A system and method for optimizing sleep quality.

Abstract

Methods for optimizing sleep for users of respiratory therapy systems are described herein. [Solution] The method includes receiving therapeutic instructions to be implemented using a respiratory therapy system during a sleep session. The therapeutic instructions include a plurality of prescribed control parameters, each of which has a value or a range of values. The method further includes receiving a desired level of restful sleep during the sleep session and adjusting one or more of the values ​​or ranges of values ​​of the plurality of prescribed control parameters to one or more adjusted values ​​or ranges of values ​​based on the desired level of restful sleep. The adjustments to the adjusted values ​​are implemented to help the user achieve the desired level of restful sleep.

Description

Technical Field

[0001] Cross - reference to Related Applications This application claims the benefit and priority of U.S. Provisional Patent Application No. 63 / 080,682, filed on September 19, 2020, which is hereby incorporated by reference in its entirety.

[0002] Technical Field The present disclosure generally relates to respiratory therapy systems, and more particularly to systems and methods for optimizing sleep comfort for users of respiratory therapy systems.

Background Art

[0003] Many individuals suffer from sleep - related disorders and / or respiratory disorders such as sleep - disordered breathing (SDB) including, for example, periodic limb movement disorder (PLMD), restless legs syndrome (RLS), obstructive sleep apnea (OSA), central sleep apnea (CSA), mixed apnea, and hypopnea, respiratory effort - related arousals (RERA), as well as chest wall disorders. These disorders are often treated using respiratory therapy systems. Users of respiratory therapy systems may have diagnosed or undiagnosed lung diseases such as chronic obstructive pulmonary disease (COPD), emphysema, fibrosis, pneumonia, obstructive lung disease (OLD), restrictive lung disease (RLD), fixed upper airway obstruction (FUAO), asthma, etc.

Summary of the Invention

[0004] According to several implementations of this disclosure, a method is provided for optimizing sleep for a user of a respiratory therapy system. The method includes receiving therapeutic instructions to be implemented using the respiratory therapy system during a sleep session. The therapeutic instructions include a plurality of prescribed control parameters, each of which has a value or a range of values. The method further includes receiving a desired level of restful sleep during the sleep session and adjusting one or more of the values ​​or ranges of the plurality of prescribed control parameters to one or more adjusted values ​​or ranges of values ​​based on the desired level of restful sleep. The adjustments to the adjusted values ​​are implemented to help the user achieve the desired level of restful sleep.

[0005] The above summary is not intended to describe any or all of the implementations of this disclosure. Further features and benefits of this disclosure are evident from the detailed description and drawings below. [Brief explanation of the drawing]

[0006] [Figure 1] This is a functional block diagram of the system relating to several implementation forms of this disclosure. [Figure 2] This is a perspective view of at least a portion of the system shown in Figure 1, the user, and the person sharing a bed, relating to several implementations of this disclosure. [Figure 3] This is a process flow diagram of a method for optimizing sleep for users of a respiratory therapy system, relating to several implementations of the present disclosure. [Figure 4A] This is a plot of regulated pressure and regulated ramp pressure relating to several implementations of the present disclosure. [Figure 4B] This is a plot of the adjusted pressure response relating to another implementation of the present disclosure.

[0007] While various modifications and alternative forms are possible, specific implementations and embodiments are shown as examples in the drawings and are described in detail herein. However, this is not intended to limit the disclosure to any particular form disclosed, and conversely, the disclosure is not intended to limit the disclosure to any particular form. It should be understood that this includes all modifications, equivalents, and substitutions that fall within the spirit and scope of this disclosure as defined by the attached claims. [Modes for carrying out the invention]

[0008] Many individuals suffer from sleep-related disorders and / or respiratory disorders. Examples of sleep-related disorders and / or respiratory disorders include periodic limb movement disorder (PLMD), restless leg syndrome (RLS), sleep-disordered breathing (SDB), obstructive sleep apnea (OSA), apnea, Cheyne-Stokes respiration (CSR), respiratory dysfunction, obesity hyperventilation syndrome (OHS), chronic obstructive pulmonary disease (COPD), neuromuscular disorders (NMD), and chest wall disorders.

[0009] These and other disorders are characterized by specific events that occur during an individual's sleep (e.g., snoring, apnea, hypopnea, inability to keep the legs still, sleep disturbances, choking, increased heart rate, labored breathing, asthma attacks, epileptic episodes, seizures, or any combination thereof).

[0010] The Apnea-Hypopnea Index (AHI) is an index used to indicate the severity of sleep apnea during a sleep session. The AHI is calculated by dividing the number of apnea and / or hypopnea events experienced by the user during the sleep session by the total sleep duration in that session. An event may be, for example, a pause in breathing lasting at least 10 seconds. An AHI of less than 5 is considered normal. An AHI of 5 to less than 15 is considered mild sleep apnea. An AHI of 15 to less than 30 is considered moderate sleep apnea. An AHI of 30 or more is considered severe sleep apnea. In children, an AHI greater than 1 is considered abnormal. Sleep apnea can be considered "controlled" when the AHI is normal, or when the AHI is normal or mild. The AHI can also be used in combination with oxygen desaturation levels to indicate the severity of obstructive sleep apnea.

[0011] To alleviate some of these sleep-related and / or respiratory disorders, users may be prescribed the use of a breathing device or system. For example, a continuous positive airway pressure (CPAP) machine can be used to increase the air pressure in the user's throat, preventing the airway from closing and / or narrowing during sleep. Therefore, the goal of the therapy is to lower the AHI to a normal level and improve the user's sleep quality.

[0012] While these breathing devices or systems can improve a user's sleep quality, they can sometimes impair user comfort. For example, a user may experience discomfort when trying to sleep due to their own air being forced into their airway. Other discomforts may relate to the system's noise, sweat or stickiness of the face mask, or discomfort and claustrophobia from wearing the mask. After a sleep session, a user may experience belching, bloating, gastric distension, and severe gas pain due to aerophagia. Aerophagia occurs when air enters the esophagus and reaches the stomach instead of entering the airway and lungs as intended. Other discomforts after a sleep session may include dryness of the nose, throat, or eyes as a result of leakage, which leads to a high airflow or poor humidification of the therapeutic air.

[0013] Discomfort experienced by individuals prescribed respiratory therapy systems is often most pronounced when users first adopt sleep therapy using the system. Poor initial user experience due to poor sleep quality increases the likelihood that users will abandon the therapy and sacrifice sleep quality in pursuit of improved sleep quality. Delaying treatment can lead to a deterioration in the user's quality of life due to their inability to achieve quality sleep.

[0014] Referring to Figure 1, several implementations of the System 100 of this Disclosure are shown. The System 100 comprises a control system 110, a memory device 114, and an electronic interface The system includes a face 119, one or more sensors 130, and one or more user devices 170. In some implementations, the system 100 optionally further includes a respiratory system 120, a blood pressure device 182, an activity tracker 190, or any combination thereof.

[0015] The control system 110 includes one or more processors 112 (hereinafter, processor 112). The control system 110 is generally used to control (e.g., operate) various components of system 100 and / or to analyze data acquired and / or generated by the components of system 100. The processors 112 may be general-purpose or special-purpose processors or microprocessors. Although one processor 112 is shown in Figure 1, the control system 110 may include any appropriate number of processors (e.g., one processor, two processors, five processors, ten processors, etc.) which may reside in a single housing or be located apart from one another. The control system 110 may be coupled to, for example, the housing of the user device 170 and / or one or more housings of the sensor 130, and / or located inside them. The control system 110 may be centralized (within one such housing) or distributed (among two or more such housings which are physically separate). In such an implementation configuration, which includes two or more housings containing the control system 110, the housings may be located close to and / or far apart from each other.

[0016] The memory device 114 stores machine-readable instructions that can be executed by the processor 112 of the control system 110. The memory device 114 can be any suitable computer-readable storage device or medium, such as a random or serial access memory device, a hard drive, a solid-state drive, or a flash memory device. Although one memory device 114 is shown in Figure 1, the system 100 may include any suitable number of memory devices 114 (e.g., one memory device, two memory devices, five memory devices, ten memory devices, etc.). The memory device 114 can be coupled to and / or located inside the housing of the respiratory therapy device 122, the housing of the user device 170, the housing of the sensor 130, or any combination thereof. Similar to the control system 110, the memory device 114 can be centralized (within one such housing) or distributed (within two or more such housings that are physically separate).

[0017] In some implementations, the memory device 114 (Figure 1) stores a user profile associated with the user. The user profile may include, for example, demographic information associated with the user, biometric information associated with the user, medical information associated with the user, self-reported user feedback, sleep parameters associated with the user (e.g., sleep-related parameters recorded from one or more previous sleep sessions), or any combination thereof. Demographic information may include, for example, information indicating the user's age, gender, race, geographical location, relationship status, family history of insomnia or sleep apnea, employment status, education level, socioeconomic status, or any combination thereof. Medical information may include, for example, information indicating one or more medical conditions associated with the user, the user's medication use, or both. Medical information data may further include the results or scores of the Multiple Sleep Latency Test (MSLT) and / or the scores or values ​​of the Pittsburgh Sleep Quality Index (PSQI). Self-reported user feedback may include information indicating a self-reported subjective sleep score (e.g., poor, average, good), a self-reported subjective stress level, a self-reported subjective fatigue level, a self-reported subjective health status, recent life events experienced by the user, or any combination thereof.

[0018] The electronic interface 119 is configured to receive data (e.g., physiological data and / or acoustic data) from one or more sensors 130, and as a result, the data can be stored in a memory device 114 and / or analyzed by the processor 112 of the control system 110. The electronic interface 119 can communicate with one or more sensors 130 using wired or wireless connections (e.g., via a cellular network, using RF communication protocols, WiFi communication protocols, Bluetooth® communication protocols, etc.). The electronic interface 119 may include an antenna, a receiver (e.g., an RF receiver), a transmitter (e.g., an RF transmitter), a transceiver, or any combination thereof. The electronic interface 119 may also include another processor and / or another memory device which are identical or similar to the processor 112 and memory device 114 described herein. In some implementations, the electronic interface 119 is coupled to or integrated with a user device 170. In other implementations, the electronic interface 119 is connected to or integrated with the control system 110 and / or the memory device 114 (for example, within the housing).

