System and method for optimizing parameters of a respiratory therapy system - Patents.com

Abstract

A method for optimizing a plurality of parameters of a respiratory therapy system includes receiving data associated with a user of the respiratory therapy system. The method further includes determining initial values ​​for each of the plurality of parameters based at least in part on the received data. Each of the plurality of parameters is associated with a comfort level of the user. The method further includes receiving usage data associated with use of the respiratory therapy system during a first time period during which each of the plurality of parameters has an initial value. The method includes generating recommended values ​​for each of the plurality of parameters for use of the respiratory therapy system during a second time period after the first time period based at least in part on the received data and the received usage data.

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 / 320,085, filed March 15, 2022, which is incorporated by reference herein in its entirety.

[0002] The present disclosure relates generally to systems and methods for optimizing parameters of a respiratory therapy system, and more particularly, to systems and methods for optimizing parameters of a respiratory therapy system based at least in part on user-related and usage data. [Background technology]

[0003] Many people suffer from sleep-related disorders and respiratory diseases, such as sleep-disordered breathing (SDB), which can include obstructive sleep apnea (OSA), central sleep apnea (CSA), other types of apnea such as mixed apnea and hypopnea, and respiratory effort-related arousals (RERA). These individuals may also suffer from other health conditions (sometimes called comorbidities) such as insomnia (characterized, for example, by difficulty falling asleep, frequent or prolonged awakenings after initially falling asleep, and / or early morning awakenings with inability to fall back asleep), periodic limb movement disorder (PLMD), restless legs syndrome (RLS), Cheyne-Stokes respiration (CSR), respiratory failure, obesity hyperventilation syndrome (OHS), chronic obstructive pulmonary disease (COPD), neuromuscular disorders (NMD), rapid eye movement (REM) behavior disorder (also known as RBD), dream behavior (DEB), hypertension, diabetes, stroke, and chest wall disorders. These patients are often treated using respiratory therapy systems (e.g., continuous positive airway pressure (CPAP) systems) that deliver pressurized air to prevent narrowing or collapse of the airway during sleep. The respiratory therapy system may include a conduit that delivers the pressurized air from a respiratory therapy device with a flow generator (e.g., a motor) to a user interface coupled to the individual's face. In certain circumstances, a user may be undesirably non-compliant with the user's prescribed usage of the respiratory therapy system. The present disclosure is directed to solving this and other problems. Summary of the Invention

[0004] According to some implementations of the present disclosure, a method for optimizing a plurality of parameters of a respiratory therapy system includes receiving data associated with a user of the respiratory therapy system. The method further includes determining an initial value for each of the plurality of parameters. Each of the plurality of parameters is associated with a comfort level of the user. The determining the initial value is based at least in part on the received data. The method further includes receiving usage data associated with use of the respiratory therapy system during a first time period. During use of the respiratory therapy system during the first time period, each of the plurality of parameters has an initial value. The method further includes generating a recommended value for each of the plurality of parameters for use of the respiratory therapy system during a second time period after the first time period. The generating the recommended value is based at least in part on the received data and the received usage data.

[0005] According to some implementations of the present disclosure, a method for optimizing a combination of two or more parameters of a respiratory therapy system includes receiving data associated with a user of the respiratory therapy system. The method further includes determining an initial value for each of the parameters of the combination of two or more parameters. Each of the parameters of the combination of two or more parameters is associated with a comfort level of the user. The determining the initial value is based at least in part on the received data. The method further includes receiving usage data associated with use of the respiratory therapy system during a first time period. During use of the respiratory therapy system during the first time period, each of the parameters of the combination of two or more parameters has its initial value. The method includes generating a recommended value for each of the parameters of the combination of two or more parameters for use of the respiratory therapy system during a second time period after the first time period. The generating the recommended value is based at least in part on the received data and the received usage data.

[0006] According to some implementations of the disclosure, a system includes a control system and a memory. The control system includes one or more processors. The memory has machine-readable instructions stored therein. The control system is coupled to the memory device and configured to execute the machine-readable instructions to implement a method for optimizing a plurality of parameters of a respiratory therapy system. The method includes receiving data associated with a user of the respiratory therapy system. The method further includes determining an initial value for each of the plurality of parameters. The determining the initial value is based at least in part on the received data. The method further includes receiving usage data associated with use of the respiratory therapy system during a first time period. During use of the respiratory therapy system during the first time period, each of the plurality of parameters has an initial value. The method further includes generating a recommended value for each of the plurality of parameters for use of the respiratory therapy system during a second time period after the first time period. The generating the recommended value is based at least in part on the received data and the received usage data.

[0007] According to some implementations of the disclosure, a system includes a control system and a memory. The control system includes one or more processors. The memory has machine-readable instructions stored therein. The control system is coupled to the memory device and configured to execute the machine-readable instructions to implement a method for optimizing a combination of two or more parameters of a respiratory therapy system. The method includes receiving data associated with a user of the respiratory therapy system. The method further includes determining initial values ​​for each of the parameters of the combination of two or more parameters. The determining the initial values ​​is based at least in part on the received data. The method further includes receiving usage data associated with use of the respiratory therapy system during a first time period. During use of the respiratory therapy system during the first time period, each of the parameters of the combination of two or more parameters has an initial value. The method further includes generating recommended values ​​for each of the parameters of the combination of two or more parameters for use of the respiratory therapy system during a second time period after the first time period. The generating the recommended values ​​is based at least in part on the received data and the received usage data.

[0008] According to some implementations of the disclosure, a system includes a respiratory therapy system, a memory device, and a control system. The respiratory therapy system is configured to deliver pressurized air to an individual. The memory device has machine-readable instructions stored therein. The control system includes one or more processors configured to execute the machine-readable instructions to perform a method. The method includes receiving data associated with a user of the respiratory therapy system. The method further includes determining initial values ​​for each of a plurality of parameters of the respiratory therapy system. Each of the plurality of parameters is associated with a comfort level of the user. The determining of the initial values ​​is based at least in part on the received data. The method further includes receiving usage data associated with use of the respiratory therapy system during a first period of time. During the first period of use of the respiratory therapy system, each of the plurality of parameters has an initial value. The method further includes generating recommended values ​​for each of the plurality of parameters for use of the respiratory therapy system during a second period of time after the first period of time. The generating of the recommended values ​​is based at least in part on the received data and the received usage data.

[0009] According to some example implementations of the present disclosure, a system includes a respiratory therapy system, a memory device, and a control system. The respiratory therapy system is configured to deliver pressurized air to an individual. The memory device has machine-readable instructions stored therein. The control system includes one or more processors configured to execute the machine-readable instructions to implement a method. The method includes receiving data associated with a user of the respiratory therapy system. The method further includes determining initial values ​​for each of the parameters of a combination of two or more parameters of the respiratory therapy system. Each of the parameters of the combination of two or more parameters is associated with a comfort level of the user. The determining of the initial values ​​is based at least in part on the received data. The method further includes receiving usage data associated with use of the respiratory therapy system during a first time period. During use of the respiratory therapy system during the first time period, each of the parameters of the combination of two or more parameters has an initial value. The method further includes generating recommended values ​​for each of the parameters of the combination of two or more parameters for use of the respiratory therapy system during a second time period after the first time period. The generating of the recommended values ​​is based at least in part on the received data and the received usage data.

[0010] The above summary is not intended to represent each implementation or every aspect of the present invention. Additional features and advantages of the present invention will be apparent from the detailed description and drawings set forth below. [Brief description of the drawings]

[0011] [Figure 1] FIG. 1 is a functional block diagram of a system for detecting rainout in a respiratory therapy system according to some implementations of the present disclosure. [Diagram 2] 2 is a perspective view of the system of FIG. 1, a user of the system, and the user's bed partner, in accordance with some implementations of the present disclosure. [Diagram 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. [Diagram 5] FIG. 1 is a process flow diagram of a method for optimizing sleep for a user of a respiratory therapy system, according to some implementations of the disclosure. [Figure 6A] 1 is a graph of regulated pressure and regulated ramp pressure according to some implementations of the present disclosure. [Figure 6B] 13 is a graph of a tuned pressure response according to some implementations of the present disclosure. [Figure 7] FIG. 1 is a process flow diagram of a method for optimizing one or more parameters of a respiratory therapy system, according to some implementations of the present disclosure. [Figure 8] 1 is a graph of an expiratory pressure relief setting with a default value and a graph of an expiratory pressure relief setting with a recommended value, according to some implementations of the present disclosure. [Figure 9] According to some implementations of the present disclosure, a user device presents recommended values ​​of multiple parameters to a user. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0012] While the present disclosure is susceptible to various modifications and alternative forms, specific implementations and embodiments thereof have been shown by way of example in the drawings and 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 on the contrary, the 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.

[0013] The present disclosure is described with reference to the accompanying drawings, in which like reference numerals are used throughout to indicate similar or equivalent elements. The drawings are not drawn to scale and are provided solely for the purpose of illustrating the present disclosure. Several aspects of the disclosure are described below with reference to example applications.

[0014] Many people suffer from sleep-related disorders and / or respiratory diseases. Examples of sleep-related and / or respiratory diseases include periodic limb movement disorder (PLMD), restless legs syndrome (RLS), sleep-disordered breathing (SDB), obstructive sleep apnea (OSA), central sleep apnea (CSA), other types of apnea, Cheyne-Stokes respiration (CSR), respiratory failure, obesity hyperventilation syndrome (OHS), chronic obstructive pulmonary disease (COPD), neuromuscular diseases (NMD), chest wall diseases, etc.

[0015] 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) such as obstructive sleep apnea (OSA), central sleep apnea (CSA) and other types of apnea, respiratory effort-related arousals (RERA), Cheyne-Stokes respiration (CSR), respiratory insufficiency, obesity hyperventilation syndrome (OHS), chronic obstructive pulmonary disease (COPD), neuromuscular disorders (NMD), chest wall disorders, etc. Obstructive sleep apnea (OSA) is a type of sleep-disordered breathing (SDB) characterized by symptoms such as obstruction of the upper airway during sleep resulting from a combination of an abnormally narrow upper airway and a decrease in normal muscle tone in the areas of the tongue, soft palate, and posterior pharyngeal wall. Central sleep apnea (CSA) is another form of sleep disorder. CSA occurs when the brain temporarily stops delivering signals to the muscles that control breathing. Other types of apnea include hypopnea, hyperpnea, and hypercapnia. Hypopnea is characterized by slow or shallow breathing due to narrowing of the airway, rather than obstruction of the airway. Hyperpnea is typically characterized by an increase in the depth or rate of breathing. Hypercapnia is typically characterized by elevated or excessive carbon dioxide concentrations in the bloodstream. It is usually caused by insufficient breathing. Respiratory effort-related arousals (RERA) events are typically characterized by increased respiratory effort for 10 seconds or more that leads to arousal from sleep, but do not meet the criteria for an apnea or hypopnea event. RERA is defined as a series of breaths characterized by increased respiratory effort that leads to arousal from sleep, but does not meet the criteria for an apnea or hypopnea event. These events must meet both of the following criteria: (1) there is a pattern of gradually increasing esophageal pressure followed by an abrupt change in pressure to a lower level causing arousal, and (2) the event lasts for 10 seconds or more. In some implementations, a nasal cannula / pressure transducer system is adequate and reliable for detecting RERA. The RERA detector may be based on an actual flow signal obtained from a respiratory therapy device. For example, a measure of flow limitation may be determined based on the flow signal. A measure of arousal may then be derived as a function of the measure of flow limitation and the measure of ventilation abruption.One such method is described in International Patent Application No. 2008 / 138040 and US Pat. No. 9,358,353, assigned to ResMed, the disclosures of each of which are incorporated herein by reference in their entireties.

[0016] Cheyne-Stokes respiration (CSR) is another form of SDB. CSR is a respiratory control disorder in which patients experience periodic alternations of increased and decreased ventilation, called CSR cycles. CSR is characterized by repeated deoxygenation and reoxygenation of arterial blood. OHS is defined as the combination of severe obesity and chronic hypercapnia on awakening in the absence of other known causes of hypoventilation. Symptoms include dyspnea, morning headache, and excessive daytime sleepiness. COPD includes any of a group of lower airway diseases that share certain characteristics in common, including increased resistance to air movement, prolonged expiratory phase of breathing, and loss of normal elasticity of the lungs. NMD includes many diseases and conditions 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 connection between the respiratory muscles and the rib cage.

[0017] Many of these disorders are characterized by specific events that occur while a person is sleeping (e.g., snoring, apnea, hypopnea, restless legs syndrome, sleep disturbances, choking, increased heart rate, difficulty breathing, asthma attacks, epileptic seizures, seizures, or a combination thereof). Different types of data can be used to monitor the health of individuals with any of the above types of sleep-related or respiratory disorders, or other disorders.

[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 the user during a sleep session by the total sleep time during the sleep session. The events are, for example, cessation of breathing that lasts for at least 10 seconds. An AHI below 5 is considered normal. An AHI between 5 and 15 is considered an indication of mild sleep apnea. An AHI between 15 and 30 is considered moderate sleep apnea. An AHI of 30 or greater is considered severe sleep apnea. For children, an AHI above 1 is considered abnormal. If the AHI is normal, or if the AHI is normal or mild, the sleep apnea can be considered "controlled". The AHI can also be used in combination with the level of oxygen desaturation 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, an electronic interface 119, one or more sensors 130, and optionally one or more user devices 170. In some implementations, the system 100 further includes a respiratory therapy system 120 (including a respiratory therapy device 122), a blood pressure device 180, an activity tracker 190, or any combination thereof. The system 100 can be used to optimize values ​​of one or more parameters of the respiratory therapy system.

[0020] The control system 110 includes one or more processors 112 (hereinafter processor 112). The control system 110 is typically used to control (e.g., operate) various components of the system 100 and to analyze data acquired and / or generated by the components of the system 100. The processor 112 may be a general-purpose or special-purpose processor or a microprocessor. Although one processor 112 is shown in FIG. 1, the control system 110 may include any suitable number of processors (e.g., one processor, two processors, five processors, ten processors), which may be located within a single housing or may be located remotely from one another. The control system 110 (or other control systems), or a portion of the control system 110, such as the processor 112 (or other processor(s) or portion(s) of the other control systems), may be used to perform one or more steps of any of the methods described and / or claimed herein. The control system 110 may be coupled to and / or located, for example, within a housing of the user device 170 and / or within a housing of one or more sensors 130. The control system 110 may be centralized (within one housing) or distributed (within two or more physically distinct housings). In such implementations that include two or more housings that include the control system 110, such housings may be located in close proximity and / or remote from one another.

[0021] The memory device 114 stores machine-readable instructions executable by the processor 112 of the control system 110. The memory device 114 may be any suitable computer-readable storage device or medium, such as, for example, a random or serial access memory device, a hard drive, a solid state drive, a flash memory device, etc. Although one memory device 114 is shown in FIG. 1, the system 100 may include any suitable number of memory devices 114 (e.g., one memory device, two memory devices, five memory devices, ten memory devices). The memory device 114 may be coupled to and / or located within a housing of a respiratory therapy device 122 of the respiratory therapy system 120, within a housing of a user device 170, within a housing of one or more sensors 130, or any combination thereof. As with the control system 110, the memory device 114 may be centralized (within one housing) or distributed (within two or more physically distinct housings).

[0022] In some implementations, the memory device 114 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 indicative of the user's age, the user's gender, the user's race, a family medical history (e.g., a family history of insomnia or sleep apnea), the user's employment status, the user's education status, the user's socio-economic status, or a combination thereof. The medical information may include, for example, information indicative of one or more medical conditions associated with the user, medication use by the user, or both. The medical information data may further include a fall risk assessment associated with the user (e.g., a fall risk score using the Mohs Fall Scale), 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 (e.g., poor, average, excellent), 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 a combination thereof.

[0023] The electronic interface 119 is configured to receive data (e.g., physiological data and / or acoustic data) from one or more sensors 130, which is stored in the memory device 114 and analyzed by the processor 112 of the control system 110. The electronic interface 119 can communicate with the one or more sensors 130 using a wired or wireless connection (e.g., RF communication protocol, WiFi communication protocol, Bluetooth® communication protocol, IR communication protocol, via cellular network, other optical communication protocol). The electronic interface 119 can include an antenna, a receiver (e.g., RF receiver), a transmitter (e.g., RF transmitter), a transceiver, or any combination thereof. The electronic interface 119 can also include one or more processors and / or one or more memory devices the same as or similar to the processor 112 and memory device 114 described herein. In some implementations, the electronic interface 119 is coupled to or integrated into the user device 170. In other implementations, the electronic interface 119 is coupled to or integrated with (e.g., within the housing) the control system 110 and / or the memory device 114.

[0024] As noted above, in some implementations, the system 100 optionally includes a respiratory therapy system 120 (also referred to as a respiratory pressure therapy system). The respiratory therapy system 120 may include a respiratory therapy device 122 (also referred to as a respiratory pressure therapy device), a user interface 124 (also referred to as a mask or patient interface), a conduit 126 (also referred to as a tube or air circuit), a display device 128, a humidification tank 129, a receptacle 182, or any combination thereof. In some implementations, the control system 110, the memory device 114, the display device 128, the one or more sensors 130, the humidification tank 129, and the receptacle 182 are part of the respiratory therapy device 122. Respiratory pressure therapy refers to the supply of air to the entrance of a user's airway at a controlled target pressure that is nominally positive relative to the atmosphere throughout the user's breathing cycle (as opposed to negative pressure therapy, such as a tank ventilator or chest guard). Respiratory therapy system 120 is generally used to treat individuals suffering from one or more sleep-related respiratory disorders (e.g., obstructive sleep apnea, central sleep apnea, mixed sleep apnea), other respiratory disorders such as COPD, or other disorders that lead to respiratory insufficiency, which may manifest during sleep or wakefulness.

[0025] The respiratory therapy device 122 is typically used to generate pressurized air delivered to a user (e.g., using one or more motors (e.g., blower motors) driving one or more compressors). In some implementations, the respiratory therapy device 122 generates a continuous constant air pressure delivered to a user. In other implementations, the respiratory therapy device 122 generates two or more predetermined pressures (e.g., a first predetermined air pressure and a second predetermined air pressure). In still other implementations, the respiratory therapy device 122 is configured to generate a variety of different air pressures within a predetermined range. For example, the respiratory therapy device 122 can provide a pressure of at least about 6 cmH2O, at least about 10 cmH2O, at least about 20 cmH2O, about 6 cmH2O to about 10 cmH2O, about 7 cmH2O to about 12 cmH2O, etc. The respiratory therapy device 122 can also provide 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). In some implementations, the control system 110, the memory device 114, the electronic interface 119, or any combination thereof, can be coupled to or located within the housing of the respiratory therapy device 122.

