System and method for predicting mask leakage

Abstract

This invention provides a system and method for predicting unintentional mask leaks in the respiratory system. [Solution] The method includes delivering pressurized air from a breathing device to a user via a conduit connected to a user interface during a sleep session. The method also includes receiving first past data associated with pressurized air delivered from the breathing device during one or more previous sleep sessions, receiving first current data associated with pressurized air being delivered from the breathing device during that sleep session, receiving second past data associated with the user during one or more previous sleep sessions, and receiving second current data associated with the user during the current sleep session via one or more second sensors. The method determines the likelihood of an unintentional leak in the breathing system occurring within a given time period.

Description

Technical Field

[0001] (Cross - Reference to Related Applications) This application claims the benefit and priority of U.S. Provisional Patent Application No. 63 / 032,325, filed on May 29, 2020. The entire disclosure of this document is incorporated herein by reference for all purposes.

[0002] The present disclosure generally relates to respiratory systems, and more particularly, to systems and methods for predicting unintentional mask leakage in a respiratory system.

Background Art

[0003] Many individuals suffer from sleep - related disorders and / or respiratory disorders such as, for example, periodic limb movement disorder (PLMD), restless legs syndrome (RLS), sleep - disordered breathing (SDB), obstructive sleep apnea (OSA), apnea, Cheyne - Stokes respiration (CSR), respiratory insufficiency, obesity hypoventilation syndrome (OHS), chronic obstructive pulmonary disease (COPD), neuromuscular disease (NMD), and chest wall disorders. These disorders are often treated using respiratory therapy systems. People with respiratory disorders may have sleep disorders, but systems designed to alleviate the physical symptoms of respiratory disorders do not address problems other than the symptoms of the disorders themselves that can interfere with the person's good sleep. Therefore, there is a need for alternative systems and methods to address the sleep disorders or unintentional features of current respiratory therapy systems that can degrade the quality of intentional sleep therapy. The present disclosure aims to solve these problems and address other needs.

Summary of the Invention

[0004] According to some implementations of this disclosure, a method for predicting unintentional leakage in a respiratory system during a current sleep session includes delivering pressurized air from a respiratory device to a user via a conduit connected to a user interface during the current sleep session. This user interface is worn near a portion of the user's face and assists the user in receiving at least a portion of the pressurized air. Past first data is received, associated with pressurized air delivered from the respiratory device during one or more previous sleep sessions. Current first data is received via one or more first sensors, associated with pressurized air being delivered from the respiratory device during the current sleep session. Past second data is received, associated with one or more orientations of the user during one or more previous sleep sessions. Current second data is received via one or more second sensors, associated with one or more orientations of the user during the current sleep session. Based at least in part on (i) past first data, (ii) current first data, (iii) past second data, and (iv) current second data, the likelihood of unintentional leakage in the respiratory system occurring within a given time period is determined.

[0005] According to some implementations of this disclosure, a method for predicting unintentional leaks during a current sleep session in a respiratory system includes determining an unintentional leak prediction value for a user of the respiratory system. The determined unintentional leak prediction value indicates the likelihood that the user will experience an unintentional leak within a given time period in the current sleep session. The unintentional leak prediction value is determined using an unintentional leak prediction algorithm configured to receive posture data as input and output an individual unintentional leak prediction value.

[0006] The above summary is not intended to describe any implementation or aspect of this disclosure. Further features and benefits of this disclosure are evident from the detailed description and figures below. [Brief explanation of the drawing]

[0007] [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 part 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 determining the likelihood of unintentional leaks occurring in several implementation forms of this disclosure. [Figure 4] These are graphs of pressure and flow data relating to several implementations of this disclosure.

[0008] While various modifications and alternative forms are possible for this disclosure, specific implementations and embodiments of this disclosure are shown as examples in the drawings and are described in detail herein. However, it should be understood that this is not intended to limit this disclosure to any particular form, and that this disclosure encompasses all modifications, equivalents, and alternatives that fall within the spirit and scope of this disclosure as defined by the appended claims. [Modes for carrying out the invention]

[0009] 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 failure, obesity hyperventilation syndrome (OHS), chronic obstructive pulmonary disease (COPD), neuromuscular disorders (NMD), and chest wall disorders.

[0010] To mitigate some of these sleep-related disorders and / or respiratory disorders, users may be prescribed the use of a breathing device or system. For example, a continuous positive airway pressure (CPAP) device can be used to increase air pressure in the throat of the breathing device user (e.g., the user) to prevent airway closure and / or narrowing during sleep. While these breathing devices or systems can improve the quality of sleep for users, several issues other than the symptoms of the disorder itself may counteract the desired improvement in sleep quality. For example, noise originating from the system, such as from unintentional leaks. Such noise can not only reduce the quality of sleep for the user but also interfere with the quality of sleep for their sleep partner. Several implementations of this disclosure describe methods for predicting unintentional leaks in the breathing system used by a subject during a current sleep session. Predicting unintentional leaks allows for the implementation of mitigation measures to stop them.

[0011] Referring to Figure 1, System 100 according to several implementations of the present disclosure is shown. System 100 includes a control system 110, a memory device 114, an electronic interface 119, one or more sensors 130, and one or more user devices 170. In some implementations, System 100 optionally further includes a breathing system 120.

[0012] 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 Figure 1 shows one processor 112, 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 located separately 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 (in one such housing) or (physically separate) They can be distributed (in two or more such housings). In such an implementation configuration, which includes two or more housings housing the control system 110, the housings can be located close to and / or far apart from each other.

[0013] 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 media, 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 breathing device 122, the housing of the user device 170, one or more housings 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 physically separate such housings).

[0014] 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, family history of insomnia, 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.

[0015] The electronic interface 119 is configured to receive data (e.g., physiological data) from one or more sensors 130, which can be stored in a memory device 114 and / or analyzed by a 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., using RF communication protocols, WiFi communication protocols, Bluetooth® communication protocols, cellular networks, 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 one or more processors and / or one or more memory devices 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).

[0016] As described above, in some implementation forms, system 100 optionally performs a breathing system The system includes a stem 120 (also referred to as a respiratory therapy system). The respiratory system 120 may include a respiratory pressure therapy device 122 (referred to herein as a respiratory 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 a combination thereof. In some implementations, one or more of the following are part of the respiratory device 122: a control system 110, a memory device 114, a display device 128, a sensor 130, and a humidifier tank 129. 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 to the atmosphere throughout the user's entire respiratory cycle (unlike negative pressure therapy, such as tank ventilators or positive / negative pressure external ventilators (cuirass)). The respiratory 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).

[0017] The breathing device 122 is generally used to generate pressurized air delivered to a user (for example, using one or more motors that drive one or more compressors). In some implementations, the breathing device 122 generates a continuous, constant air pressure delivered to the user. In other implementations, the breathing device 122 generates two or more predetermined pressures (for example, a first predetermined air pressure and a second predetermined air pressure). In yet another implementation, the breathing device 122 is configured to generate a variety of different air pressures within a predetermined range. For example, the breathing device 122 can deliver at least about 6 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 breathing device 122 can also deliver pressurized air at a predetermined flow rate between, for example, about -20 L / min and about 150 L / min while maintaining positive pressure (relative to ambient pressure).

[0018] The user interface 124 engages with a portion of the user's face and delivers pressurized air from the breathing device 122 to the user's airway to help prevent airway narrowing and / or obstruction during sleep. This may also increase the user's oxygen intake during sleep. Depending on the therapy applied, the user interface 124 may form a tight seal with, for example, a region or portion of the user's face, thereby facilitating gas delivery at a pressure sufficiently different from the ambient pressure to produce a therapeutic effect, such as a positive pressure of approximately 10 cmH2O relative to the ambient pressure. In other forms of therapy, such as oxygen delivery, the user interface may not include a seal sufficient to facilitate the delivery of gas to the airway at a positive pressure of approximately 10 cmH2O.

[0019] As shown in Figure 2, in some implementations, the user interface 124 is a face mask (e.g., a full-face mask) that covers the user's nose and mouth. The user interface 124 may include a number of straps (e.g., including hook-and-loop fasteners) for positioning and / or stabilizing the interface on a portion of the user (e.g., the face), and a shape-conforming cushion (e.g., silicone, plastic, foam, etc.) intended to provide an airtight seal between the user interface 124 and the user. The user interface 124 may also include one or more vents to allow carbon dioxide and other gases exhaled by the user 210 to escape.

[0020] The conduit 126 (also referred to as an air circuit or tube) allows air to flow between two components of the breathing system 120, such as the breathing device 122 and the user interface 124. In some implementations, this conduit may have separate branches for inhalation and exhalation. In other implementations, a single branch conduit is used for both inhalation and exhalation.

[0021] Respiratory device 122, user interface 124, conduit 126, display device One or more of the chair 128 and the humidification tank 129 may include one or more sensors (e.g., a pressure sensor, a flow sensor, or 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 the flow rate of the pressurized air supplied by the breathing device 122.