[0019] As described above, in some implementations, system 100 optionally includes a respiratory therapy system 120. The respiratory therapy system 120 may include a respiratory pressure therapy (RPT) device 122 (referred to herein as respiratory therapy device 122), a user interface 124, a conduit 126 (also referred to as a tube or air circuit), a display device 128, a humidifier tank 129, a receptacle 180, or any combination thereof. In some implementations, one or more of the control system 110, memory device 114, display device 128, sensor 130, and humidifier tank 129 are part of the respiratory therapy device 122. Respiratory pressure therapy refers to applying an air supply to the inlet of the user's airway at a controlled target pressure that is nominally positive relative to the atmosphere throughout the user's respiratory cycle (in contrast to negative pressure therapy such as a tank ventilator or positive / negative pressure external ventilator (cuirass)). The respiratory therapy system 120 is generally used to treat individuals suffering from one or more sleep-related breathing disorders (e.g., obstructive sleep apnea, central sleep apnea, or mixed sleep apnea).

[0020] The respiratory therapy device 122 has a blower motor (not shown) that is commonly used to generate pressurized air that is delivered to the user (for example, using one or more motors that drive one or more compressors). In some implementations, the respiratory therapy device 122 generates a continuous and constant air pressure that is delivered to the user. In other implementations, the respiratory therapy device 122 generates two or more predetermined pressures (for example, a first predetermined air pressure and a second predetermined air pressure). In still other implementations, the respiratory therapy device 122 is configured to generate various different air pressures within a predetermined range. For example, the respiratory therapy device 122 can deliver at least about 4 cmH2O, at least about 10 cmH2O, at least about 20 cmH2O, from about 6 cmH2O to about 10 cmH2O, from about 7 cmH2O to about 12 cmH2O, and so on. The respiratory therapy device 122 can also deliver pressurized air at a predetermined flow rate, for example, from about 20 liters per minute to about 150 liters per minute, while maintaining a positive pressure relative to the ambient pressure.

[0021] The user interface 124 engages a portion of the user's face and delivers pressurized air from the respiratory therapy device 122 to the user's airway to assist in preventing the airway from narrowing and / or collapsing during sleep. Thereby, the oxygen uptake of the user during sleep can also be increased. The user interface 124 generally engages the user's face such that the pressurized air is delivered to the user's airway through the user's mouth, the user's nose, or both the user's mouth and nose. The respiratory therapy device 122, the user interface 124, and the conduit 126 together form an air path that is in fluid connection with the user's airway. With the pressurized air, the user during sleep The oxygen uptake can also increase. Depending on the therapy applied, the user interface 124 can, for example, form a seal with the area or part of the user's face to provide a pressure that is sufficiently different from the ambient pressure to result in a therapeutic effect, such as promoting the delivery of air at a positive pressure of about 10 cmH2O relative to the ambient pressure. In other forms of therapy, such as the delivery of oxygen, the user interface may not include a seal sufficient to promote the delivery of gas supply to the airway at a positive pressure of about 10 cmH2O.

[0022] As shown in FIG. 2, in some implementations, the user interface 124 is a facial mask (such as a full face mask) that covers the nose and mouth of the user 210. Alternatively, the user interface 124 can be a nasal mask that provides air to the nose of the user 210 or a nasal pillow mask that delivers air directly to the nostrils of the user 210. The user interface 124 can include, for example, a plurality of straps that form a headgear to assist in positioning and / or stabilizing the interface on a portion of the user 210 (such as the face), and a shape-conforming cushion (such as silicone, plastic, foam, etc.) to assist in providing an airtight seal between the user interface 124 and the user 210. The user interface 124 can also include one or more ventilation holes to allow the escape of carbon dioxide and other gases exhaled by the user 210. In other implementations, the user interface 124 includes a mouthpiece (such as a night guard mouthpiece shaped to fit the teeth of the user 210, a mandibular rehabilitation device, etc.).

[0023] The conduit 126 (also referred to as an air circuit or tube) allows air to flow between two components of the respiratory therapy system 120, such as the respiratory therapy device 122 and the user interface 124. In some implementations, the conduit 126 can have separate branches for inhalation and exhalation. In other implementations, a single-branch conduit is used for both inhalation and exhalation.

[0024] One or more of the respiratory therapy device 122, user interface 124, conduit 126, display device 128, and humidification tank 129 may include one or more sensors (e.g., pressure sensors, flow sensors, or more generally, any of the other sensors 130 described herein). These one or more sensors can be used, for example, to measure the air pressure and / or flow rate of the pressurized air supplied by the respiratory therapy device 122.

[0025] The display device 128 is generally used to display images (one or more) 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 about the status of the respiratory therapy device 122 (e.g., whether the respiratory therapy device 122 is on or off, the pressure of the air being delivered by the respiratory therapy device 122, the temperature of the air being delivered by the respiratory therapy device 122, etc.) and / or other information (e.g., a sleep score and / or therapy score, also referred to as the myAir® score, as described in US2017 / 0311879A1, which is incorporated herein by reference in its entirety, the current date / time, personal information of user 210, questions requesting user feedback, and / or advice to user 210, etc.). In some implementations, the display device 128 functions as a human-machine interface (HMI) including a graphic user interface (GUI) configured to display images (one or more) as an input interface. The display device 128 may be an LED display, an OLED display, an LCD display, etc. The input interface senses input made by a human user interacting with, for example, a touchscreen or contact sensing board, a mouse, a keyboard, or a respiratory therapy device 122. It could be any sensor system configured in such a way.

[0026] The humidifying tank 129 is connected to or integrated with the respiratory therapy device 122 and includes a water reservoir that can be used to humidify the pressurized air delivered from the respiratory therapy device 122. The respiratory therapy device 122 may include one or more vents (not shown) and a heater for heating the water in the humidifying tank 129 to humidify the pressurized air provided to the user 210. In addition, in some implementations, the conduit 126 may also include a heating element (e.g., connected to and / or embedded in the conduit 126) for heating the pressurized air delivered to the user 210. The humidifying tank 129 can be fluid-coupled to a water vapor inlet in the air path to deliver water vapor into the air path via the water vapor inlet, or it can be formed in series with the air path as part of the air path itself. In some implementations, the humidifying tank 129 may not include a water reservoir and therefore may be waterless.

[0027] In some implementations, system 100 can be used to deliver at least a portion of a substance from receptacle 180 to the user's airway, at least partially based on physiological data, sleep-related parameters, other data or information, or any combination thereof. Generally, modifying the delivery of a portion of a substance into the airway may include (i) initiating the delivery of a portion of a substance into the airway, (ii) terminating the delivery of a portion of a substance into the airway, (iii) changing the amount of a substance delivered into the airway, (iv) changing the temporal characteristics of the delivery of a portion of a substance into the airway, (v) changing the quantitative characteristics of the delivery of a portion of a substance into the airway, (vi) changing any parameter associated with the delivery of a substance into the airway, or (vii) a combination of (i) to (vi).

[0028] Modifying the temporal characteristics of the delivery of a portion of a substance into the air pathway may include changing the rate at which the substance is delivered, starting and / or completing at different times, continuing over different periods, changing the temporal distribution or characteristics of the delivery, or changing the quantity distribution independently of the temporal distribution. By independently changing time and quantity, it is possible to reliably change the amount of substance released each time, separate from changing the frequency of release. In this way, several different combinations of release frequency and release amount (e.g., high frequency but low release, high frequency and high release, low frequency and high release, low frequency and low release, etc.) can be achieved. Other modifications to the delivery of a portion of a substance into the air pathway can also be utilized.

[0029] The respiratory therapy system 120 can be used as a positive airway pressure (PAP) system or ventilator, such as a continuous positive airway pressure (CPAP) system, an automated positive airway pressure (APAP) system, a biphasic or variable positive airway pressure (BPAP or VPAP) system, or any combination thereof. A CPAP system delivers a predetermined amount of pressurized air to the user 210 (determined, for example, by a sleep physician). An APAP system automatically changes the amount of pressurized air delivered to the user 210 based, for example, on respiratory data associated with the user. A BPAP or VPAP system is configured to deliver a first predetermined pressure (e.g., inspiratory positive airway pressure or IPAP) and a second predetermined pressure lower than the first predetermined pressure (e.g., expiratory positive airway pressure or EPAP).

[0030] Referring again to Figure 1, one or more sensors 130 of system 100 include a pressure sensor 132, a flow sensor 134, a temperature sensor 136, a motion sensor 138, a microphone 140, a speaker 142, a radio frequency (RF) receiver 146, an RF transmitter 148, a camera 150, an infrared sensor 152, a photoelectric fingertip plethysmography (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 electromyography (EMG) sensor 166, and an oxygen sensor 168. This includes an analyte sensor 174, a moisture sensor 176, a LiDAR sensor 178, or any combination thereof. Generally, each of the one or more sensors 130 is configured to output sensor data received and stored in a memory device 114 or one or more other memory devices.

[0031] One or more sensors 130 are illustrated and described as including, respectively, a pressure sensor 132, a flow sensor 134, a temperature sensor 136, a motion sensor 138, a microphone 140, a speaker 142, an RF receiver 146, an RF transmitter 148, a camera 150, an infrared sensor 152, a photoelectric fingertip plethysmography (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 electromyography (EMG) sensor 166, an oxygen sensor 168, an analyte sensor 174, a moisture sensor 176, and a LiDAR sensor 178, but it is more common for one or more sensors 130 to include any combination and any number of each of the sensors described and / illustrated herein.

[0032] As described herein, the system 100 can generally be used to generate physiological data associated with a user (e.g., a user of the respiratory therapy system 120 shown in Figure 2) during a sleep session. The physiological data can be analyzed to generate one or more sleep-related parameters, which may include any parameters or measurements about the user during a sleep session. One or more sleep-related parameters that can be determined about user 210 during a sleep session may include, for example, the apnea-hypopnea index (AHI) score, sleep score, flow signal, pressure signal, respiratory signal, respiratory rate, inspiratory amplitude, expiratory amplitude, inspiratory-to-expiratory ratio, number of events per hour, event pattern, stage, pressure setting of the respiratory therapy device 122, heart rate, heart rate variability, user 210 movement, temperature, EEG activity, EMG activity, wakefulness, snoring, choking, cough, wheezing, or any combination thereof.

