System and method for communicating indications of sleep-related events to a user - Patent Application 20070122997

Abstract

The method includes receiving data associated with a user's sleep session, the data including respiratory data associated with the user during at least a portion of the sleep session and audio data playable as one or more sounds during at least a portion of the sleep session. The method further includes determining a respiratory signal associated with the user during the sleep session based at least in part on at least a portion of the data. The method further includes identifying events experienced by the user during the sleep session based at least in part on at least a portion of the data. The method further includes communicating to a user via a user device a graphical representation of a portion of the respiratory signal and an event indication assisting in identifying the identified events within the graphical representation of the portion of the respiratory signal.

Description

[Technical Field]

[0001] (CROSS-REFERENCE TO RELATED APPLICATIONS) This application claims the benefit of and priority to U.S. Provisional Patent Application No. 63 / 044760, filed June 26, 2020, which is incorporated herein by reference in its entirety.

[0002] The present disclosure relates generally to systems and methods for identifying events experienced by a user during a sleep session, and more particularly to systems and methods for conveying one or more visual and / or audio indications of the identified events to the user. [Background technology]

[0003] Many people suffer from sleep-related and / or respiratory disorders, such as periodic limb movement disorder (PLMD), restless legs syndrome (RLS), sleep-disordered breathing (SDB), obstructive sleep apnea (OSA), apnea, Cheyne-Stokes respiration (CSR), respiratory failure, obesity hyperventilation syndrome (OHS), chronic obstructive pulmonary disease (COPD), neuromuscular disorders (NMD), and chest wall disorders. These disorders are typically treated using respiratory treatment systems. However, for some users, such systems are uncomfortable, difficult to use, expensive, aesthetically unpleasing, and / or the benefits associated with using the system are not recognized. As a result, some users may choose not to initiate use of a respiratory treatment system or, if respiratory treatment is not used, discontinue use of the respiratory treatment system without substantiated symptom severity. The present disclosure is directed to solving these problems and others. Summary of the Invention [Means for solving the problem]

[0004] In some implementations of the present disclosure, a method includes receiving data associated with a user's sleep session from one or more sensors. The data includes respiratory data associated with the user during at least a portion of the sleep session and one or more playable sounds associated with the user during at least a portion of the sleep session. The method further includes determining a respiratory signal associated with the user during the sleep session based at least in part on at least a portion of the data. The method further includes identifying events experienced by the user during the sleep session based at least in part on at least a portion of the data. The method further includes communicating to a user via a user device a graphical representation of a portion of the respiratory signal and an event indication that assists in identifying the identified events within the graphical representation of the portion of the respiratory signal.

[0005] In some implementations of the present disclosure, a system includes an electronic interface, a memory, and a control system. The memory stores machine-readable instructions. The control system includes one or more processors configured to execute the machine-readable instructions to perform the steps of receiving data associated with a user's sleep session, the data including respiratory data associated with the user during at least a portion of the sleep session and one or more reproducible audio data associated with the user during at least a portion of the sleep session. The control system is further configured to determine a respiratory signal associated with the user during the sleep session based at least in part on at least a portion of the data. The control system is further configured to identify events experienced by the user during the sleep session based at least in part on at least a portion of the data. The control system is further configured to communicate to a user via a user device a graphical representation of a portion of the respiratory signal and an event indication that assists in identifying the identified event within the graphical representation of the portion of the respiratory signal.

[0006] The above summary is not intended to describe each implementation or every aspect of the present disclosure. Additional features and benefits of the present disclosure will be apparent from the following detailed description and drawings. [Brief explanation of the drawings]

[0007] [Figure 1] FIG. 1 is a functional block diagram of a system according to some implementations of the present disclosure. [Figure 2] 2 is a perspective view of at least a portion of the system of FIG. 1, a user, and a bed partner, according to some implementations of the present disclosure. [Figure 3] 1 illustrates an example timeline of a sleep session, according to some implementations of the present disclosure. [Figure 4] 4 illustrates an example hypnogram associated with the sleep session of FIG. 3, according to some implementations of the present disclosure. [Figure 5] FIG. 1 is a process flow diagram of a method for identifying events experienced by a user during a sleep session, according to some implementations of the present disclosure. [Figure 6] 1 illustrates an example respiratory signal and an example audio signal associated with a user during a sleep session, according to some implementations of the present disclosure. [Figure 7] 1 shows an example graphical representation of a portion of a respiratory signal and an event indication, according to some implementations of the present disclosure. [Figure 8] 1 illustrates an exemplary snoring pattern, according to some implementations of the present disclosure. DETAILED DESCRIPTION OF THE INVENTION

[0008] While the present disclosure is susceptible to various modifications and alternative forms, specific implementations and embodiments of the present disclosure have been shown by way of example in the drawings and are herein described in detail. It is to be understood, however, that it is not intended to limit the disclosure to the particular forms disclosed, but that the present disclosure is intended to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the present disclosure as defined by the appended claims.

[0009] Many people suffer from sleep-related and / or breathing disorders, including, for example, sleep-disordered breathing (SDB), such as periodic limb movement disorder (PLMD), restless legs syndrome (RLS), obstructive sleep apnea (OSA), central sleep apnea (CSA), and other types of apnea, including mixed apneas and hypopneas, respiratory effort-related arousals (RERA), Cheyne-Stokes respiration (CSR), respiratory failure, obesity hypoventilation syndrome (OHS), chronic obstructive pulmonary disease (COPD), neuromuscular diseases (NMD), rapid eye movement (REM) behavior disorder (also known as RBD), dream enactment (DEB), hypertension, diabetes, stroke, insomnia, and chest wall disorders. These disorders are typically treated using respiratory therapy systems.

[0010] Obstructive sleep apnea (OSA) is a form of sleep-disordered breathing (SDB) characterized by events such as upper airway obstruction or obstruction during sleep due to an abnormally small upper airway combined with the normal loss of muscle tone in the tongue, soft palate, and posterior pharyngeal wall. More generally, apnea generally refers to pauses in breathing (obstructive sleep apnea) or cessation of respiratory function (often referred to as central sleep apnea) caused by air blockage. Typically, individuals stop breathing for approximately 15 to 30 seconds during an obstructive sleep apnea event.

[0011] 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 airway obstruction. Hyperpnea is generally characterized by an increase in the depth and / or rate of breathing. Hypercapnia is generally characterized by a sudden or excessive amount of carbon dioxide in the bloodstream, usually resulting from insufficient breathing.

[0012] Cheyne-Stokes respiration (CSR) is another form of sleep-disordered breathing. CSR is a disturbance in a patient's respiratory control system that results in alternating periods of waxing and waning ventilation, known as the CSR cycle. CSR is characterized by repeated deoxygenation and reoxygenation of arterial blood.

[0013] Obesity-hypoventilation syndrome (OHS) is defined as the combination of severe obesity and chronic awake hypercapnia in the absence of other causes of hypoventilation. Symptoms include dyspnea, morning headache, and excessive daytime sleepiness.

[0014] Chronic obstructive pulmonary disease (COPD) encompasses any of a group of diseases of the lower respiratory tract that share certain characteristics, such as increased resistance to air movement, a prolonged expiratory phase of breathing, and loss of the lungs' normal elasticity.

[0015] Neuromuscular disorders (NMDs) encompass a number of diseases and illnesses that impair muscle function directly through intrinsic muscle pathology or indirectly through neuropathology. Chest wall disorders are a group of thoracic deformities that result in inefficient connections between the respiratory muscles and the rib cage.

[0016] Respiratory effort-related arousal (RERA) events are typically characterized by increased respiratory effort for 10 seconds or more leading to arousal from sleep, without meeting the criteria for an apnea or hypopnea event. RERAs are defined as a series of breaths characterized by increased respiratory effort leading to arousal from sleep, but without meeting the criteria for an apnea or hypopnea. These events must meet both criteria: (1) a pattern of gradually increasing esophageal negative pressure terminated by a sudden drop in the negative pressure level and arousal; and (2) the event must last 10 seconds or more. In some implementations, a nasal cannula / pressure transducer system is sufficient and reliable for detecting RERAs. The RERA detector can be based on an actual flow signal derived from a respiratory therapy device. For example, a measure of flow limitation can be determined based on the flow signal. A measure of arousal can then be derived as a function of the measure of flow limitation and the measure of sudden increase in ventilation. One such method is described in International Patent Publication No. 2008 / 138040 and U.S. Patent Publication No. 9,358,353, both issued to ResMed, Inc., the entire disclosures of each of which are incorporated herein by reference.

[0017] These and other disorders are characterized by specific events that occur during an individual's sleep (e.g., snoring, apnea, hypopnea, restless legs, sleep disturbances, choking, increased heart rate, labored breathing, asthma attack, epileptic episode, seizure, or any combination thereof).

[0018] The apnea-hypopnea index (AHI) is an index used to indicate the severity of sleep apnea during a sleep session. The AHI is calculated by dividing the number of apnea and / or hypopnea events experienced by a user during a sleep session by the total number of hours of sleep in that sleep session. An event can be, for example, a pause in breathing lasting at least 10 seconds. An AHI of less than 5 is considered normal. An AHI of 5 to less than 15 is considered to indicate mild sleep apnea. An AHI of 15 to less than 30 is considered to indicate moderate sleep apnea. An AHI of 30 or greater 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.

[0019] 1, a system 100 according to some implementations of the present disclosure is shown. The system 100 includes a control system 110, a memory device 114, one or more sensors 130, and one or more user devices 170. In some implementations, the system 100 optionally further includes a respiratory treatment system 120.

[0020] Control system 110 includes one or more processors 112 (hereinafter processor 112). Control system 110 is generally used to control (e.g., operate) various components of system 100 and / or analyze data acquired and / or generated by the components of system 100. Processor 112 may be a general-purpose or special-purpose processor or microprocessor. While one processor 112 is shown in FIG. 1 , control system 110 may include any number of processors (e.g., one processor, two processors, five processors, ten processors, etc.), which may reside in a single housing or may be located remotely from one another. Control system 110 (or any other control system) or a portion of control system 110, such as processor 112 (or any other processor or portion of any other control system), may be used to perform any one or more steps of the methods described and / or claimed herein. Control system 110 may be coupled to and / or located within, for example, the housing of user device 170, a portion of respiratory treatment system 120 (e.g., respiratory treatment device 122), and / or the housing of one or more sensors 130. Control system 110 may be centralized (in one such housing) or distributed (in two or more such housings that are physically separate). In such implementations that include two or more housings to house control system 110, such housings may be located proximate to and / or distant from one another.