[0022] The display device 128 is generally used to display an image (single or plural) including a still image, a video image, or both, and / or information regarding the breathing device 122. For example, the display device 128 can provide information regarding the state of the breathing device 122 (e.g., whether the breathing device 122 is on or off, the pressure of the air delivered by the breathing device 122, the temperature of the air delivered by the breathing device 122, etc.) and / or other information (e.g., a sleep score, the current date / time, personal information of the 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 an image (single or plural) as an input interface. The display device 128 can be an LED display, an organic EL display, a liquid crystal display, or the like. The input interface can be, for example, a touch screen or a touch sensing substrate, a mouse, a keyboard, or any sensor system configured to sense an input made by a human user interacting with the breathing device 122.

[0023] The humidifying tank 129 is connected to or integrated with the breathing device 122. The humidifying tank 129 includes a water reservoir that can be used to humidify the pressurized air delivered from the breathing device 122. The breathing device 122 may include a heater that heats the water in the humidifying tank 129 to humidify the pressurized air provided to the user. 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) that heats the pressurized air delivered to the user. The humidifying tank 129 can be fluid-connected to the water vapor inlet of the air passage to deliver water vapor into the air passage via the water vapor inlet, or it can be formed in series with the air passage as part of the air passage itself.

[0024] In some implementations, the system 100 can be used to deliver at least a portion of a substance from the receptacle 180 to the user's airway, at least in part, based on physiological data, sleep-related parameters, other data or information, or a combination thereof. Generally, modifying the delivery of a portion of a substance to the airway may include (i) initiating the delivery of a substance to the airway, (ii) terminating the delivery of a portion of a substance to the airway, (iii) changing the amount of a substance delivered to the airway, (iv) changing the temporal characteristics of the delivery of a portion of a substance to the airway, (v) changing the quantitative characteristics of the delivery of a portion of a substance to the airway, (vi) changing any parameter associated with the delivery of a substance to the airway, or (vii) a combination of (i) to (vi).

[0025] Changing the temporal characteristics of the delivery of a portion of a substance into the air passageway can include changing the rate at which the substance is delivered, starting and / or ending at different times, continuing over different periods, changing the temporal distribution or characteristics of the delivery, changing the quantity distribution independently of the temporal distribution, etc. By changing the time and quantity independently, it is possible to reliably change the amount of substance released each time, apart from changing the release frequency of the substance. In this way, several different combinations of release frequency and release amount (e.g., high frequency but low release amount, high frequency and large amount, low frequency and large amount, low frequency and small amount, etc.) can be realized. Other modifications to the delivery of a portion of a substance into the air passageway can also be utilized.

[0026] The respiratory system 120 can be used, for example, as a positive airway pressure (PAP) system, a continuous positive airway pressure (CPAP) system, an auto-titrating positive airway pressure system (APAP), a biphasic or variable positive airway pressure system (BPAP or VPAP), a ventilator, or a combination thereof. A CPAP system delivers a predetermined air pressure (e.g., as determined by a sleep physician) to the user. An APAP system automatically varies the air pressure delivered to the user, for example, based 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).

[0027] 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 (PPG) sensor 154, an electrocardiogram (ECG) sensor 156, an electroencephalogram (EEG) sensor 158, a capacitance sensor 160, a force sensor 162, a strain gauge sensor 164, an electromyogram (EMG) sensor 166, an oxygen sensor 168, an analyte sensor 174, a moisture sensor 176, a light detection and ranging (LiDAR) sensor 178, or a 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.

[0028] One or more sensors 130 are illustrated and described as including each of the following: pressure sensor 132, flow sensor 134, temperature sensor 136, motion sensor 138, microphone 140, speaker 142, RF receiver 146, RF transmitter 148, camera 150, infrared sensor 152, photoelectric (PPG) sensor 154, electrocardiogram (ECG) sensor 156, electroencephalogram (EEG) sensor 158, capacitance sensor 160, force sensor 162, strain gauge sensor 164, electromyogram (EMG) sensor 166, oxygen sensor 168, analyte sensor 174, moisture sensor 176, and light detection and ranging (LiDAR) sensor 178. However, one or more sensors 130 may more commonly include each combination and any number of the sensors described and / or illustrated herein.

[0029] 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 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 associated with the user during the sleep session. One or more sleep-related parameters that can be determined about user 210 during a sleep session include, for example, the apnea-hypopnea index (AHI) score, sleep score, flow 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 device 122, heart rate, heart rate variability, user 210 movement, temperature, EEG activity, EMG activity, wakefulness, snoring, choking, cough, wheezing, whistling, or a combination thereof.

[0030] In some implementations, 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 during a sleep session. The sleep / wake signals may indicate one or more sleep states, including wakefulness, relaxed wakefulness, micro-wakefulness, rapid eye movement (REM) stage, first non-REM stage (often referred to as "N1"), second non-REM stage (often referred to as "N2"), third non-REM stage (often referred to as "N3"), or a combination thereof.

[0031] The sleep / wake signal can also be accompanied by a timestamp to determine the user's bedtime, wake-up time, and sleep attempt time. The sleep / wake signal can 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, or one sample per minute. In some implementations, the sleep / wake signal may also indicate respiratory signals during the sleep session, respiratory rate, inspiratory amplitude, expiratory amplitude, inspiratory-to-expiratory ratio, number of events per hour, event patterns, pressure settings of the respiratory device 122, or a combination thereof. These events (one or more) may include snoring, apnea, central apnea, obstructive apnea, mixed apnea, hypopnea, mask leakage (e.g., from the user interface 124), lower limb immobility, sleep disturbance, suffocation, increased heart rate, dyspnea, asthma attack, epileptic interstitial, seizure, or a combination thereof. One or more sleep-related parameters that can be used to determine a user during a sleep session based on sleep / wake signals include, for example, total time in bed, total sleep duration, sleep latency, post-sleep wakefulness parameters, sleep efficiency, fragmentation index, or a combination thereof.

[0032] Generally, a sleep session includes any point in time after the user 210 lies down or sits in the bed 230 (or another area or object intended for sleeping), and / or after the breathing device 122 is powered on and / or the user interface 124 is put on. Therefore, a sleep session may include (i) the period when user 210 is using the CPAP system but before attempting to fall asleep (for example, when user 210 is lying in bed 230 reading a book), (ii) the period when user 210 is attempting to fall asleep but is still awake, (iii) the period when user 210 is in light sleep (also known as stages 1 and 2 of non-REM sleep), (iv) the period when user 210 is in deep sleep (also known 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.

[0033] A sleep session is typically defined as ending when user 210 removes user interface 124, turns off the breathing device 122, and / or gets out of bed 230. In some implementations, a sleep session may include additional time periods or be limited to only a portion of the periods disclosed above. For example, a sleep session may be defined to begin when the breathing device 122 starts supplying pressurized air to the airway or user 210, end when the breathing device 122 stops supplying pressurized air to user 210's airway, and include a period in between that includes some or all of the time when user 210 is asleep or awake.

[0034] 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., atmospheric pressure sensor) that generates sensor data indicating the user's breathing (e.g., inhalation and / or exhalation) and / or ambient pressure of the breathing system 120. In such implementations, the pressure sensor 132 can be connected to or integrated with the breathing 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 potentiometer sensor, or a combination thereof.

[0035] The flow sensor 134 outputs flow data that can be stored in the memory device 114 and / or analyzed by the processor 112 of the control system 110. In some implementations, the flow sensor 134 outputs the airflow from the breathing device 122, the airflow through the conduit 126, the airflow through the user interface 124, and These are used to determine a combination thereof. In such an implementation, the flow sensor 134 can be connected to or integrated with the breathing device 122, the user interface 124, or the conduit 126. The flow sensor 134 may be a mass flow sensor such as a rotary flow meter (e.g., a Hall effect flow meter), a turbine flow meter, an orifice flow meter, an ultrasonic flow meter, a hot-wire sensor, an eddy current sensor, a membrane sensor, or a combination thereof.

[0036] 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 device 122 and / or through the conduit 126, the temperature within the user interface 124, the ambient temperature, or a 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 a combination thereof.

[0037] 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 system 120, such as the respiratory 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 a signal representing the user's sleep state can be obtained, for example, through the user's breathing 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.

[0038] Microphone 140 outputs sound data that can be stored in memory device 114 and / or analyzed by processor 112 of control system 110. Microphone 140 can be used to record sounds (one or more) (e.g., sounds from user 210) during a sleep session to determine one or more sleep-related parameters (e.g., using control system 110). Microphone 140 can be connected to or integrated with breathing device 122, user interface 124, conduit 126, or user device 170. In some implementations, system 100 includes multiple microphones (e.g., two or more microphones with beamforming capabilities and / or arrays of microphones) such that sound data generated by each of the multiple microphones can be used to distinguish sound data generated by another of the multiple microphones.

[0039] Speaker 142 outputs sound waves that are audible to the user of system 100 (for example, 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 (for example, in response to an event such as an unintentional mask leak). Speaker 142 can be connected to or integrated with the breathing device 122, user interface 124, conduit 126, or external device 170.

[0040] The microphone 140 and speaker 142 can be used as separate devices. In some implementations, the microphone 140 and speaker 142 can be used as separate devices. For example, it can be incorporated into the acoustic sensor 141 as described in WO2018 / 050913, which is incorporated entirely herein by reference. In such an implementation, 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 that are inaudible to the human ear (e.g., less than 20 Hz or greater than about 18 kHz) so as not to disturb the sleep of the user 210 or the person sharing a bed 220 (Figure 2). Based at least in part on the data from the microphone 140 and / or the speaker 142, the control system 110 can determine the location or orientation of the user 210 (Figure 2), and / or one or more of the sleep-related parameters described herein.