[0033] One or more sensors 130 can be used to generate, for example, physiological data, acoustic data, or both. Physiological data generated by one or more of the sensors 130 can be used by the control system 110 to determine sleep-wake signals and one or more sleep-related parameters associated with the user 210 (Figure 2) during a sleep session. Sleep-wake signals can indicate one or more sleep states, including wakefulness, relaxed wakefulness, micro-wakefulness, or distinct sleep stages, such as the rapid eye movement (REM) stage, the first non-REM stage (often referred to as "N1"), the second non-REM stage (often referred to as "N2"), the third non-REM stage (often referred to as "N3"), or any combination thereof. Methods for determining sleep states and / or sleep stages from physiological data generated by one or more sensors, such as one or more sensors 130, are described, for example, in US10,492,720B2, US2014 / 0088373A1, WO2017 / 132726, WO2019 / 122413, and US2020 / 0383580A1 (each of which is incorporated herein by reference in its entirety).

[0034] In some implementations, the sleep-wake signals described herein may be accompanied by timestamps indicating the time the user entered bed, the time the user left bed, the time the user attempted to fall asleep, etc. The sleep-wake signals may be measured during a sleep session by one or more sensors 130 at a predetermined sampling rate, for example, one sample per second, one sample per 30 seconds, one sample per minute, etc. In some implementations, the sleep-wake signals may also indicate respiratory signals during the sleep session, respiratory rate, inspiratory amplitude, expiratory amplitude, inspiratory-to-expiratory ratio, number of events per unit time, event patterns, pressure settings of the respiratory therapy device 122, or any combination thereof. These events (one or more) may include snoring, apnea, central apnea, obstructive apnea, mixed apnea, hypopnea, (for example) This may include mask leakage (from user interface 124), inability to keep the lower limbs still, sleep disturbance, suffocation, increased heart rate, labored breathing, asthma attack, epileptic episode, seizure, or any combination thereof. One or more sleep-related parameters that can be determined about a user during a sleep session based on sleep-wake signals include, for example, total time in bed, total sleep time, sleep latency, post-sleep wakefulness parameters, sleep efficiency, fragmentation index, or any combination thereof. As further detailed herein, one or more sleep-related scores can be determined by analyzing physiological data and / or sleep-related parameters.

[0035] Generally, a sleep session includes any time after the user 210 is lying or sitting in bed 230 (or another area or object intended for sleeping), and / or has turned on the breathing device 122, and / or is wearing the user interface 124. Therefore, a sleep session may include (i) the period when user 210 is using the CPAP system but before user 210 is attempting to fall asleep (for example, when user 210 is lying in bed 230 reading a book), (ii) the period when user 210 is beginning to attempt to fall asleep but is still awake, (iii) the period when user 210 is in light sleep (also referred to as stages 1 and 2 of non-REM sleep), (iv) the period when user 210 is in deep sleep (also referred to as slow-wave sleep, SWS, or stage 3 of non-REM sleep), (v) the period when user 210 is in rapid eye movement (REM) sleep, (vi) the period when user 210 is periodically awake between light sleep, deep sleep, or REM sleep, or (vii) the period when user 210 is awake and does not fall asleep again.

[0036] A sleep session is typically defined as ending when user 210 removes user interface 124, turns on breathing device 122, and / or gets out of bed 230. In some implementations, a sleep session may include a further period or be limited to only a portion of the period disclosed above. For example, a sleep session may be defined as beginning when breathing device 122 starts supplying pressurized air to the airway or user 210, ending when breathing device 122 stops supplying pressurized air to user 210's airway, and encompassing a period during which user 210 falls asleep or wakes up.

[0037] 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., an atmospheric pressure sensor) that generates sensor data indicating the user's (e.g., inhaling and / or exhaling) respiration and / or ambient pressure of the respiratory therapy system 120. In such implementations, the pressure sensor 132 can be connected to or integrated with the respiratory therapy device 122. The pressure sensor 132 may be, for example, a capacitive sensor, an electromagnetic sensor, a piezoelectric sensor, a strain gauge sensor, an optical sensor, a potentiometric sensor, or any combination thereof.

[0038] The flow sensor 134 outputs flow data that can be stored in the memory device 114 and / or analyzed by the processor 112 of the control system 110. An example of a flow sensor (e.g., flow sensor 134) is described in U.S. Patent No. 10,328,219B2, which is incorporated herein by reference in its entirety. In some implementations, the flow sensor 134 is used to determine the airflow from the respiratory therapy device 122, the airflow through the conduit 126, the airflow through the user interface 124, or any combination thereof. In such implementations, the flow sensor 134 can be connected to or integrated with the respiratory therapy device 122, the user interface 124, or the conduit 126. The flow sensor 134 is a mass flow sensor such as, for example, a rotary flow meter (e.g., Hall effect flow meter), a turbine flow meter, an orifice flow meter, an ultrasonic flow meter, a hot-wire sensor, an eddy current sensor, a membrane sensor, or any combination thereof. It may be a case of . In some implementations, the flow sensor 134 is configured to measure exhaust flow (e.g., intentional "leak"), unintentional leak (e.g., mouth leak and / or mask leak), patient flow (e.g., air entering and / or exiting the lungs), or any combination thereof. In some implementations, the flow data can be analyzed to determine the user's cardiogenic oscillation. In some examples, the pressure sensor 132 can be used to determine the user's blood pressure.

[0039] 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 indicating the core body temperature of the user 210 (Figure 2), the skin temperature of the user 210, the temperature of the air flowing from the respiratory therapy device 122 and / or through the conduit 126, the temperature inside the user interface 124, the ambient temperature, or any combination thereof. The temperature sensor 136 may be, for example, a thermocouple sensor, a thermistor sensor, a silicon bandgap temperature sensor or semiconductor-based sensor, a resistance temperature detector, or any combination thereof.

[0040] The motion sensor 138 outputs motion data that can be stored in the memory device 114 and / or analyzed by the processor 112 of the control system 110. The motion sensor 138 can be used to detect the movement of the user 210 during a sleep session and / or to detect the movement of any of the components of the respiratory therapy system 120, such as the respiratory therapy device 122, the user interface 124, or the conduit 126. The motion sensor 138 may include one or more inertial sensors, such as an accelerometer, a gyroscope, and a magnetometer. In some implementations, the motion sensor 138 may, as an alternative or additional, generate one or more signals representing the user's body movements, from which signals representing the user's sleep state can be obtained, for example, through the user's respiratory movements. In some implementations, the motion data from the motion sensor 138 can be used in conjunction with additional data from another sensor 130 to determine the user's sleep state.

[0041] The microphone 140 outputs sound and / or audio data that can be stored in the memory device 114 and / or analyzed by the processor 112 of the control system 110. The audio data generated by the microphone 140 can be reproduced as one or more sounds (one or more) during a sleep session (e.g., sounds from user 210). The audio data from the microphone 140 can also be used (e.g., using the control system 110) to identify one or more sleep-related parameters or events experienced by user 210 during a sleep session, as further detailed herein. The microphone 140 can be connected to or integrated with the respiratory therapy device 122, the user interface 124, the conduit 126, or the user device 170. In some implementations, the system 100 includes multiple microphones (e.g., two or more microphones with beamforming and / or arrays of microphones), and as a result, the sound data generated by each of the multiple microphones can be used to distinguish sound data generated by another of the multiple microphones.

[0042] Speaker 142 outputs sound waves audible to the user of system 100 (e.g., user 210 in Figure 2). Speaker 142 can be used, for example, as an alarm clock, or to play an alert or message to user 210 (e.g., in response to an event). In some implementations, speaker 142 can be used to transmit audio data generated by microphone 140 to user 210. Speaker 142 is connected to the respiratory therapy device 122, user interface 12 4. It can be connected to or integrated with the conduit 126 or the user device 170.

[0043] The microphone 140 and speaker 142 can be used as separate devices. In some implementations, the microphone 140 and speaker 142 can be combined with an acoustic sensor 141 (e.g., a sonar sensor), as described, for example, in WO2018 / 050913 and WO2020 / 104465 (each of which is incorporated herein by reference in whole). In such implementations, the speaker 142 generates or emits sound waves at predetermined intervals, and the microphone 140 detects reflections of the sound waves emitted from the speaker 142. The sound waves generated or emitted by the speaker 142 have frequencies inaudible to the human ear (e.g., less than 20 Hz or more than about 18 kHz) so as not to disturb the sleep of the user 210 or the person sharing the bed 220 (Figure 2). The control system 110 can determine, at least in part, the location of the user 210 (Figure 2) and / or one or more of the sleep-related parameters described herein, such as respiratory signal, respiratory rate, inspiratory amplitude, expiratory amplitude, inspiratory-to-expiratory ratio, number of events per hour, event pattern, sleep state, sleep stage, pressure setting of the respiratory therapy device 122, or any combination thereof, based at least in part on data from the microphone 140 and / or speaker 142. In such a context, the sonar sensor may be understood to be involved in active acoustic sensing, such as generating and / or transmitting ultrasonic and / or low-frequency ultrasonic sensing signals (e.g., within a frequency range such as approximately 17–23 kHz, 18–22 kHz, or 17–18 kHz) in the air. Such a system may be considered in conjunction with WO2018 / 050913 and WO2020 / 104465 described above (each of which is incorporated herein in whole by reference).

[0044] In some implementations, the sensor 130 includes (i) a first microphone which is identical or similar to microphone 140 and integrated into acoustic sensor 141, and (ii) a second microphone which is identical or similar to microphone 140 but is separate and distinct from the first microphone integrated into acoustic sensor 141.

[0045] The RF transmitter 148 generates and / or emits radio waves having a predetermined frequency and / or amplitude (e.g., within the high frequency band, within the low frequency band, long wave signal, short wave signal, etc.). The RF receiver 146 detects the reflection of the radio waves emitted from the RF transmitter 148, and this data can be analyzed by the control system 110 to determine the location of the user 210 (Figure 2) 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 RF transmitter 148 are shown as separate and distinct elements in Figure 1, in some implementations the RF receiver 146 and RF transmitter 148 are combined as part of an RF sensor 147 (e.g., a RADAR sensor). In some such implementations the RF sensor 147 includes a control circuit. Specific forms of RF communication can include Wi-Fi, Bluetooth (registered trademark), and others.