[0021] The storage device 114 stores machine-readable instructions executable by the processor 112 of the control system 110. The storage device 114 may be any suitable computer-readable storage device or medium, such as, for example, a random or serial access memory device, a hard drive, a solid-state drive, a flash memory device, etc. Although one storage device 114 is shown in FIG. 1 , the system 100 may include any suitable number of storage devices 114 (e.g., one storage device, two storage devices, five storage devices, ten storage devices, etc.). The storage device 114 may be coupled to and / or located within the housing of the respiratory treatment device 122, the housing of the user device 170, the housing of one or more sensors 130, or any combination thereof. Like the control system 110, the storage device 114 may be centralized (within one such housing) or distributed (within two or more such housings that are physically separate).

[0022] In some implementations, the storage device 114 (FIG. 1) stores a user profile associated with the user. The user profile may include, for example, demographic information associated with the user, biometric information associated with the user, medical information associated with the user, self-reported user feedback, sleep parameters associated with the user (e.g., sleep-related parameters recorded from one or more previous sleep sessions), or any combination thereof. The demographic information may include, for example, information indicating the user's age, the user's gender, the user's race, a family history of insomnia, the user's employment status, the user's education status, the user's socioeconomic status, or any combination thereof. The medical information may include, for example, information indicating one or more medical conditions associated with the user, medication usage by the user, or both. The medical information data may further include a Multiple Sleep Latency Test (MSLT) result or score and / or a Pittsburgh Sleep Quality Index (PSQI) score or value. The self-reported user feedback may include information indicative of a self-reported subjective sleep score (poor, fair, good, etc.), the user's self-reported subjective stress level, the user's self-reported subjective fatigue level, the user's self-reported subjective health status, recent life events experienced by the user, or any combination thereof.

[0023] As described herein, the processor 112 and / or storage 114 of the control system 110 can receive data (e.g., physiological data and / or audio data) from one or more sensors 130, which is stored in the storage 114 and / or analyzed by the processor 112. The processor 112 and / or storage 114 can communicate with the one or more sensors 130 using a wired or wireless connection (e.g., using an RF communication protocol, a Wi-Fi® communication protocol, a Bluetooth® communication protocol, a cellular network, etc.). In some implementations, the system 100 can include an antenna, a receiver (e.g., an RF receiver), a transmitter (e.g., an RF transmitter), a transceiver, or any combination thereof. These components can be coupled to or integrated into the housing of the control system 110 (e.g., the same housing as the processor 112 and / or storage 114) or the user device 170.

[0024] As mentioned above, in some implementations, system 100 optionally includes a respiratory treatment system 120. Respiratory treatment system 120 includes a respiratory pressure treatment device 122 (also referred to herein as respiratory treatment device 122), a user interface 124 (also referred to as a mask or patient interface), a conduit 126 (also referred to as a tubing or air circuit), a display 128, a humidifier 129, or a combination thereof. In some implementations, control system 110, memory 114, display 128, one or more sensors 130, and humidifier 129 are part of respiratory treatment device 122. Respiratory pressure treatment refers to the application of delivering air to the entrance of a user's airways at a controlled target pressure that is nominally positive relative to the atmosphere throughout the user's respiratory cycle (as opposed to negative pressure therapy, such as an iron lung or chest pad, for example). Respiratory treatment system 120 is typically used to treat individuals suffering from one or more sleep-related breathing disorders (e.g., obstructive sleep apnea, central sleep apnea, mixed sleep apnea).

[0025] Respiratory treatment device 122 is typically used to generate pressurized air delivered to a user (e.g., using one or more motors driving one or more compressors). In some implementations, respiratory treatment device 122 generates a continuous, constant air pressure delivered to a user. In other implementations, respiratory treatment device 122 generates two or more predetermined pressures (e.g., a first predetermined air pressure and a second predetermined air pressure). In yet other implementations, respiratory treatment device 122 is configured to generate a variety of different air pressures within a predetermined range. For example, respiratory treatment device 122 can deliver at least about 6 cmH2O, at least about 10 cmH2O, at least about 20 cmH2O, between about 6 cmH2O and about 10 cmH2O, or between about 7 cmH2O and about 12 cmH2O. Respiratory treatment device 122 can also deliver pressurized air at a predetermined flow rate, e.g., between about -20 L / min and about 150 L / min, while maintaining a positive pressure (relative to ambient pressure).

[0026] The user interface 124 engages a portion of the user's face to deliver pressurized air from the respiratory treatment device 122 to the user's airway, helping to prevent the airway from narrowing and / or closing during sleep. This may also increase the user's oxygen intake while sleeping. Typically, the user interface 124 engages the user's face to deliver pressurized air to the user's airway through the user's mouth, nose, or both the user's mouth and nose. The respiratory treatment device 122, the user interface 124, and the conduit 126 form an airway fluidly connected to the user's airway. The pressurized air also increases the user's oxygen intake while sleeping. Depending on the therapy being applied, the user interface 124 may, for example, form a seal with an area or portion of the user's face to facilitate delivery of gas at a pressure sufficiently different from ambient pressure to effect therapy, e.g., a positive pressure of approximately 10 cmH2O relative to ambient pressure. In other forms of therapy, such as oxygen delivery, the user interface may not include a seal sufficient to facilitate delivery of a gas supply to the airways at a positive pressure of approximately 10 cmH2O.

[0027] As shown in FIG. 2 , in some implementations, the user interface 124 is a face mask that covers the user's nose and mouth. Alternatively, the user interface 124 may be a nasal mask that provides air to the user's nose or a nasal pillows mask that delivers air directly to the user's nostrils. The user interface 124 may include multiple straps (e.g., including hook-and-loop fasteners) for positioning and / or stabilizing the interface on a portion of the user (e.g., the face) and a conformable cushion (e.g., silicone, plastic, foam, etc.) that helps provide an airtight seal between the user interface 124 and the user. The user interface 124 may also include one or more vents to allow carbon dioxide and other gases exhaled by the user 210 to escape. In other implementations, the user interface 124 is a mouthpiece (e.g., a night guard mouthpiece shaped to fit the user's teeth, a mandibular repositioning device, etc.) that directs pressurized air into the user's mouth.

[0028] A conduit 126 (also called an air circuit or tubing) allows air to flow between two components of the respiratory treatment system 120, such as the respiratory treatment device 122 and the user interface 124. In some implementations, there may be separate conduits for inhalation and exhalation. In other implementations, a single conduit is used for both inhalation and exhalation. The conduit 126 includes a first end coupled to an outlet of the respiratory treatment device 122 and an opposite second end coupled to the user interface 124. The conduit 126 can be coupled to the respiratory treatment device 122 and / or the user interface 124 using various techniques (e.g., press-fit connection, snap-fit ​​connection, threaded connection, etc.). In some implementations, the conduit 126 includes one or more heating elements that heat the pressurized air flowing through the conduit 126 (e.g., heat the air to a predetermined temperature or within a predetermined temperature range). The heating elements may be coupled to and / or embedded in the conduit 126. In such an implementation, the end of the conduit 126 coupled to the respiratory treatment device 122 can include electrical contacts electrically coupled to the respiratory treatment device 122 to drive one or more heating elements of the conduit 126.

[0029] One or more of the respiratory treatment device 122, the user interface 124, the conduit 126, the display 128, and the humidifier 129 may include one or more sensors (e.g., a pressure sensor, a flow sensor, or more generally, any of the other sensors 130 described herein) that can be used, for example, to measure the air pressure and / or flow rate of the pressurized air supplied by the respiratory treatment device 122.

[0030] Display device 128 is generally used to display images and / or information, including still images, moving images, or both, related to respiratory treatment device 122. For example, display device 128 can provide information regarding the status of respiratory treatment device 122 (e.g., whether respiratory treatment device 122 is on / off, the pressure of air being delivered by respiratory treatment device 122, the temperature of air being delivered by respiratory treatment device 122, etc.) and / or other information (e.g., a sleep score and / or a treatment score (also referred to 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, personal user information, etc.). In some implementations, display device 128 functions as a human-machine interface (HMI) that includes as an input interface a graphical user interface (GUI) configured to display images. Display device 128 can be an LED display, an OLED display, an LCD display, etc. The input interface may be, for example, a touch screen or touch sensitive board, a mouse, a keyboard, or any sensor system configured to sense inputs made by a human user interacting with respiratory treatment device 122 .

[0031] Humidifier 129 is coupled to or integrated with respiratory treatment device 122 and includes a reservoir of water capable of humidifying pressurized air delivered from respiratory treatment device 122. Respiratory treatment device 122 may include a heater that heats water in humidifier 129 to humidify the pressurized air provided to the user. Additionally, in some implementations, conduit 126 may also include a heating element (e.g., coupled to and / or embedded in conduit 126) that heats the pressurized air delivered to the user.

[0032] Respiratory treatment system 120 can be used, for example, as a mechanical ventilator or a positive airway pressure (PAP) system, such as a continuous positive airway pressure (CPAP) system, an automatic positive airway pressure (APAP) system, a bilevel or variable positive airway pressure (BPAP or VPAP) system, or any combination thereof. A CPAP system delivers a predetermined air pressure (e.g., determined by a sleep physician) to a user. An APAP system automatically varies the air pressure delivered to a user based, for example, on respiratory data associated with the user. A BPAP or VPAP system is configured to deliver a first predetermined pressure (e.g., inspiratory positive airway pressure or IPAP) and a second predetermined pressure (e.g., expiratory positive airway pressure or EPAP) that is lower than the first predetermined pressure.

[0033] Referring to FIG. 2, a portion of system 100 (FIG. 1) according to some implementations is shown. A user 210 and bed partner 220 of respiratory treatment system 120 are positioned in bed 230 and lying on mattress 232. A user interface 124 (e.g., a full face mask) may be worn by user 210 during a sleep session. User interface 124 is fluidly coupled and / or connected to respiratory treatment device 122 via conduit 126. Respiratory treatment device 122 then delivers pressurized air to user 210 via conduit 126 and user interface 124 to increase air pressure in the user's 210 throat and help prevent airway closure and / or narrowing during sleep. Respiratory treatment device 122 may be placed on a nightstand 240 directly adjacent to bed 230, as shown in FIG. 2, or more generally, on any surface or structure generally adjacent to bed 230 and / or user 210.