[0041] 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 (either the RF receiver 146 and the RF transmitter 148, or another RF pair) can also be used for wireless communication between the control system 110, the breathing device 122, one or more sensors 130, the user device 170, or a combination thereof. Although the RF receiver 146 and the RF transmitter 148 are shown as separate and distinct elements in Figure 1, in some implementations the RF receiver 146 and the RF transmitter 148 are combined as part of an RF sensor 147. In some such implementations, the RF sensor 147 includes a control circuit. The specific form of RF communication could be Wi-Fi, Bluetooth (registered trademark), etc.

[0042] 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 with each other at all times using Wi-Fi signals. The Wi-Fi mesh system can be used to generate motion data based on changes in the Wi-Fi signal (e.g., differences in received signal strength) that occur between the routers and satellites (one or more) due to the movement of objects or people partially interfering with the signal. The motion data may represent movement, respiration, heart rate, behavior, or a combination thereof.

[0043] Camera 150 outputs image data that can be reproduced as one or more images (e.g., still images, video images, thermal images, or a 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., changes in the posture of subject 210), respiratory signals, respiratory rate, inspiratory amplitude, expiratory amplitude, inspiratory-to-expiratory ratio, number of events per hour, event patterns, sleep state, sleep stage, or a combination thereof. Furthermore, the control system 110 can use the image data from camera 150 to determine, for example, the user's posture and orientation, the user's chest movement, the airflow through the user's mouth and / or nose, the time when the user 210 entered bed 230, and the time when the user 210 left bed 230.

[0044] 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 combination with the camera 150 to measure the presence, location, and / or movement of the user 210. The IR sensor 152 can detect infrared light with wavelengths between approximately 700 nm and 1 mm, for example, while the camera 150 can detect visible light with wavelengths between approximately 380 nm and 740 nm.

[0045] 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).

[0046] The ECG sensor 156 outputs physiological data associated with the electrical activity of the user 210's heart (Figure 2A). 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.

[0047] 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 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 its associated headgear (e.g., a strap).

[0048] 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 a 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, or a combination thereof.

[0049] 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 covering the user 210's nose and mouth, the analyte sensor 174 may be positioned within the face mask to monitor the user 210's mouth breathing. In some implementations, the analyte sensor 174 may detect carbonaceous chemicals or compounds This is a volatile organic compound (VOC) sensor that can be used to detect substances. 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 data output by the analyte sensor 174 positioned inside the user 210's face mask detects the presence of an analyte, the processor 112 can use this data as an indication that the user 210 is breathing through their mouth.

[0050] 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 breathing device 122, etc.). Therefore, in some implementations, the moisture sensor 176 can be positioned in the user interface 124 or conduit 126 to monitor the humidity of the pressurized air from the breathing 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 around the user 210, such as the air in the user 210's bedroom.

[0051] One or more LiDAR (Light Detection and Ranging) 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 generally uses pulsed lasers to measure time of flight. LiDAR is also called 3D laser scanning. In one use case of such a sensor, a stationary 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. One or more LiDAR sensors 178 can also use artificial intelligence (AI) to automatically create a geofence for a RADAR system by detecting and classifying features in space that may pose problems for the RADAR system, such as glass windows (which may be highly reflective to RADAR). LiDAR can also be used to estimate a person's height, as well as changes in height that occur when a person sits down, falls down, etc. LiDAR can be used to form a 3D mesh representation of the environment. In further applications, LiDAR can reflect off solid surfaces (e.g., radio-transparent materials) through which radio waves pass, enabling the classification of different types of obstacles.

[0052] In some implementations, one or more sensors 130 include electrocutaneous 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 combinations thereof.

[0053] Although shown separately in Figure 1, a 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 device 122, user interface 124, conduit 126, humidifier tank 129, control system 110, user device 170, or a combination thereof. For example, the acoustic sensor 141 and / or RF sensor 147 can be integrated and / or connected to the user device 170. In such an implementation, the user device 170 can be considered a secondary device that generates additional or secondary data used by system 100 (e.g., control system 110) according to some aspects of the present disclosure. In this configuration, at least one of the one or more sensors 130 is not connected to the respiratory device 122, the control system 110, or the user device 170, and is positioned generally adjacent to the user 210 during the 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.).

[0054] 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, restless legs, sleep disturbance, suffocation, increased heart rate, dyspnea, asthma attack, epileptic interstitial, seizure, elevated blood pressure, or any combination thereof. Many of these sleep-related parameters are physiological parameters, but some of these sleep-related parameters can be considered non-physiological parameters. Other types of physiological and non-physiological parameters can be determined from either data from one or more sensors 130 or other types of data.

[0055] The user device 170 (Figure 1) includes a display device 128. The user device 170 may be a mobile device such as a smartphone, tablet, game console, smartwatch, or laptop computer. Alternatively, the user device 170 may be an external sensing system, a television (e.g., a smart TV), or another smart home device (e.g., a smart speaker such as Google Home, Amazon Echo, or Alexa). In some implementations, this user device is a wearable device (e.g., a smartwatch). The display device 172 is generally used to display images (one or more) including still images, video images, or both. In some implementations, the 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. The display device 172 may be an LED display, an OLED display, a liquid crystal display, etc. The input interface may be, for example, a touchscreen or contact sensing board, a mouse, a keyboard, or any sensor system configured to sense input made by a human user interacting with the user device 170. In some implementations, one or more user devices may be used by and / or included in the system 100.

[0056] 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 breathing 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 capable of edge cloud processing), or may reside on one or more servers (e.g., remote servers, local servers, etc., or a combination thereof).

[0057] Although System 100 is shown to include all of the above components, various implementations of this disclosure may generate physiological data and determine a recommended bedtime for the user. The number of components that can be included in the system for this purpose may be more or less than that. 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. 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 breathing system 120, at least one of one or more sensors 130, and a user device 170. Thus, any(s) of the components shown and described herein can be used and / or combined with one or more other components to form a variety of systems for determining a user's recommended bedtime.

[0058] Generally, referring to Figure 2, parts of system 100 (Figure 1) relating to several implementation configurations are shown. The user 210 and bedmate 220 of the respiratory system 120 are in bed 230 and lying on mattress 232. A user interface 124 (e.g., a full-face mask) may be worn by the user 210 during a sleep session. The user interface 124 is fluidly connected to and / or connected to a respiratory device 122 via a conduit 126. The respiratory 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, helping to prevent airway obstruction and / or narrowing during sleep. The respiratory device 122 can be positioned on a nightstand 240 directly adjacent to the bed 230, or more generally, on any surface or structure substantially adjacent to the bed 230 and / or the user 210, as shown in Figure 2.

[0059] In some implementations, the control system 110, the memory 214, one or more sensors 130, or any 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 in a first posture 255A on and / or inside one or more components of the breathing system 120 adjacent to the bed 230 and / or user 210. One or more sensors 130 may be connected to the breathing system 120, the user interface 124, the conduit 126, the display device 128, the humidification tank 129, or a combination thereof.

[0060] Alternatively or additionally, at least one of the one or more sensors 130 may be located in a second position 255B above and / or inside the bed 230 (for example, one or more sensors 130 are 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 in a third position 255C above and / or inside the mattress 232 adjacent to the bed 230 and / or the user 210 (for example, one or more sensors 130 are 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 in a fourth position 255D above and / or inside the pillow, which is generally adjacent to the bed 230 and / or the user 210.

[0061] Alternatively or additionally, at least one of the one or more sensors 130 may be located in a fifth posture 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 in a sixth posture 255F such that at least one of the one or more sensors 130 is coupled to and / or positioned on user 210 (for example, one or more sensors 130 are embedded in or coupled to fabric, clothing 212, and / or a smart device worn by user 215). It is more common for at least one of the sensors 130 to be oriented at any suitable location relative to the user 210 so that one or more sensors 130 can generate sensor data associated with the user 210.

[0062] Generally, users prescribed the use of the respiratory system 120 tend to experience improved sleep quality and reduced daytime fatigue after using the respiratory system 120 during sleep compared to not using the respiratory 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) device used to increase the 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 waking up, due to reduced oxygen intake caused by narrowing or collapse of the airway during sleep. CPAP devices minimize the events that cause the person to wake up or be otherwise disturbed due to reduced oxygen intake by preventing airway narrowing or collapse.

[0063] The breathing device 122 attempts to maintain a medically prescribed air pressure during sleep, but in some cases, the user interface 124 may move or change position while the user 210 is sleeping. Movement of the interface 124 may cause pressurized air from the breathing system 120 to leak at the interface between the user interface 124 and the user 210's face / head. For example, the user 210 may sleep on their back while wearing the user interface 124, but during nighttime sleep, they may unconsciously change position so that their cheeks are flush with the pillow 260. In such a position, the user interface 124 may move from a relatively snug position where no unintentional air leakage occurs to a new position that allows and / or induces unintentional air leakage from the breathing system 120. Unintentional air leakage from the user interface 124 may produce an audible noise that disturbs the user 210 and / or the person sharing the bed 220, thus interfering with and / or adversely affecting the sleep session of both parties. Furthermore, unintentional air leaks can dry out the user's skin, leading to dry mouth, dry eyes, or any combination thereof.

