / or apnea events with a significantly reduced inspiratory volume. The second metric can also indicate how much the user's stress level increases with an increase in the severity of the apnea event. Thus, in some implementations, the second metric can include the relationship between the user's stress response and various different types of apnea events, the proportion of various types of apnea events that result in different stress responses, and others.

[0106] In a further implementation, the respiratory therapy system can be configured to learn the relationship between a second metric and the treatment by the respiratory therapy system (e.g., CPAP pressure). In yet another implementation, the respiratory therapy level (e.g., CPAP pressure level) may be varied to enhance the learning of the relationship between the therapy and the second metric, and in yet another implementation, the treatment level may be adjusted to control the second metric (e.g., increase or decrease the CPAP pressure to reduce a metric related to stress or sympathetic nerve activity).

[0107] In some implementations, the second metric is related to the user's respiration during a sleep session. For example, the second metric may include the user's respiratory cadence (e.g., the speed of the user's respiration and / or the presence or absence of the start and stop of the user's respiration), the amplitude of the user's respiration (e.g., the volume of the user's inhalation and / or exhalation), the expiratory time constant (e.g., the time constant of the exponential decay of the lung volume during exhalation), the expiratory shape (e.g., the time curve shape of the lung volume during exhalation), the expiratory amplitude (e.g., the expiratory volume or expiratory force), the expiratory speed (e.g., the speed at which the lung volume decreases during exhalation), the expiratory time constant (e.g., the speed of the exponential increase of the lung volume during exhalation), the inspiratory shape (e.g., the shape of the plot of the lung volume over time during inhalation), the inspiratory amplitude (e.g., the inspiratory volume and / or force), the inspiratory speed (e.g., the speed at which the lung volume increases during inhalation), the tidal volume, the respiratory rate, or other respiratory metrics (e.g., respiration-related metrics).

[0108] These respiratory metrics can be related to any number of different health conditions. For example, a user's breathing cadence and / or tidal volume can be related to obesity, COPD, pneumonia, asthma, and other conditions. A user's shortness of breath (e.g., the user's breathing cadence being fast and / or the tidal volume being shallow) indicates that the user is obese or suffering from COPD, pneumonia, asthma, etc. In another example, the shape of inhalation and / or exhalation indicates the presence of an obstruction in the user's airway. In a further example, tidal volume can be related to heart failure. The instability of tidal volume (e.g., the tidal volume varying with breathing) indicates the presence of heart failure or the risk of progression to heart failure. In a further example, any one or more of (i) the breathing cadence, (ii) the shape, speed, and amplitude of inhalation, and (iii) the shape, speed, and amplitude of exhalation indicates the presence of Cheyne-Stokes respiration.

[0109] In another example, the second metric may be related to the time the user is awake or asleep during a sleep session, and may include the amount of time spent falling asleep, the total amount of time spent sleeping, the user's bedtime, the time the user wakes up, the consistency of the user's breathing rate, etc. These metrics can indicate that the user is awake for longer than intended, sleeping for shorter than intended, that the user's breathing does not slow down and become consistent until later than intended during the sleep session (indicating that the user is asleep), etc., and these indicate that the user is suffering from insomnia. For example, information related to sleep stages such as a hypnogram (e.g., the hypnogram 400 of FIG. 3) can also be used to determine whether the user is suffering from insomnia.

[0110] These metrics or other metrics can also indicate the presence of other neurological disorders such as anxiety, claustrophobia, stress, etc. Generally, the second metric can be related to the cardiac state, respiratory state, neurological state, and other types of states. Generally, metrics related to the neurological state include heart rate (a high heart rate indicates anxiety), blood pressure (high blood pressure may indicate anxiety), sleep latency (low sleep latency may indicate anxiety), arousal threshold (e.g., the ease with which a user wakes up, a low arousal threshold may indicate anxiety), amount of body movement during a sleep session (e.g., uncontrolled leg movement may indicate restless leg syndrome), metrics related to arousal during a sleep session (an irregular sleep-wake pattern may indicate a disruption of the circadian rhythm, may lead to morning depression, and may also be a possibility of nocturnal epilepsy), interface leakage rate (a high leakage rate from a user interface such as user interface 124 may indicate that the user interface is worn loosely due to claustrophobia), number of times the user removes the user interface during a sleep session (a large number of times may indicate claustrophobia), and may include various metrics related to the user's response to the questionnaire, which will be described in more detail below.