[0034] 1 , the one or more sensors 130 of the system 100 may 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 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 sensor 178, or any combination thereof. Generally, each of the one or more sensors 130 is configured to output sensor data that is received and stored by the storage device 114 or one or more other storage devices.

[0035] Although the one or more sensors 130 are illustrated 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 infrared sensor 152, a photoelectric (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, and a LiDAR sensor 178, more generally, the one or more sensors 130 may include any combination and number of the sensors described and / or illustrated herein.

[0036] The one or more sensors 130 can be used, for example, to generate physiological data, audio data, or both. The physiological data generated by the one or more sensors 130 can be used by the control system 110 to determine a sleep-wake signal and one or more sleep-related parameters associated with the user during a sleep session. The sleep-wake signal can indicate one or more sleep states including wakefulness, relaxed wakefulness, micro-arousals, rapid eye movement (REM) stage, a first non-REM stage (often referred to as "N1"), a second non-REM stage (often referred to as "N2"), a third non-REM stage (often referred to as "N3"), or any combination thereof. Methods for determining sleep states and / or sleep stages based on physiological data generated by one or more sensors, such as one or more sensors 210, are described, for example, in International Publication No. WO 2014 / 047310, U.S. Patent Application Publication No. 2014 / 0088373, WO 2017 / 132726, WO 2019 / 122413, WO 2019 / 122414, and U.S. Patent Application Publication No. 2020 / 0383580, each of which is incorporated by reference in its entirety. Sleep-wake signals may be measured by sensor 130 during a sleep session at a predetermined sampling rate, such as, for example, 1 sample per second, 1 sample per 30 seconds, 1 sample per minute, etc. One or more sleep-related parameters that may be determined for the user during a sleep session based on the sleep-wake signals include total time in bed, total sleep time, sleep onset latency, a sleep sleep effect parameter, sleep efficiency, fragmentation index, or any combination thereof.

[0037] The sleep-wake signal may also be time-stamped to indicate the time the user gets into bed, the time the user gets out of bed, the time the user attempts to fall asleep, etc. The sleep-wake signal may be measured by one or more sensors 130 at a predetermined sampling rate during the sleep session, such as one sample per second, one sample per 30 seconds, one sample per minute, etc. In some implementations, the sleep-wake signal may also indicate the respiratory signal, respiratory rate, inspiratory amplitude, expiratory amplitude, inspiratory-expiratory ratio, number of events per hour, event patterns, pressure setting of the respiratory therapy device 122, or any combination thereof during the sleep session. The events may include snoring, apnea, central apnea, obstructive apnea, mixed apnea, hypopnea, mask leak (e.g., from the user interface 124), restless legs, sleep disorder, choking, increased heart rate, labored breathing, asthma attack, epileptic episode, seizure, or any combination thereof. The one or more sleep-related parameters that may be determined for the user during a sleep session based on the sleep-wake signal may include total time in bed, total sleep time, sleep onset latency, a wake-after-sleep parameter, sleep efficiency, a fragmentation index, or any combination thereof. As described in further detail herein, the physiological data and / or the sleep-related parameters may be analyzed to determine one or more sleep-related scores.

[0038] Physiological and / or audio data generated by one or more sensors 130 can also be used to determine a respiratory signal associated with the user during a sleep session. The respiratory signal generally indicates the user's breathing during a sleep session. The respiratory signal may indicate, for example, respiratory rate, respiratory rate variability, inspiratory amplitude, expiratory amplitude, inspiratory-expiratory ratio, number of events per hour, event patterns, pressure settings of the respiratory therapy device 122, or any combination thereof. The events may include snoring, apnea, central apnea, obstructive apnea, mixed apnea, hypopnea, mask leak (e.g., from the user interface 124), restless legs, sleep disorder, choking, increased heart rate, labored breathing, asthma attack, epileptic episode, seizure, or any combination thereof.

[0039] Pressure sensor 132 outputs pressure data that may be stored in memory 114 and / or analyzed by processor 112 of control system 110. In some implementations, pressure sensor 132 is an air pressure sensor (e.g., a barometric sensor) that generates sensor data indicative of a user's breathing (e.g., inhalation and / or exhalation) and / or ambient pressure of respiratory treatment system 120. In such implementations, pressure sensor 132 may be coupled to or integrated with respiratory treatment device 122. Pressure sensor 132 may be, for example, a capacitive sensor, an electromagnetic sensor, a piezoelectric sensor, a strain gauge sensor, an optical sensor, a potentiometric sensor, or any combination thereof.

[0040] 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. Examples of flow sensors (e.g., flow sensor 134) are described in International Publication No. WO 2012 / 012835 and U.S. Patent Application Publication No. 10,328,219, which are incorporated herein by reference in their entireties. In some implementations, the flow sensor 134 is used to determine the airflow rate from the respiratory treatment device 122, the airflow rate through the conduit 126, the airflow rate through the user interface 124, or any combination thereof. In such implementations, the flow sensor 134 can be coupled to or integrated with the respiratory treatment 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 film sensor, or any combination thereof.

[0041] The temperature sensor 136 outputs temperature data that may 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 210 ( FIG. 2 ), the skin temperature of the user 210, the temperature of the air flowing from the respiratory treatment device 122 and / or through the conduit 126, 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 semiconductor-based sensor, a resistance temperature detector, or any combination thereof.

[0042] 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 210 during a sleep session and / or the movement of any of the components of the respiratory treatment system 120, such as the respiratory treatment device 122, the user interface 124, or the conduit 126. The motion sensor 138 can include one or more inertial sensors, such as an accelerometer, a gyroscope, and a magnetometer. In some implementations, the motion sensor 138 may alternatively or additionally generate one or more signals representative of the user's body movements and derive a signal from these signals representative of the user's sleep state, for example, via the user's respiratory movements. In some implementations, the motion data from the motion sensor 138 can be used in conjunction with additional data from another sensor 130 to determine the user's sleep state.

[0043] The microphone 140 outputs audio data that can be stored in the storage device 114 and / or analyzed by the processor 112 of the control system 110. The audio data generated by the microphone 140 can be played back as one or more sounds (e.g., sounds from the user 210) during a sleep session. As described in further detail herein, the audio data from the microphone 140 can also be used to identify events experienced by the user during a sleep session (e.g., using the control system 110). The microphone 140 can be coupled to or integrated with the respiratory treatment device 122, the user interface 124, the conduit 126, or the user device 170. In some implementations, the system 100 includes multiple microphones (e.g., two or more microphones and / or an array of microphones using beamforming), so that the audio data generated by each of the multiple microphones can be used to identify audio data generated by other microphones of the multiple microphones.

[0044] The speaker 142 outputs sound waves audible to a user (e.g., user 210 of FIG. 2 ) via the system 100. The speaker 142 can be used, for example, as an alarm clock or to play alerts or messages to the user 210 (e.g., in response to an event). In some implementations, the speaker 142 can be used to communicate audio data generated by the microphone 140 to the user. The speaker 142 can be coupled to or integrated with the respiratory treatment device 122, the user interface 124, the conduit 126, or the user device 170.

[0045] The microphone 140 and the speaker 142 can be used as independent devices. In some implementations, the microphone 140 and the speaker 142 may be combined into an acoustic sensor 141 (e.g., a SONAR sensor), for example, as described in International Publication Nos. WO 2018 / 050913 and WO 2020 / 104465, each of which is incorporated by reference in its entirety. In such implementations, the speaker 142 generates or emits sound waves at predetermined intervals, and the microphone 140 detects reflections of the sound waves emitted from the speaker 142. The sound waves generated or emitted by the speaker 142 have a frequency that is inaudible to the human ear (e.g., below 20 Hz or above about 18 kHz) so as not to disturb the sleep of the user 210 or the user's bed partner 220 ( FIG. 2 ). The control system 110 can determine the location of the user 210 (FIG. 2) and / or one or more sleep-related parameters described herein based at least in part on data from the microphone 140 and / or the speaker 142.

[0046] In some implementations, sensor 130 includes (i) a first microphone that is the same as or similar to microphone 140 and integrated into acoustic sensor 141, and (ii) a second microphone that is the same as or similar to microphone 140 but is independent and separate from the first microphone that is integrated into acoustic sensor 141.

[0047] The RF transmitter 148 generates and / or emits radio waves having a predetermined frequency and / or a predetermined amplitude (e.g., within a high-frequency band, within a low-frequency band, a long-wave signal, a short-wave signal, etc.). The RF receiver 146 detects reflections of the radio waves emitted from the RF transmitter 148, and this data can be analyzed by the control system 110 to determine the location of the user 210 ( FIG. 2 ) and / or one or more sleep-related parameters described herein. Additionally, the RF receiver (either the RF receiver 146 or the RF transmitter 148, or another RF pair) can be used for wireless communication between the control system 110, the respiratory treatment device 122, one or more sensors 130, the user device 170, or any combination thereof. While the RF receiver 146 and the RF transmitter 148 are shown in FIG. 1 as separate and distinct elements, in some implementations the RF receiver 146 and the RF transmitter 148 are combined as part of the RF sensor 147. In some such implementations, the RF sensor 147 includes control circuitry. The specific form of RF communication may be Wi-Fi, Bluetooth, or the like.

[0048] In some implementations, RF sensor 147 is part of a mesh system. An example of a mesh system is a Wi-Fi® mesh system, which may include mesh nodes, mesh routers, and mesh gateways, each of which may be mobile / movable or fixed. In such implementations, the Wi-Fi® mesh system includes a Wi-Fi® router and / or a Wi-Fi® controller, and one or more satellites (e.g., access points), each of which includes an RF sensor the same as or similar to RF sensor 147. The Wi-Fi® router and satellites continuously communicate with each other using Wi-Fi® signals. A Wi-Fi® mesh system can be used to generate motion data based on changes in the Wi-Fi® signal between the router and the satellite (e.g., differences in received signal strength) due to the movement of an object or person partially obstructing the signal. This motion data can indicate movement, breathing, heart rate, gait, falls, behavior, etc., or any combination thereof.