[0026] The user interface 124 contacts a portion of the user's face and delivers pressurized air from the respiratory therapy device 122 to the user's airways to prevent the airways from narrowing or collapsing during sleep. This 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 effectively effect the therapy, for example, approximately 10 cmH2O positive pressure relative to ambient pressure. For other therapies, such as delivery of oxygen, the user interface may not include a seal sufficient to easily deliver a gas supply at approximately 10 cmH2O positive pressure to the airways.

[0027] In some implementations, the user interface 124 is or includes a face mask that covers the user's nose and mouth (e.g., as shown in FIG. 2). Alternatively, the user interface 124 is or includes a nasal mask that supplies air to the user's nose or a nasal pillow mask that delivers air directly to the user's nostrils. The user interface 124 can include a strap assembly with multiple straps (e.g., including hook and loop fasteners) for positioning and / or stabilizing the user interface 124 at a portion of the user's interface 124 on a desired location of the user (e.g., face) and a conforming cushion (e.g., silicone, plastic, foam) that helps provide an airtight seal between the user interface 124 and the user. In some implementations, the user interface 124 can include a connector 127 and one or more vents 125. The one or more vents 125 can be used to allow carbon dioxide and other gases exhaled by the user to escape. In other implementations, the user interface 124 includes a mouthpiece (e.g., a night guard mouthpiece molded to the user's teeth, a mandibular repositioning device, etc.). In some implementations, the connector 127 is separate from the user interface 124 (and / or the conduit 126), but is coupleable to the user interface 124. The connector 127 is configured to connect and fluidly couple the user interface 124 to the conduit 126.

[0028] The conduit 126 allows air flow between two components of the respiratory therapy system 120 (e.g., the respiratory therapy device 122 and the user interface 124). In some implementations, there may be separate limbs of the conduit for inhalation and exhalation. In other implementations, a single limb conduit is used for both inhalation and exhalation. In general, the respiratory therapy system 120 forms an air pathway that extends between the motor of the respiratory therapy device 122 and the user and / or the user's airway. Thus, the air pathway typically includes at least the motor of the respiratory therapy device 122, the user interface 124, and the conduit 126.

[0029] One or more of the respiratory therapy device 122, the user interface 124, the conduit 126, the display device 128, and the humidification tank 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). These one or more sensors may be used, for example, to measure the air pressure and / or flow rate of the pressurized air supplied by the respiratory therapy device 122.

[0030] The display device 128 is typically used to display image(s), including still images, video images, or both, and / or information about the respiratory therapy device 122 . For example, the display device 128 can provide information regarding the status of the respiratory therapy device 122 (e.g., whether the respiratory therapy device 122 is on / off, the pressure of the air supplied by the respiratory therapy device 122, the temperature of the air supplied by the respiratory therapy device 122), and / or other information (e.g., a sleep score or treatment score (such as the myAir® score described in International Patent Application No. 2016 / 061629 and U.S. Patent No. 2017 / 0311879, each of which is incorporated by reference in its entirety herein), the current date / time, personal information about the user, a questionnaire for the user, etc. In some implementations, the display device 128 functions as a human machine interface (HMI) including a graphic user interface (GUI) configured to display image(s) as an input interface. The display device 128 can be an LED display, an OLED display, an LCD display, etc. The input interface can be, for example, a touch screen or touch-sensitive board, a mouse, a keyboard, or any sensor system configured to sense input by a human user interacting with the respiratory therapy device 122.

[0031] The humidification tank 129 is coupled to or integrated with the respiratory therapy device 122 and includes a reservoir of water that can be used to humidify the pressurized air delivered from the respiratory therapy device 122. The respiratory therapy device 122 includes a heater that heats the water in the humidification tank 129 to humidify the pressurized air delivered to the user. Additionally, in some implementations, the conduit 126 also includes a heating element (e.g., coupled to and / or embedded in the conduit 126) that heats the pressurized air delivered to the user. The humidification tank 129 is fluidly coupled to a water vapor inlet of the air path and can deliver water vapor to the air path via the water vapor inlet, or can be formed in-line with the air path as part of the air path itself. In other implementations, the respiratory therapy device 122 or the conduit 126 can include a waterless humidifier. The waterless humidifier can incorporate sensors that interface with other sensors located elsewhere in the system 100.

[0032] In some implementations, system 100 can be used to deliver at least a portion of the substance from receptacle 182 to the user's airway based at least in part on physiological data, sleep-related parameters, other data or information, or any combination thereof. In general, altering the delivery of the portion of the substance to the airway may include (i) initiating delivery of the substance to the airway, (ii) terminating delivery of the portion of the substance to the airway, (iii) altering an amount of the substance delivered to the airway, (iv) altering a temporal characteristic of the delivery of the portion of the substance to the airway, (v) altering a quantitative characteristic of the delivery of the portion of the substance to the airway, (vi) altering a parameter associated with the delivery of the substance to the airway, or (vii) a combination of (i) through (vi).

[0033] Varying the temporal characteristics of the delivery of the portion of the substance into the air path can include changing the rate at which the substance is delivered, starting and / or ending at different times, continuing for different durations, changing the time distribution or characteristics of the delivery, changing the amount distribution independent of the time distribution, etc. Independent variations in time and amount can vary the amount of substance released each time apart from changing the frequency of release of the substance. In this way, various combinations of release frequency and amount can be achieved (e.g., higher frequency but lower amount, higher frequency with higher amount, lower frequency with higher amount, lower frequency with lower amount). Other modifications for the delivery of the portion of the substance into the air path can also be utilized.

[0034] Respiratory therapy system 120 may be used as a ventilator or a positive airway pressure (PAP) system, such as, for example, a continuous positive airway pressure (CPAP) system, an automatic positive airway pressure system (APAP), a bi-level or variable positive airway pressure system (BPAP or VPAP), or any combination thereof. A CPAP system provides a user with a predetermined air pressure (e.g., determined by a sleep physician). An APAP system automatically varies the air pressure provided to a user, for example, based at least in part on respiratory data associated with the user. A BPAP or VPAP system is configured to deliver a first predetermined pressure (e.g., inspiratory positive pressure or IPAP) and a second predetermined pressure (e.g., expiratory positive pressure or EPAP) that is lower than the first predetermined pressure.

[0035] Referring to FIG. 2, a portion of the system 100 (FIG. 1) according to some implementations is shown. A user 210 and a bed partner 220 of the respiratory therapy system 120 are positioned on a bed 230 and lying on a mattress 232. A user interface 124 (e.g., a full face mask) can be worn by the user 210 during a sleep session. The user interface 124 is fluidly coupled and / or connected to the respiratory therapy device 122 via a conduit 126. The respiratory therapy device 122 then delivers pressurized air to the user 210 via the conduit 126 and the user interface 124 to increase air pressure in the throat of the user 210 to help prevent the airway from closing or narrowing during sleep. The respiratory therapy device 122 includes a display device 128, which allows the user to interact with the respiratory therapy device 122. The respiratory therapy device 122 can also include a humidification tank 129 that stores water used to humidify the pressurized air. The respiratory therapy device 122 may be placed on a nightstand 240 directly adjacent to the bed 230, as shown in Figure 2, or more generally on any surface or structure generally adjacent to the bed 230 and / or the user 210. The user may also wear the blood pressure device 180 and activity tracker 190 while lying on the mattress 232 of the bed 230.

[0036] 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 (IR) sensor 152, a photoplethysmography (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 humidity sensor 176, a light detection and ranging (LiDAR) sensor 178, or any combination thereof. In general, each of the one or more sensors 130 is configured to output sensor data that is received and stored in the memory device 114 or one or more other memory devices. The sensor 130 may also include an electrooculogram (EOG) sensor, a peripheral oxygen saturation (SpO2) sensor, a galvanic skin response (GSR) sensor, a carbon dioxide (CO2) sensor, or any combination thereof.

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

[0038] The one or more sensors 130 may be used to generate, for example, physiological data, acoustic data, or both, associated with a user of the respiratory therapy system 120 (e.g., user 210 of FIG. 2), the respiratory therapy system 120, both the user and the respiratory therapy system 120, or other entities, objects, activities, etc. The physiological data generated by the one or more sensors 130 may 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 may be indicative of one or more sleep stages, including distinct sleep stages (sometimes referred to as sleep states), such as sleep, wake, relaxed wake, micro-arousal, or rapid eye movement (REM) stages (which may include both typical and non-typical REM stages), 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 stages from physiological data generated by one or more sensors, such as sensor 130, are described, for example, in International Patent Application No. 2014 / 047310, U.S. Patent No. 10,492,720, U.S. Patent No. 10,660,563, U.S. Patent No. 2020 / 0337634, International Patent Application No. 2017 / 132726, International Patent Application No. 2019 / 122413, U.S. Patent No. 2021 / 0150873, International Patent Application No. 2019 / 122414, and U.S. Patent No. 2020 / 0383580, each of which is incorporated by reference in its entirety into this specification.

[0039] The sleep-wake signal may also be time-stamped to indicate when the user got into bed, when the user got out of bed, when the user attempted 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, for example, one sample per second, one sample per 30 seconds, one sample per minute, etc. Examples of one or more sleep-related parameters that may be determined for the user during the sleep session based at least in part on the sleep-wake signal include total time in bed, total time asleep, total time awake, sleep onset latency, wake parameters after sleep onset, sleep efficiency, fragmentation index, time to sleep onset, breathing rate consistency, time to sleep onset, time to wake up, rate of sleep disturbance, number of movements, or any combination thereof.

[0040] Physiological and / or acoustic data generated by one or more sensors 130 may also be used to determine a respiratory signal associated with the user during a sleep session. The respiratory signal typically indicates the respiration or breathing of the user during a sleep session. The respiratory signal may indicate, for example, respiratory rate, respiratory rate variability, inhalation amplitude, exhalation amplitude, inhalation exhalation amplitude ratio, inhalation exhalation duration ratio, number of events per hour, event patterns, pressure settings of the respiratory therapy device 122, or any combination thereof. The event(s) may include snoring, apnea, central apnea, obstructive apnea, mixed apnea, hypopnea, RERA, flow limitation (e.g., an event of no increase in flow despite an increase in negative intrathoracic pressure indicating increased effort), mask leak (e.g., from the user interface 124), restless legs syndrome, sleep disorders, choking, increased heart rate, heart rate variability, dyspnea, asthma attack, epileptic seizure, seizure, fever, coughing, sneezing, snoring, shortness of breath, presence of illness such as cold or flu, elevated stress levels, and the like. The event can be detected by any means known in the art, for example, as described in U.S. Pat. No. 5,245,995, U.S. Pat. No. 6,502,572, International Application No. 2018 / 050913, and International Application No. 2020 / 104465, each of which is incorporated by reference in its entirety.

[0041] The pressure sensor 132 outputs pressure data that can be stored in the memory device 114 and / or analyzed by the processor 112 of the control system 110. In some implementations, the pressure sensor 132 is 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 the respiratory therapy system 120. In such implementations, the pressure sensor 132 is coupled to or integrated into the respiratory therapy device 122. The pressure sensor 132 can be, for example, a capacitive sensor, an electromagnetic sensor, an inductive sensor, a resistive sensor, a piezoelectric sensor, a strain gauge sensor, an optical sensor, a potential difference sensor, or any combination thereof. In one example, the pressure sensor 132 can be used to determine the user's blood pressure.

[0042] The flow sensor 134 outputs flow data that can be stored in the memory device 114 or analyzed by the processor 112 of the control system 110. In some implementations, the flow sensor 134 is used to determine the airflow rate from the respiratory therapy 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 therapy device 122, the user interface 124, or the conduit 126. The flow sensor 134 can be, for example, a mass flow sensor such as a rotary flow meter (e.g., a Hall effect flow meter), a turbine flow meter, an orifice flow meter, an ultrasonic flow meter, a hot wire sensor, a vortex sensor, a membrane sensor, or any combination thereof.

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

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

[0045] The microphone 140 outputs acoustic data that can be stored in the memory device 114 or analyzed by the processor 112 of the control system 110. The acoustic data generated by the microphone 140 can be played back as one or more sound(s) (e.g., sounds from the user) during a sleep session to determine (e.g., using the control system 110) one or more sleep-related parameters, as described in more detail herein. The acoustic 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), as described in more detail herein. In other implementations, the acoustic data from the microphone 140 represents noises associated with the respiratory therapy system 120. In some implementations, the acoustic data from the microphone 140 can be analyzed to detect the presence of liquid in the respiratory therapy system 120, particularly the user interface 124 and / or the conduit 126, as described in more detail herein. In some implementations, the system 100 includes multiple microphones (e.g., two or more microphones and / or an array of microphones with beamforming), and audio data generated by each of the multiple microphones can be used to distinguish audio data generated by another of the multiple microphones. The microphones 140 can be coupled to or integrated with the respiratory therapy system 120 (or system 100) in generally any configuration. For example, the microphones 140 can be located inside the respiratory therapy device 122, the user interface 124, the conduit 126, or other components. The microphones 140 can also be located adjacent to or coupled to the exterior of the respiratory therapy device 122, the exterior of the user interface 124, the exterior of the conduit 126, or other components. The microphones 140 can also be a component of the user device 170 (e.g., the microphones 140 are smartphone microphones).The microphone 140 may be integrated into the user interface 124, the conduit 126, the respiratory therapy device 122, or any combination thereof. In general, the microphone 140 may be located at any point in or near the air path of the respiratory therapy system 120, which includes at least the motor, the user interface 124, and the conduit 126 of the respiratory therapy device 122. Thus, the air path is also referred to as the acoustic path.

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

[0047] The microphone 140 and the speaker 142 can be used as separate devices. In some implementations, the microphone 140 and the speaker 142 can be combined into an acoustic sensor 141 (e.g., a SONAR sensor), as described, for example, in International Patent Application Nos. 2018 / 050913 and 2020 / 104465, each of which is incorporated by reference herein in its entirety. In such implementations, the speaker 142 generates or emits sound waves at a predetermined interval and / or frequency, and the microphone 140 detects the reflection of the sound waves emitted from the speaker 142. The sound waves generated or emitted by the speaker 142 have a frequency 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 or the user's bed partner (e.g., bed partner 220 of FIG. 2). Based at least in part on data from microphone 140 and / or speaker 142, control system 110 can determine the user's location and / or one or more of the sleep-related parameters described herein, such as respiration signal, respiration rate, inhalation amplitude, exhalation amplitude, inhalation-exhalation ratio, number of events per hour, pattern of events, sleep stage, pressure setting of respiratory therapy device 122, mouth leak status, or any combination thereof. In this context, a SONAR sensor may be understood to relate to active acoustic sensing, such as by generating / transmitting an ultrasonic or low-frequency ultrasonic sensing signal (e.g., in a frequency range of about 17-23 kHz, 18-22 kHz, or 17-18 kHz) into the air. Such systems may be discussed in connection with the aforementioned International Patent Application No. 2018 / 050913 and International Patent Application No. 2020 / 104465. In some implementations, speaker 142 is a bone conduction speaker. In some implementations, the one or more sensors 130 include (i) a first microphone that is the same as or similar to microphone 140 and integrated into acoustic sensor 141, and (ii) a second microphone that is the same as or similar to microphone 140 but separate and different from the first microphone integrated into acoustic sensor 141.

[0048] The RF transmitter 148 generates and / or emits radio waves having a predetermined frequency and / or a predetermined amplitude (e.g., in a high frequency band, in a low frequency band, a long wave signal, a short wave signal). 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 user's location and / or one or more of the sleep-related parameters described herein. The RF receiver (RF receiver 146 and RF transmitter 148 or another RF pair) can also be used for wireless communication between the control system 110, the respiratory therapy device 122, one or more sensors 130, the user device 170, or any combination thereof. Although 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 (e.g., a radar sensor). In some such implementations, the RF sensor 147 includes control circuitry. Specific forms of RF communication include WiFi and Bluetooth (registered trademark).

[0049] In some implementations, the RF sensor 147 is part of a mesh system. One example of a mesh system is a WiFi mesh system, which may include mesh nodes, mesh router(s), and mesh gateway(s), each of which may be mobile / movable or fixed. In such implementations, the WiFi mesh system includes a WiFi router and / or a WiFi controller and one or more satellites (e.g., access points), each of which includes an RF sensor that is the same or similar to the RF sensor 147. The WiFi router and satellites continuously communicate with each other using WiFi signals. The WiFi mesh system can be used to generate motion data based at least in part on changes (e.g., differences in received signal strength) that occur in the WiFi signal between the router and satellite(s) due to an object or person moving and partially blocking the signal. The motion data can be indicative of movement, breathing, heart rate, walking, falls, activity, etc., or any combination thereof.

[0050] The camera 150 outputs image data playable as one or more images (e.g., still images, video images, thermal images, or combinations thereof) that can be stored in the memory device 114. The image data from the camera 150 can be used by the control system 110 to determine one or more of the sleep-related parameters described herein. For example, the image data from the camera 150 can be used to locate the user, determine the time the user entered the user's bed (e.g., bed 230 of FIG. 2), or determine the time the user left the bed 230. The camera 150 can also be used to track eye movement, pupil dilation (if one or both of the user's eyes are open), blink frequency, or any changes during REM sleep. The camera 150 can also be used to track the user's location, which can affect the duration and / or severity of apneic episodes for a user with positional obstructive sleep apnea.

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

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

[0053] The PPG sensor 154 outputs physiological data associated with the user. This data can be used to determine one or more sleep-related parameter(s), such as, for example, heart rate, heart rate pattern, heart rate variability, heart cycle, respiratory rate, inhalation amplitude, exhalation amplitude, inhalation-exhalation ratio, estimated blood pressure parameters, or any combination thereof. The PPG sensor 154 can be worn by the user, embedded in clothing or fabric worn by the user, embedded in or coupled to the user interface 124 or its associated headgear (e.g., straps).