[0111] Step 508 of method 500 includes causing some operations to be performed at least partially based on the determined value of the second metric. In some implementations, since the second metric indicates the identity, presence, and / or severity of the health state in the user, it may be used to determine the identity, presence, and / or severity of the health state in the user. And many different operations can be taken.

[0112] In some implementations, one or more settings of the respiratory therapy system may be adjusted based at least in part on the determined value of the second metric. In one example, the respiratory therapy system initially operates as a positive airway pressure system, such as a CPAP system. In response to the value of the second metric indicating the presence of a cardiac condition (e.g., heart failure) or the risk of progression to a cardiac condition (e.g., heart failure), one or more settings of the respiratory therapy device can be adjusted (either manually or automatically by a control system) such that the respiratory therapy system operates as an adaptive servo-ventilation system. A cardiac condition (e.g., heart failure) often causes apnea that cannot be treated with a CPAP system, and the use of a CPAP system often exacerbates the occurrence and / or severity of these apneas. When the respiratory therapy system operates as an adaptive servo-ventilation system, the respiratory therapy system can monitor the user's breathing, increase the user's tidal volume to ensure tidal volume stability, and reduce the occurrence and / or severity of these apneas.

[0113] In another example, settings related to the supply of pressurized air, such as the pressure of the air, the flow rate of the air, the ramp time of the pressurized air (e.g., the time required for the air pressure to increase from the start of use of the respiratory therapy system to the desired pressure (e.g., the desired treatment pressure)), the humidity of the pressurized air, etc., can be changed. A drug or other substance may be supplied and / or injected into the user's airway via the pressurized air. The drug may be configured to assist the user in falling asleep. This substance may be a smell configured to calm the user and assist the user in falling asleep. In one example, when operating under high pressure, the volume of the respiratory therapy system affects the user's insomnia and, in some cases, worsens the user's insomnia. If a second metric indicates that the user is suffering from insomnia, the ramp time can be increased, thereby allowing the respiratory therapy system to provide the user with a longer sleep time until the desired pressure is reached. In a further example, if the second metric indicates the presence of insomnia, the treatment response sensitivity ramp time can be increased and / or the air pressure can be maintained at a relatively low level (e.g., less than the desired treatment and can increase more quickly in the presence of lighter events such as snoring or flow restriction) until the respiratory therapy system detects the user's falling asleep (e.g., based at least in part on a sleep / wake signal).

[0114] In another example, for a user suffering from COPD, asthma, allergies, sinus infections, rhinitis, or other respiratory-related diseases, the humidity of the pressurized air can be increased. In a further example, if a second metric indicates that the user is suffering from a condition treatable by inhalation of a drug, the drug may be supplied and / or infused into the user's airway. In some of these implementations, the value of the first metric related to the SDB can affect how the settings of the respiratory therapy system are adjusted. In other implementations, adjusting the settings may include changing the type of user interface currently in use. The microphone 140 and / or the speaker 142 can be used, for example, to direct an acoustic signal to the user interface and then analyze the acoustic data related to the reflection of the acoustic signal to characterize the user interface. Once the type of user interface currently in use is determined, a proposal to change the type of user interface can be generated based at least in part on the determined value of the second metric. Generally, the settings of the respiratory therapy system have a desired therapeutic effect. The settings of the respiratory therapy system can be adjusted as needed to change the desired therapeutic effect.

[0115] In a further example, adjusting the settings of the respiratory therapy system can include adjusting the pressure of the pressurized air to account for the CO 2 level around the interface. The pressure of the pressurized air may be decreased if the user interface is leaking and / or if the user's ventilation rate is low.

[0116] In some implementations, notifications and / or reports may be generated and sent to the user and / or a third party. The third party may include friends, family members, spouses, significant others, caregivers, healthcare providers, etc. The notifications and / or reports can identify the detected condition and / or the severity of the detected condition. The notifications and / or reports can also propose future treatment methods to assist in the treatment of the disease. For example, if a second metric indicates the presence of insomnia, the notifications and / or reports can propose cognitive behavioral therapy. The notifications and / or reports can be displayed to the user on an electronic display device such as the display device 172 of the user device 170.

