System and method for continuous care

Abstract

A method includes receiving ventilator data associated with a user of a respiratory therapy system during a sleep session while a user interface of the respiratory therapy system is engaged with the user, receiving sensor data associated with the user during the sleep session while the user interface is engaged with the user and while the user interface is not engaged with the user, accumulating the ventilator data and the sensor data, the ventilator data including historical ventilator data and current ventilator data, the sensor data including historical sensor data and current sensor data, and training a machine learning algorithm with the historical ventilator data and the historical sensor data such that the machine learning algorithm is configured to (i) receive the current ventilator data and the current sensor data as input and (ii) determine a predicted activity level that the user will experience over a predetermined time period as output.

Description

[0001] Divisional application statement

[0002] This application is a divisional application of Chinese invention patent application filed on September 11, 2020, with application number 2020800789410 and title "System and Method for Continuous Care".

[0003] Cross-references to related applications

[0004] This application claims the benefit and priority of U.S. Provisional Patent Application No. 62 / 899,833, filed September 13, 2019, the entire contents of which are incorporated herein by reference. Technical Field

[0005] The present invention generally relates to systems and methods for determining sleep-related parameters of a user during a sleep period, and more specifically, to systems and methods for determining two or more sets of sleep-related parameters of a user during a sleep period using two or more separate and distinct sensors. Background Technology

[0006] Many individuals suffer from sleep-related breathing disorders associated with one or more events occurring during sleep periods, such as snoring, sleep apnea, hypopnea, restless legs, sleep disturbances, apnea, increased heart rate, dyspnea, asthma attacks, seizures, epileptic seizures, or any combination thereof. These individuals are typically treated with respiratory therapy systems (e.g., continuous positive airway pressure (CPAP) systems), which deliver pressurized air to help prevent the individual's airway from narrowing or collapsing during sleep periods. Respiratory therapy systems can generate physiological data associated with sleep periods, which can then be used to determine sleep-related parameters and / or generate reports indicating sleep quality. However, gaps exist in the physiological data generated for sleep periods if the user does not use the respiratory therapy system for a portion of their sleep period (e.g., the user removes the user interface). This invention aims to address these problems and resolve other needs. Summary of the Invention

[0007] According to some implementations of the present invention, a method includes receiving data associated with a user's airway from one or more sensors. The method further includes receiving second physiological data associated with a user's sleep period from a second sensor. The method further includes determining a first set of sleep-related parameters associated with the user's sleep period based at least in part on the first physiological data. The method further includes determining a second set of sleep-related parameters associated with the user's sleep period based at least in part on the second physiological data. The method further includes calibrating the second sensor based at least in part on a comparison between the first set of sleep-related parameters and the second set of sleep-related parameters.

[0008] According to some implementations of the present invention, a system includes a breathing device, a user interface, sensors, a memory, and a control system. The breathing device is configured to provide pressurized air during sleep periods and generate first physiological data associated with a user of the breathing device. The user interface is coupled to the breathing device via a catheter. The user interface is configured to engage with the user during sleep periods to help direct the supplied pressurized air into the user's airway. The sensors are configured to generate second physiological data associated with the user of the breathing device during sleep periods. The memory stores machine-readable instructions. The control system includes one or more processors configured to execute machine-readable instructions to: analyze the first physiological data to determine a first set of sleep-related parameters of the user during sleep periods. The control system is further configured to analyze the second physiological data to determine a second set of sleep-related parameters of the user during sleep periods. The control system is further configured to calibrate the sensors based at least in part on a comparison of the first set of sleep-related parameters with the second set of sleep-related parameters.

[0009] According to some implementations of the present invention, a system includes a user interface, a breathing device, sensors, a memory, and a control system. The user interface is a mask configured to engage with a user during a sleep period. The breathing device is coupled to the user interface via a catheter. The breathing device is configured to generate first physiological data associated with the user during the sleep period when the user interface is engaged with the user. Sensors are configured to generate second physiological data associated with the user during the sleep period, both when the user interface is engaged with the user and when the user interface is not engaged with the user. The memory stores machine-readable instructions. The control system includes one or more processors configured to execute machine-readable instructions to: analyze the first physiological data, the second physiological data, or both to determine a first set of sleep-related parameters of the user during a first portion of the sleep period when the user interface is engaged with the user. The control system is further configured to: analyze the second physiological data to determine a second set of sleep-related parameters of the user during a second portion of the sleep period when the user interface is not engaged with the user. The control system is further configured to: generate a report associated with both the first and second set of sleep-related parameters.