[0064] Other sources of unintentional air leakage at the interface between the user interface 124 and the user 210's face / head are also possible. For example, over time, the user interface 124 or a part of it may wear down, causing the seal at the interface to be less complete than when the user interface 124 was new. Alternatively, the strap or strap portion that holds the user interface 124 in place may loosen over time, resulting in an insufficient seal that can lead to unintentional leakage.

[0065] As used throughout this disclosure, the term "unintentional leak" refers to an unintended flow of air from the breathing system 120 to the atmosphere. In some implementations, the user interface 124 includes vents designed to allow exhaled gases and air to flow from the breathing system 120, and these vents are referred to as intentional leaks or vent flows, as they are designed and intended to create such flows. That is, gases escaping through vents are not considered unintentional leaks, as they include intended air outflows from the breathing system 120. In contrast to intended air outflows from the breathing system 120, unintentional leaks may occur as a result of an incomplete seal between the user interface 124 (e.g., a mask) and the user 210's face / head, as described above. In some implementations, an incomplete seal between the user interface 124 and the user 210's face / head may result in exhaled gases being released, such as when the user interface or a part of it (e.g., a cushion) needs to have sufficient slack to ensure user comfort, or when the user has facial features (e.g., a beard). Some leakage may occur from the suction system 120, but this is not considered unintentional. In other cases, unintentional leakage or air to the atmosphere may occur somewhere in the air circuit of the breathing system 120 to the surroundings. For example, in some implementations, some air leakage from the breathing system 120 may occur somewhere in the circuit of the breathing system 120. For example, leakage at the junction between the conduit 126 and the user interface 124 may be due to design requirements and / or tolerances, such as the ease of movement between the conduit 126 and the user interface 124. In some implementations, a threshold is set for unintentional leakage, and no action is taken until the unintentional leakage exceeds that set threshold.

[0066] In some implementations, unintentional air leakage from the breathing system 120 is considered acceptable and not serious enough to require action (e.g., a new user interface or new cushion, a change in therapeutic pressure(s) or any combination thereof). In such implementations, the acceptable amount of unintentional leakage or air leakage from the breathing system 120 may be when the volume of the unintentional leakage is less than or equal to a certain percentage of the total volume of air flowing through the breathing system 120. For example, unintentional leakage of about 20%, or about 10%, or about 5%, or about 3%, or about 2%, or about 1% may be considered acceptable. Acceptable unintentional leakage in terms of airflow may be when the airflow is, for example, less than about 5 liters / min, less than about 4 liters / min, less than about 3 liters / min, less than about 2 liters / min, or less than about 1 liter / min. The duration of the unintentional leakage can also be used to determine whether the unintentional leakage is acceptable. For example, this could occur when user 210 transitions from one sleeping position to another, or when user 210 is in a position that causes unintentional leakage. For example, in some implementations, if the duration of unintentional leakage is less than approximately 1 second, less than approximately 5 seconds, less than approximately 10 seconds, less than approximately 30 seconds, or less than approximately 1 minute, the unintentional leakage can be considered acceptable. In some implementations, the combined flow rate and time can be considered when determining whether an unintentional leakage is acceptable. For example, if the total volume of air released due to the unintentional leakage during the duration of the leakage is less than approximately 5 liters, less than approximately 4 liters, less than approximately 3 liters, less than approximately 2 liters, or less than approximately 1 liter, the unintentional leakage can be considered acceptable.

[0067] In some implementations, this unintentional leakage is combined with intentional leakage and referred to as the total leakage of the respiratory system 120. In some such implementations, the total leakage may be considered acceptable if it is below a threshold. For example, the total leakage may be less than approximately 30 liters / min, or less than approximately 29 liters / min, or less than approximately 28 liters / min, or less than approximately 27 liters / min, or less than approximately 26 liters / min, or less than approximately 25 liters / min, or less than approximately 24 liters / min, or less than approximately 23 liters / min, or less than approximately 22 liters / min, or less than approximately 21 liters / min, or less than approximately 20 liters / min. The tolerance of the total leakage may also be based in part on the amount of time during which the total leakage is below the threshold. For example, in some implementations, the total leakage may be considered acceptable if it is below 24 liters / min for at least 70 percent of the measured amount of time (e.g., a sleep session).

[0068] Referring to Figure 3, the flowchart illustrates a method for predicting unintentional leakage in a respiratory system during a current sleep session, relating to several implementations of this specification.

[0069] In step 310, pressurized air is delivered from the breathing device (e.g., breathing device 122) to the user (e.g., user 210) during the current sleep session. The sleep session is as described above, for example, when user 210 lies or sits in bed 230 / turns on breathing device 122 / wears user interface 124. The period is demarcated by the time between when user 210 removes user interface 124, turns off the breathing device 122, and / or gets out of bed 230. The current sleep session is the sleep session being monitored to predict unintentional leaks.

[0070] This method includes four additional steps after the delivery of pressurized air from the breathing device is initiated in step 310. In step 320, a first historical data associated with the delivered pressurized air is received. In step 330, a first current data associated with the delivered pressurized air is received. In step 340, a second historical data associated with the user's orientation is received. In step 350, a second current data associated with the user's orientation is received. Steps 320, 330, 340, and 350 can be performed in any order. Steps 320 and 340 can also be performed before step 310.

[0071] Previous data, either the first or second past data, refers to data collected or acquired during a previous sleep session, not during the current sleep session. In some implementations, the previous sleep session ends immediately before the start of the current sleep session, for example, during the course of an entire night or period of time during which user 210 was planning to sleep. For example, in some implementations, the time between the end of the previous sleep session and the start of the current sleep session is less than approximately 30 seconds, less than approximately 1 minute, less than approximately 30 minutes, less than approximately 1 hour, less than approximately 4 hours, less than approximately 12 hours, or less than approximately 18 hours. In some implementations, a longer period occurs between the end of the previous sleep session and the start of the current sleep session, such as the period spanning from the end of the first night to the start of the second night. For example, in some implementations, the time between the end of the previous sleep session and the start of the current sleep session is more than approximately 18 hours, more than approximately 24 hours, more than approximately 2 days, more than approximately 3 days, more than approximately 4 days, more than 5 days, more than approximately 6 days, more than approximately 1 week, or more than approximately 1 month. In some implementations, the previous historical data is the average of one or more sleep sessions that occurred before the current sleep session. In some implementations, this average is between 1 and 1000 sleep sessions, 1 and 500 sleep sessions, 1 and 100 sleep sessions, 1 and 50 sleep sessions, 1 and 10 sleep sessions, 1 and 3 sleep sessions, or 2 sleep sessions.

[0072] Optionally, and according to several implementations, receiving past or present first data associated with pressurized air delivered from a breathing device includes receiving the first data via one or more first sensors, which include one or more pressure sensors, one or more flow sensors, one or more humidity sensors, one or more microphones, or any combination thereof. For example, one or more sensors in the system 100 shown in Figures 1 and 2. In some implementations, the one or more sensors include one or more pressure sensors, one or more flow sensors, or at least one pressure sensor and at least one flow sensor.

[0073] In some implementations, receiving past or present second data associated with one or more orientations of the user involves receiving the second data via one or more second sensors, including one or more cameras, one or more video cameras, one or more pressure sensors, one or more microphones, one or more speakers, one or more accelerometers, one or more gyroscopes, one or more radio frequency sensors, one or more acoustic sensors, or any combination thereof. Optionally, the radio frequency sensors may be one or more ultra-wideband sensors, one or more impulse radar ultra-wideband sensors, or one or more frequency-modulated continuous wave radar sensors. For example, one or more sensors relating to the system 100 shown in Figures 1 and 2 can be used.

[0074] In step 360, the likelihood of an unintentional leak occurring in the breathing system is determined. The likelihood of an unintentional leak can be limited to unintentional leaks occurring within a given time period. This likelihood can be associated with unintentional leaks occurring within, for example, 10 seconds, 30 seconds, 1 minute, 5 minutes, 10 minutes, or any other time period (one or more). In some implementations, this likelihood is determined using past first pressure data (step 320), past second data associated with user orientation (step 340), current first pressure data associated with delivered pressurized air (step 330), current second data associated with user orientation, or any combination thereof.

[0075] In step 370, an unintentional leak mitigation measure is taken when the likelihood of an unintentional leak occurring is met. This likelihood can be expressed as a certainty, for example, using an integer selected between 1 and 10 (e.g., 1 to 5, 1 to 3, or any range), where 1 is the least likely and 10 is the most likely. In some other implementations, this likelihood is expressed as a rating such as very unlikely, unlikely, moderately likely, likely, and very likely. In other implementations, this likelihood can be expressed as a percentage likelihood. According to some implementations, this likelihood is the percentage likelihood of an unintentional leak occurring, with thresholds of at least about 50%, at least about 60%, at least about 70%, at least about 80%, at least about 90%, or at least about 99%.

[0076] In some implementations, these mitigation measures include emitting sound, changing therapeutic pressure, changing expiratory pressure reduction (EPR) settings, changing humidification levels, modifying or moving the device, turning on or increasing the brightness of a light, turning on or increasing the output of a fan, or any combination thereof. In some implementations, the type of mitigation measure taken depends on the percentage likelihood of an unintentional leak occurring. For example, if a first mitigation measure is taken at a 60% threshold, and the first mitigation measure is ineffective or leads to an increase in likelihood, causing the likelihood to rise to 90%, a second mitigation measure may be taken. If the likelihood of an unintentional leak still increases, escalation with a third or subsequent mitigation measure may occur. In some implementations, mitigation measures can be taken even after an unintentional leak has occurred. In some implementations, even if the likelihood remains unchanged or decreases after the first mitigation measure, a second mitigation measure may be taken, for example, if the likelihood is still above the threshold.