[0117] In some implementations, the techniques of method 500 may be used to monitor the effectiveness of treating a health condition using a respiratory therapy system. In these implementations, a second metric is tracked over a plurality of sleep sessions, and the value of the second metric is determined for each sleep session. The settings of the respiratory therapy system for each sleep session can be determined along with the duration of use of the respiratory therapy system for each sleep session. The settings and duration of use of the respiratory therapy system may be related to any changes in the presence and / or severity of the health condition. Next, the optimal settings and / or duration of use for treating the health condition may be determined.

[0118] In some implementations, as described above, the second metric is related to the user's stress level. In these implementations, the operation may include adjusting various settings of the respiratory therapy system based on the determined stress level of the user. In one example, the user's stress level is used to adjust in real time how the respiratory therapy system responds to apnea events. The respiratory therapy system can operate as a constant pressure (CPAP system) or a bilevel positive airway pressure system (BiPAP system or VPAP system) that supplies pressurized air to the user's airway at two different constant pressures. When the user is experiencing an elevated stress level, the respiratory therapy system can increase the pressure of the supplied air when a severe apnea event is detected. The increase in air pressure contributes to ending a severe apnea event more quickly and / or minimizing the reduction in inhalation that occurs during a severe apnea event. An increase in pressure is generally not done because it can have negative side effects such as drying out the user's airway or waking up the user. However, when the user experiences an elevated stress level during a sleep session (especially during a severe apnea event), the benefit of ending a severe apnea event sooner by increasing the air pressure may outweigh any drawbacks of the pressure increase. Thus, in these implementations, the respiratory therapy system can increase the air pressure in response to a severe apnea event only when the user's stress level has increased. When the user's stress level has not increased, the respiratory therapy system can maintain the same pressure level even if a severe apnea event occurs.

[0119] In some implementations, when a user typically experiences an elevated stress level during a sleep session, the pressure increases in response to a severe apnea event. When the severe apnea event ends, the pressure can drop to a standard level. In other implementations, when a user experiences an elevated stress level during a severe apnea event, the pressure increases in response to the severe apnea event. When the severe apnea event ends and / or the user's stress level returns to normal, the pressure can drop to a standard level. In further implementations, such an increase in pressure can occur not only during severe apnea events but also during mild apnea events. Thus, in some implementations, the respiratory therapy system can increase the pressure of the air delivered to the user's airway in response to an apnea event (severe or mild) only if a second metric indicates an increase in the user's stress level. If the second metric does not indicate an increase in the user's stress level during the apnea event, the respiratory therapy system can continue to operate at a standard pressure.

[0120] Referring to FIG. 6, a method 600 for monitoring a user during a sleep session while the user is using a respiratory therapy system (e.g., respiratory therapy system 12) is shown. Generally, a control system (e.g., control system 110 of system 100) is configured to perform the various steps of method 600. A memory device (e.g., memory device 114 of system 100) can be used to store any type of data used in the steps of method 600 (or other methods).

[0121] Step 602 of method 600 includes generating data during a current sleep session related to a user of the respiratory therapy system. Step 602 of method 600 is the same as or similar to method 502 of method 500. Step 604 includes receiving historical data related to one or more previous sleep sessions. Generally, the historical data can be of the same type as the data generated during the current sleep session, except that it is associated with one or more previous sleep sessions of the user.

[0122] Step 606 of method 600 includes analyzing the generated data to determine values of metrics related to the health state of users other than the SDB. Step 606 of method 600 is the same as or similar to step 506 of method 500. Thus, the metrics for which values are determined may be the second metrics for which values are determined in step 506. Step 608 of method 600 includes analyzing historical data to determine values of metrics for each of one or more previous sleep sessions. Step 610 of method 600 includes comparing the determined values of metrics for each of one or more previous sleep sessions with the determined values of metrics for the current sleep session. This comparison can reveal whether the severity of the user's condition is increasing or decreasing over time.