[0049] Camera 150 outputs image data playable as one or more images (e.g., still images, video images, thermal images, or a combination thereof) that can be stored in storage device 114. Control system 110 can use the image data from camera 150 to determine one or more sleep-related parameters described herein, such as, for example, one or more events (e.g., periodic limb movement or restless legs syndrome), a respiratory signal, a respiratory rate, an inspiratory amplitude, an expiratory amplitude, an inspiratory-expiratory ratio, a number of events per hour, an event pattern, a sleep state, a sleep stage, or any combination thereof. Furthermore, image data from camera 150 can be used to, for example, identify a user's position, determine chest movement of user 210 ( FIG. 2 ), determine mouth and / or nasal airflow of user 210, determine the time user 210 enters bed 230, and determine the time user 210 leaves bed 230 ( FIG. 2 ). In some implementations, camera 150 includes a wide-angle or fisheye lens. For example, image data from camera 150 can be used to identify the user's location, determine when user 210 enters bed 230 (FIG. 2), and determine when user 210 leaves bed 230.

[0050] The infrared (IR) sensor 152 outputs infrared image data that can be played back as one or more infrared images (e.g., still images, moving images, or both) that can be stored in the storage 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 temperature of the user 210 and / or the movement of the user 210. The IR sensor 152 can also be used in combination with the camera 150 in measuring the presence, location, and / or movement of the user 210. The IR sensor 152 can detect infrared light having a wavelength between about 700 nm and about 1 mm, for example, while the camera 150 can detect visible light having a wavelength between about 380 nm and about 740 nm.

[0051] The PPG sensor 154 outputs physiological data associated with the user 210 (FIG. 2), which may be used to determine one or more sleep-related parameters, such as, for example, heart rate, heart rate variability, cardiac cycle, respiratory rate, inspiration amplitude, expiration amplitude, inspiration-to-expiration ratio, estimated blood pressure parameters, or any combination thereof. The PPG sensor 154 is worn by the user 210, may be embedded in clothing and / or fabric worn by the user 210, embedded in and / or coupled to the user interface 124 and / or its associated headgear (e.g., straps, etc.).

[0052] The ECG sensor 156 outputs physiological data associated with the electrical activity of the heart of the user 210. In some implementations, the ECG sensor 156 includes one or more electrodes placed on or around a portion of the user 210 during a sleep session. The physiological data from the ECG sensor 156 can be used to determine, for example, one or more sleep-related parameters described herein.

[0053] The EEG sensor 158 outputs physiological data associated with the electrical activity of the brain of the user 210. In some implementations, the EEG sensor 158 includes one or more electrodes placed on or around the scalp of the user 210 during a sleep session. The physiological data from the EEG sensor 158 can be used, for example, to determine the sleep state of the user 210 at any given time during the sleep session. In some implementations, the EEG sensor 158 can be integrated into the user interface 124 and / or its associated headgear (e.g., straps, etc.).

[0054] The capacitance sensor 160, the force sensor 162, and the strain gauge sensor 164 output data that may be stored in the memory device 114 and used by the control system 110 to determine one or more sleep-related parameters described herein. The EMG sensor 166 outputs physiological data associated with electrical activity produced by one or more muscles. The oxygen sensor 168 outputs oxygen data indicative of the oxygen concentration of a gas (e.g., in the conduit 126 or at the user interface 124). The oxygen sensor 168 may be, for example, an ultrasonic oxygen sensor, an electrical 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 sphygmomanometer sensor, an oximetry sensor, or any combination thereof.

[0055] The analyte sensor 174 can be used to detect the presence of analytes in the breath of the user 210. Data output by the analyte sensor 174 can be stored in the memory device 114 and used by the control system 110 to determine the identity and concentration of any analytes in the user's 210 breath. In some implementations, the analyte sensor 174 is positioned near the mouth of the user 210 to detect analytes in the breath exhaled from the user's 210 mouth. For example, if the user interface 124 is a face mask that covers the nose and mouth of the user 210, the analyte sensor 174 can be positioned within the face mask to monitor the user's 210 mouth breathing. In other implementations, if the user interface 124 is a nasal mask or nasal pillows mask, the analyte sensor 174 can be positioned near the nose of the user 210 to detect analytes in the breath exhaled from the user's nose. In yet another implementation, if the user interface 124 is a nasal mask or nasal pillows mask, the analyte sensor 174 can be positioned near the mouth of the user 210. In this implementation, the analyte sensor 174 can be used to detect whether air is inadvertently leaking from the mouth of the user 210. In some implementations, the analyte sensor 174 is a volatile organic compound (VOC) sensor that can be used to detect carbon-based chemicals or compounds. In some implementations, the analyte sensor 174 can also be used to detect whether the user 210 is breathing through their nose or mouth. For example, if the presence of an analyte is detected by data output by the analyte sensor 174 positioned near the mouth of the user 210 or in a face mask (in implementations where the user interface 124 is a face mask), the control system 110 can use this data as an indicator that the user 210 is breathing through their mouth.

[0056] The moisture sensor 176 outputs data that can be stored in the memory device 114 and used by the control system 110. The moisture sensor 176 can be used to detect moisture in various areas surrounding the user (e.g., inside the conduit 126 or the user interface 124, near the face of the user 210, near the connection between the conduit 126 and the user interface 124, near the connection between the conduit 126 and the respiratory treatment device 122, etc.). Thus, in some implementations, the moisture sensor 176 can be coupled to or integrated within the user interface 124 or the conduit 126 to monitor the humidity of the pressurized air from the respiratory treatment device 122. In other implementations, the moisture sensor 176 is located near any area where humidity levels need to be monitored. The moisture sensor 176 can also be used to monitor the humidity of the air in the ambient environment surrounding the user 210, for example, in the user's bedroom.

[0057] The light detection and ranging (LiDAR) sensor 178 can be used for depth sensing. Such optical sensors (e.g., laser sensors) can be used to detect objects and create three-dimensional (3D) maps of surrounding environments, such as living spaces. LiDAR typically uses a pulsed laser to measure time of flight. LiDAR is also known as 3D laser scanning. In one use case of such a sensor, a fixed or mobile device (such as a smartphone) equipped with the LiDAR sensor 166 can measure and map an area more than five meters away from the sensor. LiDAR data can be fused with point cloud data estimated, for example, by an electromagnetic RADAR sensor. The LiDAR sensor 178 can also automatically create geofences for RADAR systems by using artificial intelligence (AI) to detect and classify spatial features that may pose problems to the RADAR system, such as glass windows (which may be highly reflective to RADAR). LiDAR can also be used to estimate a person's height, as well as changes in their natural height, such as when they sit or fall. LiDAR can be used to create 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 may enable classification of different types of obstacles due to reflections from such surfaces.

[0058] 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, an oximetry sensor, a sonar sensor, a RADAR sensor, a blood glucose sensor, a color sensor, a pH sensor, an air quality sensor, a tilt sensor, a rain sensor, a soil moisture sensor, a water flow sensor, an alcohol sensor, or any combination thereof.

[0059] 1 , any combination of one or more sensors 130 may be integrated into and / or coupled to any one or more components of system 100, including respiratory treatment device 122, user interface 124, conduit 126, humidifier 129, control system 110, user device 170, or any combination thereof. For example, microphone 140 and speaker 142 are integrated into and / or coupled to user device 170, and pressure sensor 132 and / or flow sensor 134 are integrated into and / or coupled to respiratory treatment device 122. In some implementations, at least one of one or more sensors 130 is not coupled to respiratory treatment device 122, control system 110, or user device 170, but is positioned generally adjacent to user 210 during a sleep session (e.g., positioned on or in contact with a portion of user 210, worn by user 210, coupled to or placed on a nightstand, coupled to a mattress, coupled to a ceiling, etc.).

[0060] Data from one or more sensors 130 can be analyzed to determine one or more sleep-related parameters, which may include a respiratory signal, a respiratory rate, a respiratory pattern, an inspiratory amplitude, an expiratory amplitude, an inspiratory-expiratory ratio, the occurrence of one or more events, the number of events per hour, an event pattern, a sleep state, an apnea-hypopnea index (AHI), or any combination thereof. The one or more events may include snoring, apnea, central apnea, obstructive apnea, mixed apnea, hypopnea, mask leak, coughing, restless legs, sleep disorder, choking, increased heart rate, labored breathing, asthma attack, epileptic episode, seizure, elevated blood pressure, or any combination thereof. While many of these sleep-related parameters are physiological parameters, some 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.

[0061] User device 170 ( FIG. 1 ) includes a display device 172. User device 170 may be, for example, a mobile device such as a smartphone, tablet, game console, smartwatch, laptop, etc. Alternatively, user device 170 may be an external sensing system, a television (e.g., a smart television), or another smart home device (e.g., a smart speaker such as Google Home, Amazon Echo, Alexa, etc.). In some implementations, user device 170 is a wearable device (e.g., a smart watch). Display device 172 is typically used to display images, including still images, moving images, or both. In some implementations, display device 172 functions as a human-machine interface (HMI) that includes a graphical user interface (GUI) configured to display images and an input interface. Display device 172 may be an LED display, an OLED display, an LCD display, etc. The input interface may be, for example, a touchscreen or touch-sensitive board, a mouse, a keyboard, or any sensor system configured to sense inputs made by a human user interacting with user device 170. In some implementations, one or more user devices may be used by and / or included in the system 100 .

[0062] 1 as separate and distinct components of system 100, in some implementations control system 110 and / or storage 114 are integrated into user device 170 and / or respiratory treatment device 122. Alternatively, in some implementations control system 110 or portions thereof (e.g., processor 112) may be located in the cloud (e.g., integrated into a server, integrated into an Internet of Things (IoT) device, connected to the cloud, subject to edge cloud processing, etc.), located on one or more servers (e.g., remote servers, local servers, etc., or any combination thereof).

[0063] Although system 100 is shown as including all of the above components, in various implementations of the present disclosure, the system can include more or fewer components for generating physiological data and determining recommended notifications or actions for the user. For example, a first alternative system includes control system 110, storage 114, and at least one of one or more sensors 130. As another example, a second alternative system includes control system 110, storage 114, at least one of one or more sensors 130, and user device 170. As yet another example, a third alternative system includes control system 110, storage 114, respiratory treatment system 120, at least one of one or more sensors 130, and user device 170. Thus, any portion of the components shown and described herein can be used and / or combined with one or more other components to form a variety of systems.