[0077] Sounds that can be used as mitigation measures may 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 used herein are referred to as pink noise, brown noise, violet noise, etc. In some implementations, these sounds (e.g., white noise, pink noise, brown noise, violet, etc.) help mask noise from unintentional leaks that are expected to occur. In some implementations, these sounds (e.g., soothing sounds and music) can help maintain a sleep state for the user or a co-sleep partner, gently awaken them, or encourage the user to change their sleeping position to one where unintentional leaks do not occur or are less likely to occur.

[0078] In some implementations, this sound can be provided by one or more speakers 142 of system 100. Optionally, system 100 includes multiple speakers 142 to emit localized sound. Speakers 142 may include in-ear speakers, behind-the-ear speakers, adjacent-ear speakers, earbuds, earpods, or any combination thereof. Speakers 142 may be wired or wireless speakers (e.g., headphones, bookshelf speakers). These may be (type speakers, floor-standing speakers, television speakers, in-wall speakers, ceiling speakers, etc.). In some implementations, the speaker 142 is worn by the user 210 and / or the person sharing the bed 220. In some such implementations, the sound is localized through the type of speaker 142 so that the provided speaker 142 can supply masking noise without affecting the person sharing the bed. In such implementations, each localized speaker 142 can be provided to the breathing user and / or the person sharing the bed.

[0079] Speaker 142 is optionally attached to one or more of the straps or strap sections of the user interface 124. Thus, the user 210 and / or the person sharing the bed 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 lower-frequency components using active adaptive masking and / or adaptive noise cancellation, so that the perceived sound is more pleasant to the ear and has a greater relaxing effect (the latter is well suited to sounds that change slowly and are predictable).

[0080] In some implementations, the sound is emitted gradually. For example, the control system 100 can cause speaker 142 to emit a sound at a first volume, and then gradually increase the volume from the first volume to a second volume, which is louder, over a period of time. For example, speaker 142 can emit a sound at a relatively low volume initially, and then gradually increase the volume so as not to abruptly introduce a new sound that would awaken or disturb user 210 and / or roommate 220. The period over which the volume is increased can be 1 second, 5 seconds, 10 seconds, 20 seconds, 30 seconds, or any other amount of time. In some implementations, the sound can start at a low volume after the likelihood has met a threshold, and the sound can gradually increase until the likelihood decreases, for example, if user 210 changes direction and the likelihood does not decrease. In some implementations, once the likelihood no longer meets a threshold (for example, falls below it), the sound can either stop instantly or gradually weaken until it stops completely. The duration of the volume reduction can be approximately 1 second, 5 seconds, 10 seconds, 20 seconds, 30 seconds, or any other amount of time.

[0081] Modifications to pressure therapy that can be used as mitigation measures may include, but are not limited to, increasing or decreasing the pressure of the air delivered to the user 210. Pressure increases and decreases may be based on the orientation of the user 210. For example, if the user 210 is oriented to move the user interface 124 in a way that increases the likelihood of an unintentional leak (e.g., with respect to the mattress 232 or pillow 260), the pressure can be decreased to reduce the likelihood of an unintentional leak occurring. In some implementations, the pressure can be increased to wake the user 210, or to cause slight discomfort to cause the user 210 to change orientation without waking them.

[0082] In response to unintentional leak predictions, the expiratory pressure reduction (EPR) setting can be changed. Generally, EPR is a feature included in some breathing devices (e.g., CPAP machines). By adjusting various comfort settings, users can reduce the feeling of shortness of breath experienced by some users. For example, suppose there is a 2 cmH2O droplet between inhalation and exhalation. If this function is manual, the system 100 can change the EPR setting without manual user input as described in this implementation, thereby reducing the occurrence of unintentional leaks.

[0083] In some implementations, the mitigating measure used is a change in the humidification level, which increases the humidity of the air delivered to user 210. For example, if dry skin of user 210 increases the percentage likelihood of unintentional leakage, which may reduce the sealing efficiency between interface 123 and user 210. User 210 may also experience discomfort due to dryness of the nose or mouth as the humidification level decreases, which may lead to a change of orientation that is predicted to increase the likelihood of unintentional leakage. In some implementations, the change in the humidification level is a decrease in humidification. For example, if user 210 experiences discomfort due to the accumulation of moisture or water (e.g., in the sealing portion of user interface 124), which may lead to a change of orientation that is predicted to increase the likelihood of unintentional leakage.

[0084] Devices that are activated or moved as mitigation measures include, but are not limited to, smart pillows, adjustable bed frames, adjustable mattresses, fans, adjustable blankets, or any combination thereof. These devices are under the control of, for example, 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 activated 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 orientation of user 210 if the user's head is positioned in a way that raises the likelihood of unintentional leakage above a threshold. In another or additional implementation, an adjustable bed frame may include parts that can be raised or lowered when driven by a motor, and that can change the orientation of user 210, for example, by shifting user 210 to a side-lying or supine position. In some implementations, a fan, such as one mounted on a nightstand 240, a fan in a window, or a ceiling fan, is turned on in response to the likelihood of unintentional leakage, blowing air towards and reorienting the user 210. Alternatively, the fan is turned off when the likelihood of unintentional leakage meets a threshold. In some implementations, the fan is turned on or off to provide a calming environment for the user 210, who is expected to reorient themselves into a position where unintentional leakage may occur due to discomfort associated with airflow or its absence. In some implementations, the fan generates white noise. The fan can gradually increase the speed and movement of the air so as not to arouse or disturb the user 210 and / or the person sharing the bed 220 with sudden changes in air movement or noise from the fan.

[0085] In some implementations, the leak mitigation measure involves injecting a substance into the pressurized air delivered to the user interface 124. For example, a substance can be filled into a receptacle 180 having 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 from the user 210, who may, for example, change direction.

[0086] Optionally, this substance may include drugs such as anti-inflammatory drugs, asthma treatment drugs, and heart attack drugs. Generally, it can deliver any type of drug used to treat any illness, symptom, or disease to the user 210's airway. For example, if a symptom causes shaking, repositioning, or movement of the user 210, which increases the likelihood of unintentional leakage, the likelihood of unintentional leakage can be reduced by injecting the drug. If the substance is a drug, it generally contains one or more active ingredients and one or more excipients. Excipients are media for transporting the active ingredients and include fillers, packing agents, diluents, anti-adhesion agents, binders, and coatings. It may contain substances such as colorants, disintegrants, fragrances, flow enhancers, lubricants, preservatives, adsorbents, sweeteners, binders, or any combination thereof. The active ingredient is generally the part of the drug that actually produces the effect it delivers.

[0087] This substance may optionally be an aromatic compound (e.g., a substance that delivers fragrance and / or aroma to the user 210's airway), a sleep aid (e.g., a substance that helps user 210 fall asleep), a consciousness-stimulating compound (e.g., a substance that helps user 210 stay awake, also known as a sleep inhibitor), cannabidiol oil, or essential oils (such as lavender, valerian, clary sage, sweet marjoram, Roman chamomile, or bergamot). This substance may generally be a solid, liquid, gas, or any combination thereof. This substance may, as an alternative or addition, contain one or more nanoparticles.

[0088] In some implementations, the current second data indicates that the user has moved from a first posture to a second posture. The first posture is, for example, supine, lateral, prone, or fetal position. The second posture is one of supine, lateral, prone, or a different fetal position from the first posture. Optionally, the first posture involves the user lying supine, and the second posture involves the user lying on their side. Optionally, the first posture involves the user lying supine, and the second posture involves the user lying prone. Optionally, the first posture involves the user lying on their side, and the second posture involves the user in a fetal position.

[0089] Alternatively or optionally, the likelihood of an unintentional leak occurring is re-evaluated after an unintentional leak mitigation measure is taken in step 370. For example, this may involve repeating one or more of steps 330, 340, 350, 360, and 360 outlined in Figure 3. If, after these repetitions, the likelihood of an unintentional leak does not meet the threshold after the re-evaluation in step 360, additional mitigation measures, such as in step 370, may be taken. These steps can be repeated more than 3, 10, 20, or 30 times to predict and mitigate unintentional leaks throughout a sleep session. In some implementations, the number of times mitigation measures are used is tracked and stored, for example, in a memory device 114. In some implementations, this information can be displayed, for example, to the user 210 or caregiver. This information can be displayed after one or more sleep sessions using an external device 170, such as a smartphone.