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

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

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

[0057] The analyte sensor 174 can be used to detect the presence of an analyte in the user's exhaled breath. 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 the analyte in the user's exhaled breath. In some implementations, the analyte sensor 174 is positioned near the user's mouth to detect the analyte in the breath exhaled from the user's mouth. For example, if the user interface 124 is a face mask that covers the user's nose and mouth, the analyte sensor 174 can be positioned in the face mask to monitor the user's mouth breathing. In other implementations, for example, if the user interface 124 is a nasal mask or nasal pillows mask, the analyte sensor 174 can be positioned near the user's nose to detect the analyte in the breath exhaled from the user's nose. In yet other implementations, if the user interface 124 is a nasal mask or nasal pillows mask, the analyte sensor 174 can be positioned near the user's mouth. In this implementation, the analyte sensor 174 can be used to detect whether air is accidentally leaking from the user's mouth. In some implementations, the analyte sensor 174 is a volatile organic compound (VOC) sensor that can be used to detect carbon-based chemicals or compounds, such as carbon dioxide. In some implementations, the analyte sensor 174 can also be used to detect whether a user is breathing through their nose or mouth. For example, if data output by the analyte sensor 174 located near the user's mouth or in a face mask (in implementations where the user interface 124 is a face mask) detects the presence of an analyte, the control system 110 can use this data as an indication that the user is breathing through their mouth.

[0058] The humidity sensor 176 outputs data that is stored in the memory device 114 and that can be used by the control system 110. The humidity sensor 176 can be used to detect humidity in various areas around the user (e.g., inside the conduit 126 or the user interface 124, near the user's face, near the connection of the conduit 126 to the user interface 124, near the connection of the conduit 126 to the respiratory therapy device 122). Thus, in some implementations, the humidity sensor 176 can be coupled or integrated with the user interface 124 or the conduit 126 to monitor the humidity of the pressurized air from the respiratory therapy device 122. In other implementations, the humidity sensor 176 is located near any area where humidity levels need to be monitored. The humidity sensor 176 can also be used to monitor the humidity of the air in the user's surrounding environment, for example, in the user's bedroom. The humidity sensor 176 can also be used to track the user's biological response to changes in the environment.

[0059] One or more LiDAR sensors 178 may be used for depth sensing. This type of optical sensor (e.g., laser sensor) may be used to detect objects and create a three-dimensional (3D) map of an environment, such as a living space. LiDAR typically uses a pulsed laser to perform time-of-flight measurements. LiDAR is also referred to as 3D laser scanning. An example of the use of such a sensor is that a fixed or mobile device (e.g., a smartphone) equipped with a LiDAR sensor 178 may measure and map an area more than 5 meters away from the sensor. LiDAR data may be fused with point cloud data estimated, for example, by an electromagnetic radar sensor. The LiDAR sensor 178 may automatically geofence a RADAR system by using artificial intelligence (AI) to detect and classify features in a space that may cause problems for the RADAR system, such as glass windows (which may have a high reflectivity to the RADAR). LiDAR may also be used to estimate a person's height and the change in height when a person sits or falls. LiDAR may be used to form a 3D mesh representation of the environment. Additionally, for solid surfaces through which radio waves pass (e.g., radio-transparent materials), LiDAR may reflect off such surfaces and classify different types of obstacles.

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

[0061] Data from one or more sensors 130 may be analyzed to determine one or more sleep-related parameters. This may include a respiratory signal, a respiratory rate, a respiratory pattern, an inhalation amplitude, an exhalation amplitude, an inhalation-exhalation ratio, the occurrence of one or more events, the number of events per hour, a pattern of events, the average duration of an event, a range of event durations, a ratio of the number of different events, a sleep stage, 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, intentional user interface leak, unintentional user interface leak, mouth leak, coughing, restless legs syndrome, sleep disorder, choking, increased heart rate, dyspnea, asthma attack, epileptic attack, seizure, elevated blood pressure, hyperventilation, or a combination thereof. Many of these sleep-related parameters are physiological parameters, but some of the sleep-related parameters are considered to be non-physiological parameters. Other types of physiological and non-physiological parameters may also be determined from data from one or more sensors 130 or from other types of data.

[0062] The user device 170 includes a display device 172. The user device 170 may be a mobile device, such as, for example, a smartphone, a tablet, a laptop, a gaming console, or a smart watch. Alternatively, the 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(s), such as Google Home®, Google Nest®, Amazon Echo®, Amazon Echo Show®, an Alexa®-enabled device, or the like). In some implementations, the user device 170 is a wearable device (e.g., a smart watch). The display device 172 is typically used to display image(s), including still images, video images, or both. In some implementations, the display device 172 functions as a human-machine interface (HMI) that includes a graphic user interface (GUI) and an input interface configured to display the image(s). The display device 172 may be an LED display, an OLED display, an LCD display, or the like. The input interface may be, for example, a touch screen or touch-sensitive substrate, a mouse, a keyboard, or any sensor system configured to sense input by a human user interacting with user device 170. In some implementations, one or more user devices 170 may be used by and / or included in system 100.

[0063] The blood pressure device 180 is typically used to assist in generating physiological data for determining one or more blood pressure measurements associated with a user. The blood pressure device 180 may include at least one of the one or more sensors 130 for measuring, for example, a systolic blood pressure component and / or a diastolic blood pressure component.

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

[0065] The activity tracker 190 is typically used to assist in generating physiological data for determining activity measurements associated with a user. Activity measurements may include, for example, number of steps, distance traveled, number of steps climbed, duration of physical activity, type of physical activity, intensity of physical activity, time spent standing, respiration rate, average respiration rate, resting respiration rate, maximum respiration rate, respiration rate variability, heart rate, average heart rate, resting heart rate, maximum heart rate, heart rate variability, number of calories burned, blood oxygen saturation, electrodermal activity (also called skin conductance or galvanic skin response), or any combination thereof. The activity tracker 190 includes one or more of the sensors 130 described herein, such as, for example, a motion sensor 138 (e.g., one or more accelerometers and / or gyroscopes), a PPG sensor 154, and / or an ECG sensor 156.

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

[0067] 1, the control system 110 and the memory device 114 are described and shown as separate components of the system 100, in some implementations the control system 110 and / or the memory device 114 are integrated into the user device 170 and / or the respiratory therapy device 122. Alternatively, in some implementations the control system 110, or portions thereof (e.g., the 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), located within one or more servers (e.g., remote servers, local servers, etc., or any combination thereof).

[0068] Although the system 100 is shown to include all of the above components, according to implementations of the present disclosure, a system for determining the length of a conduit may include more or fewer components. For example, a first alternative system includes the control system 110, the memory device 114, and at least one of the one or more sensors 130. As another example, a second alternative system includes the control system 110, the memory device 114, at least one of the one or more sensors 130, and the user device 170. As yet another example, a third alternative system includes the control system 110, the memory device 114, the respiratory therapy system 120, at least one of the one or more sensors 130, and the user device 170. As a further example, a fourth alternative system includes the control system 110, the memory device 114, the respiratory therapy system 120, at least one of the one or more sensors 130, the user device 170, and a blood pressure device 180 and / or an activity tracker 190. Thus, any portion or portions 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 for modifying pressure settings.

[0069] 2, in some implementations, any of the control system 110, memory device 114, one or more sensors 130, or combinations thereof may be located on and / or within any surface and / or structure generally adjacent to the bed 230 and / or user 210. For example, in some implementations, at least one of the one or more sensors 130 may be located at a first location on and / or within one or more components of the respiratory therapy system 120 adjacent to the bed 230 and / or user 210. The one or more sensors 130 may be coupled to the respiratory therapy system 120, the user interface 124, the conduit 126, the display device 128, the humidification tank 129, or combinations thereof.

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

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

[0072] In some implementations, a primary sensor, such as a microphone 140, is configured to generate acoustic data associated with the user 210 during a sleep session. The acoustic data can be based, for example, on acoustic signals in the conduit 126 of the respiratory therapy system 120. For example, one or more microphones (the same as or similar to the microphone 140 of FIG. 1) can be integrated and / or coupled to (i) a circuit board of the respiratory therapy device 122, (ii) the conduit 126, (iii) a connector between components of the respiratory therapy system 120, (iv) the user interface 124, (v) headgear (e.g., a strap) associated with the user interface, or (vi) a combination thereof. In some implementations, the microphone 140 is in fluid communication with the airflow path (e.g., the airflow path between a flow generator / motor and a distal end of the conduit). By fluid communication, it is also intended to include configurations in which the microphone is in acoustic communication with the airflow path without being in direct or physical contact with the airflow. For example, in some implementations, the microphone is located on the circuit board and is in fluid communication with the airflow path via a duct, optionally sealed by a membrane.

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

[0074] Additionally or alternatively, one or more microphones (the same as or similar to microphone 140 of FIG. 1 ) can be integrated and / or coupled to a coexisting smart device such as user device 170, a television, a clock (e.g., a mechanical watch or other smart device worn by a user), a pendant, mattress 232, bed 230, bedding placed on bed 230, a pillow, a speaker (e.g., speaker 142 of FIG. 1 ), a radio, a tablet device, a waterless humidifier, or a combination thereof. A coexisting smart device is any smart device that is within range to detect sounds emanating from the user, respiratory therapy system 120, and / or any part of system 100. In some implementations, a coexisting smart device is a smart device that is in the same room as the user during a sleep session.

[0075] Additionally or alternatively, in some implementations, one or more microphones (the same as or similar to microphone 140 of FIG. 1) can be located away from system 100 (FIG. 1) and / or user 210 (FIG. 2) so long as there is an air passage for the acoustic signal to travel to the one or more microphones. For example, one or more microphones can be located in a room separate from the room in which system 100 is located.

[0076] As used herein, a sleep session can be defined in a variety of ways, for example, based at least in part on an initial start time and end time. In some implementations, a sleep session is a period of time during which a user is asleep. That is, a sleep session has a start time and an end time, and during a sleep session, the user does not wake up until the end time. That is, periods during which the user is awake are not included in a sleep session. From this definition of a first sleep session, if a user wakes up and goes back to sleep multiple times in the same night, each sleep interval separated by an interval of waking becomes a sleep session.

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

[0078] In some implementations, a sleep session is defined as the entire time between the time in the evening when the user first gets into bed and the time the user last leaves the bed the next morning. In other words, a sleep session can be defined as the period starting from the first time (e.g., Monday, January 6, 2020) (also called the current evening) when the user first gets into bed with the intention of sleeping (unless the user intends to watch TV or operate a smartphone first before going to sleep) on a first date (e.g., Monday, January 6, 2020) at a first time (e.g., 10:00 p.m.) (also called the current evening) and ending when the user first gets out of bed with the intention of not sleeping again the next morning on a second date (e.g., Tuesday, January 7, 2020) at a second time (e.g., 7:00 a.m.) (also called the next morning).

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

[0080] 3, an example timeline 300 of a sleep session is shown. The timeline 300 includes the time of falling asleep (t 入床 ), bedtime (t 就寝 ), initial sleep time (t 睡眠 ), the first microphone wake MA1, the second microphone wake MA2, wake A, and the wake time (t 覚醒 ), and wake-up time (t 起床 ) are included.

[0081] Bed time t 入床 is associated with the time when the user first enters the bed (e.g., the bed 230 in FIG. 2) before falling asleep (e.g., when the user lies or sits in the bed). 入床 may be identified based at least in part on a bed threshold duration to distinguish between a time when the user goes to bed to sleep and a time when the user goes to bed for other reasons (e.g., to watch television). For example, the bed threshold duration may 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. 入床 is described here with respect to a bed, but more generally, 入床 can also refer to the time when a user first enters a sleeping location (e.g., a couch, chair, sleeping bag).

[0082] GTS is the time when the user first tries to fall asleep after getting into bed (t 入床 For example, after a user gets into bed, the user may engage in one or more activities (e.g., reading, watching television, listening to music, using the user device 170) to relax before attempting to fall asleep. 睡眠 ) is the time when the user first falls asleep. For example, the initial sleep time (t 睡眠 ) is the time when the user first enters the first non-REM sleep stage.

[0083] Awakening time t 覚醒is the time associated with the time the user wakes up without falling asleep again (e.g., different from when the user wakes up in the middle of the night and goes back to sleep). The user may experience one or more short (e.g., 5 seconds, 10 seconds, 30 seconds, 1 minute) involuntary microphone awakenings (e.g., microphone awakenings MA1 and MA2) after initially falling asleep. Awakening time t 覚醒 In contrast, the user falls asleep again after each of the microphone awakenings MA1 and MA2. Similarly, the user may experience one or more conscious awakenings (e.g., awakening A) after initially falling asleep (e.g., waking up to go to the bathroom, caring for a child or pet, sleepwalking). However, the user falls asleep again after awakening A. Thus, at the awakening time t 覚醒 may be defined, for example, based at least in part on an awakening threshold duration (e.g., the user is awake for at least 15 minutes, at least 20 minutes, at least 30 minutes, at least 1 hour, etc.).

[0084] Similarly, the wake-up time t 起床 is associated with the time when the user gets out of bed for the purpose of ending a sleep session (e.g., the user gets up in the middle of the night to go to the bathroom, take care of a child or pet, or sleepwalk). In other words, the wake-up time t 起床 is the time when the user last left the bed without returning to bed until the next sleep session (e.g., the next night). Therefore, the wake-up time t 起床 For example, t can be defined at least in part based on a wake threshold duration (e.g., at least 15 minutes, at least 20 minutes, at least 30 minutes, at least 1 hour after the user leaves the bed). 入床 can also be defined based at least in part on a wake threshold duration (eg, the user has been out of bed for at least 4 hours, at least 6 hours, at least 8 hours, at least 12 hours).

[0085] As mentioned above, the user can select the first t 入床 and the last t 起床In some implementations, the patient may wake up once more during the night and get out of bed between the final awakening time t 覚醒 and / or final wake-up time t 起床 t may be identified or determined based at least in part on a predetermined threshold duration after an event (e.g., falling asleep or getting out of bed). Such threshold duration may be customized for a user. For a typical user who goes to bed in the evening and wakes up and gets out of bed in the morning, the user may experience a wakefulness (t 覚醒 ) or wake up (t 起床 ) from the time the user enters the bed (t 入床 ), bedtime (t 就寝 ), or fall asleep (t 睡眠 ) can be used. For users who spend a lot of time in bed, a shorter threshold period may be used (e.g., between about 8 hours and about 14 hours). The threshold period may be initially selected and later adjusted, at least in part, based on the system monitoring the user's sleep behavior.

[0086] Total time in bed (TIB) is calculated from the time of admission t 入床 From wake-up time t 起床 t . Total sleep time (TST) is the time between the initial sleep time t and the wake time, excluding any conscious or unconscious wakefulness or microarousals in between. Typically, total sleep time (TST) will be less than the total time in bed (TIB) (e.g., 1 minute less, 10 minutes less, 1 hour less). For example, referring to timeline 300 in FIG. 3, total sleep time (TST) is the time between the initial sleep time t 睡眠 and awakening time t 覚醒 , but excluding the duration of the first microphone wake MA1, the second microphone wake MA2, and the wake A. As shown, in this example, the total sleep time (TST) is less than the total time in bed (TIB).

[0087] In some implementations, the total sleep time (TST) can be defined as the continuous total sleep time (PTST). In such implementations, the continuous total sleep time does not include a predetermined initial portion or period of the first non-REM sleep stage (e.g., light sleep stage). For example, the predetermined initial portion is between about 30 seconds and about 20 minutes, between about 1 minute and about 10 minutes, between about 3 minutes and about 5 minutes, etc. The continuous total sleep time is a measure of continuous sleep and smooths out the sleep-wake hypnogram. For example, when a user first falls asleep, the user is in the first non-REM sleep stage for a very short time (e.g., about 30 seconds), then returns to the wake stage for a short time (e.g., 1 minute), and then returns to the first non-REM sleep stage. In this example, the continuous total sleep time does not include the first instance (e.g., about 30 seconds) of the first non-REM sleep stage.

[0088] In some implementations, a sleep session is started at bedtime (t 入床 ) and wake-up time (t 起床 ), that is, a sleep session is defined as the total time in bed (TIB). In some implementations, a sleep session is defined as the total time in bed (TIB) between the initial sleep time (t 睡眠 ) and wake-up time (t 覚醒 ) In some implementations, a sleep session is defined as a total sleep time (TST). In some implementations, a sleep session is defined as a bedtime (t 就寝 ) and wake up time (t 覚醒 ) In some implementations, a sleep session is defined to end at bedtime (t 就寝 ) and wake-up time (t 起床 ) In some implementations, a sleep session is defined to end at bedtime (t 入床 ) and wake up time (t 覚醒 ) In some implementations, a sleep session is defined to end at an initial sleep time (t 睡眠 ) and wake-up time (t 起床 ) is defined as ending at

[0089] 4, an exemplary hypnogram 400 corresponding to the timeline 300 (FIG. 3) is shown according to some implementations. 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 a sleep stage at any time during a sleep session.

[0090] The sleep-wake signal 401 can be generated based at least in part on physiological data associated with the user (e.g., data generated by one or more sensors 130 described herein). The sleep-wake signal can indicate one or more sleep stages including wakefulness, relaxed wakefulness, micro-awakening, REM sleep stage, first non-REM sleep stage, second non-REM sleep stage, third non-REM sleep stage, or any combination thereof. In some implementations, one or more of the first non-REM sleep stage, the second non-REM sleep stage, and the third non-REM sleep stage can be grouped and classified as a light sleep stage or a deep sleep stage. For example, the light sleep stage includes the first non-REM sleep stage, and the deep sleep stage includes the second non-REM sleep stage and the third non-REM sleep stage. In FIG. 4, the hypnogram 400 is shown as including a light sleep stage axis 430 and a deep sleep stage axis 440, but in some implementations, the hypnogram 400 can include an axis for each of the first non-REM sleep stage, the second non-REM sleep stage, and the third non-REM sleep stage. In other implementations, the sleep-wake signal may also indicate a respiration signal, a respiration rate, an inhalation amplitude, an exhalation amplitude, an inhalation-exhalation amplitude ratio, an inhalation-exhalation duration 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 memory device 114.

[0091] 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 time (WASO), sleep efficiency (SE), sleep fragmentation index, sleep blocks, or any combination thereof.

[0092] Sleep onset latency (SOL) is the time to bed (t 就寝 ) and initial sleep time (t 睡眠 ) 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 bedtime to a predetermined duration of sleep. In some implementations, the predetermined amount of sustained sleep includes, for example, at least 10 minutes of sleep in the second non-REM sleep stage, the third non-REM sleep stage, and / or the REM sleep stage, and no more than 2 minutes of wakefulness, the first non-REM sleep stage, and / or movement therebetween. In other implementations, sustained sleep onset latency requires, for example, up to 8 minutes of sustained sleep in the second non-REM sleep stage, the third non-REM sleep stage, and / or the REM sleep stage. In other implementations, the predetermined amount of sustained sleep may include at least 10 minutes of sleep in the first non-REM sleep stage, the second non-REM sleep stage, the third non-REM sleep stage, and / or the REM sleep stage after the initial sleep time. In such an implementation, a predetermined amount of sustained sleep may exclude microphone wakeups (eg, a 10 second microphone wakeup does not restart the 10 minute period).