[0123] Finally, step 612 of method 600 includes causing an action to be performed based at least in part on the comparison. If the comparison in step 610 indicates an increase in the severity of the health state, various different actions can be taken. In some implementations, a notification may be sent to the user or a third party (e.g., a friend, family member, spouse, partner, caregiver, or healthcare provider). This notification can provide information regarding the increase in the severity of the health state. This notification can be displayed to the user on an electronic display device such as the user device 170. In another implementation, one or more settings of the respiratory therapy system may be changed to better treat the health state.

[0124] In yet another implementation, a proposal for future treatment can be sent to the user. Proposals for future treatment may include proposals to visit a physician or dentist, proposals to follow a medication regimen or medical regimen, proposals to change the settings of the respiratory therapy system, proposals to use a separate device to treat the health state (e.g., a proposal to use a portable oxygen concentrator), or proposals to use a separate device to confirm the increased severity of the health state.

[0125] The separation device may be any device that can be used to confirm an increased severity. For example, the device may be a medical measurement device that can be used to measure some attributes or parameters indicating the severity of the user's health condition. The separation device may include a pulse oximeter configured to measure the user's oxygen saturation, a blood pressure monitor configured to monitor the user's blood pressure, a heart rate monitor configured to measure the user's heart rate, a blood glucose meter configured to measure the user's blood glucose level, an electroencephalogram (EEG) configured to measure brain activity (for monitoring central arousal and / or sleep stages during a sleep session), an electrooculogram (EOG) configured to detect eye movements during a sleep session, a wearable device configured to detect movement during a sleep session, a mattress sensor configured to detect movement during a sleep session, or other devices.

[0126] In other implementations, the comparison between the data from the current sleep session and the data from past sleep sessions can indicate that the severity of the health condition has increased, decreased, or remained unchanged, or has decreased. If the severity of the health condition is increasing, this operation may include suggesting to the user to adjust the settings and / or use of the respiratory therapy system in order to increase the expected therapeutic effect of the respiratory therapy system according to the increasing severity of the health condition. If the severity of the health condition has decreased or the unchanged state is maintained, this operation may include suggesting to the user to continue using the respiratory therapy system with the current settings or to adjust the settings and / or use of the respiratory therapy system to decrease the desired therapeutic effect of the respiratory therapy system according to the unchanged or decreased severity of the health condition. Other types of operations can also be taken.

[0127] In another implementation, data from previous sleep sessions can be analyzed to predict the future state of a user's various health conditions. This can include predicting the appropriate time for any type of future intervention or treatment change, and sending notifications and / or reports regarding the prediction of future intervention or treatment changes to the user or a third party. The prediction may also be constantly updated as more data is obtained from each sleep session.

[0128] In some implementations, historical data from one or more previous sleep sessions is analyzed to track the user's stress level over time, and a second metric includes a metric of the user's stress level during the current sleep session. By comparison, if it is shown that the user's stress level does not decrease during the sleep session, the settings of the respiratory therapy system can be adjusted to help reduce the user's stress level. For example, the settings of the respiratory therapy system can be adjusted to increase the ramp-up time of the pressurized air delivered to the user's airway or to decrease the pressure of the pressurized air delivered to the user's airway. Generally, when the user first starts using the respiratory therapy system at the start of a sleep session, the respiratory therapy system increases the pressure of the air supplied to the user's airway from a low initial pressure to a high working pressure within a first period (such as 5 minutes, 10 minutes, 30 minutes, 1 hour, etc.). The time required for the pressure to rise from the initial pressure to the working pressure is called the ramp-up time. If the user's stress level increases (e.g., in response to first wearing the user interface), this operation may include increasing the ramp-up time of the respiratory therapy system such that the pressure of the air supplied to the user increases from the initial pressure to the working pressure within a second period that is longer than the first period. This operation may additionally or alternatively include decreasing the working pressure of the respiratory therapy system. In these implementations, the working pressure may be changed to a modified working pressure that is less than the original working pressure.

[0129] In some implementations, the historical data may be used to determine a user's baseline stress level during different types of apnea events, and the second metric may include a metric of the user's stress level during the current sleep session. If the second metric indicates that the user is experiencing a high stress level (compared to the baseline) when experiencing a particular apnea event, the pressure of the pressurized air delivered to the user's airway can be increased to assist in reducing the duration and / or severity of the apnea event, even if the duration and / or severity of the apnea event is typically treated at an increased pressure. Similarly, if the second metric indicates that the user is not experiencing an elevated stress level when experiencing a particular apnea event, the pressure of the pressurized air delivered to the user's airway can be maintained constant, even if the duration and / or severity of the apnea event is typically treated at an increased pressure.