[0046] In some implementations, the RF sensor 147 is part of a mesh system. An example of a mesh system is a Wi-Fi mesh system which may include mesh nodes, mesh routers (one or more), and mesh gateways (one or more) (each of which may be mobile / movable or fixed). In such an implementation, the Wi-Fi mesh system includes Wi-Fi routers and / or Wi-Fi controllers, and one or more satellites (e.g., access points) (each of which includes an RF sensor identical or similar to the RF sensor 147). The Wi-Fi routers and satellites communicate continuously with each other using Wi-Fi signals. The system can be used to generate motion data based on changes in the Wi-Fi signal between the router and satellite(s) (e.g., differences in received signal strength) by an object or person moving and partially interfering with the signal. The motion data may indicate motion, breathing, heart rate, walking, falls, actions, or any combination thereof.

[0047] Camera 150 outputs image data that can be reproduced as one or more images (e.g., still images, video images, thermal images, or any combination thereof) that can be stored in memory device 114. The control system 110 can use the image data from camera 150 to determine one or more of the sleep-related parameters described herein, such as one or more events (e.g., periodic limb movements or restless limb syndrome), respiratory signals, respiratory rate, inspiratory amplitude, expiratory amplitude, inspiratory-to-expiratory ratio, number of events per unit time, event patterns, sleep state, sleep stage, or any combination thereof. Furthermore, the image data from camera 150 can be used to determine, for example, the user's position, the chest movement of user 210 (Figure 2), the airflow around user 210's mouth and / or nose, the time user 210 entered bed 230, and the time user 210 left bed 230. In some implementations, camera 150 includes a wide-angle lens or a fisheye lens.

[0048] The infrared (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 during a sleep session, including the user 210's temperature and / or movement. The IR sensor 152 can also be used in conjunction with the camera 150 to measure the user 210's presence, location, and / or movement. The IR sensor 152 can detect infrared light having wavelengths from, for example, about 700 nm to about 1 mm, while the camera 150 can detect visible light having wavelengths from about 380 nm to about 740 nm.

[0049] The PPG sensor 154 outputs physiological data associated with user 210 (Figure 2) that can be used to determine one or more sleep-related parameters, such as heart rate, heart rate variability, cardiac cycle, respiratory rate, inspiratory amplitude, expiratory amplitude, inspiratory-to-expiratory ratio, estimated blood pressure parameters (one or more), or combinations thereof. The PPG sensor 154 can be worn by user 210, embedded in clothing and / or fabrics worn by user 210, embedded in and / or connected to the user interface 124 and / or associated headgear (e.g., a strap).

[0050] The ECG sensor 156 outputs physiological data associated with the electrical activity of the user 210's heart. In some implementations, the ECG sensor 156 includes one or more electrodes positioned on or around a portion of the user 210 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.

[0051] The EEG sensor 158 outputs physiological data associated with the electrical activity of the user 210's brain. In some implementations, the EEG sensor 158 includes one or more electrodes positioned on or around the user 210's scalp during a sleep session. The physiological data from the EEG sensor 158 can be used, for example, to determine the user 210's sleep state and / or sleep stage at any given 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., a strap).

[0052] The capacitance sensor 160, force sensor 162, and strain gauge sensor 164 output data that can be stored in the memory device 114 and used by the control system 110 to determine one or more of the sleep-related parameters described herein. The EMG sensor 166 outputs physiological data associated with electrical activity produced by one or more muscles. The oxygen sensor 168 outputs oxygen data indicating the oxygen concentration of the gas (e.g., in the conduit 126 or in the user interface 124). The oxygen sensor 168 may be, for example, an ultrasonic oxygen sensor, an electro-oxygen sensor, a chemical oxygen sensor, an optical oxygen sensor, a pulse oximeter (e.g., an SpO2 sensor), or any combination thereof. In some implementations, one or more sensors 130 also include a galvanic skin response (GSR) sensor, a blood flow sensor, a respiratory sensor, a pulse sensor, a blood pressure sensor, an oxygen measurement sensor, or any combination thereof.

[0053] The analyte sensor 174 can be used to detect the presence of analytes in the exhaled breath of the user 210. The data output by the analyte sensor 174 can be stored in the memory device 114 and used by the control system 110 to determine the identity and concentration of any analyte contained in the user 210's breath. In some implementations, the analyte sensor 174 is positioned near the user 210's mouth to detect analytes contained in the breath exhaled from the user 210's mouth. For example, if the user interface 124 is a face mask that covers the user 210's nose and mouth, the analyte sensor 174 may be positioned inside the face mask to monitor the user 210's mouth breathing. For example, if the user interface 124 is a nasal mask or nasal pillow mask, in other implementations, the analyte sensor 174 may be positioned near the user 210's nose to detect analytes contained in the breath exhaled through the user's nose. In other implementations, if the user interface 124 is a nasal mask or nasal pillow mask, the analyte sensor 174 may be positioned near the user 210's mouth. In this implementation, the analyte sensor 174 can be used to detect whether air is inadvertently leaking from the user 210's mouth. In some implementations, the analyte sensor 174 is a volatile organic compound (VOC) sensor that can be used to detect carbonaceous chemicals or compounds. In some implementations, the analyte sensor 174 can also be used to detect whether the user 210 is breathing through their nose or mouth. For example, if the presence of an analyte is detected by data output from the analyte sensor 174 positioned near the user 210's mouth or (in implementations where the user interface 124 is a face mask) within the face mask, the control system 110 can use this data as an indication that the user 210 is breathing through their mouth.

[0054] The moisture sensor 176 outputs data that can be stored in the memory device 114 and used by the control system 110. The moisture sensor 176 can be used to detect moisture in various areas surrounding the user (e.g., inside the conduit 126 or user interface 124, near the user 210's face, near the connection between the conduit 126 and the user interface 124, near the connection between the conduit 126 and the respiratory therapy device 122, etc.). Therefore, in some implementations, the moisture sensor 176 can be connected to or integrated with the user interface 124 or integrated with the conduit 126 to monitor the humidity of the pressurized air from the respiratory therapy device 122. In other implementations, the moisture sensor 176 is placed near any area where the moisture level needs to be monitored. The moisture sensor 176 can also be used to monitor the humidity of the surrounding environment of the user 210, for example, the humidity of the air in the user 210's bedroom.

[0055] One or more light-detecting ranging (LiDAR) sensors 178 can be used for depth sensing. This type of optical sensor (e.g., laser sensor) can be used to detect objects and create a three-dimensional (3D) map of the surrounding environment, such as a living space. LiDAR can generally use pulsed lasers to measure time of flight. LiDAR is also called 3D laser scanning. In one use case of such a sensor, a fixed or mobile device (such as a smartphone) having a LiDAR sensor 178 can measure and map an area more than 5 meters away from the sensor. LiDAR data can be fused with point cloud data estimated by, for example, an electromagnetic RADAR sensor. The LiDAR sensor(s) 178 can also use artificial intelligence (AI) to automatically create a geofence for the RADAR system by detecting and classifying features in space that may pose a problem for the RADAR system, such as glass windows (which may be highly reflective to RADAR). LiDAR can also be used, for example, to provide estimation of not only a person's height but also changes in height when a person is sitting or lying down. LiDAR can be used to form a 3D mesh representation of the environment. In further applications, for solid surfaces through which radio waves can pass (e.g., radio wave-transparent materials), LiDAR can reflect signals from such surfaces, potentially enabling the classification of different types of obstacles.

[0056] In some implementations, one or more sensors 130 include galvanic skin response (GSR) sensors, blood flow sensors, respiration sensors, pulse sensors, blood pressure sensors, oxygen measurement sensors, sonar sensors, RADAR sensors, blood glucose sensors, color sensors, pH sensors, air quality sensors, tilt sensors, rain sensors, soil moisture sensors, water flow sensors, alcohol sensors, or any combination thereof.

[0057] Although shown separately in Figure 1, any combination of one or more sensors 130 can be integrated and / or connected to any one or more components of system 100, including the respiratory therapy device 122, user interface 124, conduit 126, humidification tank 129, control system 110, user device 170, activity tracker 190, or any combination thereof. For example, the microphone 140 and speaker 142 may be integrated and / or connected to the user device 170, and the pressure sensor 130 and / or flow sensor 132 may be integrated and / or connected to the respiratory therapy device 122. In some implementations, at least one of the one or more sensors 130 is not connected to the respiratory therapy device 122, the control system 110, or the user device 170, but is positioned generally adjacent to the user 210 during a sleep session (for example, positioned on or in contact with a part of the user 210, worn by the user 210, connected to or positioned on a nightstand, connected to a mattress, connected to the ceiling, etc.).

[0058] By analyzing data from one or more sensors 130, one or more sleep-related parameters can be determined, which may include respiratory signals, respiratory rate, respiratory pattern, inspiratory amplitude, expiratory amplitude, inspiratory-to-expiratory ratio, occurrence of one or more events, number of events per hour, event pattern, sleep state, apnea-hypopnea index (AHI), or any combination thereof. One or more events may include snoring, apnea, central apnea, obstructive apnea, mixed apnea, hypopnea, mask leak, cough, inability to rest the lower extremities, sleep disturbance, suffocation, increased heart rate, labored breathing, asthma attack, epileptic episode, seizure, elevated blood pressure, or any combination thereof. Many of these sleep-related parameters are physiological parameters, but some can be considered non-physiological parameters. Other types of physiological and non-physiological parameters can also be determined from data from one or more sensors 130 or other types of data.

[0059] User device 170 (Figure 1) includes a display device 172. User device 170 may be a mobile device such as a smartphone, tablet, game console, smartwatch, or laptop computer. Alternatively, user device 170 may be an external sensing system, a television (e.g., a smart TV), or another smart home device (e.g., a smart speaker such as Google Home, Amazon Echo, or Alexa). In some implementations, the user device is a wearable device (e.g., a smartwatch). Display device 172 is generally used to display images (one or more) including still images, video images, or both. In some implementations, display device 172 functions as a human-machine interface (HMI) including a graphic user interface (GUI) configured to display images (one or more) and an input interface. Display device 172 may be an LED display, an OLED display, an LCD display, etc. The input interface may be a touchscreen or contact-sensing substrate, a mouse, a keyboard, or any sensor system configured to sense input made by a human user interacting with user device 170. In some implementations, one or more user devices may be used by and / or included in system 100.