[0093] The wake onset after sleep (WASO) is associated with the total time the user is awake between the initial sleep time and the wake time. Thus, the wake onset after sleep includes brief micro-awakenings during a sleep session, whether conscious or unconscious (e.g., micro-awakenings MA1 and MA2 shown in FIG. 4). In some implementations, the wake onset after sleep (WASO) is defined as a persistent wake onset after sleep (PWASO) that includes only total durations of awakenings of 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).

[0094] Sleep efficiency (SE) is determined as a 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 sleep time is 7.5 hours, the sleep efficiency for that sleep session is 93.75%. Sleep efficiency is an indicator of the user's sleep hygiene. For example, if the user goes to bed and spends time on other activities (e.g., watching TV) before going to sleep, the sleep efficiency is reduced (e.g., the user is penalized). In some implementations, the sleep efficiency (SE) can be calculated at least in part based on the total time in bed (TIB) and the total time the user is attempting to sleep. In such implementations, the total time the user is attempting to sleep is defined as the duration between a Go To Bed Time (GTS) and a wake-up time as described herein. For example, if the total sleep time is 8 hours (e.g., from 11:00 PM to 7:00 AM), with a bedtime of 10:45 PM and a wake-up time of 7:15 AM, in such implementations, the sleep efficiency parameter is calculated to be approximately 94%.

[0095] The fragmentation index is determined based at least in part on the number of awakenings during the sleep session. For example, if a user has two microphone awakenings (e.g., microphone awakening MA1 and microphone awakening MA2 shown in FIG. 4), the fragmentation index can be expressed as 2. In some implementations, the fragmentation index is scaled between a predetermined range of integers (e.g., 0 to 10).

[0096] A sleep block is associated with a transition between any stage of sleep (e.g., NREM sleep stage 1, NREM sleep stage 2, NREM sleep stage 3, and / or REM sleep) and a wakefulness stage. Sleep blocks can be calculated, for example, with a resolution of 30 seconds.

[0097] In some implementations, the systems and methods described herein generate or analyze a hypnogram including a sleep-wake signal to determine bedtime (t 入床 ), bedtime (t 就寝 ), initial sleep time (t 睡眠), one or more first microphone wake-ups (e.g., MA1 and MA2), wake-up time (t 覚醒 ), wake-up time (t 起床 ), or any combination thereof based at least in part on the hypnogram sleep-wake signals.

[0098] In other implementations, one or more sensors 130 are used to measure bed entry time (t 入床 ), bedtime (t 就寝 ), initial sleep time (t 睡眠 ), one or more first microphone wake-ups (e.g., MA1 and MA2), wake-up time (t 覚醒 ), wake-up time (t 起床 ), or any combination thereof, which defines a sleep session. 入床 can be determined based at least in part on data generated by, for example, the motion sensor 138, the microphone 140, the camera 150, or any combination thereof. The bedtime can be determined based at least in part on data from, for example, the motion sensor 138 (e.g., data indicative of no user movement), the camera 150 (e.g., data indicative of no user movement and / or data indicative of the user turning off the lights), the microphone 140 (e.g., data indicative of the television being turned off), the user device 170 (e.g., data indicative of the user no longer using the user device 170), the pressure sensor 132 and / or the flow sensor 134 (e.g., data indicative of the user turning on the respiratory therapy device 122, data indicative of the user wearing the user interface 124), or any combination thereof.

[0099] Typically, a user prescribed to use the respiratory therapy system 120 tends to experience better quality sleep and less daytime fatigue after using the respiratory therapy system 120 while sleeping compared to not using the respiratory therapy system 120 (especially if the user suffers from sleep apnea or other sleep-related disorders). For example, the user 210 suffers from obstructive sleep apnea and may utilize the user interface 124 (e.g., a full-face mask) to deliver pressurized air from the respiratory therapy device 122 via the conduit 126. The respiratory therapy device 122 is a continuous positive airway pressure (CPAP) device used to increase air pressure in the throat of the user 210 to prevent the airway from closing or narrowing during sleep. People with sleep apnea may experience airway narrowing or obstruction during sleep, reducing oxygen intake and waking up or disrupting sleep. The CPAP device prevents the airway from narrowing or collapsing, minimizing waking up or other disturbances caused by reduced oxygen intake. Although the respiratory therapy device 122 attempts to maintain a medically prescribed air pressure during sleep, the user may experience discomfort while sleeping as a result of the therapy.

[0100] FIG. 5 illustrates a method 500 for optimizing sleep for a user of a respiratory therapy system (e.g., respiratory therapy system 120) including a respiratory therapy device (e.g., respiratory therapy device 122) configured to deliver pressurized air and a user interface (e.g., user interface 124) connected to the respiratory therapy device via a conduit (e.g., conduit 126). The user interface is configured to interact with a user and helps direct the pressurized air to the user's airway. In general, a control system (e.g., control system 110 of system 100) having one or more processors is configured to perform the steps of method 500. A memory device (e.g., memory device 114 of system 100) can be used to store machine-readable instructions executed by the control system to perform the steps of method 500. The memory device can also store any type of data utilized in the steps of method 500. In general, method 500 can be implemented using a system (e.g., system 100) including a respiratory therapy system, a control system, and a memory device.

[0101] At step 510, therapy instructions to be implemented using respiratory therapy system 120 for a sleep session are received. The therapy instructions may be entered or preprogrammed into respiratory therapy system 120 by a caregiver or user 210. The therapy instructions may include a number of prescribed control parameters. For example, the therapy instructions may include a prescribed pressure and pressure range (e.g., a target pressure, a minimum pressure and a maximum pressure). The therapy instructions may also include a prescribed ramp rate, or range of ramp rates, to achieve the prescribed pressure.

[0102] In some implementations, the control parameters include sounds, expiratory pressure relief (EPR) settings, humidification levels, device movement, lights turning on or changing brightness, fans turning on or changing power, or any combination thereof.

[0103] Sounds that can be used as control parameters include, but are not limited to, white noise, pink noise, brown noise, violet noise, soothing sounds, music, alarms, warnings, beeps, or combinations thereof. As used herein, some variations of flat-shaped white noise sounds are referred to as pink noise, brown noise, violet noise, etc. In some implementations, the sounds (e.g., white noise, pink noise, brown noise, violet) help mask noises from the respiratory therapy system or the environment. In some implementations, the sounds (e.g., soothing sounds or music) can help the user or bed partner maintain a sleep state, gently wake the user, or cause the user to change sleep positions, such as positions that maintain a sleep session or sleep state.

[0104] In some implementations, sound may be provided by one or more speakers 142 of the system 100. Optionally, the system 100 includes multiple speakers 142 that provide localized sound emission. The speakers 142 include in-ear speakers, above-ear speakers, speakers adjacent to the ear speakers, earphones, earpods, or any combination thereof. The speakers 142 are wired or wireless speakers (e.g., headphones, bookshelf speakers, floor-standing speakers, television speakers, in-wall speakers, in-ceiling speakers). In some implementations, the speakers 142 are worn by the user 210 and / or the bed partner 220. In some such implementations, the provided speakers 142 can provide masking noise without affecting the bed partner because the sound is identified by the type of speaker 142. In such implementations, the breathing user 210 and / or the bed partner 220 can be provided with the respective identified speakers 142.

[0105] Optionally, the speaker 142 is attached to one or more straps or strap segments of the user interface 124. Thus, the user 210 (FIG. 2) and / or bed partner 220 can choose whether to perceive a relatively flat-shaped white noise sound or a quieter (lower level and / or low-pass filtered) shaped noise signal. In some such implementations, high-frequency sounds / noises (e.g., "harsh" sounds) are reduced while still providing sounds that mask environmental noises. The system 100 can select a set of optimized fill-in sound frequencies to achieve a target noise profile. For example, if a particular sound component already exists in the frequency spectrum (e.g., associated with a room box fan, CPAP blower motor, etc.), the system 100 can select a fill-in sound with sound parameters / characteristics that fill the quiet frequency bands, for example, up to a target amplitude level. Thus, the system 100 can use active adaptive masking and / or adaptive noise cancellation to adaptively attenuate high and / or low frequency components so that the perceived sound is more pleasant and relaxing to the ear (the latter being suitable for more slowly changing and predictable sounds).

[0106] Generally, EPR is a feature provided in some respiratory therapy devices (e.g., CPAP machines) that allows the user to adjust various comfort settings to reduce the feeling of breathlessness experienced by some users, such as a drop of 2 cmH2O between inspiration and expiration. While this feature can be performed manually, in some implementations of this description, the EPR settings are implemented by the system 100 without direct user input to provide the desired sleep comfort.

[0107] In some implementations, the control parameter is the humidity of the air provided to the user 210. For example, the humidity level can be selected to reduce discomfort due to dry sinuses or mouth. The humidity level can also be selected to optimize the seal between the interface 124 and the user 210.

[0108] The devices that are actuated or moved as control parameters include, but are not limited to, a smart pillow, an adjustable bed frame, an adjustable mattress, a fan, an adjustable blanket, or a combination thereof. For example, the devices are controlled by the control system 110. In some implementations, the smart pillow, smart mattress, or adjustable blanket can include one or more inflatable compartments or bladders that can be inflated or deflated. The actuation device can thereby change the orientation of the user. For example, the pillow 260 can be a smart pillow that includes one or more inflatable bladders that change the orientation of the user 210 if the user's head is in an orientation that increases the likelihood of an increase in the AHI. In another or additional implementation, the adjustable bed frame can include a section that can be raised or lowered by the drive of a motor to change the orientation of the user 210, such as forcing the user 210 to roll from side to back. In some implementations, a fan (e.g., a fan placed on the nightstand 240, a window fan, or a ceiling fan) turns on in response to the likelihood of the orientation of the user 210 increasing the AHI. In some implementations, the fan generates a white noise. The fan may gradually increase speed and air movement so as not to wake or disturb the user 210 or bed partner 220 with sudden changes in air movement or sound from the fan.

[0109] In some implementations, the control parameter is the injection of a substance into the pressurized air supplied to the user interface 124. For example, the receptacle 182 can be filled with the substance and the receptacle is provided with an outlet in direct or indirect fluid communication with the conduit 126. The substance can be configured or selected to cause a physical reaction by the user 210. For example, the user 210 can change direction.

[0110] Optionally, the substance may include pharmaceuticals such as anti-inflammatory drugs, drugs to treat asthma attacks, drugs to treat heart attacks, etc. In general, any type of drug used to treat any illness, condition, disease, etc. may be delivered to the airway of the user 210. When the substance is a drug, it generally includes one or more active ingredients and one or more excipients. Excipients act as a vehicle to carry the active ingredient and may include substances such as bulking agents, fillers, diluents, anti-adherents, binders, coating agents, colorants, disintegrants, flavors, glidants, lubricants, preservatives, adsorbents, sweeteners, fillers, or combinations thereof. The active ingredient is generally the part of the drug that actually causes the effect provided by the drug.

[0111] The substance may optionally be an aroma compound (e.g., a substance that delivers a scent and / or fragrance to the airways of the user 210), a sleep aid (e.g., a substance that helps the user 210 fall asleep), a consciousness awakening compound (e.g., a substance that helps the user 210 wake up, also called a sleep inhibitor), cannabidiol oil, an essential oil (e.g., lavender, valerian, clary sage, sweet marjoram, roman chamomile, bergamot). The substance is generally a solid, liquid, gas, or any combination thereof. Alternatively or additionally, the substance may include one or more nanoparticles.

[0112] In some implementations, the control parameters prescribed for the treatment are a function of sleep stage or sleep structure. For example, at the transition from wakefulness to N1 stage, a pressure ramp may be initiated to achieve a first target pressure from ambient pressure, according to the user's prescription. The ramp may be implemented to allow the user 210 to gradually get used to the pressure change. During the subsequent N2 and N3, the first target pressure may be maintained according to the user's prescription. Higher pressures are often prescribed for REM sleep stages than for previous sleep states, since most users are more likely to experience increased apnea during REM sleep. Thus, a ramp to a higher pressure may be implemented for REM sleep. The sleep state may be determined by monitoring physiological parameters using sensors (e.g., one or more sensors 130, blood pressure device 180, or activity tracker 190 of FIG. 1), as described above.

[0113] In step 520, a desired sleep comfort level is input to the respiratory therapy system 120. The desired sleep comfort level may be selected based on how important sleep comfort is to the user 210 during a sleep session. For example, a first user 210 may be encouraged to select a high comfort level. A more experienced user 210 may not prioritize sleep comfort or may not experience significant discomfort when using the respiratory therapy system 120 at the default control settings. In some cases, a user 210 may generally desire high sleep comfort, but may need high quality sleep and may be willing to accept a lower comfort level during a particular sleep session, and may select a low desired sleep comfort level.

[0114] As an alternative or in addition to the user setting a desired sleep comfort level, in some implementations, the system 100 can automatically set a sleep comfort level for the user 210. The automatically set sleep comfort level can be based at least in part on data associated with the user 210 and / or on the user's experience and / or data based on the length of time the respiratory therapy system 120 and / or sleep therapy have been used. For example, the automatically set sleep comfort level can be set based on the number of days the user has been using sleep therapy or the number of hours logged using the respiratory therapy system 120.

[0115] In some implementations, one aspect of sleep comfort relates to how a user 210 or a group of users evaluates their sleep experience. A user or group of users can evaluate their sleep comfort experience after a sleep session. The evaluation system can include various criteria including data on reported symptoms such as aerophagia, difficulty falling asleep, dry nasal passages / mouth, muscle pain, dry skin, etc. The criteria can be further quantified, such as rating pain from aerophagia as low (e.g., mild gas), moderate lingering discomfort (e.g., bloating), or high (e.g., cramping). Difficulty falling asleep can be evaluated by whether the user recalls checking the time or noticing noise or air pressure from the respiratory therapy system 120. The occurrence of dry skin, sinuses / mouth, and muscle pain can also be reported and used to evaluate sleep comfort. Evaluation can be done by questionnaire, with or without the help of a caregiver. In some implementations, the questionnaire may also collect information such as whether the user 210 experienced insomnia, difficulty sleeping, or awakening during pressure therapy, a bed partner's rating of sleep comfort (e.g., how it affected the bed partner), and other motivational feedback questions such as increased activity the next day after pressure therapy. The questionnaire is presented and the rating is entered by an interactive app via the user interface 124, a touch screen on the breathing apparatus 122, voice input, or an external device 170 (e.g., a smartphone).

[0116] An example of a short questionnaire in which a user (Joe) rated his sleep comfort is shown in Table 1.

[0117] [Table 1]

[0118] The sample questionnaire assesses sleep comfort (or discomfort) due to aerophagia, dry nose / mouth, dry skin, discomfort at bedtime, muscle pain, and sleep quality. The questionnaire rates these factors on a 5-point scale from low to high. Sleep quality relates to how well rested the user 210 feels, for example, clear-headed and alert. Sleep quality is often inversely related to sleep comfort, even if sleep quality does not characterize sleep comfort. Including sleep quality in the sleep comfort assessment helps determine how much sleep comfort can be altered without reducing it to a level where treatment is not effective.

[0119] An overall sleep comfort score can be determined as a function of the sleep comfort of each item listed in Table 1. Equation 1 illustrates one implementation of how the sleep comfort questionnaire can be used to provide an overall sleep score.

[0120] Equation 1: Overall evaluation sleep comfort score = (m1) (air swallowing disorder) + (m2) (nose / mouth dryness) + (m3) (dry skin) + (m4) (sleep onset) + (m5) (muscle pain) - (m6) (sleep quality)

[0121] In Equation 1, m1 is a weighting factor selected for aerophagia, m2 is a weighting factor selected for nasal / oral dryness, m3 is a weighting factor for dry skin, m4 is a weighting factor for falling asleep, m5 is a weighting factor for muscle pain, and m6 is a weighting factor for sleep quality. The value of the overall sleep comfort score may also be normalized to have a minimum value of 0 or 1 and a maximum value of 5, 10, 20, 50, or 100. The weighting factor relates to the importance of a particular item to sleep comfort. For example, the item "aerophagia" may ultimately be more important to sleep quality than the item "falling asleep," and a larger weighting factor is selected or assigned to aerophagia than falling asleep. The weighting factor may be assigned by the first machine learning algorithm, for example, by providing data from multiple sleep sessions of one or more users. The "true" sleep comfort levels for each individual sleep session are also used to train the first algorithm. Here, "true" sleep comfort refers to an overall assessment of sleep comfort provided by the user, which can be assigned a value for training the first machine learning algorithm.

[0122] The first machine learning algorithm may also determine overall features, including items other than those listed in Table 1, to provide the most accurate overall assessment sleep comfort score. In some implementations, the first machine learning algorithm may learn how individual users 210 rate subjective criteria. For example, a first user may rate muscle pain as having a negative impact on sleep comfort more than a second user. The first algorithm may learn this difference between the first and second users and appropriately modify the function that determines the overall assessment sleep comfort score depending on which user is using the respiratory therapy system 120. For example, if Equation 1 is used, the weighting factor m5 associated with muscle pain for the first user will be less than the same weighting factor for the second user.

[0123] In some implementations, aspects of sleep comfort involve monitoring the user 210 or the user of the respiratory therapy system 120 during a sleep session using sensors such as the sensor 130. In some implementations, the sleep structure of the sleep session is determined as described above, and the sleep comfort level achieved is determined as a function of the sleep structure. For example, the user 210 may be detected moving during a non-REM sleep session. The user may unconsciously attempt and succeed in removing the user interface 124 during N1 or other sleep phases. In some implementations, the user may assume sleep positions during the non-REM sleep phase (e.g., supine and sideways), which reduces sleep comfort. These activities may be factors that affect sleep comfort and can be monitored using sensors such as the motion sensor 138, the camera 150, the microphone 140, the blood pressure device 180, the activity tracker 190, or any combination thereof. Other indicators of sleep comfort that can be monitored include pump or other noise from the respiratory therapy system 120 and leakage (e.g., mask leakage) at the user interface 124. Ambient temperature as measured by temperature sensor 136 can also be indicative of sleep comfort. For example, temperatures higher or lower than an ideal temperature (e.g., 63° F.) can affect sleep comfort. Although the user 210 may not immediately understand how the various factors monitored by the sensors are affecting their sleep, the sensors can not only monitor these factors in real time, but also track them after a sleep session to provide data for analysis.