[0130] Method 600 generally refers to comparing data from the current sleep session with data from previous sleep sessions, but generally, method 600 is suitable for any comparison between different data sets generated between different sleep sessions. Thus, it is also possible to compare data generated during the current sleep session with data generated during a subsequent sleep session to determine the value of the second metric for the current and subsequent sleep sessions and to take some actions based at least in part on this comparison.

[0131] Referring to FIG. 7, a method 700 for monitoring a user during a sleep session while the user is using a respiratory therapy system (e.g., respiratory therapy system 12) is shown. Generally, a control system (e.g., control system 110 of system 100) is configured to perform the various steps of method 700. A memory device (e.g., memory device 114 of system 100) can be used to store any type of data used in the steps of method 700 (or other methods).

[0132] Step 702 of method 700 includes generating data during a current sleep session related to a user of a respiratory therapy system. Step 702 of method 700 is generally the same as or similar to step 602 of method 600 and / or step 502 of method 500. Step 704 of method 700 includes analyzing the generated data to determine a value of a first metric related to sleep disordered breathing (SDB). Step 704 of method 700 is generally the same as or similar to step 504 of method 500. Step 706 of method 700 includes analyzing the generated data to determine a value of a second metric related to a health condition other than SDB. Step 706 of method 700 is generally the same as or similar to step 506 of method 500 and step 606 of method 600.

[0133] Step 708 of method 700 includes receiving additional data related to the user when the user is awake. Generally, the additional data is related to the user but not related to the user's use of the respiratory therapy system. The data may include demographic information such as the user's age, gender, sex difference, geographical location, height, weight, neck size, and occupation; medical information related to the user such as the user's smoking status; audio data related to the user such as the user's responses to a questionnaire or other data related to the user's speech; and other types of additional data.

[0134] The questionnaire can include questions designed to elicit further information about the user from the user. These questions can be related to the quality and / or quantity of the user's sleep, the user's health, the user's recent activities, the user's physical activity / exercise, the user's stress level, the user's physical health, the user's mental health, other data, or any combination thereof. The data related to the user's response to the questionnaire can include what the user answered.

[0135] However, in some implementations, the data may include how the user actually answers the question. Attributes such as the user's tone of voice, the cadence of the user's speech, the structure of the answers provided (e.g., one complete sentence per one word), the speed and accuracy of the user's answers, etc., can provide insights into various states including neurological states such as anxiety and depression. For example, a faster cadence, a monotone / limited tone, a high ratio of breathing sounds to the audio during the response, large intervals between words, and / or any vibrations in the user's audio can indicate the presence of neurological states such as anxiety and / or depression.

[0136] In some implementations, the question may be presented to the user via the display device 128 of the respiratory therapy system 120 and / or the display device 172 of the user device 170. In other implementations, the question may be sent to the user via the speaker 142 (e.g., played loudly). The user's answer to the questionnaire can be input manually (e.g., via the display device 128 of the respiratory therapy system 128 or the user device 170). However, the user can also speak their answer out loud, and a sensor (e.g., microphone 140) can generate audio data indicative of the user's answer. The audio data represents attributes or characteristics of the user's answer that indicate the user's health state.

[0137] The additional data can include not only the user's answer to the questionnaire but also other audio data. For example, the user may be recorded during the day (e.g., using microphone 142 or other devices). Audio data related to the user's speech and other audio emitted by the user (e.g., coughs, wheezes, etc.) can indicate various different states including respiratory states (e.g., wheezing, pneumonia, allergies, respiratory infections, etc.) and neurological states (depression, anxiety, bipolar disorder, etc.).

[0138] Step 710 of method 700 includes analyzing the received additional data (data related to the user during waking hours) to determine a value of a third metric related to the health state. Step 712 of method 700 includes causing an operation to be performed based at least in part on the determined value of the third metric.

[0139] Generally, the third metric related to the health state is different from the second metric related to the health state. By determining the value of the third metric from the additional data related to the user during waking hours, a more accurate representation of the health state can be obtained and an operation can be taken.