[0060] The blood pressure device 182 is typically used to assist in generating cardiovascular data for determining one or more blood pressure measurements associated with the user 210. The blood pressure device 182 may include, for example, at least one of one or more sensors 130 for measuring systolic and / or diastolic blood pressure components.

[0061] In some implementations, the blood pressure device 182 is a blood pressure monitor comprising a user-wearable inflatable cuff and a pressure sensor (e.g., the pressure sensor 132 described herein). For example, as shown in the example in Figure 2, the blood pressure device 182 can be worn on the upper arm of a user 210. In such implementations where the blood pressure device 182 is a blood pressure monitor, the blood pressure device 182 also includes a pump (e.g., a manually operated valve) for inflating the cuff. In some implementations, the blood pressure device 182 is connected to a respiratory device 122 of a respiratory therapy system 120, which delivers pressurized air to inflate the cuff. More commonly, the blood pressure device 182 is communicatively connected to and / or physically integrated with a control system 110, memory 114, respiratory therapy system 120, user device 170, and / or activity tracker 190.

[0062] The activity tracker 190 is typically used to help generate physiological data for determining activity measurements associated with the user 210. The activity tracker 190 may include one or more of the sensors 130 described herein, such as a motion sensor 138 (e.g., one or more accelerometers and / or gyroscopes), a PPG sensor 154, and / or an ECG sensor 156. Using the physiological data from the activity tracker 190, it may be possible to determine, for example, steps taken, distance traveled, steps uphill, duration of physical activity, type of physical activity, intensity of physical activity, time spent standing, respiratory rate, mean respiratory rate, resting respiratory rate, maximum respiratory rate, respiratory rate variability, heart rate, mean heart rate, resting heart rate, maximum heart rate, heart rate variability, calories burned, blood oxygen saturation, skin electrical activity (also referred to as skin conductance or galvanic skin response), or any combination thereof. In some implementations, the activity tracker 190 is connected (e.g., electronically or physically) to the user device 170.

[0063] In some implementations, the activity tracker 190 is a wearable device that the user 210 can wear, such as a smartwatch, wristband, ring, or patch. Yes. For example, referring to Figure 2, the activity tracker 190 is worn on the wrist of user 210. The activity tracker 190 can also be linked to or integrated with clothing or garments worn by user 210. As a further alternative, the activity tracker 190 can also be linked to or integrated with user device 170 (e.g., within the same housing). It is more common for the activity tracker 190 to be communicatively linked to or physically integrated with the control system 110, memory device 114, respiratory therapy system 120, user device 170, and / or blood pressure device 182 (e.g., within the same housing).

[0064] Although the control system 110 and the memory device 114 are shown and illustrated in Figure 1 as separate and distinct components of 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 a part thereof (e.g., the processor 112) may reside in the cloud (e.g., integrated into a server, integrated into an Internet of Things (IoT) device, connected to the cloud, and subject to edge cloud processing), or may reside on one or more servers (e.g., remote servers, local servers, etc., or any combination thereof).

[0065] Although System 100 is shown as including all of the components described above, depending on the implementation of this disclosure, the components included in the system may be more or less. For example, a first alternative system includes a control system 110, a memory device 114, and at least one of one or more sensors 130, but does not include a respiratory therapy system 120. Another example is a second alternative system including a control system 110, a memory device 114, at least one of one or more sensors 130, and a user device 170. Yet another example is a third alternative system including a control system 110, a memory device 114, a respiratory therapy system 120, at least one of one or more sensors 130, and optionally a user device 170. Thus, any(s) of the components illustrated and described herein can be used and / or combined with one or more other components to form a variety of systems.

[0066] Generally, referring to Figure 2, parts of system 100 (Figure 1) relating to several implementation configurations are shown. The user 210 and co-sleep partner 220 of the respiratory therapy system 120 are located in bed 230 and lying on mattress 232. A user interface 124 (hereinafter also referred to herein as a mask, e.g., a full-face mask) may be worn by the user 210 during a sleep session. The user interface 124 is fluidically connected to and / or connected to a respiratory device 122 via a conduit 126. As a result, the respiratory therapy device 122 delivers pressurized air to the user 210 via the conduit 126 and user interface 124 to increase air pressure in the user 210's throat, thereby helping to prevent the airway from closing and / or narrowing during sleep. The respiratory therapy device 122 can be positioned on a nightstand 240 directly adjacent to the bed 230, as shown in Figure 2, or more generally, on any surface or structure substantially adjacent to the bed 230 and / or the user 210.

[0067] In some implementations, the control system 110, the memory 214, one or more sensors 130, or a combination thereof, may be located on and / or inside any surface and / or structure substantially 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 position 255A on and / or inside 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 located on the respiratory therapy system 120, the user interface 124, or the conduit. It can be connected to 126, a display device 128, a humidifier tank 129, or a combination thereof.

[0068] Alternatively or additionally, at least one of the one or more sensors 130 may be located at a second position 255B above and / or inside the bed 230 (for example, one or more sensors 130 may be connected to and / or integrated with the bed 230). Furthermore, alternatively or additionally, at least one of the one or more sensors 130 may be located at a third position 255C above and / or inside the mattress 232 adjacent to the bed 230 and / or user 210 (for example, one or more sensors 130 may be connected to and / or integrated with the mattress 232). Alternatively or additionally, at least one of the one or more sensors 130 may be located at a fourth position 255D above and / or inside the pillow, generally adjacent to the bed 230 and / or user 210.

[0069] Alternatively or additionally, at least one of the one or more sensors 130 may be located at a fifth position 255E above and / or inside the nightstand 240, which is generally adjacent to the bed 230 and / or user 210. Alternatively or additionally, at least one of the one or more sensors 130 may be located at a sixth position 255F such that at least one of the one or more sensors 130 is linked to and / or positioned on user 215 (for example, one or more sensors 130 are embedded in or linked to fabric, clothing 212, and / or smart devices worn by user 210). More commonly, at least one of the one or more sensors 130 is positioned at any suitable position relative to user 210 so that one or more sensors 130 can generate sensor data associated with user 210.

[0070] Generally, users prescribed the use of the respiratory therapy system 120 tend to experience higher quality sleep and reduced daytime fatigue after using the respiratory therapy system 120 during sleep, compared to not using the respiratory therapy system 120 (especially when the user suffers from sleep apnea or other sleep-related disorders). For example, user 210 may suffer from obstructive sleep apnea and may rely on a user interface 124 (e.g., a full-face mask) to deliver pressurized air from the respiratory device 122 via a conduit 126. The respiratory device 122 may be a continuous positive airway pressure (CPAP) machine used to increase air pressure in the throat of user 210 to prevent the airway from closing and / or narrowing during sleep. People with sleep apnea may experience sleep disturbances, such as narrowing or collapse of their airway during sleep, leading to reduced oxygen intake and waking. CPAP machines minimize the occurrence of events that cause the user to wake up or be otherwise disturbed due to reduced oxygen intake by preventing airway narrowing or collapse. While the breathing device 122 attempts to maintain a medically prescribed air pressure (single or multiple) during sleep, the user may experience sleep discomfort due to the therapy.

[0071] Referring to Figure 3, the flowchart illustrates the method 300 for optimizing sleep for users of the respiratory therapy system, relating to several implementation forms of this description.

[0072] In step 310, therapeutic instructions are received to be implemented using the respiratory therapy system 120 during the sleep session. These instructions can be entered into the respiratory therapy system 120 by the caregiver or user 210, or they can be pre-programmed. The therapeutic instructions may include several prescribed control parameters. For example, the instructions may include prescribed pressures and pressure ranges (e.g., target pressure, minimum and maximum pressures). The instructions may also include prescribed ramp rates or ranges of ramp rates to achieve the prescribed pressure(s).

[0073] In some implementations, control parameters include sound, exhalation pressure reduction (EPR) settings, humidification level, device movement, light activation or brightness change, fan activation or output change, or any combination thereof.

[0074] 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, alerts, beeps, or combinations thereof. Some variations of the flat-shaped white noise sound used herein are referred to as pink noise, brown noise, violet noise, etc. In some implementations, sounds (e.g., white noise, pink noise, brown noise, violet, etc.) help to mask noise from a respiratory therapy system or environment. In some implementations, sounds (e.g., soothing sounds and music) help the user or co-habitant maintain a sleep state, gently awaken, or change the user's sleeping position to, for example, a position that maintains a sleep session or sleep state.

[0075] In some implementations, sound can be provided by one or more speakers 142 of the system 100. Optionally, the system 100 includes multiple speakers 142 to emit localized sound. Speakers 142 may include earbuds, earpods, ear speakers, earbuds, or any combination thereof, located within earbuds, above earbuds, adjacent to earbuds, ear speakers, or any combination thereof. Speakers 142 may be wired or wireless speakers (e.g., headphones, bookshelf speakers, floor-standing speakers, television speakers, wall-mounted speakers, ceiling-mounted speakers, etc.). In some implementations, the speakers 142 are worn by the user 210 and / or the person sharing the bed 220. In some such implementations, since sound is localized through this type of speaker 142, the provided speakers 142 can supply masking noise without affecting the person sharing the bed. In this implementation, each localized speaker 142 can be provided to the respiratory user 210 and / or the person sharing the same bed 220.

[0076] Optionally, speaker 142 is attached to one or more of the straps or strap segments of the user interface 124. Thus, user 210 (Figure 2) and / or roommate 220 have the choice of perceiving either 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 / noise (e.g., "more jarring" sounds) are reduced while providing a masking sound to the ambient noise. System 100 can achieve a target noise profile by selecting an optimized set of fill-in sound frequencies. For example, if a certain sound component is already present in the frequency spectrum (e.g., a box fan or CPAP blower motor in the room), system 100 can select a fill-in sound with sound parameters / characteristics that fill a quieter frequency band, for example, up to a target amplitude level. In this way, system 100 can adaptively attenuate high-frequency and / or low-frequency components using active adaptive masking and / or adaptive noise cancellation, resulting in perceived sounds that are more pleasant to the ear and have a greater relaxing effect (the latter is particularly well-suited to sounds that change slowly and are highly predictable).