[0124] Table 2 lists some examples of factors that can be monitored using sensors.

[0125] [Table 2]

[0126] Table 2 shows the number of non-REM sleep movements, the percentage of time spent lying on one's back and on one's side, the average room temperature, the number of mask leaks, the occurrence of noise above a whisper (e.g., about 40 dB), and the AHI. The AHI does not directly measure sleep comfort and may therefore be inversely related to or have an adverse effect on sleep comfort. Reducing the AHI is an important goal of sleep therapy for various sleep disorders, so including the AHI allows for a balanced consideration.

[0127] The measured overall sleep comfort score can be determined as a function of these sensor measurables. Any useful function can be implemented. An example of a simple function is shown in Equation 2. Equation 2: Measured total sleep comfort score = (m7) (N-REM movement) + (m8) (percentage of time on back / side) + (m9) (average room temperature) + (m 10 )(#mask leakage count)+(m 11 )(#Noise)-(m 12 )(AHI) where m7 is the weighting factor selected for N-REM exercise, m8 is the weighting factor selected for the percentage of time in supine / side position, m9 is the weighting factor for average room temperature, m 10 is the weighting factor for the number of mask leaks, m 11 is the weighting factor for the number of noise occurrences exceeding 40 dB, m 12is a weighting factor for the AHI. The weighting factor and the overall form of the function can be determined using a second machine learning algorithm. The measured total sleep comfort score value can also be normalized to have a minimum value of 0 or 1 and a maximum value of 5, 10, 20, 50, or 100. Data for a user or multiple users across multiple sleep sessions can be input to the second machine learning algorithm. The actual sleep comfort can be used to train the second machine learning algorithm. In some implementations, the first machine learning algorithm provides an assessed sleep comfort that can be used to train the second machine learning algorithm to determine the measured sleep comfort. For example, the assessed sleep comfort from the first algorithm is used as the true sleep comfort to train the second algorithm.

[0128] In some implementations, data of user 210 rated sleep comfort (e.g., Table 1) is combined with data of sensor measured sleep comfort factors (e.g., Table 2). For example, the user reported sleep comfort and measured sleep comfort can be used to determine an overall sleep comfort score. For example, the overall sleep comfort score function can be a combination of Equations 1 and 2. The overall sleep comfort score value can also be normalized, for example, to have a minimum value of 0 or 1 and a maximum value of 5, 10, 20, 50, or 100. In some implementations, the first algorithm and the second algorithm are combined into one machine learning algorithm.

[0129] Returning to step 520 of FIG. 5, the desired sleep comfort level is a value selected from a series of values ​​that increase stepwise between a first value indicating that the comfort experience is not important to the user and a second value indicating that the user desires the best possible sleep comfort experience. This value can be scaled similarly to the sleep comfort score used, i.e., the assessed sleep comfort score (e.g., Table 1, Equation 1), the measured sleep comfort score (e.g., Table 2, Equation 2), or the overall sleep comfort score (e.g., a combination of the assessed and measured sleep comfort scores). For example, the minimum and maximum values ​​of the various scores correspond to the minimum and maximum values ​​that the user 210 can select for the desired sleep comfort. The value can be a continuous scale, such as an analog volume control, or it can be digital. In other implementations, the sleep comfort can have a digital value. For example, the value can be an integer from 1 to 10, where 1 indicates that the user does not desire improved sleep comfort and 10 indicates that the user desires the best possible sleep comfort. The sleep comfort level can be selected or adjusted depending on the sleep comfort desired by the user.

[0130] In some implementations, the desired comfort level is selected when the user 210 goes to bed for a sleep session, while in other implementations the user 210 can change the comfort level during the sleep session. For example, the user 210 may wake up after a combination of non-REM and REM sleep and determine that he is uncomfortable or cannot fall asleep again. The user 210 can decide to increase the sleep comfort level accordingly. Alternatively, the user 210 may wake up during a sleep session, realize that it is 4:00 a.m., determine that he needs to get a few more hours of quality sleep, and decrease the sleep comfort level to improve his sleep quality.

[0131] As shown in step 530, in some optional implementations, historical control parameters and historical sleep comfort levels can be input to the respiratory therapy system 120. As used herein, "history" is associated with one or more previous sleep sessions. For example, the historical sleep comfort may be a user-selected value 8 (e.g., on a scale of 1 to 10) that the user may have selected in a sleep session immediately prior to the current sleep session. In this case, the historical control parameters are the control parameters of a previous sleep session implemented using the respiratory therapy system 120 with a goal of a desired sleep comfort level of 8. After evaluating the previous sleep session, the user may determine whether the actual achieved comfort level (e.g., historical sleep comfort level) is lower or higher than the input level. The received historical parameters and sleep comfort level can be used for the current sleep session to more accurately achieve the desired sleep comfort level in a sleep session where a gap in the user input is indicated. For example, the user can select a higher or lower sleep comfort level based on personal experience to self-adjust the desired sleep comfort level.

[0132] In some implementations, the use of historical data is related to training the system and can be implemented using artificial intelligence. For example, the third machine learning algorithm can include data used in the first machine learning algorithm (user-reported sleep comfort), the second machine learning algorithm (user-measured sleep comfort), and historically adjusted control parameters. In some implementations, the third machine learning algorithm is or includes elements of the first and second machine learning algorithms.

[0133] At step 540, the control parameters are adjusted from the prescribed control parameters and the adjusted control parameters are implemented during the sleep session. If the prescribed control parameters are implemented to improve the sleep quality of the user 210, the adjusted control parameters are implemented to achieve a desired sleep comfort level for the user 210.

[0134] In some implementations, the received therapy instructions are provided to assist a user in achieving a target therapy parameter during a sleep session, and the adjusted one or more values ​​or value ranges of the plurality of control parameters provide a therapy parameter that is different from the target therapy parameter. In some implementations, the adjusted one or more values ​​or value ranges of the plurality of control parameters provide a therapy parameter that is greater than the target therapy parameter. In some other implementations, the adjusted one or more values ​​or value ranges provide a therapy parameter that is less than the target therapy parameter.

[0135] In some implementations, the received therapy instructions are provided to assist the user in achieving a target AHI during the sleep session. In some implementations, the achieved AHI is approximately the same as the goal of the therapy, while in other implementations, the adjusted control parameters may result in an AHI that is greater (e.g., worse) than the target AHI.

[0136] Thus, sleep comfort can be improved at the expense of reduced sleep quality. In some implementations, control parameters are adjusted to maximize sleep quality and maximize sleep comfort. For example, maximizing sleep quality and sleep comfort can be features of a machine learning algorithm, such as a third machine learning algorithm.

[0137] In some implementations, the pressure, pressure range, or pressure ramp is adjusted up or down from a prescribed pressure, pressure range, or pressure ramp. In some implementations, the pressure, pressure range, or pressure ramp is adjusted to be lower than the prescribed pressure for at least a portion of the sleep session. In some implementations, the pressure, pressure range, or pressure ramp is adjusted to be higher than the prescribed pressure for at least a portion of the sleep session. In some implementations, the pressure or pressure range is adjusted to be higher than the prescribed pressure for at least a portion of the sleep session, and the average adjusted pressure over the entire sleep session is lower than the average prescribed pressure over the entire sleep session.

[0138] In some implementations, the sound provided by one or more speakers 142 of the system 100 is adjusted from a default sound. In some implementations, the volume, duration, or duration of the white noise, pink noise, brown noise, violet noise, soothing sound, or music is increased, decreased, or increased from a default. In some implementations, an alarm or warning that indicates a poor sleeping posture with respect to sleep quality is turned off. Silencing the alarm allows the user 210 to continue sleeping despite the poor posture, resulting in a more comfortable sleep.

[0139] In some implementations, the EPR setting is adjusted up or down from the default value. For example, if the EPR setting is default to a drop of 0.5 cmH2O between inspiration and expiration, the adjusted setting will be 1 cmH2O, 1.5 cmH2O, or 2.0 cmH2O.

[0140] In some implementations, the humidity of the air delivered to the user 210 is adjusted up or down from a default value. For example, in some implementations, the humidity is increased to reduce discomfort due to dry skin or dry sinuses / mouth. In other implementations, the humidity is decreased to reduce discomfort caused by the user 210 feeling uncomfortable due to the slickness or stickiness of the user interface 124 (e.g., a face mask). A decrease in humidity may lead to decreased sleep quality, for example due to increased moisture leakage from the face, but increased sleep comfort.

[0141] In some implementations, the adjusted control parameters include devices that are activated or moved. In a prescribed treatment, the device may cause the user 210 to change position to reduce or avoid mask leak. If the user 210 has a tendency to move into a position that causes mask leak, repeated activation of the device in an attempt to force the user into a different position may cause sleep discomfort, for example, forcing the user into a position that causes muscle pain or induces aerophagia.

[0142] In implementations where the control parameter is the injection of a substance into the pressurized air delivered to the user interface 124, the adjustment can be a decrease or an increase in the substance delivered. For example, a compound that stimulates awareness can be prescribed to limit sleep time. Adjustments can delay delivery of the substance to a later period of sleep, thereby increasing sleep time and improving sleep quality.

[0143] In some implementations, the prescribed control parameters maintain the ideal sleep structure. Adjusted control parameters may change the ideal sleep structure. For example, adjusting the control parameters may increase the occurrence of apneas, resulting in less REM sleep.

[0144] In some implementations, the selected desired sleep comfort level does not provide a measurable improvement in sleep quality, but can be used to enable a user 210, such as a first user, to adopt a sleep therapy regimen. The user 210, optionally with guidance from a caregiver, can gradually reduce the comfort level to improve sleep quality in a "weaning" process. In some implementations, the weaning process is part of a program over days, weeks, or months and may be an automatically implemented function of the control system 110. In some implementations, the weaning program may be a function of one or more of the machine learning algorithms described herein.

[0145] Step 550 illustrates an optional implementation in which control parameters are adjusted during a sleep session depending on the current sleep comfort and the desired sleep comfort. The current sleep comfort is an estimate of the user's sleep comfort and does not require direct or conscious input from the user. For example, the current sleep comfort can be determined by monitoring the user 210 using a sensor such as the sensor 130, blood pressure device 180, or activity tracker 190 of FIG. 1. While Table 2 illustrates the sleep comfort measured over the entire sleep session, various factors that can be monitored using sensors can be sampled and aggregated during the sleep session. How these factors change during the sleep session can be used to predict the sleep comfort achieved during the sleep session. If the predicted sleep comfort trajectory based on the current sleep comfort deviates from the desired level of sleep comfort, corrective action can be taken. Corrective action can be taken by changing the control parameters. For example, if the temperature is high and sleep comfort is predicted to decrease, the thermostat can be reset or the fan can be turned on. If muscle pain is predicted to occur due to the sleeping posture of the user 210, a device such as a smart pillow, a smart mattress, or an adjustable blanket may be activated to change the posture of the user 210. The prediction may be implemented using a predictive algorithm. The predictive algorithm may be a fourth machine learning algorithm, which may include the first, second, and third algorithms described above.

[0146] Step 560 is an optional step that includes determining a sleep comfort level achieved by the user 210 during the sleep session. The achieved sleep comfort level can be determined, for example, using the user-rated sleep comfort and the measured sleep comfort, as described above. Optionally, the sleep comfort is determined using a fifth machine learning algorithm, which is any combination of the first, second, third, and fourth machine learning algorithms described above. In some implementations, the sleep comfort is reported to the user, for example, via the external device 170.

[0147] According to some implementations, any of a plurality of prescribed control parameters may be adjusted to improve sleep comfort. For example, the plurality of control parameters may include a prescribed pressure, a prescribed pressure range, a prescribed pressure ramp range, and a prescribed step pressure change range, and one or more of the prescribed pressure, the prescribed pressure range, the prescribed pressure ramp range, and the prescribed step pressure change range are adjusted to improve sleep comfort. In some implementations, the prescribed pressure is adjusted to an adjusted pressure that is lower than the prescribed pressure or the prescribed pressure range. In some implementations, the prescribed pressure range is adjusted to a pressure range that is lower than the prescribed pressure range. For example, the average or mean value of the prescribed pressure range may be adjusted lower, or one or more of the maximum pressure or minimum pressure may be adjusted lower.

[0148] FIG. 6A is a graph illustrating an implementation according to some aspects of the disclosure. The graph in FIG. 6A illustrates a pressure ramp that can be implemented at or near the beginning of a sleep session to, for example, help a user fall asleep at the beginning of a sleep session. The graph on the left illustrates an adjusted target maximum pressure 602, an adjusted pressure ramp 603, and a respiratory flow rate 601. The graph on the right illustrates a therapeutic prescribed maximum pressure 604, a prescribed pressure ramp 605, and a respiratory flow rate 601. A prescribed maximum pressure 604 implemented by the respiratory therapy system 120 may be prescribed to the user. The prescribed maximum pressure 604 may be, for example, 15 mmH2O. This pressure is prescribed to provide a target AHI, such as 10 or less, per sleep session. The prescribed target AHI and pressure may be determined, for example, during a caregiver-supervised titration experiment. The user 210 may find that the prescribed maximum pressure 604 and / or the prescribed pressure ramp 605 reduce sleep comfort. For example, the user 210 may experience symptoms of aerophagia after a sleep session in which the prescribed maximum pressure 604 was implemented. Alternatively or additionally, the user 210 may feel that the prescribed pressure ramp 605 increases the pressure too quickly, making it difficult to fall asleep. The adjusted target maximum pressure 602 and the adjusted pressure ramp 603 are responsive to the user 210 selecting a desired sleep comfort level. For example, a user who feels bloated when the prescribed pressure ramp 605 is implemented selects a sleep comfort level to reduce the bloating and increase sleep comfort. Similarly, the more gradual increase of the adjusted pressure ramp 603 compared to the prescribed pressure ramp 605 allows the user 210 a more gentle transition to fall asleep. The adjusted target maximum pressure 602 and the adjusted pressure ramp 603 may provide better sleep comfort compared to the prescribed maximum pressure 604 and the prescribed pressure ramp 605, but may result in reduced sleep quality. For example, using the prescribed maximum pressure 604 may result in a higher AHI being achieved compared to the AHI achieved using the adjusted target maximum pressure 602 .

[0149] As another example, the user 210 may find that the prescribed pressure ramp 605 starts out at too low a pressure, not providing a sufficient amount of breathing air and creating an unpleasant hunger for air, referred to as "air hunger." This can result in reduced sleep comfort and difficulty falling asleep. The prescribed pressure ramp 605 is then adjusted to the user's desired level of sleep comfort, and with an appropriately adjusted pressure ramp 603 in place, the user 210 is provided with enough breathing air to fall asleep while receiving treatment.

[0150] The plot shown in FIG. 6A also illustrates another aspect according to some implementations. A delta 606 between the prescribed maximum pressure 604 and the adjusted target maximum pressure 602 is shown. If higher pressures result in more discomfort during sleep, a larger delta 606 indicates that the user 120 has selected a higher sleep comfort. In comparison, a smaller delta 606 indicates a lower selected sleep comfort. Although shown as applied to pressure, other control factors can be manipulated similarly, with the delta between the prescribed value and the adjusted value responding to the desired sleep comfort level. In some implementations, an increase in the control parameter improves sleep comfort. For example, if the prescribed control parameter is the concentration of a drug added via the receptacle 180, an increase in the drug may improve sleep comfort but may increase apneas. In this case, the increase in the drug is an adjustment of the control parameter from the prescribed control parameter to a higher level.

[0151] FIG. 6B illustrates an implementation according to another aspect of the description. The plot on the left shows an adjusted pressure and ramp profile. The graph in FIG. 6B illustrates a pressure ramp that can be performed if a user experiences an event during a sleep session. The graph on the left shows respiratory flow 608 and time segments 616, 618A, and 620. After time segment 616, an apnea is shown at time segment 618A. In response to the apnea, the respiratory therapy system 120 performs a ramp 609 from an initial pressure 610 to a second higher pressure 612. After extension 611, the apnea stops and normal breathing continues at time segment 620. The plot on the right shows a prescribed pressure and ramp profile. After time segment 616 at the initial pressure 610, an apnea occurs at time segment 618B. In response, a prescribed pressure ramp 614 to a higher target pressure 617 is performed. The prescribed pressure ramp 614 is steeper (more positive) than the ramp 609, and the pressure 617 is also higher than the second pressure 612. The prescribed therapy uses more aggressive control parameters, resulting in a shorter apnea segment 618B than the apnea segment 618A. In this implementation, segment 618B is shorter than 618A. This is because apnea stops sooner after implementation of the prescribed pressure ramp 614 than after implementation of the pressure ramp 609. Specifically, the delay 611 shown in the left graph is not seen in the right graph. The more aggressive control parameters implemented with the prescribed pressure ramp 614 and pressure 617 may more effectively eliminate apnea, but may lead to sleep discomfort. The less aggressive pressure ramp 609 and second pressure 612 reduce discomfort.

[0152] FIG. 7 illustrates a method 700 for optimizing one or more parameters of a respiratory therapy system (e.g., respiratory therapy system 120). In some implementations, the respiratory therapy system includes a respiratory therapy device (e.g., respiratory therapy device 122) configured to supply pressurized air and a user interface (e.g., user interface 124) coupled to the respiratory therapy device via a conduit (e.g., conduit 126). The user interface is configured to interact with a user and helps direct the pressurized air to the user's airway. In general, a control system (e.g., control system 110 of system 100) with one or more processors is configured to perform the steps of method 700. A memory device (e.g., memory device 114 of system 100) can be used to store machine-readable instructions executed by the control system to perform the steps of method 700. The memory device can also store any type of data used in the steps of method 700. In general, method 700 can be implemented using a system (e.g., system 100) including a respiratory therapy system, a control system, and a memory device.

[0153] Typically, a respiratory therapy system is used according to a variety of parameters. These parameters include the settings of the respiratory therapy system, but may also include other parameters associated with the user's use of the respiratory therapy system, such as the light level of the room in which the user uses the respiratory therapy system, the volume of the room in which the user uses the respiratory therapy system, the position of the user when using the respiratory therapy system, etc. All these parameters contribute to the user's experience when using the respiratory therapy system and typically have different values. The values ​​of all these parameters are related to the effectiveness of the respiratory therapy system in treating the problem (e.g., SDB or OSA) currently being experienced by the user.