[0140] In some implementations, the value of the second metric may be adjusted based at least in part on the value of the third metric. In these implementations, the accuracy of the determined value of the second metric may be limited if it is based at least in part only on data related to the use of the respiratory therapy system. However, the value of the second metric can be determined more accurately by considering data related to the user during waking hours and / or the value of the third metric.

[0141] In other implementations, the value of the second metric can indicate that the user may have multiple different health states. However, it may not be possible to determine which of the multiple health states the user is actually suffering from based at least in part only on the value of the second metric. The value of the third metric can provide more detailed information that enables determining which of the multiple health states the user is suffering from. In other implementations, the reverse may be true. The value of the third metric can indicate that the user may be suffering from multiple different health states, and the value of the second metric can be used to determine which of the multiple health states the user is suffering from.

[0142] In other implementations, the identity of the health condition the user is suffering from is determined only from the second metric (or the third metric). Next, the value of the third metric (or the second metric) can be used to determine the severity of the health condition.

[0143] In general, methods 500, 600, and 700 can be implemented using a system that includes a control system having one or more processors and a memory storing machine-readable instructions. The control system can be coupled to a storage device, and methods 500, 600, and 700 can be implemented when the machine-readable instructions are executed by at least one of the processors of the control system. Methods 500, 600, and 700 can also be implemented using a computer program product (e.g., a non-transitory computer-readable medium) that includes instructions that, when executed by a computer, cause the computer to perform the steps of methods 500, 600, and 700.

[0144] One or more additional implementations and / or claims of the present disclosure can be formed by combining one or more elements or aspects or steps or portions (singular or plural) from any one or more of the following claims 1 to 125 with one or more elements or aspects or steps or portions (singular or plural) from any one or more of the other claims 1 to 125 or combinations thereof.

[0145] Although the present disclosure has been described with reference to one or more particular implementations or embodiments, those skilled in the art will recognize that many changes are possible without departing from the spirit and scope of the present disclosure. These implementations and their obvious variations are each intended to fall within the spirit and scope of the present disclosure. And it is also contemplated that additional implementations according to various aspects of the present disclosure can combine any number of features from any of the implementations described herein.

Claims

1. A system for monitoring a user's sleep session, comprising: a respiratory therapy system; a respiratory therapy device configured to supply pressurized air and comprising a housing with one or more sensors disposed therein; a respiratory therapy system including a user interface connected to the respiratory therapy device via a conduit, engaging the user, and configured to direct the supplied pressurized air into the user's airway; a memory storing machine-readable instructions; a control system coupled to the memory device, the control system executing the machine-readable instructions to: generate data related to the use of the respiratory therapy device by the user during a current sleep session, the data including at least a first portion generated by the one or more sensors disposed within the housing of the respiratory therapy device; analyze the generated data to determine a value of a first metric related to a sleep-disordered breathing (SDB) condition; analyze the generated data to determine a value of a second metric related to a health condition other than the SDB condition, the health condition including insomnia; and a control system including one or more processors configured to determine to adjust one or more settings of the respiratory therapy system or to determine a recommendation to the user or a third party for future treatment, at least in part based on the determined value of the second metric indicating the presence of insomnia.

2. The system of claim 1, wherein the value of the second metric indicates the presence, severity, or both of the health condition.

3. The system of claim 1 or 2, wherein the first metric includes a respiratory signal, respiratory rate, respiratory pattern, inspiratory amplitude, expiratory amplitude, inspiratory / expiratory ratio, occurrence of one or more events, number of events per hour, pattern of events, average duration of events, range of event durations, ratio of different numbers of events, sleep stage, time spent in each of a plurality of sleep stages, sleep stage pattern, apnea-hypopnea index (AHI), sleep score, treatment score, total sleep time, total bedtime, wake time, wake-up time, sleep profile, sleep disorder index, total light sleep time, total deep sleep time, total REM sleep time, total sleep time during treatment, total sleep time after treatment, number of awakenings, sleep latency, or any combination thereof.

4. The one or more sensors disposed within the housing of the respiratory therapy device include a flow sensor, a pressure sensor, a microphone, or any combination thereof, the system according to any one of claims 1 to 3.