[0010] According to some implementations of the present invention, a method includes receiving ventilator data associated with a user during a sleep period when a user interface of a respiratory therapy system is engaged with a user. The method also includes receiving sensor data associated with the user during the sleep period, both when the user interface is engaged with the user and when the user interface is not engaged with the user. The method further includes accumulating ventilator data and sensor data, the ventilator data including historical ventilator data and current ventilator data, and the sensor data including historical sensor data and current sensor data. The method also includes training a machine learning algorithm with the historical ventilator data and historical sensor data, such that the machine learning algorithm is configured to (i) receive current ventilator data and current sensor data as input, and (ii) determine a predicted level of activity that the user will experience during a predetermined time period as output.

[0011] According to some implementations of the present invention, a system includes a user interface, a breathing device, sensors, a memory, and a control system. The user interface is configured to engage a user during a sleep period. The breathing device is coupled to the user interface via a catheter. The breathing device is configured to generate ventilator data associated with the user during the sleep period when the user interface is engaged with the user. Sensors are configured to generate sensor data associated with the user of the breathing device during the sleep period, both when the user interface is engaged with the user and when the user interface is not engaged with the user. The memory stores machine-readable instructions. The control system includes one or more processors configured to execute machine-readable instructions to accumulate ventilator data and sensor data, the ventilator data including historical ventilator data and current ventilator data, and the sensor data including historical sensor data and current sensor data. The control system is further configured to train a machine learning algorithm using the historical ventilator data and historical sensor data, such that the machine learning algorithm is configured to (i) receive current ventilator data and current sensor data as input, and (ii) determine a predicted level of activity that the user will experience during a predetermined time period as output.

[0012] The above overview is not intended to represent every embodiment or aspect of the invention. Additional features and advantages of the invention will become apparent from the detailed description and accompanying drawings. Attached Figure Description

[0013] Figure 1 This is a functional block diagram of a system for generating physiological data associated with a user during sleep, according to some embodiments of this description.

[0014] Figure 2 This is according to some embodiments of the present invention. Figure 1 A perspective of the system, users, and bed partners;

[0015] Figure 3This is a process flowchart of a method for determining sleep-related parameters associated with a user during sleep periods according to some implementations of the present invention;

[0016] Figure 4 This is a process flowchart of a method for generating reports associated with sleep periods according to some embodiments of the present invention;

[0017] Figure 5 This is a flowchart of a method for training a machine learning algorithm based on historical ventilator equipment data and historical sensor data, according to some implementations of the present invention.

[0018] Figure 6 An exemplary timeline of sleep periods according to some implementations of the present invention is shown; and

[0019] Figure 7 It is according to some embodiments of the present invention and Figure 3 An exemplary sleep graph associated with sleep periods.

[0020] While the present invention is susceptible to various modifications and substitutions, its specific implementations and embodiments have been illustrated by way of example in the accompanying drawings and will be described in detail herein. However, it should be understood that this is not intended to limit the invention to the specific forms disclosed, but rather, the invention is intended to cover all modifications, equivalents, and substitutions falling within the spirit and scope of the invention as defined by the appended claims. Detailed Implementation

[0021] Many individuals suffer from sleep-related and / or breathing disorders. Examples of sleep-related and / or breathing disorders include 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-related hyperventilation syndrome (OHS), chronic obstructive pulmonary disease (COPD), neuromuscular disease (NMD), and chest wall disorders.

[0022] Obstructive sleep apnea (OSA) is a form of sleep-disordered breathing (SDB) characterized by events during sleep periods resulting from a combination of abnormally small upper airway obstruction and loss of normal muscle tone in the areas of the tongue, soft palate, and posterior oropharyngeal walls, leading to closure or blockage of the upper airway. More generally, apnea generally refers to the cessation of breathing caused by air blockage (obstructive sleep apnea) or cessation of respiratory function (often referred to as central apnea). Typically, during an obstructive sleep apnea event, an individual will stop breathing for approximately 15 to 30 seconds.