[0064] As used herein, a sleep session can be defined in several ways, for example, based on an initial start time and an end time. In some implementations, a sleep session is a duration during which a user is asleep, i.e., the sleep session has a start time and an end time, and the user does not wake up during the sleep session until the end time. In other words, the time the user is awake is not included in the sleep session. According to this first definition of a sleep session, if a user wakes up and falls asleep multiple times throughout the night, each sleep segment separated by a wakefulness segment constitutes a sleep session.

[0065] Alternatively, in some implementations, a sleep session has a start time and an end time, and during a sleep session, the user can wake up without the sleep session ending as long as the cumulative duration the user is awake is below a wakefulness duration threshold. The wakefulness duration threshold may be defined as a percentage of the sleep session. The wakefulness duration threshold may 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, or any other threshold percentage. In some implementations, the wakefulness duration threshold is defined as a certain period of time, such as about 1 hour, about 30 minutes, about 15 minutes, about 10 minutes, about 5 minutes, about 2 minutes, or any other period of time.

[0066] In some implementations, a sleep session is defined as the total time from when a user first gets into bed at night to when the user last gets out of bed the next morning. In other words, a sleep session can be defined as the period starting at a first time (e.g., 10:00 PM) on a first date (e.g., Monday, January 6, 2020), which may be referred to as tonight, when the user first gets into bed with the intention to go to sleep (unless the user first intends to watch TV or play with their smartphone before going to sleep), and ending at a second time (e.g., 7:00 AM) on a second date (e.g., Tuesday, January 7, 2020), which may be referred to as the next morning, when the user first gets out of bed with the intention not to go to sleep again the next morning.

[0067] In some implementations, a user can manually set the start of a sleep session and / or manually end a sleep session. For example, a user can select (e.g., by clicking or tapping) one or more user-selectable elements displayed on display 172 of user device 170 (FIG. 1) to manually start or end a sleep session.

[0068] Generally, a sleep session includes any time after the user 210 lies or sits in bed 230 (or another area or object used for sleeping) and turns on the respiratory treatment device 122 and puts on the user interface 124. Thus, a sleep session may include (i) a time period during which the user 210 is using the CPAP system but before attempting to fall asleep (e.g., a time period during which the user 210 is lying in bed 230 reading a book), (ii) a time period during which the user 210 begins to attempt to fall asleep but is still awake, (iii) a time period during which the user 210 is in light sleep (also called stages 1 and 2 of non-rapid eye movement (NREM) sleep), (iv) a time period during which the user 210 is in deep sleep (also called slow wave sleep (SWS) or stage 3 of NREM sleep), (v) a time period during which the user 210 is in rapid eye movement (REM) sleep, (vi) a time period during which the user 210 is waking periodically between light sleep, deep sleep, or REM sleep, or (vii) a time period during which the user 210 is awake and does not return to sleep.

[0069] In general, a sleep session can be defined to end when the user 210 removes the user interface 124, turns off the respiratory treatment device 122, and leaves the bed 230. In some implementations, a sleep session can include additional time periods or be limited to only some of the time periods disclosed above. For example, a sleep session can be defined to encompass a period that begins when the respiratory treatment device 122 begins to deliver pressurized air to the airway or user 210, ends when the respiratory treatment device 122 stops delivering pressurized air to the airway of the user 210, and includes some or all of the time points in between when the user 210 is asleep or awake.

[0070] Referring to Figure 3, an exemplary timeline 300 of a sleep session is shown. The timeline 300 begins with bedtime (t bed ) and bedtime (t GTS ) and initial sleep time (t sleep), the first micro-awakening MA1, the second micro-awakening MA2, the awakening A, and the wake-up time (t wake ) and wake-up time (t rise ) and,

[0071] bedtime t bed is associated with the time when the user first goes to bed (e.g., bed 230 in FIG. 2) (e.g., when the user lies or sits in bed) before falling asleep. bed can be identified based on a bedtime threshold duration to distinguish between a time when a user goes to bed to sleep and a time when a user goes to bed for other reasons (e.g., to watch television). For example, the bedtime 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. As used herein, a bedtime t refers to a time when a user goes to bed with reference to a bed. bed However, more generally, the bedtime t bed may represent the time when the user first settles into some location (e.g., sofa, chair, sleeping bag, etc.) to sleep.

[0072] The time of sleep onset (GTS) is the time when the user goes to bed (t bed ) and the time when the user first attempts to fall asleep. For example, after getting into bed, the user may engage in one or more activities (e.g., reading, watching television, listening to music, using user device 170, etc.) to relax before attempting 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 ) may be the time when the user first entered the first non-REM sleep stage.

[0073] wake up time t wakeis the time associated with the time the user wakes up without going back to sleep (e.g., different from the time the user wakes up in the middle of the night and goes back to sleep). After the user initially falls asleep, they may experience one of many involuntary micro-awakenings (e.g., micro-awakenings MA1 and MA2) with short durations (e.g., 5 seconds, 10 seconds, 30 seconds, 1 minute, etc.). wake In contrast to the above, the user falls asleep again after each micro-awakening MA1 and MA2. Similarly, the user may have one or more conscious awakenings (e.g., Awakening A) (e.g., waking up to go to the bathroom, caring for a child or pet, sleepwalking, etc.) after initially falling asleep. However, the user falls asleep again after Awakening A. Therefore, the wake-up time t wake may be defined, for example, based on the wake-up threshold duration (eg, the user has been awake for 15 minutes or more, 20 minutes or more, 30 minutes or more, 1 hour or more, etc.).

[0074] Similarly, the wake-up time t rise is associated with the time when the user gets up and is absent from bed with the intent of ending a sleep session (as opposed to, for example, the user getting up to go to the bathroom during the night, caring for a child or pet, sleepwalking, etc.). In other words, the wake-up time t rise is the time when the user last left bed without returning to bed until the next sleep session (e.g., the next night). Therefore, the wake-up time t rise may be defined, for example, based on a wake-up threshold duration (e.g., the user has left bed for 15 minutes or more, 20 minutes or more, 30 minutes or more, 1 hour or more, etc.). bed can also be defined based on wake threshold duration (eg, when the user has been out of bed for more than 4 hours, more than 6 hours, more than 8 hours, more than 12 hours, etc.).

[0075] As mentioned above, the user must first bed to the last t rise In some implementations, the patient may wake up and leave the bed one or more times during the night. wakeand / or last wake-up time t rise is identified or determined based on a predetermined threshold duration of time following an event (e.g., falling asleep or leaving bed). Such threshold duration may be customized for the user. For a typical user who goes to bed at night and wakes up and leaves bed in the morning, the threshold duration may be any time between about 12 hours and about 18 hours (between the time the user wakes up (t) wake ) or wake up (t rise ) to bedtime (t bed ) and fall asleep (t GTS ) or sleep (t sleep ) can be used. For users who sleep longer, a shorter threshold period (e.g., between about 8 hours and about 14 hours) can be used. The threshold period may be initially selected and / or later adjusted based on the system monitoring the user's sleep behavior.

[0076] Total time in bed (TIB) is the time from bedtime t bed From wake-up time t rise t . Total sleep time (TST) is the duration from the initial sleep time t to the wake-up time, excluding conscious or unconscious awakenings and / or microarousals. Typically, total sleep time (TST) will be shorter than total time in bed (TIB) (e.g., 1 minute shorter, 10 minutes shorter, 1 hour shorter, etc.). For example, referring to timeline 300 of FIG. 3, total sleep time (TST) is the duration from the initial sleep time t sleep and alarm time t wake , but excluding the duration of the first micro-awakening MA1, the second micro-awakening MA2, and Awakening A. As shown, in this example, the total sleep time (TST) is less than the total time in bed (TIB).

[0077] In some implementations, total sleep time (TST) may be defined as total continuous sleep time (PTST). In such implementations, total continuous sleep time excludes a predetermined initial portion or period of the first non-REM stage (e.g., a light sleep stage). For example, the predetermined initial portion may be between about 30 seconds and about 20 minutes, between about 1 minute and about 10 minutes, between about 3 minutes and about 5 minutes, etc. Total continuous sleep time is a measure of continuous sleep and smooths the sleep-wake hypnogram. For example, when a user first falls asleep, the user may enter the first non-REM stage for a very short time (e.g., about 30 seconds), then return to the wake stage for a short time (e.g., 1 minute), before returning to the first non-REM stage. In this example, total continuous sleep time excludes the first instance (e.g., about 30 seconds) of the first non-REM stage.

[0078] In some implementations, a sleep session begins at bedtime (t bed ) and wake-up time (t rise ), i.e., a sleep session is defined as the total time in bed (TIB). In some implementations, a sleep session is defined as ending at the initial sleep time (t sleep ) and wake up at the alarm time (t wake ) In some implementations, a sleep session is defined as total sleep time (TST). In some implementations, a sleep session is defined as a sleep session ending at sleep onset time (t GTS ) and wake up at the alarm time (t wake ) In some implementations, a sleep session is defined as ending at sleep onset time (t GTS ) and wake-up time (t rise ) In some implementations, a sleep session is defined as ending at bedtime (t bed ) and wake up at the alarm time (t wake ) In some implementations, a sleep session is defined as ending at an initial sleep time (t sleep ) and wake-up time (t rise ) is defined as ending in

[0079] Generally, a sleep session may include any time after the user 210 lies or sits in bed 230 (or another area or object used for sleeping) and turns on the respiratory treatment device 122 and puts on the user interface 124. Thus, a sleep session may include (i) a time period during which the user 210 is using the CPAP system but before attempting to fall asleep (e.g., a time period during which the user 210 is lying in bed 230 reading a book), (ii) a time period during which the user 210 begins to attempt to fall asleep but is still awake, (iii) a time period during which the user 210 is in light sleep (also called stages 1 and 2 of non-rapid eye movement (NREM) sleep), (iv) a time period during which the user 210 is in deep sleep (also called slow wave sleep (SWS) or stage 3 of NREM sleep), (v) a time period during which the user 210 is in rapid eye movement (REM) sleep, (vi) a time period during which the user 210 is waking periodically between light sleep, deep sleep, or REM sleep, or (vii) a time period during which the user 210 is awake and does not return to sleep.

[0080] In general, a sleep session can be defined to end when the user 210 removes the user interface 124, turns off the respiratory treatment device 122, and leaves the bed 230. In some implementations, a sleep session can include additional time periods or be limited to only some of the time periods disclosed above. For example, a sleep session can be defined to encompass a period that begins when the respiratory treatment device 122 begins to deliver pressurized air to the airway or user 210, ends when the respiratory treatment device 122 stops delivering pressurized air to the airway of the user 210, and includes some or all of the time points in between when the user 210 is asleep or awake.