[0090] The data received in any one of steps 320, 330, 334, or 335 can be processed by the breathing system 100. For example, the control system may include an unintentional leak prediction algorithm stored as machine-readable instructions in memory, such as memory device 114. This unintentional leak prediction algorithm can be started, for example, when a sleep session begins. The start of a sleep session is detected, for example, by one or more sensors 130. In some implementations, the sleep session begins and the algorithm starts when pressurized air is delivered to the user (step 310). In some implementations, the algorithm can be started, but remains in sleep or monitoring mode until the sleep session begins, after which it is considered started. In some implementations, the sleep session is started manually by user 210, and the algorithm is started by turning on the breathing device 122, wearing the user interface 124, or reclining in bed, etc. When pressurized air is delivered to the user, the algorithm can receive data from one or more of steps 320, 330, 340, and 350 as input. This algorithm can, for example, output an unintentional leak prediction value based on the likelihood calculated by the algorithm in step 360 for the individual. The determined unintentional leak prediction value is the likelihood that the user will experience unintentional leaks within a given time period during the current sleep session. This indicates the degree. In some implementations, this predetermined amount of time is less than approximately 5 seconds, less than approximately 10 seconds, less than approximately 20 seconds, less than approximately 30 seconds, less than approximately 40 seconds, less than approximately 50 seconds, less than approximately 60 seconds, less than approximately 2 minutes, less than approximately 5 minutes, less than approximately 10 minutes, or less than approximately 1 hour. The steps prior to processing using the unintentional leak prediction algorithm may include receiving data via one or more sensors 130 as described above, and transferring or transmitting the data to a control system, including storing the data in a memory device 114. In some implementations, the system 100 can activate or operate the device to mitigate the occurrence of unintentional leaks.

[0091] In some implementations, the method for predicting unintentional leaks further includes identifying the type of user interface. For example, in some implementations, this method includes identifying user interfaces such as a full-face mask, a nose face mask, or a nose pillow. In some implementations, the step of determining the likelihood of unintentional leaks is further, at least partially, based on the identified type of user interface. In some other implementations, the user interface is identified from reference models and diagrams such as makeup.

[0092] Predicting unintentional leaks and identifying user interfaces can optionally be based on measuring pressure and flow within the breathing system 120. Pressure and flow data are used to generate a graph 400 with an intentional leak characteristic curve 410, as shown in Figure 4. Unintentional leaks can be predicted by deviations in pressure and flow from the characteristic curve 410. The shape of the characteristic curve 410 can also commonly be used to identify user interfaces.

[0093] Figure 4 shows the average total flow rate. JPEG2026094441000002.jpg913 (Unit: liters / minute) vs. average device pressure JPEG2026094441000003.jpg815 The relationship in cmH2O (unit: cmH2O) is depicted, and in the figure, the average value is the average of multiple breathing cycles. The shape of curve 410 depends on the mask, such as the shape and volume (e.g., enclosed by the user's face and the mask), as well as the size and configuration of the mask's vents. Therefore, the type of mask can be determined by the shape of curve 410. Average flow represented by curve 410 JPEG2026094441000004.jpg913 This corresponds to the outflow from device systems such as the respiratory system 120, and this flow is intentional flow, such as the outflow from the vents of the mask (e.g., user interface 124). Therefore, deviations from curve 410 relate to unintentional flow.

[0094] When user 210 inhales and exhales while wearing an interface such as user interface 124, device 122 such as CPAP attempts to maintain a constant pressure. However, small fluctuations occur around the target or set pressure. This is the mean pressure. JPEG2026094441000005.jpg815 This corresponds to a slight increase or decrease. If there is an intentional flow (i.e., no unintentional flow), JPEG2026094441000006.jpg815 When it vibrates up and down, the average flow value JPEG2026094441000007.jpg913 It follows curve 410 precisely. Unintentional leaks are due to the mean pressure and flow ( JPEG2026094441000008.jpg1025 ) corresponds to points such as 420, and deviations from the right side of curve 410 and to the right. Mean pressure JPEG2026094441000009.jpg815 At point 420, where the pressure is approximately 11 cmH2O, if the expected leakage occurs, the average flow will be... JPEG2026094441000010.jpg913 This would be approximately 30 LPM. However, in reality, the average flow JPEG2026094441000011.jpg913 Since it is measured at approximately 40 LPM, the characteristic curve indicates unintentional leakage with a delta d of about 40 LMP.

[0095] If an obstruction occurs within the system, such as when the mask vents are blocked by the user's posture or when the conduit 126 is blocked (e.g., bent or twisted), another unintentional flow, referred to herein as unintentional obstruction, may occur. In these cases, this flow will be lower than predicted by the intentional leakage graph 410. Unintentional obstructions appear as points or deviations to the left of the curve 410. In some implementations, unintentional obstruction data can be used to receive past second data associated with one or more orientations of the user during one or more previous sleep sessions. In some implementations, unintentional obstruction data can be used to receive current data associated with one or more orientations of the user during the current sleep session.

[0096] Increasing the pressure applied to the user interface can increase the rate of unintentional leaks. This increased pressure can weaken the seal between the mask (e.g., user interface 124) and the user's face, allowing air to escape. The user can strengthen and improve the seal between themselves and the mask by tightening the straps on the user interface, but even at sufficiently high pressure, some amount of unintentional leakage will still occur.

[0097] In some implementations, the intentional flow curve 410 shown in Figure 4 is derived as follows: A flow path is formed by a breathing device (e.g., breathing device 122), a mask with one or more vents (e.g., user interface 124), and a conduit (e.g., conduit 126). This conduit generates a first impedance Z1, which in turn results in a pressure drop ΔP, a function of the total flow rate Qt. The interface pressure Pm is the difference between the device pressure Pd and the pressure drop ΔP through the conduit, where ΔP(Q) is the pressure drop through the conduit. It is a characteristic.

[0098]

number

[0099] The vents in the mask create a second impedance Z2. The vent flow Qv is related to the interfacial pressure Pm via the vent characteristic f as follows:

[0100]

number

[0101] Combining equation (1) with equation (2), the device pressure Pd can be written as follows:

[0102]

number

[0103] This unintentional leak, which is unknown and unpredictably variable, creates a third impedance Z3. The fourth impedance Z4, capacitance Clung, and variable pressure source Plung represent the user's characteristics. Thus, the total flow rate Qt is equal to the sum of the airflow rate Qv, the leak rate Qleak, and the breathing rate Qr.

[0104]

number

[0105] In some implementations, the average respiratory flow rate Qr becomes zero after multiple respiratory cycles (e.g., respiratory cycles). This is because the average flow entering and leaving the lungs is naturally zero. Therefore, by taking the average of each flow rate over multiple respiratory cycles, the insufflation flow rate can be approximated by the following equation.

[0106]

number

[0107] The tilde (~) indicates the average value across multiple respiratory cycles. This averaging process can be performed by low-pass filtering with a time constant long enough to include multiple respiratory cycles. This time constant can be any appropriate duration, such as 5 seconds, 10 seconds, 30 seconds, or 1 minute. Other time intervals are also possible.

[0108] Combining equations (3) and (5), we get the average device pressure. JPEG2026094441000017.jpg815 It can be written as follows:

[0109]

number

[0110] If there is no leakage (e.g., Qleak=0), then the average total flow rate. JPEG2026094441000019.jpg913 This can be called the bias flow rate Qb. Equation (6) is given by the bias flow rate Qb and the average device pressure that characterizes the respiratory therapy system. JPEG2026094441000020.jpg815 To reflect the relationship between them, it can be written as follows:

[0111]

number

[0112] This relationship, which is the intentional leakage characteristic curve of the system, is determined by the ventilation characteristic f(Q) and the conduit pressure drop characteristic ΔP(Q).

[0113] A characteristic intentional flow graph, as shown in Figure 4, represents the average total flow rate for at least two pressures, i.e., two average flows. JPEG2026094441000022.jpg913 (Unit: liters / minute) vs. average device pressure JPEG2026094441000023.jpg815 It can be created by measuring (unit: cmH2O). JPEG2026094441000024.jpg815 and JPEG2026094441000025.jpg913 The data points can be fitted and described by equations. For example, in some implementations, the intentional leakage characteristic curve 410 can be approximated using polynomials, such as the following quadratic equation.

[0114]

number

[0115] The two non-zero constants (or coefficients) k1 and k2, which are parameters of the intentional leakage characteristic curve in this quadratic equation, characterize the series connection of the aeration characteristic f and the air circuit pressure drop characteristic ΔP. In some implementations, this polynomial defines the intentional leakage of a system (e.g., the aeration flow of the system) by providing the corresponding flow rate of intentional leakage for a given pressure.

[0116] In some implementations, this polynomial may have three or more non-zero constants, such as three, four, or five non-zero constants. This polynomial can be expressed, for example, as follows:

[0117]

number

[0118] In some implementations, this polynomial can be raised to powers such as 3, 4, and 5. This polynomial can be expressed, for example, as follows:

[0119]

number

[0120] In some implementations, this polynomial can be raised to powers such as 3, 4, and 5. This polynomial can be expressed, for example, as follows:

[0121]

number

[0122] As discussed herein, a respiratory therapy system typically includes components such as a respiratory device 122, a conduit 126, and a user interface 124. Various different models that can influence pressure and flow characteristics may be used. For example, a model may include various vents in a face mask and conduits of various lengths and diameters. To improve control of the therapy delivered to the user interface, it may be beneficial to estimate therapeutic parameters such as pressure, airflow rate, and unintentional flow rate within the user interface. In a system that uses therapeutic parameter estimation, the accuracy of the estimation of therapeutic parameters can be increased by knowing the type of components the user is using, thereby improving the effectiveness of the therapy.