[0154] However, the parameter values ​​may also affect the user's comfort when using the respiratory therapy system, and therefore the user's compliance with the prescribed usage of the respiratory therapy system. In general, user compliance may be measured in a variety of ways. In some cases, compliance is defined as adherence to the prescribed usage of the respiratory therapy system during an initial 90-day period. In other cases, compliance is defined as short-term compliance or long-term compliance, both of which may depend on external requirements set by insurance guidelines, medical guidelines, industry guidelines (e.g., standards set by an industry), government guidelines (e.g., guidelines from an appropriate governing or regulatory body), etc. In some cases, compliance is measured as the total number of days (or sleep sessions) D 総 The respiratory therapy system must be used for a minimum number of days (or sleep sessions) D during the period including 分 Defined as using the minimum number of days D 分 can be defined as the raw number of days or as the total number of days D 総 In some cases, compliance can be defined as the percentage of the number of hours the respiratory therapy system is used per session (e.g., per sleep session). 分 The minimum number of hours h 分 can be defined as either the raw number of hours or as a percentage of the length of a sleep session. In some cases, compliance is measured by determining whether the respiratory therapy system is used for (i) the total number of days D 総 Minimum number of days in D 分 and (ii) the minimum number of hours per use, h 分For example, in some implementations, compliance is defined as using the respiratory therapy system at least 70% of the time during the first 90 days and using the respiratory therapy system for at least four hours per night (over the number of nights the respiratory therapy system was used, or over the entire 90 nights). As used herein, the term compliance generally refers to following any type of prescribed use or usage plan of a respiratory therapy system, regardless of the duration of the prescribed use, the source of the prescribed use, or factors affecting the prescribed use.

[0155] Method 700 relates to techniques for optimizing multiple parameters of a respiratory therapy system to improve a user's comfort when using the respiratory therapy system and thus improve the user's compliance with the prescribed usage of the respiratory therapy system. Often, various parameters of a respiratory therapy system can have different values ​​while still providing an effective therapeutic benefit to the user. Optimizing the multiple parameters can include identifying a particular value or subrange of values ​​from within a broad range of values ​​that improves the user's comfort while using the respiratory therapy system while still providing a therapeutic benefit to the user.

[0156] At step 710, data associated with a user of the respiratory therapy system (also referred to herein as user data) is received. In some implementations, the received data is data specific to the user, such as personal data and demographic data. The received data includes the user's age, sex, gender, and other physical characteristics. The received data also includes clinical data of the user that is typically associated with the user's use of the respiratory therapy system. For example, the clinical data may include the user's AHI associated with previous use of the respiratory therapy system, prescribed operating parameters of the respiratory therapy system (e.g., prescribed maximum pressure of pressurized air, prescribed pressure ramp parameters, etc.), the user's sleepiness / restlessness score, the user's reason for using the respiratory therapy system (e.g., data related to SDB and / or OSA experienced by the user), or other relevant clinical and / or medical data. The received data may also indicate the type of user interface the user wears when using the respiratory therapy system (e.g., face mask, nasal pillows), the type of sleep test the user underwent, and other factors. Sleep tests (sometimes referred to as sleep studies or polysomnography) are performed by monitoring the user using various sensors during a sleep session. These sensors include EEG sensors, EOG sensors, EMG sensors, ECG sensors, and other sensors. The sleep study can be used to determine whether a user is experiencing OSA or SDB while sleeping and whether the user suffers from other conditions. The sleep study can be home-based (e.g., performed by the user in bed at home), laboratory-based (e.g., performed while the user is in a clinical setting such as a medical facility), or other settings.

[0157] The received data may also be associated with the user's preferences regarding use of the respiratory therapy system. These preferences may relate to the type of user interface the user prefers to wear, the position the user prefers to sleep in, the lighting and volume in the room during a sleep session, how the respiratory therapy system adjusts the pressure of the pressurized air to address an event (e.g., apnea), the pressure of the pressurized air when the user is trying to fall asleep, the balance between low pressure for comfort and high pressure to address an event quickly, etc.

[0158] In some implementations, the data associated with the user may include the user's age, the user's age group (e.g., under 45, between 45 and 60, over 60), the user's gender, the starting pressure at which the user will use the respiratory therapy system (e.g., the pressure at which the respiratory therapy system first begins at the beginning of a sleep session when the user first dons the user interface and is likely still awake), the minimum therapeutic pressure at which the user will use the respiratory therapy system (e.g., the minimum operating pressure of the respiratory therapy system when the user is asleep, which is typically defined by the user's healthcare provider), the user's baseline AHI, the user's baseline AHI group (e.g., minimal, mild, moderate, severe, unknown), the type of sleep test the user has taken (e.g., a home sleep test, a lab-based sleep test), the type of sleep test the user has taken (e.g., a sleep test that is at low or high ... The respiratory therapy system may include information such as the reason for using the respiratory therapy system (e.g., daytime sleepiness, light sleeper, concerns from the user's bed partner, other health risks, other reasons, or a combination of these reasons), the user's level of sleepiness when not using the respiratory therapy system (e.g., unknown, moderate, slight, very, extreme), the type of user interface used by the user (e.g., full face, nasal, nasal pillow), when the user first began treatment with the respiratory therapy system (e.g., 0-3 months ago, 3-12 months ago, more than 1 year ago, more than 5 years ago, never, etc.), whether the user is monitored by a healthcare provider while using the respiratory therapy system, a unique identifier for the respiratory therapy device and / or user interface (e.g., a unique identification number), other data, or any combination of the above.

[0159] In some implementations, the data associated with the user includes data associated with the sleep stages that the user spends time in during a sleep session. For example, the data associated with the user can indicate how much time the user spends on average in different sleep stages while sleeping, what percentage of an average sleep session is spent in each sleep stage, other types of data, or a combination thereof.

[0160] The data may be received at any suitable location, such as a device or combination of devices that implements method 700. In some implementations, the data is received and stored in a memory device of the system (e.g., memory device 114). In other implementations, the data is received and stored elsewhere.

[0161] At step 720, initial values ​​for each of one or more parameters (also referred to herein as initial parameter values) of the respiratory therapy system are determined for use of the respiratory therapy system during the first time period. The one or more parameters of the respiratory therapy system may include any parameters associated with use of the respiratory therapy system by a user. The initial values ​​of the parameters may be based at least in part on the received data.

[0162] For example, the one or more parameters may include various settings of the respiratory therapy system. In some implementations, the parameters are associated with and / or affect a comfort level of a user during a sleep session. In these implementations, the parameters include a pressure ramp setting, an event response setting, an expiratory pressure relief (EPR) setting, a temperature of the pressurized air provided by the respiratory therapy system, a humidity of the pressurized air provided by the respiratory therapy system, a nominal pressure of the pressurized air during a sleep session, a temperature of the pressurized air, a temperature of a conduit connecting a user interface and a respiratory therapy device, a therapy mode of the respiratory therapy system (e.g., APAP, CPAP, BPAP, AUTO), a minimum pressure, a maximum pressure, a trigger time, and the like.

[0163] Generally, a pressure ramp setting controls the gradual increase in the pressure of the pressurized air at the beginning of a sleep session (e.g., when a user first wears the user interface during a sleep session). The pressure can increase from a starting pressure (e.g., 0) to a pre-determined higher pressure. This allows the pressure to be gradually increased until the user falls asleep, preventing the user from feeling a high pressure when they are trying to fall asleep. A pressure ramp setting typically includes multiple settings that control this increase. For example, a pressure ramp duration setting (also called a pressure ramp rate setting) defines how long it takes for the pressure to increase to the higher pressure (e.g., how quickly the pressure increases) after the ramp is performed at the beginning of a sleep session. A final pressure setting defines the higher pressure to which the pressure will increase.

[0164] In some cases, the higher pressure to which the pressure is increased is the starting pressure used when the user is asleep during the sleep session. In other cases, the higher pressure is lower than the starting pressure. In these cases, the pressure is maintained at the higher pressure until the user is determined to be asleep, and then the pressure is increased to the starting pressure. In still other cases, the pressure is increased from the initial pressure to a pressure less than the higher pressure at the beginning of the sleep session, and then increased from that pressure to the higher pressure when the user is determined to be asleep. Furthermore, the pressure is increased to the higher pressure only after the user is determined to be asleep. A higher pressure ramp duration value (e.g., a slower increase) and a lower final pressure value (e.g., an increase to a lower pressure) are generally more comfortable for the user while the user is still awake at the beginning of the sleep session, but may not be optimal for treating the user's condition (e.g., OSA or SDB). This is because it may take longer for the respiratory therapy system to reach a pressure prescribed to treat the user's condition (e.g., a pressure prescribed to achieve a target AHI during the sleep session).

[0165] The possible values ​​of the pressure ramp setting may vary depending on the implementation. For example, in some implementations, the possible values ​​of the pressure ramp setting are "on" and "off". When set to on, a pressure ramp is implemented at the beginning of a sleep session with a predefined duration and final pressure. When set to off, no pressure ramp is implemented. In other implementations, the pressure ramp setting may have an additional value of "auto". When set to auto, a pressure ramp is performed when it is determined that the user is asleep. Before the user falls asleep, the auto value may cause the pressure ramp setting to perform no pressure ramp or a small pressure ramp. In other implementations, the possible values ​​of the pressure ramp setting may be specific numeric values ​​of the pressure ramp duration setting and the final pressure setting. In additional implementations, the possible values ​​of the pressure ramp setting are specific numeric values ​​of the pressure ramp setting, each of which refers to a distinct combination of a pressure ramp duration value and a final pressure value.

[0166] An event response setting refers to how the respiratory therapy system adjusts the pressure of the pressurized air when a user experiences an event. For example, when a user experiences an event (e.g., apnea) during a sleep session, the pressure of the pressurized air can be gradually increased from the current pressure to a pre-set higher pressure to aid in ending the event. In general, the event response setting defines how the respiratory therapy system implements a pressure ramp in response to an event (e.g., increasing the pressure over a period of time), where the pressure ramp is similar to a pressure ramp that may be implemented at the start of a sleep session with reference to the pressure ramp setting. However, in some implementations, the event response can additionally or alternatively define how other pressure changes besides pressure ramps are implemented by the event response setting.

[0167] The event response setting itself can include multiple settings, such as an event response duration setting and an event response final pressure setting. The event response duration setting defines the rate at which the pressure is changed (e.g., increased) in response to the occurrence of an event. The event response final pressure setting defines the pressure (e.g., higher pressure) to which the pressure is changed (e.g., increased) in response to an event. Higher values ​​for the event response duration (e.g., a slower increase in the pressure ramp implemented to help mitigate the event) and lower values ​​for the event response final pressure (e.g., an increase to a lower pressure) are generally more comfortable for the user, but these values ​​may be less effective in treating the event.

[0168] As with the pressure ramp setting, the possible values ​​of the event response setting may vary by implementation. For example, in some implementations, the possible values ​​of the event response setting are "soft" and "standard." When set to soft, the respiratory therapy system responds to an event by performing a pressure ramp that is generally less aggressive (e.g., longer duration and lower final pressure) than the pressure ramp performed in response to the event when the value of the event response setting is set to standard. In other implementations, the possible values ​​of the event response setting may be specific numeric values ​​of the event response duration setting and the event response final pressure setting. In additional implementations, the possible values ​​of the event response setting are specific numeric values ​​of the event response settings, each of which references a distinct combination of the event response duration value and the event response final pressure. In still other implementations, the possible values ​​of the event response setting are "on" and "off." When set to on, a defined pressure ramp is implemented in response to the occurrence of an event. When set to off, no pressure ramp is implemented in response to the occurrence of an event.

[0169] Pressure ramp settings and event response settings are typically two different settings, although both can define the pressure ramp performed by the respiratory therapy system. Pressure ramp settings typically define a pressure ramp that can be performed at (or near) the beginning of a sleep session and are designed to help the user fall asleep. Event response settings typically define how the respiratory therapy system changes the pressure of the pressurized air in response to an event, and often define the pressure ramp that is performed by the respiratory therapy system in response to an event. However, event response settings can define other pressure change responses.

[0170] As described herein, the EPR setting allows the respiratory therapy system to provide different pressures for the pressurized air depending on whether the user is currently inhaling or exhaling. Thus, depending on the value of the EPR setting, the pressure of the pressurized air may drop between inhalation and exhalation. When the EPR setting is active, the pressure of the pressurized air is lower during exhalation compared to inhalation. The possible values ​​of EPR may vary depending on the implementation. For example, in some implementations, the possible values ​​of the EPR setting are "on" and "off". When set to off, there is no pressure drop during exhalation, and when set to on, there is a pressure drop during exhalation. In other implementations, the "on" setting is implemented as "on-1", "on-2", and "on-3". On-2 provides a larger pressure drop during exhalation than On-1, and On-3 provides a larger pressure drop during exhalation than On-2. In further implementations, the possible values ​​of the EPR setting are specific numeric values ​​that correspond to an amount of pressure drop during exhalation.

[0171] Parameters may also include parameters such as the light level of the room in which the user uses the respiratory therapy system, the volume level of the room in which the user uses the respiratory therapy system, the location in which the user uses the respiratory therapy system, etc. In general, all parameters can be adjusted to modify the user's experience using the respiratory therapy system. Some parameters can be adjusted only by the user, some parameters can be adjusted only by the user's caregiver (e.g., physician, caregiver), and some parameters can be adjusted by both the user and the user's caregiver (and in some cases others). For example, in some implementations, pressure ramp settings, event response settings, EPR settings, temperature of the pressurized air, and humidity of the pressurized air can all be adjusted by both the user and the user's caregiver. In some implementations, the set or starting pressure (e.g., standard pressure of the pressurized air during a sleep session), tidal volume, and other settings are only adjustable by the user's caregiver.

[0172] In some implementations, one or more parameters are associated with a user's comfort level during use of the respiratory therapy system and can be adjusted to help adjust the user's comfort level during use of the respiratory therapy system. For example, a longer pressure duration and a lower final pressure during the pressure ramp are generally more comfortable for the user. Thus, the value of the pressure ramp setting can be adjusted to adjust the user's comfort level. In another example, a lower pressure during exhalation is generally more comfortable for the user. Thus, the value of the EPR setting can be adjusted to help adjust the user's comfort level. Increased user comfort leads to increased compliance with the respiratory therapy system. Values ​​of other parameters or settings can also be changed to help adjust the user's comfort level and improve compliance with the respiratory therapy system.

[0173] In some implementations, the initial values ​​of the parameters are generated by a first trained model, which may include one or more trained machine learning algorithms. The first model may receive as input one or more types of data associated with the user described herein and / or other data. The first model analyzes the data and outputs the initial values ​​of the parameters. The first model may use a variety of models / algorithms, such as a casual interface recommendation algorithm, a content-based filtering recommendation algorithm, a reinforcement learning-based recommendation algorithm, a collaborative filtering recommendation algorithm, etc. In some implementations, the first model may actually be a combination of multiple different models and / or algorithms used in combination to determine the initial values ​​of the parameters. For example, the first model may be composed of separate models, each of which generates the initial values ​​of one or more different parameters. In another example, the first model may include a first sub-model that receives user data and generates intermediate data, and a second sub-model that receives the intermediate data (and / or user data) and generates the initial values ​​of the parameters.

[0174] The first model can be trained using training data generated from use of the respiratory therapy system by other users. The data input into the trained first model affects how various parameter settings affect the user's comfort level (as discussed above, the comfort level can be measured / estimated by examining the user's compliance or other metrics). By training the first model based on how other users responded to various settings of the parameters, the trained first model can analyze the input data for the user in question to determine initial values ​​for the parameters. In some implementations, the trained first model is a causal inference model that determines initial values ​​for the parameters based on received data associated with the user.

[0175] In some implementations, the first model directly outputs the initial values ​​of the parameters. In some of these implementations, the first model matches the user to one of a number of existing user profiles based on data input to the first model (e.g., data associated with the user), and then determines the initial values ​​of the parameters based at least in part on the matched user profile. The user profiles can be generated based on data received from a number of users using the respiratory therapy system. The user profiles can be based on a variety of factors, such as the user's age, the user's gender, a preferred type of user interface, a preferred comfort setting, clinical data, and other factors. Typically, data from multiple users is used to establish the various user profiles, although data from any number of user profiles can be used. In some implementations, data from a user that is matched to one of the user profiles was previously used to generate the user profile. Typically, a user is matched to the user profile that has data most similar to the user. In some of these implementations (where the first model directly outputs initial parameter values), each profile has a predefined set of initial values ​​for each of the parameters (and / or ranges of initial values ​​for the parameters), which the first model can select after determining which profile the user fits. In other of these implementations (where the first model directly outputs initial parameter values), the first model determines initial values ​​for the parameters based on the profile, but the initial values ​​are not predefined for the profile.

[0176] In other implementations, the first model does not directly output initial parameter values, but instead simply matches the user to one of a number of existing user profiles. Once the first model matches the user to a profile, predefined initial parameter values ​​(e.g., values ​​or ranges of values) for that profile can be manually selected (e.g., the user and / or a third party can update settings of the respiratory therapy system using the initial parameter values ​​of the matched profile).

[0177] In yet other implementations, the multiple user profiles associated with the first model correspond to and / or are simply combinations of various different combinations of all possible initial parameter values. For example, when adjusting two parameters, the first profile may be Parameter 1 = Initial Value 1 + Parameter 2 = Initial Value 1, the second profile may be Parameter 1 = Initial Value 1 + Parameter 2 = Initial Value 2, the nth profile may be Parameter 1 = Initial Value 1 + Parameter 2 = Initial Value 2, and so on. a + Parameter 2 = Initial value b and so on. Thus, in any implementation described herein, if the first model matches a user to a user profile based at least in part on user data, the first model may match the user to one of the different combinations of initial parameter values ​​based at least in part on the user data. Similarly, in any implementation described herein, if the first model determines a combination of initial parameter values ​​from at least the user data, the first model may match the user to one of the user profiles based at least on the user data, each user profile being a combination of initial parameter values. Generally, these implementations do not individually determine (i) which user profile the user will match to and (ii) what the initial parameter values ​​are. Instead, there may be multiple different combinations of initial parameter values, each of which is said to constitute a user profile. The first model is trained to analyze the user data and determine which combination of initial parameter values ​​is optimal for the user, in effect matching the user to one of the user profiles.

[0178] In step 730, usage data is received. The usage data is associated with the user's use of the respiratory therapy system during one or more sleep sessions within a first time period during which initial values ​​of the parameters are used. The first time period typically includes an initial period of n sleep sessions (e.g., n days). The usage data may be collected continuously during the first time period, intermittently during the first time period, or only after the first time period has ended. The usage data is typically received and stored in the same location as the data received in step 710. This location may be, for example, a memory device of the system implementing method 700, another location, etc.