[0081] 4, an exemplary hypnogram 400 corresponding to the timeline 300 (FIG. 3) according to some implementations is shown. As shown, the hypnogram 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 with one of the axes 410-440 indicates the sleep stage at any given time during a sleep session.

[0082] A sleep-wake signal 401 can be generated (e.g., generated by one or more sensors 130 described herein) based on physiological data associated with the user. The sleep-wake signal can indicate one or more sleep states, including wakefulness, relaxed wakefulness, micro-arousal, REM stage, first NREM stage, second NREM stage, third NREM stage, or any combination thereof. In some implementations, one or more of the first NREM stage, second NREM stage, and third NREM stage can be grouped and classified as a light sleep stage or a deep sleep stage. For example, the light sleep stage can include the first NREM stage, and the deep sleep stage can include the second NREM stage and the third NREM stage. As shown in FIG. 4 , the hypnogram 400 includes a light sleep stage axis 430 and a deep sleep stage axis 440; however, in some implementations, the hypnogram 400 can include axes for each of the first NREM stage, the second NREM stage, and the third NREM stage. In other implementations, the sleep-wake signal may indicate a respiratory signal, a respiratory rate, an inhalation amplitude, an exhalation amplitude, an inhalation-exhalation ratio, a number of events per hour, an event pattern, or any combination thereof. Information describing the sleep-wake signal may be stored in the storage device 114.

[0083] The hypnogram 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), sleep fragmentation index, sleep blocks, or any combination thereof.

[0084] Sleep onset latency (SOL) is the time to bed (t GTS ) to the initial sleep time (t sleep ) In other words, sleep onset latency indicates the time it takes for a user to actually fall asleep after first attempting to fall asleep. In some implementations, sleep onset latency is defined as sustained sleep onset latency (PSOL). Sustained sleep onset latency differs from sleep onset latency in that it is defined as the duration from the time of sleep onset to a predetermined amount of continuous sleep. In some implementations, the predetermined amount of continuous sleep may include, for example, at least 10 minutes of sleep in the second NREM stage, the third NREM stage, and / or a REM stage including 2 minutes or less of wakefulness, the first NREM stage, and / or transitions therebetween. In other words, sustained sleep onset latency, for example, requires a maximum of 8 minutes of continuous sleep in the second NREM stage, the third NREM stage, and / or a REM stage. In other implementations, the predetermined amount of continuous sleep may include at least 10 minutes of sleep in the first NREM stage, the second NREM stage, the third NREM stage, and / or a REM stage after the initial sleep time. In such an implementation, the predetermined amount of continuous sleep may exclude any micro-arousals (eg, a 10-second micro-arousal that does not start the 10-minute period).

[0085] A wake-up-after-sleep-onset (WASO) is associated with the total duration a user is awake between the initial sleep time and the wake-up time. Thus, a wake-up-after-sleep-onset (WASO) includes brief microarousals (e.g., microarousals MA1 and MA2 shown in FIG. 4 ) during a sleep session, whether conscious or unconscious. In some implementations, a wake-up-after-sleep-onset (WASO) is defined as a persistent wake-up-after-sleep-onset (PWASO) that includes only total durations 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.).

[0086] Sleep efficiency (SE) is determined as the ratio of total time in bed (TIB) to total sleep time (TST). For example, if the total time in bed is 8 hours and the total sleep time is 7.5 hours, the sleep efficiency for that sleep session is 93.75%. Sleep efficiency indicates a user's sleep hygiene. For example, if a user goes to bed and spends time on other activities (e.g., watching TV) before going to sleep, sleep efficiency decreases (e.g., the user is penalized). In some implementations, sleep efficiency (SE) can be calculated based on the total time in bed (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 GTS time to the wake-up time as described herein. For example, in an implementation where the total sleep time is 8 hours (e.g., from 11 PM to 7 AM), the fall asleep time is 10:45 PM, and the wake-up time is 7:15 AM, the sleep efficiency parameter is calculated to be approximately 94%.

[0087] The fragmentation index is determined based at least in part on the number of arousals during a sleep session. For example, if a user had two micro-arousals (e.g., micro-arousals MA1 and MA2 shown in FIG. 4), the fragmentation index may be expressed as 2. In some implementations, the fragmentation index is scaled between a predetermined range of integers (e.g., between 0 and 10).

[0088] A sleep block is associated with a transition between any sleep stage (e.g., a first non-REM stage, a second non-REM stage, a third non-REM stage, and / or REM) and a wake stage. For example, a sleep block can be calculated with a 30-second resolution.

[0089] In some implementations, the systems and methods described herein generate or analyze a hypnogram including sleep-wake signals and determine a time to bed (t) based at least in part on the sleep-wake signals of the hypnogram. bed ), sleep onset time (t GTS ), initial sleep time (t sleep ), one or more first micro-arousals (e.g., MA1 and MA2), wake-up time (twake ), wake-up time (t rise ) or any combination thereof.

[0090] In other implementations, one or more sensors 130 detect time to bed (t bed ), sleep onset time (t GTS ), initial sleep time (t sleep ), one or more first micro-arousals (e.g., MA1 and MA2), wake-up time (t wake ), wake-up time (t rise ) or any combination thereof, can be used to define a sleep session. For example, bedtime t bed may be determined based on data generated by, for example, motion sensor 138, microphone 140, camera 150, or any combination thereof. For example, the time of sleep onset may be determined based on data from motion sensor 138 (e.g., data indicating that the user is not moving), data from camera 150 (e.g., data indicating that the user is not moving and / or data indicating that the user has turned off the lights), data from microphone 140 (e.g., data indicating that the television is turned off), data from user device 170 (e.g., data indicating that the user is no longer using user device 170), data from pressure sensor 132 and / or flow sensor 134 (e.g., data indicating that the user has turned on respiratory treatment device 122, data indicating that the user has put on user interface 124, etc.), or any combination thereof.

[0091] 5, a method 500 for identifying events experienced by a user during a sleep session and communicating visual and / or audio indications of the events to the user after the sleep session according to some implementations of the present disclosure is shown. One or more steps of method 500 can be implemented using any element or aspect of system 100 (FIGS. 1-2) described herein.

[0092] Step 501 of method 500 includes generating and / or receiving data associated with a user's sleep session. The data may include, for example, respiratory data associated with the user, audio data associated with the user, or both respiratory and audio data. The respiratory data is data indicative of the user's breathing (e.g., respiratory rate, respiratory rate variability, ventilation, inspiratory amplitude, expiratory amplitude, and / or inspiratory-expiratory ratio) during at least a portion of the sleep session (e.g., 10% or more of the sleep session, 50% or more of the sleep session, 75% or more of the sleep session, 90% or more of the sleep session, etc.). The audio data is playable as one or more sounds (e.g., snoring, choking, breathing, pauses in breathing, labored breathing, etc.) recorded during the sleep session.

[0093] In some implementations, the respiratory data is generated by a first sensor of the one or more sensors 130, and the audio data is generated by a second sensor of the one or more sensors 130. For example, the respiratory data can be generated by pressure sensor 132 and / or flow sensor 134, and the audio data can be generated by microphone 140. In this example, pressure sensor 130 and / or flow sensor 134 can be coupled to or integrated with any component or aspect of respiratory treatment system 120 described herein, and microphone 140 can be coupled to or integrated with user device 170. In other implementations, the respiratory data and audio data are generated by the same one or more sensors 130. In such implementations, the respiratory data and audio data can be generated, for example, by acoustic sensor 141. Data can be received from one or more sensors 130, for example, by electronic interface 119 and / or user device 170 ( FIG. 1 ) described herein.

[0094] By time-stamping the respiratory and audio data, portions of the audio data may be associated with corresponding portions of the respiratory data associated with the same time interval. For example, as described below, if a user experiences an event during a time interval during a sleep session, the corresponding audio may be identified based on the time-stamp information.

[0095] Step 502 of method 500 includes determining a respiratory signal associated with the user during the sleep session based at least in part on the data received in step 501. The respiratory signal may be determined based at least in part on the respiratory data, the audio data, or both. For example, control system 110 may determine the respiratory signal associated with the user during the sleep session by analyzing the data received in step 501 (e.g., data stored in storage 114). Information associated with and / or describing the determined respiratory signal may be stored, for example, in storage 114 (FIG. 1).

[0096] Referring to FIG. 6, an exemplary respiratory signal 610 associated with a user during a portion of a sleep session is shown. The y-axis represents the amplitude of the respiratory signal 610, and the x-axis represents time (e.g., minutes and / or seconds). The respiratory signal 610 includes multiple inhalation segments and multiple exhalation segments. Each inhalation segment corresponds to a user's breath (inhalation), and each exhalation segment corresponds to a user's breath (exhalation). In some implementations, the integral of one inhalation segment of the respiratory signal 610 is equal to the integral of one exhalation segment of the respiratory signal 610. 6, the respiration signal 610 includes a first portion 612 between time t1 and time t2, a second portion 614 between time t2 and time t3, a third portion 616 between time t3 and time t4, a fourth portion 618 between time t4 and time t5, a fifth portion 620 between time t5 and time t6, a sixth portion 622 between time t6 and time t7, and a seventh portion 624 between time t7 and time t8. The respiration signal 610 may indicate, among other things, one or more events experienced by the user during a portion of the first sleep session.

[0097] In some implementations, step 502 of method 500 further includes determining an audio signal associated with a sleep session based at least in part on the audio data received in step 501. Referring to FIG. 6, an exemplary audio signal 630 associated with a user during a portion of a sleep session is shown. The y-axis represents the frequency of the audio signal 630, and the x-axis represents time (e.g., minutes). As shown in FIG. 6, the audio signal 630 generally corresponds to (e.g., is associated with) the breathing signal 610. In particular, the frequency of the audio signal 630 generally corresponds to (e.g., is associated with) the amplitude of the breathing signal 610. For example, the average amplitude of a first portion 612 of the breathing signal 610 between times t1 and t2 is greater than the average amplitude of a second portion 614 of the audio signal 610 between times t2 and t3 (e.g., indicating a pause in breathing between times t2 and t3). Similarly, the average frequency of the audio signal 630 between times t1 and t2 is greater than the average frequency of the audio signal 630 between times t2 and t3 (e.g., indicating a pause in breathing between times t2 and t3). The audio data (step 501) and / or the determined audio signal (step 502) can be used to assist in identifying events experienced by the user during a sleep session.