[0123] To gain knowledge about the types of components, some respiratory devices include a menu system that allows the user to input and / or select the type of system component, including the user interface being used (e.g., brand, manufacturer, shape, model, serial number, mask series, size, etc.). Once the component type is input and / or selected by the user, the respiratory device can select the appropriate operating parameters of the flow generator that best cooperate with the selected component, allowing for more accurate monitoring of therapeutic parameters during therapy. However, in some cases, the user may not correctly select or select the component type, leaving errors in the respiratory device unaddressed or remaining unaware of the type of component being used.

[0124] Therefore, in some implementations, the unintentional leak prediction algorithm may include code or subroutines used to identify the user interface. For example, if the conduit pressure drop characteristic ΔP is known (for example, because the type of conduit constituting the conduit is known, or through previous calibration operations), the parameters of the intentional leak characteristic curve can effectively characterize the vent and, consequently, indicate the type of user interface.

[0125] In some implementations, while user 210 is using the same user interface 124, past first data and past second data are received. In some implementations, when user 210 is using the first user interface 124, past first data, past second data, current first data, and current second data are received, and when user 210 is using the second user interface 124, current first data and current second data are received. In some such implementations, an unintentional leak prediction algorithm can adjust for or compensate for changes caused by changes in the user interface.

[0126] In some implementations, when used in conjunction with known conduits, a user interface can be identified by comparing the computed parameters k1 and k2 with a data structure such as an array or database that has pairs (k1, k2) associated with known user interface types. The user interface type that is associated with the stored pair (k1, k2) and is closest to the computed parameters k1 and k2 may be considered the user interface type.

[0127] As an alternative, pressure drop JPEG2026094441000030.jpg925 However, average device pressure JPEG2026094441000031.jpg815 After subtracting from each value, the quadratic equation can be fitted to the resulting intentional mask leakage characteristic curve. The obtained parameters k1 and k2 can then be compared to a data structure of pairs (k1, k2) associated with a known user interface type to identify that user interface or to access data for the operation of the respiratory device associated with the use of a particular user interface.

[0128] Therefore, the detected parameters can be compared with the expected parameters over a period of time to collect longitudinal and cross-sectional data. In some implementations, the system can determine manufacturing variability by understanding the mask of a batch and use this to improve manufacturing quality.

[0129] In some implementations, the system can check for variations over time, determine whether the mask seal itself is degrading over time (because the system can determine the duration of use of a particular mask based on signatures such as acoustic signatures), and understand what conditions are causing unintentional leaks (for example, whether they are posture-dependent, have changed based on recommendations to tighten or loosen headgear, whether the seal has followed an expected degradation cycle (assuming periodic cleaning), or whether it indicates accelerated wear).

[0130] In some implementations, the user interface being used can be determined by user input, optical detection of the user interface, detection of the user interface via RFID, detection of the user interface via echo signature, detection of the conduit via a heated tube connector with electronic components, or any combination thereof. Thus, an initial curve representing a correctly functioning new mask of this type can be selected. The initial curve is specific to the breathing device, operating mode, operating parameters, other settings such as expiratory pressure reduction (EPR) or bilevel, the user interface (e.g., brand, manufacturer, form, model, serial number, mask series, size, etc.), or any combination thereof. It is possible.

[0131] In some implementations, the system may also select a model of the expected behavior of this type of mask as it has been partially or completely worn out over time, as an initial curve from a lookup table or cloud system. These expected models may account for varying levels of vent blockage, conduit blockage (such as for masks with airflow through soft tubes around the head), and varying levels of seal wear, headgear stretching, etc. By selecting an appropriate initial model, the system can detect intentional and unintentional leaks occurring throughout a sleep session and / or across multiple sleep sessions.

[0132] In some implementations, the system provides output to an external device 170, such as a smartphone, which provides user data regarding the predicted amount of unintentional leaks detected or the percentage likelihood of detected unintentional leaks. For example, during a sleep session, or during several sleep sessions.

[0133] One or more further implementations and / or claims of the present disclosure can be formed by combining one or more elements, aspects, steps, or parts thereof from one or more of any one or more of the following claims 1 to 35 with one or more elements, aspects, steps, or parts thereof from one or more of the other claims 1 to 35 or any combination thereof.

[0134] 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 are possible without departing from the intent and scope of this disclosure. Each of these implementations and its clearest modifications is intended to fall within the intent and scope of this disclosure. Furthermore, it is intended that further implementations in various aspects of this disclosure may combine any number of features from any of the implementations described herein.

[0135] The following are additional notes to this disclosure. (Additional note 1) A method for predicting unintentional leakage in the respiratory system during the current sleep session, The device delivers the pressurized air from the breathing device to the user during the current sleep session via a conduit connected to a user interface that is worn near a portion of the user's face and assists the user in receiving at least a portion of the pressurized air. Receiving past first data associated with pressurized air delivered from the breathing device during one or more previous sleep sessions, The current first data associated with the pressurized air delivered from the breathing device during the current sleep session is received via one or more first sensors, Receiving past second data associated with one or more orientations of the user during one or more previous sleep sessions, The system receives current second data associated with one or more orientations of the user during the current sleep session via one or more second sensors, Determining the likelihood of an unintentional leak occurring in the respiratory system within a predetermined time period, based at least in part on (i) past first data, (ii) current first data, (iii) past second data, and (iv) current second data; Methods that include... (Additional note 2) The method according to Appendix 1, further comprising causing a sound to be emitted in response to the likelihood meeting a threshold. (Additional note 3) The method according to Appendix 2, wherein the sound includes white noise, pink noise, brown noise, violet noise, soothing sounds, music, alarms, alerts, beeps, or any combination thereof. (Additional note 4) The method described in Appendix 2 or Appendix 3, wherein the sound emitted is gradually released. (Additional note 5) The method according to any one of appendices 1 to 4, further comprising changing one or more pressure settings of the therapy in response to the likelihood meeting a threshold. (Additional note 6) The method according to Appendix 5, further comprising changing the expiratory pressure reduction (EPR) setting in response to the likelihood meeting a threshold. (Additional note 7) The method according to any one of the appendices 1 to 6, further comprising changing the humidification level in response to the likelihood meeting a threshold. (Additional note 8) The method according to any one of the appendices 1 to 7, further comprising moving the device in response to the likelihood meeting a threshold. (Additional note 9) The method according to Appendix 8, wherein the device is a smart pillow, an adjustable bed frame, an adjustable mattress, a fan, an adjustable blanket, or any combination thereof. (Additional note 10) The method according to any one of appendices 1 to 9, further comprising injecting a substance into the pressurized air being delivered to the user interface, in response to the likelihood meeting a threshold. (Additional note 11) The substance is configured to induce a physical response by the user, Appendix 10 Methods used. (Additional note 12) The method according to any one of the appendices 1 to 11, wherein the one or more first sensors include one or more pressure sensors, one or more flow sensors, one or more humidity sensors, one or more microphones, or any combination thereof. (Additional note 13) The method according to any one of the appendices 1 to 12, wherein the one or more second sensors include one or more cameras, one or more video cameras, one or more pressure sensors, one or more microphones, one or more speakers, one or more accelerometers, one or more gyroscopes, one or more radio frequency sensors, one or more acoustic sensors, or any combination thereof. (Additional note 14) The method according to Appendix 13, wherein the one or more radio frequency sensors include one or more ultra-wideband sensors, one or more impulse radar ultra-wideband sensors, one or more frequency-modulated continuous wave radar sensors, or any combination thereof. (Additional note 15) The method according to any one of appendices 1 to 14, further comprising identifying the type of user interface. (Additional note 16) The method according to Appendix 15, wherein the identified type of user interface is a full-face interface, a nose interface, or a nose pillow interface. (Additional note 17) The method of Appendix 15 or Appendix 16, wherein determining the likelihood is at least in part further based on the identified type of user interface. (Additional note 18) The method according to any one of appendices 1 to 17, wherein the current second data indicates that the user has moved from a first posture to a second posture. (Additional note 19) The method according to Appendix 18, wherein the first posture involves the user lying on their back, and the second posture involves the user lying on their side. (Additional note 20) The method according to Appendix 18, wherein the first posture involves the user lying on their back, and the second posture involves the user lying face down. (Additional note 21) The method according to any one of the appendices 1 to 19, wherein the likelihood is a percent likelihood. (Additional note 22) The method according to any one of the appendices 1 to 21, wherein the predetermined amount of time is less than approximately 5 seconds, less than approximately 10 seconds, less than approximately 20 seconds, less than approximately 30 seconds, less than approximately 40 seconds, less than approximately 50 seconds, less than approximately 60 seconds, less than approximately 2 minutes, less than approximately 5 minutes, less than approximately 10 minutes, or less than approximately 1 hour. (Additional note 23) A control system including one or more processors, It comprises a memory that stores machine-readable instructions, A system in which the method described in any one of appendices 1 to 22 is performed when the control system is connected to the memory and the machine-executable instructions in the memory are executed by at least one of the one or more processors of the control system. (Additional note 24) A system for predicting unintentional leakage in a respiratory system during a current sleep session, comprising a control system having one or more processors configured to perform the method described in any one of the appendices 1 to 22. (Additional note 25) A computer program product that, when executed by a computer, comprises instructions causing the computer to perform the method described in any one of the appendices 1 to 22. (Additional note 26) The computer program product described in Appendix 25, wherein the computer program product is a non-temporary computer-readable medium. (Additional note 27) A method for predicting unintentional leakage in the respiratory system during the current sleep session, A method comprising determining an unintentional leak prediction value for a user of the breathing system, wherein the determined unintentional leak prediction value indicates the likelihood that the user will experience unintentional leaks within a predetermined time period in the current sleep session, and the unintentional leak prediction value is determined using an unintentional leak prediction algorithm configured to receive posture data as input and output the unintentional leak prediction value for the individual. (Additional note 28) The method according to Appendix 27, wherein the algorithm is further configured to receive physiological data associated with the user of the respiratory system as input. (Additional note 29) The method according to Appendix 27 or 28, wherein the predetermined time amount is less than approximately 5 seconds, less than approximately 10 seconds, less than approximately 20 seconds, less than approximately 30 seconds, less than approximately 40 seconds, less than approximately 50 seconds, less than approximately 60 seconds, less than approximately 2 minutes, less than approximately 5 minutes, less than approximately 10 minutes, or less than approximately 1 hour. (Additional note 30) The method according to any one of the appendices 27 to 29, further comprising taking certain action in response to the aforementioned unintentional leakage prediction value meeting a threshold. (Additional note 31) The method according to Appendix 30, wherein the measures include emitting sound, changing the therapeutic pressure, changing the humidification level, modifying the device, turning on a light, turning on a fan, or any combination thereof. (Additional note 32) A control system including one or more processors, It comprises a memory that stores machine-readable instructions, A system in which the method described in any one of appendices 27 to 31 is performed when the control system is connected to the memory and the machine-executable instructions in the memory are executed by at least one of the one or more processors of the control system. (Additional note 33) A system for predicting unintentional leakage in a respiratory system during a current sleep session, comprising a control system having one or more processors configured to perform the method described in any one of the appendices 27 to 31. (Additional note 34) A computer program product that, when executed by a computer, comprises instructions causing the computer to perform the method described in any one of the appendices 27 to 31. (Additional note 35) The computer program product described in Appendix 34, wherein the computer program product is a non-temporary computer-readable medium.