[0179] The usage data includes information regarding the user's use of the respiratory therapy system during the time period using the initial values ​​of the parameters (hereinafter also referred to as usage data). In some implementations, the usage data includes an average user interface leakage for sleep sessions in the first time period (e.g., average volume of air per leakage, average volume of leakage air per sleep session), a standard deviation of the user interface leakage for sleep sessions in the first time period, an average therapeutic pressure of pressurized air for sleep sessions in the first time period, a standard deviation of the therapeutic pressure of pressurized air for sleep sessions in the first time period, an average duration of use of the respiratory therapy system during sleep sessions in the first time period in which the respiratory therapy system was actually used (e.g., average minutes of use per sleep session for sleep sessions in the first time period in which the respiratory therapy system was actually used), a standard deviation of the duration of use of the respiratory therapy system during sleep sessions in the first time period in which the respiratory therapy system was actually used, an average duration of use of the respiratory therapy system during all sleep sessions in the first time period (e.g., average minutes of use per sleep session for sleep sessions in the first time period in which the respiratory therapy system was actually used), a standard deviation of the duration of use of the respiratory therapy system during sleep sessions in the first time period in which the respiratory therapy system was actually used, a standard deviation of the duration of use of the respiratory therapy system during all sleep sessions in the first time period (e.g., average minutes of use per sleep session for sleep sessions in the first time period in which the respiratory therapy system was actually used), ... The data may include, for example, an average number of minutes of use per session, a standard deviation of the duration of use of the respiratory therapy system during all sleep sessions in the first time period, a number of sleep sessions in which the respiratory therapy system was used during the first time period, an average number of times the user interface was removed during sleep sessions in the first time period, a standard deviation of the number of times the user interface was removed during sleep sessions in the first time period, an average residual AHI during sleep sessions in the first time period, a standard deviation of the residual AHI during sleep sessions in the first time period, an average residual apnea index (AI, which is generally the same as AHI but does not include hypopnea events) during sleep sessions in the first time period, a standard deviation of the residual AI during sleep sessions in the first time period, an average number of RERA events during sleep sessions in the first time period, a standard deviation of the number of RERA events during sleep sessions in the first time period, other data, or any combination of the above. Any of the above averages and standard deviations may be determined over any suitable amount of time, including per second, per minute, per hour, per sleep session, and other amounts of time.

[0180] The usage data may also include data associated with the sleep stages in which the user spent time during the first time period of sleep sessions in which the initial values ​​of the parameters were used. The data may include the amount of time the user spent in each of the sleep stages during each sleep session, the average amount of time spent in each of the sleep stages during the first time period of sleep sessions (which may be all sleep sessions and / or all sleep sessions in which the respiratory therapy system was used), the standard deviation of the amount of time spent in each of the sleep stages during the first time period of sleep sessions, the sleep stage in which the longest amount of time was spent during each sleep session (which may be all sleep sessions and / or all sleep sessions in which the respiratory therapy system was used), other types of data, or any combination thereof.

[0181] The usage data may also include subjective input from the user. This typically includes any information provided by the user related to using the respiratory therapy system during the first time period using the initial values ​​of the parameters. For example, the subjective input may include user submitted information associated with a comfort level of the user interface worn during the sleep session during the first time period, a comfort level of the user's breathing during the sleep session during the first time period (e.g., whether the user had difficulty breathing in or out), an amount of restlessness experienced by the user during the first time period, or a combination thereof. The user may provide the subjective input in any suitable manner. For example, the user may provide the subjective input using a mobile device such as a smartphone or tablet computer. The user may also use an external computing device such as a laptop computer or desktop computer. If the respiratory therapy system is capable of accepting user input, the user may further use the respiratory therapy system itself. Typically, the user may use any user device of the system (e.g., user device 170).

[0182] The usage data can be received in a variety of ways. In some implementations, the usage data for one or more sleep sessions in the first time period is received only after all sleep sessions in the first time period are completed. In other implementations, the usage data for the first time period is received continuously during the first time period. In these implementations, the usage data for the first time period typically includes multiple portions of usage data, each portion of usage data corresponding to a respective one or more sleep sessions in the first time period. Each portion of usage data may be received after completion of a respective sleep session or may be received continuously during a respective sleep session. In either case, the usage data for the first time period is received continuously during the first time period.

[0183] In step 740, recommended values ​​(also referred to herein as recommended parameter values) for one or more parameters of the respiratory therapy system are generated. The recommended parameter values ​​may be based at least in part on the usage data, and optionally used in combination with user data and / or other types of data. As described herein, the recommended parameter values ​​are typically parameter values ​​determined to be most likely to improve user compliance (or most likely to optimize other quantities or parameters, as described herein) and available for use when the user is using the respiratory therapy system during the second time period.

[0184] In some implementations, the first time period includes a predetermined number of sleep sessions (e.g., a predetermined number of days / nights). In these implementations, the recommended values ​​of one or more parameters may be generated after the predetermined number of sleep sessions are completed. However, an initial recommended value of the parameter may be generated after the completion of a first sleep session and then updated after the completion of each subsequent sleep session within the first time period until the predetermined number of sleep sessions are completed.

[0185] In other implementations, the first time period may include a different number of sleep sessions. In these implementations, the recommended values ​​for the parameters may be generated after any number of sleep sessions within the first time period based on a variety of factors. The recommended values ​​may be generated only after the last sleep session (whichever sleep session) of the first time period, or an initial recommended value may be generated after the first sleep session and then updated after the completion of each subsequent sleep session until the last sleep session (whichever sleep session) is completed. In some cases, the recommended values ​​for the parameters may be generated if it is determined that the user's compliance with the respiratory therapy system during the first time period after a particular sleep session does not meet a predetermined threshold. In other cases, the final recommended value is generated if it is determined that the difference between the current value of at least one parameter and its continuously updated recommended value meets a predetermined threshold.

[0186] Additionally, a recommendation may be generated based on subjective input from the user. For example, a recommendation is generated if the user indicates that the current value of a parameter is undesirable for some reason (e.g., the user indicates discomfort during a sleep session). The user may be prompted for subjective input at any time. For example, the user may be prompted for subjective input regarding use of the respiratory therapy system every n sleep sessions (most likely every n days). The user may also proactively provide subjective input.

[0187] In some cases, recommendations may be generated when a user switches to a different user interface. Parameter recommendations may be better suited to the new user interface type and are typically designed to help the user maintain (or improve) compliance with the new user interface type. For example, usage data may indicate that a user has switched from a full face mask to nasal pillows. When this change occurs, recommendations are generated for parameters that work better with nasal pillows.

[0188] Similar to the initial values, in some implementations, the recommended parameter values ​​are generated by a second trained model, which may include one or more trained machine learning algorithms. The second model may receive as input any of one or more types of usage data. The second model may also receive any of one or more types of user data, initial parameter values, other data, or any combination thereof. The second model analyzes at least the usage data and outputs recommended values ​​for the parameters. The second model may be a variety of models / algorithms, such as casual interface recommendation algorithms, content-based filtering recommendation algorithms, reinforcement learning-based recommendation algorithms, collaborative filtering recommendation algorithms, etc. In some implementations, the second model may actually be a combination of multiple different models and / or algorithms used in combination to determine the recommended values ​​for the parameters. For example, the second model is comprised of separate models, each of which generates recommended values ​​for one or more different parameters. In another example, the second model may include a first sub-model that receives the usage data (and / or other data) and generates intermediate data, and a second sub-model that receives the intermediate data (and / or usage data) and generates recommended values ​​for the parameters. Additionally, in some implementations, the second model may be the same model as the first model, in which case a single model generates both the initial and recommended values, while in other implementations the first and second models are different models (which may be the same type of model).

[0189] The second model, like the first model, can be trained using training data generated from use of the respiratory therapy system by other users. The data input into the trained second model will affect how various parameter settings affect the user's comfort level (as discussed above, comfort level can be measured / estimated by examining the user's compliance or other metrics). By training the second model based on how other users responded to various settings of the parameters, the trained second model can analyze the input data for the user in question to determine recommended values ​​for the parameters. In some implementations, the trained second model is a causal inference model that determines initial values ​​for the parameters based at least in part on the user's received usage data.

[0190] The second model determines recommended values ​​for the parameters based on the usage data or based on the usage data and one or more other types of data (which may include user data entered into the first model, initial parameter values, a user profile to which the user was matched by the first model, or a combination thereof). For example, in some implementations, the second model matches the user to a user profile (which may or may not be the same as the user profile determined by the first model) based on the user data, and then determines recommended parameter values ​​based on the user profile and the usage data. In some cases, each user profile may have predefined recommended values ​​(or ranges of values) for each of the parameters, which may be adjusted by the usage data. In other cases, each user profile may have multiple potential recommended values ​​(or ranges of values) for each of the parameters. After the user profile is determined based on the user data, a recommended value for each of the parameters is selected from the multiple potential recommended values ​​based on the usage data. In other implementations, the second model matches the user to the user profile based on the user data and the usage data. The user profile determined by the second model may have predefined recommended values ​​for each of the parameters, which may be output by the second model. In additional implementations, the second model may analyze the initial parameter values ​​and use the initial parameter values ​​in combination with the usage data (and in some cases in combination with the user data) to determine a user profile.

[0191] In some implementations, the second model does not directly match the user to any user profile. Instead, the second model receives the usage data and an indication of which user profile the user has been matched to by the first model (based at least in part on the user data). The second model can determine recommended parameter values ​​based on the user profile it matches to the usage data. Thus, the second model can analyze how a particular user profile responded to initial parameter values ​​and set recommended parameter values ​​accordingly.

[0192] In some implementations, the user data only influences the determination of the final parameter value if the final parameter value is based on the usage data and the user profile / set of initial parameter values ​​given to the user based on the user data. Thus, two users who receive the same initial parameter value through their user data will typically have the same final parameter value if the two users have the same or similar usage data. However, in other implementations, the final parameter value is based on the usage data and the user data itself, rather than on the user profile / set of initial parameter values ​​determined by the user data. Thus, two users who receive the same initial parameter value through their user data may have different final parameter values ​​despite the usage data being the same or similar, if the user data is sufficiently different.

[0193] In some implementations, the second model does not directly output recommended parameter values, but instead matches the user to only one of a number of user profiles, and once the second model matches the user to a profile, the user can manually select the predefined recommended parameter values ​​(e.g., values ​​or ranges of values) for that profile (e.g., the user and / or a third party can update settings of the respiratory therapy system using the recommended parameter values ​​of the matched profile).

[0194] As with the first model, in some implementations, the multiple user profiles associated with the second model are merely various different combinations of all possible recommended parameter values. Thus, in any implementation described herein, if the second model matches a user to a user profile based at least in part on usage data, the second model may have matched the user to one of the individual combinations of recommended parameter values ​​based on the usage data, a combination of usage data and user data, or usage data and other data. Similarly, in any implementation described herein, if the second model determines a combination of recommended parameter values ​​from at least the usage data, the second model may match the user to one of the user profiles based at least on the usage data, each user profile being a combination of recommended parameter values. Generally, in these implementations, it is not individually determined (i) which user profile the user is matched to and (ii) what the recommended parameter values ​​are. Instead, a second model is trained to analyze usage and / or user data to determine which combination of recommended parameter values ​​(each of which can be said to constitute a user profile) is optimal for the user, essentially matching the user to one of the user profiles.

[0195] In some implementations, the inputs to the first and / or second models include certain parameters that can be adjusted for that user. For example, a particular user may not be able to adjust the lighting or volume in a room, or may prefer a shorter duration for the pressure ramp. Thus, the inputs to the first and / or second models may include these settings, such that the first and / or second models do not output initial or recommended values ​​for parameters that the user is unable or unwilling to follow.

[0196] In some implementations, the recommended values ​​for the parameters can be sent to the user, the user's caregiver (e.g., a physician or caregiver), or both. The user and / or caregiver may need to manually update the values ​​of the parameters to the recommended values, but the parameters may also be updated automatically. In some cases, the recommended values ​​are displayed to the user on an application interface (e.g., a display screen). The application interface may be located on the respiratory therapy device, a mobile device, an external computing device, or other suitable device. The user is also presented with the option to accept or reject the recommended values. In some cases, multiple recommended values ​​may be generated and the user may be presented with which recommended value to select. In such cases, the selection of the recommended value may include options to increase or decrease the value. The user may also be provided with the option to accept or reject the recommended values ​​or to suggest their own values ​​for various settings.

[0197] In some implementations, the generation of the recommendation may be based on degradation of the user interface. For example, sensors (e.g., cameras, acoustic sensors) may be used to determine whether the user interface has degraded beyond an acceptable level. The degree of degradation may be determined visually, such as by using a camera that generates visual evidence of the degradation. The degree of degradation may also be determined using an acoustic sensor used to detect the sound of air escaping the user interface. If the degree of degradation of the user interface meets a predetermined threshold (e.g., the user interface has degraded a certain amount), a recommendation to obtain a new user interface (and / or update the type of user interface) may be generated and sent to the user. The system may optionally send a resupply order if the user selects.

[0198] In some implementations, method 700 may also include optional steps 750, 760, and 770. In optional step 750, one or more parameters are updated to recommended values ​​for use with the respiratory therapy system during a second time period, and the user uses the recommended values ​​to use the respiratory therapy system for one or more sleep sessions during the second time period. In optional step 760, subsequent usage data is received. The subsequent usage data is associated with the user's use of the respiratory therapy system during the one or more sleep sessions in the second time period. In optional step 770, subsequent recommended values ​​for one or more parameters of the respiratory therapy system are generated. The subsequent recommended values ​​may be based at least in part on the received data associated with the user, the subsequent usage data, or both. The subsequent recommended values ​​may be used for the parameters in use of the respiratory therapy system in a third time period after the second time period.

[0199] The subsequent usage data may include similar information to the usage data, except that the subsequent usage data is associated with use of the respiratory therapy system using the recommended values ​​of the parameters. The subsequent usage data may be received continuously during the second time period (e.g., after each sleep session during the second time period or continuously during sleep sessions during the second time period) or after all sleep sessions during the second time period are completed. Similar to the first time period, the second time period may have a predetermined number of sleep sessions or a variable number of sleep sessions, and the subsequent recommendations may be generated after all of the predetermined number of sleep sessions are completed or after a certain variable number of sleep sessions are completed.

[0200] In general, the values ​​of the parameters may be continuously and / or dynamically updated to improve the user's comfort and compliance with the respiratory therapy system. For example, new recommended values ​​for the parameters may be generated periodically (or continuously updated) to provide the user with the best possible experience. New recommended values ​​may be updated every n sleep sessions (or every n days). New recommended values ​​may also be updated when usage data indicates that new recommended values ​​are needed, e.g., if user compliance is declining or if subjective user input indicates that the user is not satisfied or comfortable. The user may provide subjective input at any time. The system may also periodically prompt the user for subjective input.

[0201] FIG. 8 includes two graphs illustrating one implementation of the method 700 according to an embodiment of the disclosure. Plot 802A on the left illustrates the use of a respiratory therapy system using the default EPR settings. Plot 802A illustrates a respiratory flow signal 804, an inhalation pressure signal 806A, and an exhalation pressure signal 808A. The pressure of the pressurized air during inspiration (e.g., when respiratory flow signal 804 is increasing or the slope of respiratory flow signal 804 is positive) is higher than the pressure of the pressurized air during expiration (e.g., when respiratory flow signal 804 is decreasing or the slope of respiratory flow signal 804 is negative). This pressure difference can be seen by comparing the inhalation pressure signal 806A to the exhalation pressure signal 808A. The lower pressure during expiration compared to inspiration is designed to provide a more comfortable experience for the user.

[0202] The plot 802B on the right shows the result of changing the value of the EPR setting to a recommended value, for example using the techniques of method 700. Plot 802B includes the same respiratory flow signal 804, inspiratory pressure signal 806B, and expiratory pressure signal 808B as the plot 802A on the left. However, the value of expiratory pressure signal 808B has been changed to be less than the value of expiratory pressure signal 808A, resulting in less pressure during exhalation. A larger pressure difference between inspiratory pressure signal 806B and expiratory pressure signal 808B may provide less mitigation of events during a sleep session, but may provide more comfort to the user and improve the user's compliance with the respiratory therapy system.

[0203] In FIG. 8, the inhalation pressure signals 806A and 806B typically have the same value. Thus, the pressure of the pressurized air during inspiration is typically the same in both cases. However, the recommended EPR setting may include a change in the value of the inhalation pressure in addition to or instead of a change in the value of the exhalation pressure. Thus, the inhalation pressure signal 806B of plot 802B may be higher or lower than that shown in FIG. 8 in some implementations.

[0204] While Figure 8 illustrates a graph showing the results when the recommended value of the EPR setting differs from the initial value, the value of any parameter associated with use of the respiratory therapy system may be altered using the techniques of method 700. For example, while the graphs of Figures 6A and 6B are used here to illustrate the implementation of method 500, changing the value of the pressure ramp setting in accordance with method 700 may result in changes similar to those shown in Figures 6A and 6B.

[0205] FIG. 9 illustrates an example of an interface of a user device 900 being used to send recommended values ​​to a user and / or the user's caregiver and provide the user with the option to accept or reject the recommended values. In FIG. 9, the user device 900 is a smartphone. However, any type of user device (e.g., tablet computer, laptop computer, smart watch) may be used to present the recommended values ​​to the user. The recommended values ​​may also be displayed on an application interface of a respiratory therapy device of a respiratory therapy system.

[0206] A table is displayed on the user device 900 showing the initial values ​​of the parameters of interest. In the illustrated implementation, the table includes cells 902A, 902B, and 902C showing the names of the parameters of interest, and cells 904A, 904B, and 904C showing the respective initial values ​​of the parameters. For purposes of illustration, the general names "Parameter 1," "Parameter 2," and "Parameter 3" are used to indicate the parameters that are to be changed, and the general initial values ​​"Parameter 1," "Parameter 2," and "Parameter 3" are used to indicate the initial values ​​of the parameters. 1i ","value 2i " and "Value 3i " is used.

[0207] An additional table is displayed on the user device 900 showing recommended values ​​for the parameters to be changed. The table includes cells 906A, 906B, and 906C showing the names of the parameters, and cells 908A, 908B, and 908C showing the recommended values ​​for the parameters. Again, for ease of explanation, generic names are used to indicate the parameters to be changed, and generic recommended values ​​"values" are used to indicate the recommended values ​​for the parameters. 1r ","value 2r " and "Value 3r " is used. In some implementations, recommendations are generated using method 700. Thus, recommendations can be generated based on respiratory therapy system usage data (which may include subjective user input) and a user profile to which the user has been matched.