5. The system according to claim 4, wherein at least a second portion of the data is generated by one or more sensors disposed external to the respiratory therapy device.

6. The second metric is related to the user's breathing during a current sleep session, The second metric includes a breathing cadence, an amplitude of the user's breathing, an expiratory time constant, an expiratory shape, an expiratory amplitude, an expiratory rate, an inspiratory time constant, an inspiratory shape, an inspiratory amplitude, an inspiratory rate, a tidal volume, a breathing rate, or any combination thereof, The system according to any one of claims 1 to 5.

7. The second metric includes a breathing cadence, and the health condition is obesity, chronic obstructive pulmonary disease (COPD), pneumonia, asthma, Cheyne-Stokes respiration, heart failure, or any combination thereof, or, The second metric includes an expiratory shape, and the health condition is that there is an obstruction in the user's airway, or, The second metric includes a tidal volume, and the instability of the tidal volume indicates the presence of heart failure or the risk of progression to heart failure, The system according to claim 6.

8. The respiratory therapy system initially operates as a positive airway pressure system and adjusts one or more settings of the respiratory therapy system to operate as an adaptive servo-ventilation system in response to the second metric indicating the presence of heart failure or the risk of progression to heart failure, the system according to claim 7.

9. The second metric includes a total bedtime, a total sleep time, a total wake time, a sleep latency, a middle wake parameter, a sleep efficiency, a fragmentation index, an amount of sleep time, a consistency of the breathing rate, a sleep time, a wake time, a proportion of sleep disorders, a number of movements, or any combination thereof, the system according to any one of claims 1 to 8.

10. Adjusting one or more settings of the respiratory therapy system includes reducing the pressure of the pressurized air supplied to the user's airway, the system according to claim 1.

11. The generated data includes data indicating the pressure of the pressurized air, the flow rate of the pressurized air, or both, Adjusting one or more settings of the respiratory therapy system includes changing the pressure of the pressurized air, changing the flow rate of the pressurized air, changing the ramp time of the pressurized air, changing the humidity of the pressurized air, supplying a drug to the user's airway via the pressurized air, or any combination thereof. The system according to any one of claims 1 to 10.

12. Responding to the value of the determined second metric indicating the presence of insomnia, anxiety, claustrophobia, or any combination thereof, adjusting one or more settings of the respiratory therapy system includes increasing the ramp time of the pressurized air. The system of claim 11.

13. Responding to the value of the determined second metric indicating the presence of COPD, asthma, allergy, sinus infection, rhinitis, or any combination thereof, adjusting one or more settings of the respiratory therapy system includes increasing the humidity of the pressurized air, supplying the drug to the user's airway, or both. The system of claim 12.

14. One or more of the processors of the control system further execute the machine-readable instructions to Track the second metric over a plurality of sleep sessions, Determine the value of the second metric for each of the plurality of sleep sessions, Determine (i) the duration of use of the respiratory therapy system for each of the plurality of sleep sessions, (ii) one or more setting values of the respiratory therapy system for each of the plurality of sleep sessions, or (iii) both. The system according to any one of claims 1 to 13, configured to identify (i) an optimal duration of use of the respiratory therapy system, (ii) an optimal value of one or more settings of the respiratory therapy system, or (iii) both, for treating a health condition.

15. One or more of the processors of the control system further execute the machine-readable instructions to Receive historical data related to one or more previous sleep sessions of the user, Analyze the historical data to determine the value of the second metric for each of the one or more previous sleep sessions. configured to compare the value of the determined second metric for each of the one or more previous sleep sessions with the value of the determined second metric for the current sleep session, one or more of the processors of the control system are configured to execute the machine-readable instructions to determine to adjust one or more settings of the respiratory therapy system in response to the comparison indicating an increase in the severity of the health condition, or to determine a recommendation to the user or a third party for future treatment, The system according to any one of claims 1 to 14.

16. The system according to claim 15, wherein the health condition includes the user's stress level, and in response to a comparison indicating an increase in the user's stress level, the pressure of the pressurized air delivered to the user by the respiratory therapy system is decreased.