[0098] Step 503 of method 503 (FIG. 5) includes identifying one or more events experienced by the user during the sleep session. For example, control system 110 can identify events experienced by the user during the sleep session by analyzing the data received in step 501 (e.g., data stored in storage 114) and / or the determined respiratory signal (step 502). The identified events can be snoring, apnea, central apnea, obstructive apnea or obstructive sleep apnea (OSA), mixed apnea, hypopnea, restless legs, sleep disorder, choking, labored breathing, asthma attack, epileptic episode, seizure, or any combination thereof.

[0099] In some implementations, step 503 includes identifying events based at least in part on the received respiratory data (step 501) and / or the determined respiratory signal (step 502). For example, with reference to FIG. 6, a first portion 612, a third portion 616, a fifth portion 620, and a seventh portion 624 of a respiratory signal 610 are associated with normal breathing (e.g., one or more inspirations and one or more expirations). The second portion 614 is associated with a first event, the fourth portion 618 is associated with a second event, and the sixth portion 620 is associated with a third event. In this particular example, the first event, the second event, and the third event are obstructive sleep apnea (OSA) events. These events can be identified in the respiratory signal 610 based on, for example, the relative amplitude of the respiratory signal 610 in the first portion 612, the third portion 616, the fifth portion 620, and the seventh portion 624 relative to the second portion 614, the fourth portion 618, and the sixth portion 620. As another example, each event in the respiratory signal 610 can be identified by comparing the amplitude of the respiratory signal 610 to a predetermined threshold. For example, one or more events during a sleep session can be identified by comparing the average value or sampling rate of the amplitude of the respiratory signal 610 over a predetermined period of time (e.g., 1 second, 3 seconds, 5 seconds, 10 seconds, 30 seconds, 1 minute, 3 minutes, etc.) to a predetermined threshold.

[0100] In some implementations, step 503 includes identifying an event based at least in part on at least a portion of the respiratory data and at least a portion of the audio data received in step 501. In such implementations, the audio data can be analyzed (e.g., by control system 110) to detect or measure the user's respiration. The audio data can also be analyzed to detect or identify a drop or decrease in audio frequency and / or amplitude, which can indicate, for example, a temporary cessation of breathing or a pause in breathing due to an obstructive sleep apnea (OSA) event. Thus, an event can be identified in response to determining that the frequency and / or amplitude of the audio data falls below a predetermined threshold for a predetermined duration (e.g., between about 10 seconds and about 45 seconds, between about 15 seconds and about 30 seconds, etc.).

[0101] However, the drop in audio amplitude may not be due to the user experiencing an event (e.g., a pause in breathing) but rather to a change in the ambient noise level in the room where the user is sleeping. Environmental factors that may affect the ambient noise include, for example, ventilation or airflow (e.g., from an HVAC system, fan, humidifier, dehumidifier, air purifier, etc.), appliances (e.g., television, speakers, etc.), and outside-room noise (e.g., roommates, neighbors, traffic, etc.). Thus, step 503 may include identifying the user's breathing by filtering the ambient noise from the audio data. For example, a machine learning algorithm may be used to filter the ambient noise.

[0102] Further, in such implementations, if step 503 includes identifying an event based at least in part on audio data, step 503 may include determining the user's position and / or orientation relative to one of the one or more sensors 130 (e.g., microphone 140) generating the audio data. For example, during a sleep session, the user may rotate or move away from the sensor generating the audio data, which may cause a corresponding decrease in audio amplitude. However, even if the amplitude of the audio signal decreases based on the user's position relative to the sensor, the relative change in amplitude of the audio signal in response to an event (e.g., an OSA event) generally remains the same. Thus, the determined user's position and / or orientation may be used to modify any of the predetermined audio thresholds described herein.

[0103] In some implementations, step 503 includes receiving the respiratory data (step 501) and / or the determined respiratory signal (step 502) and outputting an identification of one or more events using a trained machine learning algorithm to identify the events (e.g., using supervised or unsupervised learning techniques). In such implementations, the machine learning algorithm may also be trained to receive as input the audio data (step 501) and / or the determined audio signal (step 502) and output an identification of one or more events.

[0104] In some implementations, step 503 of method 500 further includes determining one or more sleep-related parameters associated with the user during the sleep session based at least in part on the received data (step 501). The one or more sleep-related parameters may be, for example, an apnea-hypopnea index (AHI), events per hour, event pattern, total sleep time, total time in bed, wake-up time, wake-up time, hypnogram, total light sleep time, total deep sleep time, total REM sleep time, number of awakenings, sleep onset latency, or any combination thereof. In some implementations, the one or more sleep-related parameters may include a sleep score, such as the sleep score described in International Publication No. WO 2015 / 006364, which is incorporated herein by reference in its entirety. The one or more sleep-related parameters may include any number of sleep-related parameters (e.g., one sleep-related parameter, two sleep-related parameters, five sleep-related parameters, 50 sleep-related parameters, etc.).

[0105] Step 504 of method 500 (FIG. 5) includes communicating one or more indications of the identified event (step 503) to the user after the sleep session. The one or more indications may include a visual indication (e.g., alphanumeric text, an image, a video, etc.) and / or an audio indication (e.g., a recording of the user's breathing (or the temporary lack thereof) during the sleep session, snoring, etc.). The one or more indications may be communicated to the user, for example, using user device 170 (e.g., using display 172 and / or speaker 142 of user device 170). The one or more indications generally describe the identified event (step 503) and / or otherwise convey information to the user that is associated with or describes the identified event.

[0106] In some implementations, step 504 is performed in response to determining, based at least in part on the received data (step 501), that the apnea-hypopnea index (AHI) during the sleep session is equal to or greater than a predetermined threshold. As described above, the AHI is determined by dividing the number of apnea and / or hypopnea events experienced by the user during the sleep session by the total number of hours of sleep in that sleep session. In such implementations, the one or more indications are conveyed to the user only if the AHI is equal to or greater than the predetermined threshold. The predetermined threshold may be, for example, an AHI of approximately 15.

[0107] In some implementations, step 503 includes identifying a single event within a portion of the sleep session. In other implementations, step 503 includes identifying multiple events within all or a portion of the sleep session. For example, with reference to the example respiratory signal 610 of FIG. 6, three separate events were identified: a first event occurring within the second portion 614, a second event occurring within the fourth portion 618, and a third event occurring within the sixth portion 622. Step 504 includes conveying one or more indications to the user for the identified single event (e.g., indications for multiple events), even if multiple events were identified in step 503.

[0108] Thus, if multiple events are identified in step 503, step 504 includes selecting an event from the identified multiple events and communicating one or more indications for the selected event. Typically, an event from the identified multiple events is selected such that one or more indications associated with the event are most likely to trigger a behavioral response by the user (e.g., continued use of the respiratory therapy system, seeking diagnosis and / or treatment, changing bedtime habits, etc.). In some implementations, a first event from the multiple events can be selected by comparing each of the multiple events to one another. For example, a first event from the multiple events can be selected in response to determining that the first event from the multiple events is associated with a change in frequency and / or amplitude of audio data / signals that is greater than the change in frequency and / or amplitude of all other events from the multiple events (e.g., a change for an interval of the signal indicating normal breathing). In another example, a first event of the plurality of events may be selected in response to determining that the first event of the plurality of events is associated with a duration during which the user stopped breathing (e.g., a duration indicated by silence in the audio data) that is longer than the durations during which the user stopped breathing for all other events of the plurality of events. In some implementations, selecting an event of the identified plurality of events includes using a linear regression algorithm. The linear regression algorithm may be used, for example, to determine a percentage likelihood for each identified event being an actual obstructive sleep apnea (OSA) event.

[0109] The one or more indications communicated to the user in step 504 may include, for example, a graphical representation, an event indication, an audio indication, or any combination thereof. Referring to Figure 7, a graphical representation 710 of at least a portion of the determined respiratory signal (step 502) is displayed on display device 172 of user device 170 (Figure 1) described herein. As can be seen by comparing Figures 6 and 7, the respiratory signal corresponding to graphical representation 710 is the same as or similar to a portion of determined respiratory signal 610 (Figure 6) described above.

[0110] 7, multiple displays 732-740 are displayed on display device 172 simultaneously with graphical representation 710. Each of displays 732-740 may generally include, for example, alphanumeric text, symbols, images, graphics, colors, or any combination thereof. Displays 732-740 may be displayed such that one or more of displays 732-740 is partially overlaid on a portion of graphical representation 710, fully overlaid on a portion of graphical representation 710, or positioned substantially adjacent to or spaced apart from graphical representation 710.

[0111] The first display 732 generally provides information that describes or explains the graphical representation 710 of the respiratory signal to assist the user in understanding and / or interpreting the displayed graphical representation 710. For example, the first display 732 may include alphanumeric text that describes the graphical representation 710 (e.g., "Respiratory Trace").

[0112] The second display 734 generally provides information associated with a first portion of the respiratory signal shown in the graphical representation 710. More specifically, the second display 734 generally assists the user in identifying portions of the respiratory signal in the graphical representation 710 that are not experiencing an event (e.g., using the alphanumeric text "Segment of Normal Breathing"). Providing information regarding normal breathing can, for example, assist in highlighting the identified event to the user.

[0113] The third display 736 generally provides information associated with the identified event to assist in identifying that event within the graphical representation 710 of the respiratory signal. The third display 736 may convey information to the user associated with or describing the identified event (e.g., "breathing stopped for 30 seconds"). In some implementations, the third display 736 is superimposed or included directly on the graphical representation 710 to highlight (e.g., using a different color, box, outline, etc.) the portion of the respiratory signal that corresponds to the identified event.

[0114] The first audio indication 740 generally provides information indicating that the user can hear audio associated with the identified event corresponding to the third indication 736. The first audio indication 740 includes a user-selectable element 742 and an audio indicator 744. Clicking or tapping on the user-selectable element 742 causes a portion of audio data comprising the identified event to be communicated to the user (e.g., played via the speaker 142). The portion of audio data may include the entire identified event and portions of the respiratory signal immediately before and / or after the identified event (e.g., 3, 5, 10, 15, etc. seconds before and / or after the event). The audio indicator 744 may include alphanumeric text (e.g., "Sleep Apnea Audio Playback") stating that the user can hear audio associated with the identified event.