[0077] Generally, EPR is a feature included in some breathing devices (e.g., CPAP machines) that allows users to alleviate the feeling of shortness of breath experienced by some users by adjusting between various comfort settings. For example, suppose there is a 2 cmH2O droplet between inhalation and exhalation. If this function can be manual, according to some of the implementations described here, The system 100 can implement EPR settings without manual input, providing the desired level of sleep comfort.

[0078] In some implementations, the control parameter is the humidity of the air delivered to the user 210. For example, the humidity level can be selected to reduce discomfort caused by dryness of the sinuses or mouth. The humidity level can also be selected to optimize the sealing between the interface 124 and the user 210.

[0079] Devices that are operated or moved as control parameters may include, but are not limited to, a smart pillow, an adjustable bed frame, an adjustable mattress, a fan, an adjustable blanket, or any combination thereof. The devices are under the control of, for example, a controller 110. In some implementations, a smart pillow, smart mattress, or adjustable blanket may include one or more inflatable compartments or bladders that can be inflated or deflated. This allows the operated device to change the user's orientation. For example, pillow 260 may be a smart pillow that includes one or more inflatable bladders that change the user's orientation when the user's head is in an orientation that increases the likelihood of raising AHI. In another or additional implementation, an adjustable bed frame may include sections that can be raised or lowered when driven by a motor, causing the user 210 to change orientation, for example, by shifting the user 210 from lying on their side to lying on their back. In some implementations, a fan, for example, a fan placed on a nightstand 240, a fan in a window, or a ceiling fan, is turned on in response to the likelihood that the user 210's orientation will increase AHI. In some implementations, the fan generates white noise. The fan can gradually increase the speed and movement of the air so as not to awaken or disturb the user 210 and / or the person sharing the bed 220 with sudden changes in air movement or noise from the fan.

[0080] In some implementations, the control parameter is the injection of a substance into pressurized air delivered to the user interface 124. For example, a substance can be filled into a receptacle 180, which has an outlet that is in direct or indirect fluid communication with a conduit 126. The substance can be configured or selected to elicit a physical response by the user 210. The user 210 may, for example, change direction.

[0081] Optionally, a substance may include drugs such as anti-inflammatory drugs, asthma attack medications, and heart attack medications. It is common for any type of drug used to treat any disease, symptom, or condition to be delivered to the user's airway. If a substance is a drug, it generally contains one or more active ingredients and one or more excipients. Excipients are the medium for transporting the active ingredients and may include substances such as fillers, packers, diluents, anti-adhesion agents, binders, coatings, colorants, disintegrants, fragrances, lubricants, preservatives, adsorbents, sweeteners, spreaders, or any combination thereof. The active ingredient is generally the part of the drug that actually produces the effect it delivers.

[0082] The substance may optionally be an aromatic compound (e.g., a substance that delivers a scent and / or aroma to the user 210's airway), a sleep aid (e.g., a substance that helps the user 210 fall asleep), a consciousness-stimulating compound (e.g., a substance that helps the user 210 wake up, also known as a sleep inhibitor), cannabidiol oil, or essential oils (such as lavender, valerian, clary sage, sweet marjoram, Roman chamomile, or bergamot). The substance may generally be a solid, liquid, gas, or any combination thereof. The substance may optionally or additionally include one or more nanoparticles.

[0083] In some implementations, the control parameters prescribed for therapy are used in relation to sleep stages or This is a function of the sleep architecture. For example, according to the user's prescription, a pressure ramp may be initiated to achieve a first target pressure from ambient pressure during the transition from the awake state to the N1 stage. The ramp can be implemented to gradually acclimate the user 210 to the pressure change. During the subsequent N2 and N3 stages, the first target pressure may be maintained according to the user's prescription. During REM sleep, a higher pressure from the previous sleep state is often prescribed, as most users are more likely to experience increased apnea during REM sleep. Therefore, a ramp to a higher pressure can be implemented during REM sleep. The sleep state can be determined by monitoring physiological parameters using sensors (e.g., one or more sensors 130, blood pressure device 182, or activity tracker 190 in Figure 1), as previously mentioned.

[0084] In step 320, the desired sleep quality level is entered into the respiratory system 120. The desired sleep quality level may be selected based on how important sleep quality is to the user 210 during a sleep session. For example, a first-time user 210 may be prompted to select a high comfort level. An experienced user 210 may not prioritize sleep quality, or may not experience significant discomfort when using the respiratory therapy system 120 with the prescribed control settings. In some cases, the user 210 may desire a high sleep quality level, but because quality sleep is necessary, they may choose a lower desired sleep quality level, even if it means lowering the comfort level during a particular sleep session.

[0085] As an alternative to or addition to the user setting a desired sleep quality level, in some implementations, the system 100 can automatically set the user 210's sleep quality level. The automatically set sleep quality level may be based at least in part on data associated with the user 210, as well as data based on the user's experience with the respiratory therapy system 120 and / or sleep therapy, and / or the length of time and / or hours (hr). This automatically set sleep quality level may be set, for example, based on the number of days the user has been using sleep therapy, or the number of recorded hours using the respiratory therapy system 120.

[0086] In some implementations, one aspect of sleep quality relates to how user 210 or the user population evaluates their sleep experience. Users(s) can evaluate their sleep experience after a sleep session. The evaluation system may include various criteria, such as data on reported aerophagia symptoms, difficulty falling asleep, sinus dryness / thirst, muscle pain, and dry skin. Criteria can be subdivided and quantified, for example, by evaluating any pain resulting from aerophagia ranging from low (e.g., mild gas) to moderate, prolonged discomfort (e.g., abdominal distension) and high (e.g., cramps). Difficulty falling asleep can be evaluated as the number of times the user was able to recall checking the time or noticing noise or air pressure from the respiratory therapy system 120. The occurrence of dry skin, sinus dryness or thirst, and muscle pain can also be reported and used to evaluate sleep quality. The evaluation can be simplified by questionnaire, with or without the assistance of a caregiver. In some implementations, the questionnaire may also collect information such as whether user 210 experienced light sleep, insomnia, or wakefulness during pressure therapy, an assessment of the sleep quality of a bedmate (e.g., the impact on the bedmate), and other motivational feedback questions, such as increased activity the day after pressure therapy. The questionnaire can be presented and evaluated via an interactive app through the user interface 124, the touchscreen of the respiratory device 122, voice input, or an external device 170 (e.g., a smartphone).

[0087] Table 1 shows an example of a short questionnaire from a user (Joe) who evaluated the quality of his sleep. [Table 1]

[0088] The example questionnaire assesses sleep quality (or discomfort) resulting from sleep-onset aerophagia, sinus dryness / dry mouth, dry skin, muscle pain, and sleep quality. The questionnaire rates these factors on a 5-point scale from low to high. Sleep quality relates to the level of rest the user felt; for example, how clear and alert they were. Sleep quality is often inversely proportional to sleep quality if it is not characterized by sleep quality. Including sleep quality in sleep quality assessments can help determine the extent to which sleep quality can be improved without lowering it to a level where therapy is ineffective.

[0089] The total score of the sleep quality evaluation can be determined as a function of the sleep quality of each item listed in Table 1. Equation 1 shows one implementation of a method for providing a total sleep evaluation score using a sleep quality evaluation questionnaire. Formula 1: Total score for sleep quality assessment = (m1) (aerophagia) + (m2) (sinus dryness / dry mouth) + (m3) (dry skin) + (m4) (falling asleep) + (m5) (muscle pain) - (m6) (sleep quality) In the formula, m1 is the weight coefficient selected for aerophagia, m2 is the weight coefficient selected for sinus dryness / thirst, m3 is the weight coefficient for dry skin, m4 is the weight coefficient for falling asleep, m5 is the weight coefficient for muscle pain, and m6 is the weight coefficient for sleep quality. The total score value of the sleep quality assessment can also be normalized to a minimum of 0 or 1 and a maximum of 5, 10, 20, 50, or 100, for example. The weight coefficients relate to the importance of specific items to sleep quality. For example, the "aerophagia" item may ultimately be more important to sleep quality than the "falling asleep" item, and a larger weight coefficient may be selected or assigned to aerophagia than to falling asleep. The weight coefficients can 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 quality level in individual sleep sessions is also used to train the first algorithm. Here, "true" sleep quality refers to the overall sleep quality assessment provided by the user(s), to which a value can be assigned for training the first machine learning algorithm.

[0090] The first machine learning algorithm can also determine a comprehensive function that includes items other than those listed in Table 1 to provide the most accurate total score for sleep quality evaluation. In some implementations, the first machine learning algorithm can learn how individual users 210 evaluate subjective criteria. For example, user 1 may rate the harmfulness of muscle pain to sleep quality higher than user 2. The first algorithm can learn this difference between user 1 and user 2 and, accordingly, modify the function depending on which user is using the respiratory therapy system 120 to evaluate sleep quality. The total score can be determined. For example, if Equation 1 is used, the weighting coefficient m5 associated with muscle pain for the first user is smaller than the same weighting coefficient for the second user.

[0091] In some implementations, one aspect of sleep quality is the monitoring of the user 210 of the respiratory therapy system 120 or users of the respiratory therapy system 120 using sensors such as sensor 130 during a sleep session. In some implementations, the sleep architecture in a sleep session is determined as described above, and the achieved level of sleep quality is determined as a function of the sleep architecture. For example, it may be detected that user 210 is moving during a non-REM sleep session. The user may be unconsciously attempting to remove the user interface 124 during N1 or other sleep phases, or may have successfully removed it. In some implementations, the user may be sleeping in a position (e.g., supine vs. lateral) during a non-REM sleep phase that leads to poor sleep quality. These activities may be factors affecting sleep quality and can be monitored using sensors such as motion sensor 138, camera 150, or microphone 140, blood pressure device 182, activity tracker 190, or any combination thereof. Other sleep quality indicators that can be monitored may include noise from the respiratory therapy system 120, such as a pump, or leaks in the user interface 124 (e.g., mask leaks). The ambient temperature measured by the temperature sensor 136 may also indicate the quality of sleep. For example, temperatures higher or lower than the ideal temperature (e.g., 63 degrees Fahrenheit) may affect the quality of sleep. While the user 210 cannot instantly know how factors monitored by various sensors are affecting their sleep, the sensors can monitor these factors in real time and track and provide data for post-sleep session analysis.