17. one or more of the processors of the control system are further configured to execute the machine-readable instructions to, determine the severity of the health condition based at least in part on the value of the determined second metric for the current sleep session, generate the data during one or more subsequent sleep sessions, determine the value of the second metric for each of the one or more subsequent sleep sessions, determine an updated severity of the health condition based at least in part on the value of the determined second metric for each of the one or more subsequent sleep sessions, configured to cause an operation to be performed based at least in part on the updated severity of the health condition, in response to the value of the determined second metric for each of the one or more subsequent sleep sessions indicating an increase in the severity of the health condition, the operation includes adjusting one or more settings of the respiratory therapy system to increase a desired therapeutic effect of the respiratory therapy system, in response to the value of the determined second metric for each of the one or more subsequent sleep sessions indicating a decrease in the severity of the health condition, the operation includes continuing to use the respiratory therapy system with the current settings or adjusting one or more settings of the respiratory therapy system to decrease a desired therapeutic effect of the respiratory therapy system, The system according to any one of claims 1 to 16.

18. one or more of the processors of the control system are further configured to execute the machine-readable instructions to, Receive additional data related to the user upon waking, Analyze the additional data to determine a value of a third metric related to the health state, Determine to adjust one or more settings of the respiratory therapy system based at least in part on the determined value of the third metric related to the health state, or determine a recommendation to the user or a third party for future treatment, the system according to any one of claims 1 to 17.

19. One or more of the processors of the control system further execute the machine-readable instructions to, Modify the determined value of the second metric based at least in part on the determined value of the third metric, Determine to adjust one or more settings of the respiratory therapy system based at least in part on the modified value of the second metric, or determine a recommendation to the user or a third party for future treatment, the system according to claim 18.

20. The value of the second metric is related to a plurality of health states, and one or more of the processors of the control system further execute the machine-readable instructions to determine the identity of the user's health state from the plurality of health states based at least in part on the determined value of the third metric, the system according to claim 18 or 19.

21. The value of the third metric is related to a plurality of health states, and one or more of the processors of the control system further execute the machine-readable instructions to determine the identity of the user's health state from the plurality of health states based at least in part on the determined value of the second metric, the system according to any one of claims 18 to 20.

22. One or more of the processors of the control system further execute the machine-readable instructions to, Determine the identity of the user's health state based at least in part on the determined value of the second metric, Determine the severity of the user's health state based at least in part on the determined value of the third metric, the system according to any one of claims 18 to 21.

23. One or more of the processors of the control system further execute the machine-readable instructions to, Determine the identity of the user's health status based at least in part on the value of the determined third metric. The system according to any one of claims 18 to 21, configured to determine the severity of the user's health status based at least in part on the value of the determined second metric. **Claim 24** One or more of the processors of the control system are further configured to execute the machine-readable instructions to determine (i) the identity of the health status, (ii) the severity of the health status, or both, based at least in part on the value of the determined second metric and additional data. The additional data includes demographic information related to the user, medical information related to the user, audio data related to the user, or any combination thereof. The system according to any one of claims 1 to 23. **Claim 25** The second metric indicates the stress level of the user and includes heart rate, heart rate variability, skin conductance, arterial pulse rate, arterial pulse shape, arterial pulse volume, arterial pulse amplitude, or any combination thereof. Adjusting one or more settings of the respiratory therapy system in response to the second metric indicating an elevated stress level of the user during an apnea event includes increasing the pressure of the pressurized air. The system according to any one of claims 1 to 24. **Claim 26** The value of the determined second metric indicates an elevated stress level of the user. Adjusting one or more settings of the respiratory therapy system includes (i) increasing the pressure of the pressurized air in response to the user experiencing an apnea event and (ii) decreasing the pressure of the pressurized air in response to the end of the apnea event. The system according to any one of claims 1 to 25. **Claim 27** During operation of the respiratory therapy system, the respiratory therapy system is configured to increase the pressure of the pressurized air from an initial pressure to a working pressure within a first period. Adjusting one or more settings of the respiratory therapy system includes (i) increasing the ramp-up time of the pressurized air supplied to the user's airway such that the pressure of the pressurized air increases from the initial pressure to the working pressure within a second period that is longer than the first period. (ii) changing the operating pressure to a changed operating pressure that is less than the operating pressure, or (iii) including both (i) and (ii), The system according to claim 26.

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