[0208] Finally, the user device 900 displays the text "Accept Changes?" above three user selectable icons 910A, 910B, and 910C. The user selectable icon 910A includes the text "Yes" that the user can select to accept the recommended values ​​and use the recommended values ​​during one or more subsequent sleep sessions. The user selectable icon 910B includes the text "No" that the user can select to reject the recommended values ​​and continue to use the initial values ​​during one or more subsequent sleep sessions. The user selectable icon 910C includes the text "Suggest Changes" that the user can select if they wish to change the recommended values ​​in any way. In some implementations, when the user selects the icon 910C, the application interface allows the user to directly input a preferred value via an interactive text box. In other implementations, the application interface allows the user to increase or decrease the recommended values, for example, by displaying user selectable icons corresponding to increase and decrease. In a further implementation, instead of displaying user selectable icons 910C, the application interface displays interactive text boxes and / or increment and decrement icons.

[0209] Thus, the parameter recommendations may be generated and then sent directly to the user and / or the user's caregiver. In some implementations, the parameters to be modified include a particular combination of two or more parameters that are associated with and / or affect (directly or indirectly) the user's comfort level. In one example, the parameters to be modified include at least two or more parameters selected from the following list: (i) a pressure ramp setting of the respiratory therapy system, (ii) an event response setting of the respiratory therapy system, (iii) an expiratory pressure relief setting of the respiratory therapy system, (iv) a temperature of the pressurized air supplied by the respiratory therapy system, and (v) a humidity of the pressurized air supplied by the respiratory therapy system.

[0210] In some implementations, the combination of two or more parameters is selected based on the likelihood of success of the recommendation weighted based on the user profile that the user is matched to. In other implementations, the combination of two or more parameters is selected based on a balance of the impact of each of the parameters on the comfort of a user belonging to a particular user profile. In general, the combination of two or more parameters may be selected or customized based on any requirement, such as improving user compliance with the prescribed usage of the respiratory therapy system, improving user comfort, or other requirements.

[0211] As described herein, in some implementations, the recommended value of one or more parameters is a value of the parameter that is estimated to improve and / or maximize compliance regardless of how compliance is designed. However, the recommended value of the parameter may also be a value that is estimated to affect other variables. In some implementations, the recommended parameter value is a value that is estimated to improve and / or maximize a self-reported comfort score from the user. In some implementations, the recommended parameter value is a value that is estimated to increase and / or maximize the amount of time spent below a particular therapeutic pressure while achieving a target AHI. In some implementations, the recommended parameter value is a value that is estimated to increase and / or maximize the probability that the user will continue to use the respiratory therapy system after a trial period of the respiratory therapy system. In some implementations, the recommended parameter value is a value that is estimated to increase and / or maximize the number of days (or sleep sessions) that the user uses the respiratory therapy system within a particular period of time. In some implementations, the recommended parameter value is a value that is estimated to increase or maximize the amount of time that the user uses the respiratory therapy system during one or more sleep sessions. In some implementations, the recommended parameter values ​​are values ​​that are estimated to reduce or minimize the number of interruptions in use of the respiratory therapy system (e.g., user removing the user interface, user interface being accidentally removed, respiratory therapy device being disconnected, conduit being disconnected, user interface being disconnected, other components being disconnected, etc.) In some implementations, the recommended parameter values ​​are values ​​that are estimated to reduce or minimize the number of user interface on / off events during a sleep session.

[0212] In general, the recommended parameter values ​​are those values ​​that are most likely (or estimated to be most likely) for the user to meet a threshold of a usage metric associated with the user's use of the respiratory therapy system. A usage metric is generally a metric that can be used to measure a user's use of the respiratory therapy system. A usage metric can be compliance with a prescribed usage plan for the respiratory therapy system, in which case meeting a threshold of the usage metric includes meeting a minimum requirement of the prescribed usage plan for the respiratory therapy system (e.g., using the respiratory therapy system for at least x hours per sleep session, using the respiratory therapy system for at least y sleep sessions within the first z months of use, etc.). In other cases, meeting a threshold of the usage metric includes achieving at least a minimum acceptable value of some metric (e.g., achieving a minimum amount of average time using the respiratory therapy system per sleep session). Additionally, meeting a threshold of the usage metric can include not exceeding a maximum acceptable value of some metric (e.g., not exceeding a maximum average number of events per sleep session).

[0213] In some implementations, the first model is trained to determine, for each different combination of initial parameter values ​​(each different combination can be said to constitute and / or correspond to a user profile), a probability that the combination will cause the user to meet a threshold usage metric during a first time period. This determination can be based at least in part on the user data, e.g., the first model can be trained to determine how each combination of initial parameter values ​​affects usage for a user corresponding to the user data. The first model can then select the combination of initial parameter values ​​(e.g., one of the user profiles) that has the highest probability among all possible combinations of initial parameter values.

[0214] In some of these implementations, the second model is trained to generate recommended parameter values ​​based on user data and / or usage data, which may include information about how a user used the respiratory therapy system using a selected combination of initial parameter values ​​(e.g., a selected user profile). For example, based on the user data and / or usage data, the second model can determine, for each combination of recommended parameter values, a probability that each combination of recommended parameter values ​​will cause the user to meet a threshold usage metric during a second time period. The recommended parameter value combination with the highest probability can then be selected (e.g., the parameter values ​​can be changed from the initial parameter values ​​to the recommended parameter values). In some cases, the selected combination of recommended parameter values ​​maximizes the probability that the user will meet the threshold usage metric during the second time period. In some cases, the selected combination of recommended parameter values ​​increases the likelihood that the threshold usage metric will be met during the second time period as compared to the first time period.

[0215] In some implementations, the method 700 also includes selecting parameters to optimize. For example, the respiratory therapy system may be used with a number of parameters each having an initial value during a first period of time. After the first period of time has elapsed, one or more of the parameters may be selected to have their values ​​modified and recommended values ​​for the selected parameters may be generated. The selection of the parameters to modify and the generation of the recommendations may be based at least in part on the usage data and / or the user profile to which the user has been matched.

[0216] In general, methods 500 and 700 can be implemented using a system including a control system with one or more processors and a memory device that stores machine-readable instructions. The control system can be coupled to the memory device, and methods 500 and 700 can be implemented when the machine-readable instructions are executed by at least one of the processors of the control system. Methods 500 and 700 can also be implemented using a computer program product (e.g., a non-transitory computer-readable medium) that includes instructions that, when executed by a computer, cause the computer to perform the steps of methods 500 and 700.

[0217] One or more elements, aspects, steps, or any portion(s) thereof from one or more of claims 1-103 below may be combined with one or more elements, aspects, steps, or any portion(s) thereof from one or more of other claims 1-103, or combinations thereof, to form one or more additional implementations and / or claims of the present disclosure.

[0218] Although the present disclosure has been described with reference to one or more particular implementations or implementations, those skilled in the art will recognize that many modifications can be made without departing from the spirit and scope of the present disclosure. Each of these implementations and obvious variations thereof are considered to be within the spirit and scope of the present disclosure. It is also contemplated that additional or alternative implementations according to aspects of the present disclosure may combine any number of features from any of the implementations described herein, such as, for example, the alternative implementations described below.

Claims

1. A method for optimizing multiple parameters of a respiratory therapy system, A step of receiving user data associated with the user of the respiratory therapy system, A step of determining the initial value of each of a plurality of parameters based at least in part on the user data, wherein each of the plurality of parameters is associated with the user's comfort level, and the step of determining the initial value of each of the plurality of parameters, The steps include receiving usage data associated with the use of a respiratory therapy system during one or more sleep sessions in a first period, each of which has an initial value for the aforementioned plurality of parameters, A method comprising the step of generating, at least in part, recommended values ​​for each of a plurality of parameters for using the respiratory therapy system during a second period following the first period, based on the user data and usage data.

2. A method for optimizing a combination of two or more parameters of a respiratory therapy system, The steps include receiving data associated with the user of the respiratory therapy system, A step of determining the initial value of each parameter of a combination of two or more parameters, based at least in part on the received data, wherein each parameter of the combination of two or more parameters is associated with the user's comfort level, The steps include receiving usage data associated with the use of a respiratory therapy system during one or more sleep sessions in a first period in which each of the aforementioned parameters has an initial value, A method comprising the step of generating, at least in part, recommended values ​​for each parameter of a combination of two or more parameters for using the respiratory therapy system during a second period following the first period, based on the received data and the received usage data.

3. A system, A respiratory therapy system configured to supply pressurized air to an individual, A memory device that stores machine-readable instructions, Execute a machine-readable instruction, A step of receiving data associated with the user of the respiratory therapy system, A step of determining an initial value for each of a plurality of parameters of the respiratory therapy system, at least in part, based on the received data, wherein each of the plurality of parameters is associated with the user's comfort level, and the step of The steps include receiving usage data associated with the use of a respiratory therapy system during one or more sleep sessions in a first period, each of which has an initial value for the aforementioned plurality of parameters, A control system comprising one or more processors configured to perform the step of generating recommended values ​​for each of a plurality of parameters for using the respiratory therapy system during a second period following the first period, based at least in part on the received data and the received usage data, system.

4. The system according to claim 3, wherein the step of generating recommended values ​​for each of the plurality of parameters is: The steps include generating multiple user profiles based on data received from multiple users, A step of matching a user to one of the plurality of user profiles based at least partially on the user data, A system comprising the steps of generating recommendations based on the received usage data and the weighted likelihood of success of the recommendations in the user profile to which the user has been matched.

5. The system according to claim 3, wherein the step of determining the initial value of each of the plurality of parameters is: A step of generating multiple user profiles based on data received from multiple users, wherein each of the multiple user profiles corresponds to a different combination of initial values ​​for the multiple parameters, A system comprising the step of matching a user to one of a plurality of user profiles, at least in part, based on the user data.

6. The system according to claim 5, wherein the recommended value for each of the plurality of parameters is generated at least in part based on the usage data and the user profile to which the user has been matched.

7. The system according to claim 3, wherein the step of generating recommended values ​​for each of the plurality of parameters is: A step of generating a plurality of user profiles based on data received from a plurality of users, wherein each of the plurality of user profiles corresponds to a different combination of initial values ​​of the plurality of parameters, A step of matching a user to one of the plurality of user profiles based at least partially on the user data, A system comprising the step of generating recommended values ​​for each of the plurality of parameters based at least partially on the usage data and the user profile to which the user has been matched.

8. The system according to claim 3, wherein the step of generating a recommended value for each of the plurality of parameters is based on the probability that the user will meet a threshold of a usage metric associated with the user's use of the respiratory therapy system during a second period.

9. The system according to claim 8, wherein the usage metric is user compliance with a usage plan for the respiratory therapy system, and users who meet the threshold of the usage metric include users who meet one or more minimum requirements of the usage plan during the second period.

10. The system according to claim 9, wherein users who satisfy the threshold of the usage metric include users who achieve at least the minimum acceptable value of the usage metric during one or more sleep sessions in the second period.

11. The system according to claim 8, wherein users who satisfy the threshold of the usage metric include users who do not exceed the maximum allowable value of the usage metric during one or more sleep sessions in the second period.

12. The system according to claim 3, wherein the step of generating initial values ​​for each of the plurality of parameters is: A step of generating a plurality of user profiles based on data received from a plurality of users, wherein each of the plurality of user profiles corresponds to a different combination of initial values ​​of the plurality of parameters, For each user profile among the plurality of profiles, the step of determining, at least partially based on user data, the probability that the user will meet the threshold of the usage metric associated with the use of the respiratory therapy system, using different combinations of initial values ​​of the plurality of parameters; A system comprising the step of determining, for each user profile among the plurality of profiles, the probability that the user will meet a threshold for a usage metric associated with the use of a respiratory therapy system, based at least partly on user data, using different combinations of initial values ​​for the plurality of parameters.

13. The system according to claim 3, wherein the step of generating recommended values ​​for each of the plurality of parameters is: A step of generating a plurality of user profiles based on data received from a plurality of users, wherein each of the plurality of user profiles corresponds to a different combination of initial values ​​of the plurality of parameters, A step of determining, for each of a plurality of user profiles, the probability that the user will meet a threshold for a usage metric during a first period, based on different combinations of initial values ​​for the plurality of parameters, wherein the usage metric is associated with the user's use of the respiratory therapy system, and the determination is made at least in part on user data; The steps include selecting one user profile from among the plurality of user profiles that has the highest probability of the user meeting the threshold of the usage metric during the first period, A system comprising the step of increasing the probability that the user will meet the threshold of the usage metric during the second period by generating recommended values ​​for each of the plurality of parameters based at least in part on the usage data and selected user profiles.

14. The system according to claim 13, wherein the recommended values ​​of the plurality of parameters are configured to maximize the probability that the user will meet the threshold of the usage metric during the second period.

15. The system according to claim 13, wherein the recommended values ​​of the plurality of parameters are configured to increase the probability that the user will meet the threshold of the usage metric during the second period compared to the first period.

16. The system according to claim 3, wherein the step of generating initial values ​​for each of the plurality of parameters is: For each of the multiple combinations of initial values ​​of the multiple parameters, the step of determining, at least partially based on user data, the probability that the user will meet the threshold of the usage metric associated with the user's use of the respiratory therapy system for each of the multiple combinations; A system comprising the step of selecting from among the aforementioned combinations a combination of initial values ​​for multiple parameters that has the highest probability of resulting in the user satisfying the threshold of the metric used.

17. The system according to claim 3, wherein one or more processors are further configured to execute the machine-readable instructions and transmit the generated recommendation value to (i) the user, (ii) the user's caregiver, or (iii) both.

18. The system according to claim 3, wherein one or more processors are further configured to execute machine-readable instructions and present the generated recommended values ​​on an application interface located on (i) a respiratory therapy device of a respiratory therapy system, (ii) a mobile device, (iii) an external computing device, or (iv) any combination thereof.

19. The system according to claim 18, wherein, if the generated recommended value is accepted, the application interface provides the options of (i) accepting or rejecting, and (ii) increasing or decreasing.

20. The system according to claim 3, wherein the received data includes (i) the user's age, (ii) the user's gender, (iii) the user's clinical data, (iv) the user interface type of the respiratory therapy system used by the user, (v) the test type used by the user, or (vi) any combination thereof.

21. The system according to claim 20, wherein the user's clinical data includes (i) the user's apnea-hypopnea index, (ii) prescribed respiratory pressure for the user, (iii) the user's drowsiness / restlessness score, (iv) the reason for therapy for the user, or (v) any combination thereof.

22. The system according to claim 3, wherein each of the plurality of parameters is adjustable by the user, the user's caregiver, or both.

23. The system according to claim 3, wherein each of the plurality of parameters is adjustable to help adjust the user's comfort level.

24. The system according to claim 3, wherein the plurality of parameters include two or more of the following: (i) pressure ramp setting of the respiratory therapy system, (ii) event response setting of the respiratory therapy system, (iii) expiratory pressure relaxation setting of the respiratory therapy system, (iv) temperature of the pressurized air delivered by the respiratory therapy system, (v) humidity of the pressurized air, or (vii) any combination thereof.

25. The system according to claim 3, wherein the usage data includes (i) the frequency of use of the respiratory therapy system, (ii) the pressurized air delivered by the respiratory therapy system, (iii) one or more leaks associated with the pressurized air, (iv) subjective input received from the user, or (v) information associated with any combination thereof.

26. The system according to claim 25, wherein the usage data includes (i) the frequency of use of the respiratory therapy system, (ii) the pressurized air delivered by the respiratory therapy system, (iii) one or more leaks associated with the pressurized air, (iv) subjective input received from a user, or (v) information associated with any combination thereof.

27. ​​The system according to claim 3, wherein the step of generating recommended values ​​for the plurality of parameters is performed using (i) a causal inference recommendation algorithm, (ii) a content-based filtering recommendation algorithm, (iii) a reinforcement learning-based recommendation algorithm, or (iv) a combination of both.

28. The system according to claim 3, wherein the step of generating recommended values ​​for the plurality of parameters is: A step of detecting the degree of deterioration of the user interface of the respiratory therapy system using an acoustic sensor associated with the respiratory therapy system, A system comprising the step of sending a recommendation to the user to acquire a new user interface when the degree of degradation meets a predetermined threshold.

29. The system according to claim 28, wherein one or more processors are further configured to execute the machine-readable instructions and transmit a resupply order for the user interface.

30. The system according to claim 3, wherein the first parameter among the plurality of parameters is an event response setting for the respiratory therapy system, and the second parameter among the plurality of parameters is an expiratory pressure relaxation setting for the respiratory therapy system.

31. The system according to claim 30, wherein the event response setting defines the duration and final pressure of a pressure ramp performed by the respiratory therapy system in response to the user experiencing an event during one or more sleep sessions.

32. The system according to claim 30, wherein the expiratory pressure relaxation setting defines a reduction in the pressure of pressurized air delivered to the user by the respiratory therapy system during the user's exhalation.

33. The system according to claim 3, wherein the first period includes a predetermined number of sleep sessions, and the recommended values ​​for each of the plurality of parameters are generated after the predetermined number of sleep sessions in the first period have been completed.

34. The system according to claim 3, wherein the first period includes a variable number of sleep sessions, and the recommended values ​​for each of the plurality of parameters are generated after the completion of one or more sleep sessions in the first period.

35. The system according to claim 3, wherein one or more processors are further configured to execute machine-readable instructions and update each of the plurality of parameters to its recommended value in order to use the respiratory therapy system during the second period.

36. The system according to claim 35, wherein one or more processors execute the machine-readable instructions Subsequent usage data associated with the use of a respiratory therapy system during one or more sleep sessions in the second period is received. A system further configured to generate subsequent recommended values ​​for each of the plurality of parameters relating to the use of the respiratory therapy system during a third period following the first period, based at least in part on the subsequent usage data.

37. The system according to claim 36, wherein the subsequent usage data includes subjective input from a user associated with the use of the respiratory therapy system using the recommended values ​​for each of the plurality of parameters.

38. The system according to claim 36, wherein the subsequent usage data is continuously received from the user during the second period, and the subsequent recommended value is generated in response to the subsequent usage data indicating that the recommended value is not optimal.

39. The system according to claim 36, wherein the subsequent usage data is received from the user after a predetermined number of sleep sessions are completed during the second period, and the subsequent recommended value is generated after a predetermined number of sleep sessions are completed during the second period.

40. The system according to claim 36, wherein the user uses a first user interface during a first period, and the subsequent recommended values ​​are generated in response to usage data indicating that the user switched from the first user interface to the second user interface during a second period.

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