[0092] Table 2 shows some examples of factors that can be monitored using sensors. [Table 2]

[0093] Table 2 lists the number of non-REM sleep cycles, the percentage of time spent lying on one's back versus the other, the average room temperature, the number of mask leaks, the number of noises exceeding whispering (e.g., approximately 40 dB), and the AHI (Auditory Hypothesis Index). The AHI is inversely proportional to sleep quality unless it is a direct measure of sleep quality, otherwise it may be proportional to sleep quality. Since lowering the AHI is an important objective in sleep therapy for various sleep disorders, including the AHI allows for a balanced assessment.

[0094] The total score of the sleep quality measurement can be determined as a function of the factors measurable by these sensors. Any useful function can be implemented. A simple embodiment of this function is shown in Equation 2. Formula 2: Total score of sleep quality measurement = (m7) (non-REM activity) + (m8) (time spent lying on back / side) %) + (m9) (average room temperature) + (m 10 )(Number of mask leaks) + (m 11 )(Number of noise occurrences)-(m 12 )(AHI) In the formula, m7 is the weighting coefficient selected for non-REM motion, m8 is the weighting coefficient selected for supine / lateral time %, m9 is the weighting coefficient for average room temperature, and m 10 m is a weighting coefficient for the number of mask leaks. 11 m is a weighting coefficient for the number of occurrences of noise exceeding 40 dB, 12is a weighting coefficient for AHI. The weighting coefficient and the overall shape of the function can be determined by using a second machine learning algorithm. The total score value of the sleep quality measurement can also be normalized to, for example, a minimum of 0 or 1 and a maximum of 5, 10, 20, 50 or 100. Data from a user or from multiple users across multiple sleep sessions can be input into the second machine learning algorithm. The true sleep quality can be used to train the second machine learning algorithm. In some implementation forms, the first machine learning algorithm provides a sleep quality rating that can be used to train the second machine learning algorithm to determine the measured sleep quality. For example, the sleep quality rating from the first algorithm is used as the true sleep quality for training the second algorithm.

[0095] In some implementations, data from sleep quality assessed by user 210 (e.g., Table 1) is combined with data from sleep quality factors measured by sensors (e.g., Table 2). For example, the total sleep quality score can be determined using user-reported sleep quality and measured sleep quality. For example, the function of the total sleep quality score may be a combination of Equation 1 and Equation 2. The total sleep quality score value can also be normalized to, for example, a minimum of 0 or 1 and a maximum of 5, 10, 20, 50 or 100. In some implementations, the first and second algorithms are combined as a single machine learning algorithm.

[0096] Returning to Figure 3 and step 320, the desired sleep quality level may be a value selected from a range of incremental values ​​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 quality experience. The value can be scaled as well as the sleep quality score used, i.e., the sleep quality evaluation score (e.g., Table 1, Equation 1), the sleep quality measurement score (e.g., Table 2, Equation 2), or the total sleep quality score (e.g., a combination of the sleep quality evaluation score and the sleep quality measurement score). 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 quality. The value may be a continuous scale, such as an analog volume control, or it may be digital. In other implementations, the sleep quality may have a digital value. The value may be an integer from 1 to 10, for example, where 1 indicates that the user does not desire improvement in sleep quality, and 10 indicates that the user desires the best possible sleep quality experience. The sleep quality level can be selected or entered via a dial according to the user's desired level of sleep quality.

[0097] In some implementations, the desired comfort level is selected when user 210 goes to bed for a sleep session, while in some other implementations, user 210 can change the comfort level during the sleep session. For example, user 210 wakes up after some combination of non-REM and REM sleep and decides that they are uncomfortable or unable to fall back asleep. User 210 can then decide to increase the comfort level. Alternatively, user 210 wakes up during a sleep session, realizes it's 4 a.m., and decides to lower the comfort level to improve their sleep quality, as they need about two more hours of quality sleep.

[0098] As shown in step 330, in some optional implementations, past control Parameters and past sleep quality levels can be entered into the respiratory therapy system 120. As used herein, "past" refers to one or more previous sleep sessions. For example, a past sleep quality level could be a user-selected value (e.g., on a scale of 1-10) of 8, which the user may have chosen for the sleep session immediately preceding the current sleep session. In this example, the past control parameter is a control parameter from a previous sleep session implemented using the respiratory therapy system 120 to target a desired sleep quality level of 8. After evaluating previous sleep sessions, the user may determine that the actual comfort level achieved (past sleep quality level) is lower or higher than what they entered. The received past parameters and sleep quality levels can be used in the current sleep session to more accurately achieve the desired sleep quality level in the current sleep session (where user input indicates the gap between them). The user can self-titrate the desired sleep quality level by, for example, selecting a higher or lower sleep quality level based on their personal experience.

[0099] In some implementations, the use of historical data relates to system training and can be implemented using artificial intelligence. For example, a third machine learning algorithm may include data used in the first machine learning algorithm (user-reported sleep quality), data used in the second machine training algorithm (user-measured sleep quality), and historically adjusted control parameters. In some implementations, the third machine learning algorithm may include elements from the first and second machine training algorithms, or be a combination thereof.

[0100] In step 340, 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 user 210, the adjusted control parameters are implemented to achieve the desired level of restful sleep for user 210.

[0101] In some implementations, the received therapy instructions are provided to help the user achieve target therapy parameters during a sleep session, and one or more adjusted values ​​or ranges of multiple control parameters provide therapy parameters that differ from the target therapy parameters. In some implementations, one or more adjusted values ​​or ranges of multiple control parameters provide therapy parameters that are higher than the target therapy parameters. In some other implementations, one or more adjusted values ​​or ranges of multiple control parameters provide therapy parameters that are lower than the target therapy parameters.

[0102] In some implementations, the received therapeutic instructions are provided to help the user achieve their target AHI during a sleep session. In some implementations, the achieved AHI is approximately the same as the therapeutic goal, while in some other implementations, the adjusted control parameters may lead to an AHI greater than (e.g., worse than) the target AHI.

[0103] Therefore, it is possible to improve the quality of sleep at the expense of lower sleep quality. In some implementations, control parameters are adjusted to maximize both sleep quality and the quality of sleep. Maximizing both sleep quality and the quality of sleep can be a characteristic of machine learning algorithms, such as the third machine learning algorithm.

[0104] In some implementations, pressure, pressure range, or pressure ramp is adjusted above or below the prescribed pressure, pressure range, or pressure ramp. In some implementations, 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 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.

[0105] In some implementations, the sound provided by one or more speakers 142 of the system 100 is adjusted from a prescribed sound. In some embodiments, the volume and duration of white noise, pink noise, brown noise, violet noise, soothing sounds, or music are increased or decreased from prescribed values. In some implementations, alarms or alerts indicating poor sleeping posture affecting sleep quality are turned off. By silencing alarms, the user 210 can improve their sleep quality while continuing to sleep even with poor posture.

[0106] In some implementations, the EPR setting is adjusted up or down from the prescribed value. For example, if the prescribed EPR setting is a 0.5 cmH2O droplet between inhalation and exhalation, the adjusted setting could be 1 cmH2O, 1.5 cmH2O, or 2.0 cmH2O.

[0107] In some implementations, the humidity of the air delivered to the user 210 is adjusted to be above or below a prescribed value. For example, in some implementations, humidity is increased to alleviate discomfort caused by dry skin or dry sinuses / mouth dryness. In some other implementations, humidity is decreased to alleviate discomfort caused by the user 210 experiencing discomfort due to the slipperiness or stickiness of the user interface 124 (e.g., face mask). Lower humidity may lead to a decrease in sleep quality, for example, due to increased face leakage, but the overall quality of sleep may improve.

[0108] In some implementations, the adjusted control parameters include a device that operates or moves. In a prescribed therapy, the device may cause the user 210 to change posture to reduce or avoid mask leakage. If the user 210 tends to adopt a posture that causes mask leakage, repeated operation of the device that attempts to force the user into a different posture may cause discomfort. For example, this could involve forcing the user into a posture that causes muscle pain or aerophagia.

[0109] In an implementation where the control parameter is the injection of a substance into pressurized air delivered to the user interface 124, the adjustment may be a decrease or an increase in the delivered substance. For example, a consciousness-stimulating compound can be prescribed to limit the sleep session. When adjusted, the delivery time of the substance during the sleep session can be delayed, thereby prolonging the sleep session and improving sleep quality.

[0110] In some implementations, prescribed control parameters maintain an ideal sleep architecture. Adjusted control parameters can alter this ideal sleep architecture. When control parameters are adjusted, for example, an increase in the occurrence of apnea may be accompanied by a decrease in the occurrence of REM sleep.

[0111] In some implementations, the selected desired sleep comfort level does not result in any measurable improvement in sleep quality, but can be used to enable user 210, such as first-time users, to adopt sleep therapy. User 210, at their discretion and with guidance from a caregiver, gradually lowers the comfort level to improve sleep quality during the "weaning" process. In some implementations, the weaning process may last for several days, weeks, or months. It may be part of a program lasting several months and a feature of an automatically implemented control system 110. In some implementations, the weaning program may be a feature of one or more of the machine learning algorithms described herein.

[0112] Step 350 shows an optional implementation where control parameters are adjusted during a sleep session based on the current and desired sleep levels. The current sleep level is the user's estimated sleep level and does not require direct or conscious input from the user. The current sleep level can be determined by monitoring the user 210 using sensors such as sensor 130, blood pressure device 182, or activity tracker 190 in Figure 1. If Table 2 shows the sleep levels measured throughout the entire sleep session, various factors that can be monitored using sensors can be sampled and aggregated during the sleep session. The process by which these factors change during the sleep session can be used to predict the sleep level achieved during the sleep session. If the predicted sleep level trajectory based on the current sleep level is derived from the desired sleep level, corrective measures can be implemented. Corrective measures can be implemented by changing control parameters. For example, if the temperature is high and is predicted to lower the sleep level, the thermostat can be reset and the fan can be turned on. If muscle pain is predicted based on user 210's sleeping position, a device such as a smart pillow, smart mattress, or adjustable blanket can be activated to prompt user 210 to change their position. The prediction can be implemented using a prediction algorithm. The prediction algorithm may be a fourth machine learning algorithm, which may include the first, second, and third algorithms described above.