[0115] In some implementations, a play bar 738 is also displayed on the display device 172 along with the graphical representation 710. In response to a user-selectable element 742 being selected, the play bar 738 moves along the graphical representation 710 (e.g., in a direction toward the third display 736) to play the audio and indicate which portion of the graphical representation 710 the audio corresponds to. In such implementations, the play bar 738 may also be selectable or interactive to allow the user to fast-forward or rewind the audio playback (e.g., by tapping and dragging the play bar 738 in either direction).

[0116] Referring to FIG. 8 , a plot 800 illustrating a snoring pattern is shown. The y-axis corresponds to the frequency of speech or sound (e.g., measured in kHz), and the x-axis corresponds to time during a sleep session. The snoring pattern includes a series of snores 802-812. In this non-limiting example, the snoring pattern exhibits increasing respiratory effort (e.g., labored breathing, which may be a risk factor for sleep-disordered breathing (SBD)) throughout the series of snores 802-812. The snoring pattern shown in this non-limiting example is referred to as gradually increasing snores, where successive snores increase in volume to a gradually increasing volume and then decrease in volume. For example, snores 802-812 gradually increase in volume to a gradually increasing volume, such as for snore 812, followed by normal breathing or quiet snoring, which resembles snores 802 and 804, for example. In plot 800, darker lines or shading correspond to louder sounds at certain frequencies (e.g., greater sound amplitude or intensity is indicated by darker colors and, optionally, thicker lines or shading). Plot 800 (e.g., displayed on display device 172) may be communicated to the user alone in step 504 or in combination with any of the other displays described above. Selection of plot 800 may be based on patterns characteristic of increasingly stronger snores. That is, snore events may be selected based on changes in audio amplitude, such as patterns of changes in audio amplitude corresponding to increasingly stronger snores. Additionally, by communicating plot 800 (e.g., displayed on display device 172) to the user and communicating associated audio data to the user (e.g., via speaker 142), the user may hear the snores reflected in plot 800.

[0117] Some users of the respiratory treatment systems described herein (e.g., CPAP systems) find such systems uncomfortable, difficult to use, expensive, and / or aesthetically unpleasing. Some users of these systems do not realize the benefits of use after initially initiating treatment. As a result, these users may stop using their respiratory treatment systems as prescribed (e.g., nightly) or even stop using the respiratory treatment systems altogether. In fact, users may avoid seeking diagnosis and / or treatment for symptoms associated with conditions that may require the use of a respiratory treatment system.

[0118] Sounds associated with certain events, such as snoring, choking, or labored breathing, that occur when the user is not using the respiratory treatment system may be quite loud (e.g., from the perspective of bedside attendant 220 in FIG. 2 ), yet the user is unable to hear these noises because they are asleep. If the user could actually hear the sounds associated with these events and understand the severity of these symptoms, they may be more likely to or encouraged to seek treatment, use the respiratory treatment system, and / or adhere to recommended respiratory treatments in the future to reduce or eliminate these events. For example, the time period during which the user stops breathing during an OSA event may be approximately 15 seconds to approximately 30 seconds. If the user could hear the entire relatively long silent period during which they stopped breathing, they would be more likely to understand the severity or seriousness of their symptoms and be more likely to seek treatment and / or use the respiratory treatment system as prescribed. Thus, displaying an indication of an identified event can communicate the associated sounds to the user and help encourage or trigger a behavioral response by the user, such as using the respiratory treatment system as prescribed, seeking diagnosis and treatment for their symptoms, and / or changing their sleep habits.

[0119] In some implementations, steps 501-504 may be repeated in one or more additional sleep sessions after the first sleep session (e.g., the third sleep session, the fourth sleep session, the tenth sleep session, the hundredth sleep session, etc.).

[0120] One or more elements, aspects, steps or portions thereof from any one or more of the following claims 1-37 may be combined with one or more elements, aspects, steps or portions thereof from any one or more of the other claims 1-37 or combinations thereof to form one or more additional implementations and / or claims of the present disclosure.

[0121] While the present disclosure has been described with reference to one or more particular embodiments or implementations, those skilled in the art will recognize that many modifications are possible without departing from the spirit and scope of the present disclosure. Each of these implementations and obvious variations thereof is considered to be within the spirit and scope of the present disclosure. It is also contemplated that additional implementations according to aspects of the present disclosure may combine any number of features from any of the implementations described herein.

Claims

1. 1. A method of operating a system including a processor, the processor comprising: receiving data associated with a user's sleep session from one or more sensors, the data including (i) respiratory data associated with the user during at least a portion of the sleep session; and (ii) one or more playable sounds associated with the user during at least a portion of the sleep session; determining a respiratory signal associated with the user during the sleep session based at least in part on at least a portion of the data; identifying an event experienced by the user during the sleep session based at least in part on at least a portion of the data, the event being an apnea event; communicating a portion of the audio data associated with the identified event to the user via a user device; communicating to a user via the user device (i) a graphical representation of the portion of the respiratory signal, and (ii) an event indication that assists in identifying the identified event within the graphical representation of the portion of the respiratory signal; Perform a process including the event display is displayed at least partially superimposed on or adjacent to a graphical representation of a portion of the displayed respiratory signal; The method, wherein the event display includes a first display for identifying portions of the respiratory signal in the graphical representation where the user is not experiencing an event, a second display for identifying portions of the respiratory signal in the graphical representation that correspond to the identified event, and a playback bar that indicates which portion of the graphical representation the audio data corresponds to.

2. 2. The method of claim 1, wherein the processor performs processing further comprising displaying, via the user device, a user-selectable audio element, and wherein the step of conveying, via the user device, a portion of the audio data associated with the identified event to the user is performed in response to a selection of the user-selectable audio element.

3. The method of claim 1 or 2, wherein the event display includes a graphical display, alphanumeric text, or both.

4. the processor: determining one or more sleep-related parameters associated with the sleep session of the user based at least in part on the data; communicating an indication associated with the determined one or more sleep-related parameters to the user via the user device; The method according to any one of claims 1 to 3, further comprising the steps of:

5. 5. The method of claim 4, wherein the one or more sleep-related parameters comprise an Apnea Hypopnea Index (AHI), events per hour, event pattern, sleep score, total sleep time, total time in bed, wake-up time, wake-up time, hypnogram, total light sleep time, total deep sleep time, total REM sleep time, number of awakenings, sleep onset latency, or any combination thereof.

6. 6. The method of claim 1, wherein the identified events further include snoring, restless legs, sleep disorders, choking, labored breathing, asthma attack, epileptic episode, or seizure, and the identified apnea event is central apnea, obstructive apnea, mixed apnea, or hypopnea.

7. The method of any one of claims 1 to 6, wherein at least one of the one or more sensors is physically coupled to or physically integrated with the user device.

8. The method of any one of claims 1 to 7, wherein the one or more sensors are a microphone, an acoustic sensor, an RF sensor, a pressure sensor, a motion sensor, a flow sensor, or any combination thereof.

9. The method of any one of claims 1 to 8, wherein the user device is a smartphone, a tablet, a laptop, a television, a wearable device, a smart mirror, or a respiratory treatment device.

10. the processor: identifying a second event experienced by the user during the sleep session based at least in part on the data; communicating to the user via the user device a second event indication that assists in identifying the identified second event within the graphical representation of the portion of the respiratory signal; The method according to any one of claims 1 to 9, further comprising:

11. 11. The method of claim 1, wherein the portion of the respiratory signal is associated with (a) between about 20 seconds and about 10 minutes of the sleep session, or (b) about 3 minutes of the sleep session.

12. The method of any one of claims 1 to 11, wherein identifying the event comprises selecting the event from a plurality of events experienced by the user during the sleep session.

13. The method of claim 12 , wherein selecting the event from the plurality of events comprises using a linear regression algorithm.

14. 14. The method of claim 12 or 13, wherein selecting the event from the plurality of events comprises analyzing the data to identify (i) one or more breathing pauses of the user during the sleep session, (ii) frequencies of the one or more sounds in the audio data during the sleep session, (iii) changes in frequencies of the one or more sounds in the audio data during the sleep session, (iv) amplitudes of the one or more sounds in the audio data during the sleep session, (v) changes in amplitudes of the one or more sounds in the audio data during the sleep session, or (vi) any combination thereof.

15. The event selected from the plurality of events is (a) a respiratory pause that is greater than each other respiratory pause of the plurality of events; (b) a change in audio frequency that is greater than the change in audio frequency of each of the other events of the plurality of events; (c) a change in audio amplitude that is greater than the change in audio amplitude of each of the other events of the plurality of events; and (d) a pattern of change in the audio amplitude 15. The method of claim 14, wherein the method is associated with one selected from the group consisting of:

16. The method of any one of claims 1 to 15, wherein at least part of the data for determining the respiratory signal comprises at least part of the respiratory data.

17. 17. The method of claim 16, wherein at least a portion of the data determining the respiratory signal also includes a portion of the audio data.

18. The method of any preceding claim, wherein identifying the event comprises using a trained machine learning algorithm.

19. the playback bar is displayed superimposed on or adjacent to a graphical representation of a portion of the respiratory signal. The method according to any one of claims 1 to 18.

20. a control system including one or more processors; a memory storing machine-readable instructions, The control system is coupled to the memory, and the machine-readable instructions in the memory, when executed by at least one of the one or more processors of the control system, perform the method of any one of claims 1 to 19.

21. A computer program product comprising instructions which, when executed by a computer, cause the computer to carry out the method of any one of claims 1 to 19.

22. 22. The computer program product of claim 21, wherein the computer program product is a non-transitory computer-readable medium.

23. a respiratory treatment device configured to supply pressurized air; 21. The system of claim 20, further comprising a respiratory treatment system coupled to the respiratory treatment device via a conduit and including an interface configured to engage a user and assist in directing the supplied pressurized air to the user's airway.

24. 24. The system of claim 23, wherein a first sensor of the one or more sensors is coupled to or integrated with a portion of the respiratory treatment system.

25. 25. The system of claim 24, wherein a second sensor of the one or more sensors is coupled to or integrated with the user device, the first sensor configured to generate the respiratory data and the second sensor configured to generate the audio data.

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