Claims

1. A method for predicting unintentional leakage in the respiratory system during the current sleep session, The system delivers pressurized air from a continuous positive airway pressure (CPAP) breathing device to the user during the current sleep session via a conduit connected to a user interface that is worn near a portion of the user's face and assists the user in receiving at least a portion of the pressurized air. Receiving past first data, including the pressure and flow rate of pressurized air delivered from the CPAP breathing device during one or more previous sleep sessions, Receiving current first data, including the pressure and flow rate of the pressurized air delivered from the CPAP breathing device during the current sleep session, via one or more first sensors, Receiving past second data associated with one or more orientations of the user during one or more previous sleep sessions, Receiving current second data associated with one or more orientations of the user during the current sleep session via one or more second sensors, wherein the past second data and the current second data are either an image of the user's orientation, acoustic data from the user's orientation, a radio frequency signal, or a pressure reading from the user's orientation. The processor determines, at least in part, the likelihood, expressed as a numerical integer or numerical percentage, that an unintentional leak in the respiratory system occurs within a predetermined time period, based on comparing the past first data with the current first data and comparing the past second data with the current second data, wherein the past first data is plotted as a mean pressure and mean flow rate curve, and the likelihood is determined based on deviations from the curve of the mean pressure and mean flow rate plot determined from the current first data, and whether a change in direction has occurred based on the current second data, with each of a plurality of directions having a likelihood of leak determined from the past second data. Methods that include...

2. The method according to claim 1, further comprising the configuration such that a sound is emitted when the likelihood, expressed as a numerical percentage value, exceeds a predetermined percentage threshold.

3. The method according to claim 2, wherein the sound includes white noise, pink noise, brown noise, violet noise, soothing sounds, music, alarms, alerts, beeps, or any combination thereof.

4. The method according to claim 2 or claim 3, wherein the sound emitted is emitted gradually.

5. The method according to any one of claims 1 to 4, further comprising changing one or more pressure settings of the therapy in response that the likelihood, expressed as a numerical percentage value, exceeds a predetermined percentage threshold.

6. The method according to claim 5, further comprising changing the expiratory pressure reduction (EPR) setting in response to the likelihood, expressed as a numerical percentage value, exceeding a predetermined percentage threshold.

7. The method according to any one of claims 1 to 6, further comprising changing the humidification level in response that the likelihood, expressed as a numerical percentage value, exceeds a predetermined percentage threshold.

8. The method according to any one of claims 1 to 7, further comprising inflating or deflating a smart pillow, activating an adjustable bed frame, inflating or deflating an adjustable mattress, changing the speed of a fan, inflating or deflating an adjustable blanket, or changing the orientation of the user by any combination thereof, in response to the likelihood, expressed as a numerical percentage value, exceeding a predetermined percentage threshold.

9. The method according to any one of claims 1 to 8, further comprising injecting a substance into the pressurized air delivered to the user interface in response that the likelihood, expressed as a numerical percentage value, exceeds a predetermined percentage threshold.

10. The method according to claim 9, wherein the substance is configured to induce a physical response by the user.

11. The method according to any one of claims 1 to 10, wherein the one or more first sensors include one or more pressure sensors, one or more flow sensors, one or more humidity sensors, one or more microphones, or any combination thereof.

12. The method according to any one of claims 1 to 11, further comprising identifying the type of user interface, wherein the identification is performed by correlating the shape of the curve with the shape of the curve of a known interface, the curve of the known interface being determined from past pressure and flow rate data of the known interface.

13. The method according to claim 12, wherein the identified type of user interface is a full-face interface, a nose interface, or a nose pillow interface.

14. The method according to claim 12 or 13, wherein determining the likelihood is at least partially further based on the identified type of user interface.

15. The method according to any one of claims 1 to 14, wherein the current second data indicates that the user has moved from a first posture to a second posture.

16. The first posture involves the user lying on their back, and the second posture involves the user The method according to claim 15, wherein the -er is turned sideways.

17. The method according to claim 15, wherein the first posture involves the user lying on their back, and the second posture involves the user lying face down.

18. The method according to any one of claims 1 to 17, wherein the predetermined amount of time is 5 seconds, 10 seconds, 20 seconds, 30 seconds, 40 seconds, 50 seconds, 60 seconds, 2 minutes, 5 minutes, 10 minutes, or 1 hour.

19. A control system including one or more processors, It comprises a memory that stores machine-readable instructions, A system in which the method according to any one of claims 1 to 18 is carried out, wherein the control system is connected to the memory, and the machine-readable instructions in the memory are executed by at least one of the one or more processors of the control system.

20. A system for predicting unintentional leakage in a respiratory system during a current sleep session, comprising a control system having one or more processors configured to carry out the method according to any one of claims 1 to 18.

21. A computer program product that, when executed by a computer, comprises instructions causing the computer to perform the method according to any one of claims 1 to 18.

22. The computer program product according to claim 21, wherein the computer program product is a non-temporary computer-readable medium.

23. A method for predicting unintentional leakage during the current sleep session in a continuous positive airway pressure (CPAP) breathing system, A method comprising a processor determining an unintentional leak prediction value for a user of the CPAP breathing system, wherein the determined unintentional leak prediction value indicates the likelihood that the user will experience unintentional leakage within a predetermined time period in the current sleep session, and the unintentional leak prediction value is determined using an unintentional leak prediction algorithm configured to receive the user's posture data, historical pressure and flow data obtained from past sleep sessions, and current pressure and flow data obtained from the current sleep session as inputs, and to output the user's unintentional leak prediction value, expressed as a numerical integer or numerical percentage, based on a comparison of the historical pressure and flow data with the current pressure and flow data, wherein the historical pressure and flow data is plotted as a mean pressure and mean flow curve, and the unintentional leak prediction value is determined based on the deviation of the mean pressure and mean flow plot from the curve determined from the pressure and flow data.

24. The method according to claim 23, wherein the unintentional leak prediction algorithm is further configured to receive physiological data associated with the user of the CPAP breathing system as input.

25. The method according to claim 23 or 24, wherein the predetermined amount of time is 5 seconds, 10 seconds, 20 seconds, 30 seconds, 40 seconds, 50 seconds, 60 seconds, 2 minutes, 5 minutes, 10 minutes, or 1 hour.

26. The method according to any one of claims 23 to 25, further comprising emitting a sound, changing the therapeutic pressure, changing the humidification level, inflating or deflating a smart pillow, activating an adjustable bed frame, inflating or deflating an adjustable mattress, inflating or deflating an adjustable blanket, turning on a light, turning on a fan or changing the fan speed, in response to the unintentional leak prediction value, expressed as a numerical percentage value, exceeding a predetermined percentage threshold.

27. A control system including one or more processors, It comprises a memory that stores machine-readable instructions, A system in which the method according to any one of claims 23 to 26 is carried out, wherein the control system is connected to the memory, and the machine-readable instructions in the memory are executed by at least one of the one or more processors of the control system.

28. A system for predicting unintentional leakage in a respiratory system during a current sleep session, comprising a control system having one or more processors configured to carry out the method according to any one of claims 23 to 26.

29. A computer program product that, when executed by a computer, comprises instructions causing the computer to perform the method according to any one of claims 23 to 26.

30. The computer program product according to claim 29, wherein the computer program product is a non-temporary computer-readable medium.

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