[0113] Step 360 is an optional step that includes determining the level of sleep quality achieved by user 210 during the sleep session. The achieved level of sleep quality can be determined, as described above, for example, using the user's rated sleep quality and the measured sleep quality. The sleep quality can also optionally be determined using a fifth machine learning algorithm, which may be any combination of the first, second, third, and fourth machine learning algorithms described above. In some embodiments, the sleep quality is reported to the user, for example, through an external device 170.

[0114] In some implementations, any of several prescribed control parameters can be adjusted to improve sleep quality. For example, the multiple 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, prescribed pressure range, prescribed pressure ramp range, and prescribed step pressure change range are adjusted to improve sleep quality. In some implementations, the prescribed pressure is adjusted to an adjusted pressure below the prescribed pressure or prescribed pressure range. In some implementations, the prescribed pressure range is adjusted to a pressure range lower than the prescribed pressure range. For example, the average (or mean) of the prescribed pressure range can be adjusted to be lower, or one or more of the maximum or minimum pressures can be adjusted to be lower.

[0115] Figure 4A is a plot illustrating implementations according to several aspects of the present disclosure. The plot on the left shows the adjusted target maximum pressure 402, the adjusted pressure ramp 403, and the respiratory flow 401. The plot on the right shows the prescribed therapeutic maximum pressure 404, the prescribed pressure ramp 405, and the respiratory flow 401. The user may be prescribed a maximum pressure 404 to be implemented in the respiratory therapy system 120. The target maximum pressure 404 may be, for example, 15 mmH2O. This pressure is prescribed to provide a target AHI of 10 or less per sleep session. The prescribed target AHI and pressure can be determined, for example, during a titration experiment supervised by a caregiver. User 210 is prescribed A user may notice that the set pressure 404 and / or ramp 405 reduces their quality of sleep. For example, a user 210 may experience symptoms of aerophagia after a sleep session in which the maximum pressure 404 is implemented. Alternatively or additionally, a user 210 may notice that ramp 405 increases the pressure too rapidly, making it difficult to fall asleep. The adjusted maximum pressure 402 and adjusted pressure ramp 403 are based on the user 210 selecting their desired level of sleep. For example, a user who felt abdominal distension when the prescribed pressure ramp 405 was implemented may select a sleep level that reduces abdominal distension and improves their quality of sleep. Similarly, the gradual increase of the pressure ramp 403 compared to the prescribed pressure ramp 405 allows the user 210 to transition to sleep more gently. The adjusted target maximum pressure 402 and adjusted pressure ramp 403 may decrease the quality of sleep while increasing the quality of sleep compared to the prescribed target maximum pressure 404 and prescribed pressure ramp 403. The AHI achieved may be higher when using the prescribed maximum pressure 404 compared to the AHI achieved using the adjusted maximum pressure 402, for example.

[0116] As another example, user 210 may notice that at the start of the target pressure ramp 405, the pressure is too low to deliver a sufficient volume of air, resulting in an unpleasant feeling of air hunger known as "air hunger." This can reduce the quality of sleep and make it difficult to fall asleep. The target pressure ramp 405 can then be adjusted by the user to a desired level of sleep quality, resulting in a regulated, moderate pressure ramp 403. This pressure ramp allows user 210 to receive therapy while being provided with sufficient air to fall asleep.

[0117] The plot shown in Figure 4A also illustrates another aspect relating to several implementations. The delta 406 between the prescribed target maximum pressure 404 and the adjusted target maximum pressure 402 is shown. A large delta 406 indicates that the user 120 selected a higher desired level of sleep comfort if higher pressure results in greater sleep discomfort. Conversely, a small delta 406 indicates that a lower level of sleep comfort was selected. Although shown as applicable to pressure, other control factors can be manipulated similarly, and the delta between the prescribed value and the adjusted value is in response to the desired level of sleep comfort. In some implementations, increasing the control parameter increases sleep comfort. For example, if the prescribed control parameter is the concentration of the drug added via the receptacle 180, increasing the drug may increase sleep comfort, but also increase apnea. In this case, increasing the drug is an adjustment of the control parameter from the prescribed level to a higher level.

[0118] Figure 4B shows an implementation of another aspect of this description. The plot on the left shows the adjusted pressure and ramp profile. The plot on the left shows the breathing flow 408 and time intervals 416, 418a, and 420. Apnea after time interval 416 is shown in time interval 418a. In response to the apnea, the breathing system 120 implements a ramp 409 from the initial pressure 410 to the second highest pressure 412. After a delay 411, the apnea stops and normal breathing continues in time interval 420. The plot on the right shows the prescribed pressure and ramp profile. After time interval 416 at pressure 410, apnea occurs in time interval 418b. In response, a ramp 414 is implemented to a higher target pressure 416. Ramp 414 is steeper (positive) than ramp 409, and pressure 416 is also higher than pressure 412. Because the aggressive control parameters used in the prescribed therapy, the apnea interval 418b is shorter than the apnea interval 418a. In this implementation, apnea stopped earlier after the implementation of pressure ramp 414 than after the implementation of pressure ramp 409, so interval 418b is shorter than 418a. Specifically, the delay 411 shown in the left plot is not seen in the right plot. The aggressive control parameters implemented using the prescribed pressure ramp 414 and pressure 416 can more effectively eliminate apnea, but may cause discomfort. Discomfort is associated with the less aggressive pressure ramp 409. And it decreases due to pressure 412.

[0119] One or more elements, aspects, or steps or any part thereof from any one or more of any of the following claims 1 to 36 may be combined with one or more elements, aspects, or steps or any part thereof from any one or more of the other claims 1 to 36 or any combination thereof to form one or more additional implementations and / or claims of the present disclosure.

[0120] While this disclosure has been described with reference to one or more specific embodiments or implementations, those skilled in the art will recognize that numerous modifications can be made without departing from the spirit and scope of this disclosure. Each of these implementations and its definite variations is intended to fall within the spirit and scope of this disclosure. Additional implementations according to aspects of this disclosure are also intended to combine any number of features from any of the implementations described herein.

Claims

1. A computer implementation method for generating personalized sleep quality settings for configuring a respiratory therapy system for a user, The control system receives therapeutic instructions to be implemented using the respiratory therapy system during a sleep session, wherein the therapeutic instructions include a plurality of prescribed control parameters that define how the respiratory therapy system operates during the sleep session, and each of the plurality of prescribed control parameters has a value or a range of values. The control system receives user profile data associated with the user, wherein the user profile data includes at least demographic and medical information associated with the user. The control system receives baseline therapy context data associated with the user, wherein the baseline therapy context data includes at least one diagnostic index indicating the severity of the user's sleep-related breathing disorder, the diagnostic index including an apnea-hypopnea index (AHI) value or category; information identifying the type of user interface of the respiratory therapy system configured to deliver pressurized air to the user; and at least one pressure-related control parameter from the therapy instructions, the pressure-related control parameter including a minimum therapy pressure and an initial ramp pressure or start pressure. The control system trains a machine learning algorithm using training data that includes, for each of multiple sleep sessions of multiple users using each respiratory therapy system, at least corresponding user profile data, corresponding baseline therapy context data, at least two values ​​or ranges of values ​​from the prescribed control parameters implemented in the sleep session, and sleep outcome data, wherein the sleep outcome data represents at least one of sleep-related parameters, therapy adherence indicators, and a sleep quality score determined from user-reported sleep quality data and / or sensor-derived sleep-related parameters, and the machine learning algorithm learns a mapping to predict sleep quality-related outcomes for a given user based on the user profile data, the baseline therapy context data, and the prescribed control parameters. The following are provided as input to the trained machine learning algorithm for the user's current sleep session: the therapy instructions for the current sleep session, the user's user profile data, and the user's baseline therapy context data. The control system, by executing the trained machine learning algorithm, determines adjusted values ​​or ranges of adjusted values ​​for at least two of the plurality of prescribed control parameters in order to generate a personalized sleep quality setting configuration for the current sleep session, wherein the adjusted values ​​or ranges of adjusted values ​​for each of the at least two prescribed control parameters differ from the corresponding values ​​or ranges of values ​​in the therapy instructions, and the machine learning algorithm predicts that the adjusted values ​​or ranges of adjusted values ​​will improve the user's sleep quality-related results during the current sleep session compared to operating according to the therapy instructions without the adjusted values. A user device configured to present the personalized sleep quality setting configuration for setting up the respiratory therapy system, and at least one of the respiratory therapy systems for setting up the respiratory therapy system, wherein the control system outputs the personalized sleep quality setting configuration. Includes, A method by which the personalized sleep quality setting configuration can be used to help the user achieve an improved sleep quality during the current sleep session.

2. The method according to claim 1, wherein the demographic information includes at least one of the user's age and the user's gender.

3. The method according to claim 1, wherein the baseline therapy context data further includes information indicating whether the user interface is a full-face mask, a nasal mask, or a nasal pillow mask.

4. The method according to claim 1, wherein the at least one diagnostic indicator includes an apnea-hypopnea index (AHI) value or an AHI severity category selected from normal, mild, moderate, and severe.

5. The method according to claim 1, wherein the plurality of prescribed control parameters include at least two sleep quality-related parameters selected from prescribed pressure ramp rate, prescribed initial ramp pressure, expiratory pressure reduction (EPR) setting, and humidification level.

6. The method according to claim 1, wherein determining the personalized sleep quality setting configuration includes determining an adjusted pressure ramp rate that is less than the prescribed pressure ramp rate and an adjusted initial ramp pressure that is greater than the prescribed initial ramp pressure.

7. The method according to claim 1, wherein determining the personalized sleep quality setting configuration includes determining an adjusted EPR setting in which the pressure drop between inspiration and expiration is greater than that of a prescribed EPR setting.

8. The method according to claim 1, wherein the sleep outcome data includes at least one sleep-related parameter selected from the apnea-hypopnea index (AHI), total sleep duration, sleep efficiency, and fragmentation index.

9. The method according to claim 1, wherein the sleep outcome data includes a therapy compliance index that includes at least one of the usage time of the respiratory therapy system during a sleep session and the number of sleep sessions in which the usage time exceeds a predetermined threshold time.

10. The method according to any one of claims 1 to 9, wherein the machine learning algorithm is trained using training data including at least two past adjusted values ​​or ranges of past adjusted values ​​of the prescribed control parameters from a plurality of past sleep sessions, and associated past sleep quality-related results.

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