[0023] Other types of apnea include hypoventilation, hyperventilation, and hypercapnia. Hypoventilation is typically characterized by slow or shallow breathing caused by a narrowed airway, rather than airway obstruction. Hyperventilation is typically characterized by increased respiratory depth and / or rate. Hypercapnia is typically characterized by an excess of carbon dioxide in the bloodstream and is usually caused by hypoventilation.

[0024] Cheyne-Stokes respiration (CSR) is another form of sleep-disordered breathing. CSR is a dysregulation of the patient's respiratory controller, in which there is a rhythmic alternation of waxing and waning ventilation called the CSR cycle. CSR is characterized by repetitive hypoxia and reoxygenation of arterial blood.

[0025] Obesity hyperventilation syndrome (OHS) is defined as a combination of severe obesity and chronic hypercapnia upon waking, without other known causes of hypoventilation. Symptoms include dyspnea, morning headache, and excessive daytime sleepiness.

[0026] Chronic obstructive pulmonary disease (COPD) includes any of the lower airway diseases that share certain common characteristics, such as increased resistance to air movement, prolonged expiratory phase of breathing, and loss of normal lung elasticity.

[0027] Neuromuscular diseases (NMD) encompass a wide range of conditions and ailments that impair muscle function directly through intrinsic muscular pathology or indirectly through neuropathology. The chest wall is a group of thoracic deformities that result in inefficient connection between the respiratory muscles and the thoracic cavity.

[0028] These and other conditions are characterized by specific events that occur when an individual is sleeping (such as snoring, sleep apnea, insufficiency of breathing, restless legs, sleep disorders, suffocation, increased heart rate, difficulty breathing, asthma attacks, seizures, epilepsy, or any combination thereof).

[0029] The Apnea-Hypopnea Index (AHI) is an index used to indicate the severity of sleep apnea during a sleep period. The AHI is calculated by dividing the number of apnea and / or hypopnea events experienced by the AHI user during a sleep period by the total number of hours of sleep in that period. An event can be, for example, an apnea lasting at least 10 seconds. An AHI less than 5 is considered normal. An AHI greater than or equal to 5 but less than 15 is considered an indicator of mild sleep apnea. An AHI greater than or equal to 15 but less than 30 is considered an indicator of moderate sleep apnea. An AHI greater than or equal to 30 is considered an indicator of severe sleep apnea. In children, an AHI greater than 1 is considered abnormal. When the AHI is normal, or when the AHI is normal or mild, sleep apnea can be considered “controlled.” The AHI can also be used in conjunction with oxygen desaturation levels to indicate the severity of obstructive sleep apnea.

[0030] refer to Figure 1 System 100 includes a control system 110, a respiratory therapy system 120, one or more sensors 130, and external devices 170. As described herein, system 100 is typically used to generate a first set of physiological data associated with a user during sleep using the respiratory system 120, and to generate a second set of physiological data using one or more sensors 130 external to the respiratory system 120, which can then be analyzed to determine the first set of sleep-related parameters and the second set of sleep-related parameters.

[0031] Control system 110 includes one or more processors 112 (hereinafter, processor 112). Control system 110 is typically used to control various components of system 100 and / or analyze data acquired and / or generated by the components of system 100. Processor 112 may be a general-purpose or special-purpose processor or a microprocessor. Although in Figure 1 A processor 112 is shown, but the control system 110 may include any suitable number of processors (e.g., one processor, two processors, five processors, ten processors, etc.), which may be located in a single housing or positioned remotely from each other. The control system 110 may be coupled to and / or located within, for example, the housing of an external device 170, a portion of a respiratory system 120 (e.g., a housing), and / or the housing of one or more sensors 130. The control system 110 may be centralized (within one such housing) or distributed (within two or more physically different such housings). In this embodiment, which includes two or more housings containing the control system 110, such housings may be positioned close to and / or far from each other.

[0032] Memory 114 stores machine-readable instructions executable by the processor 112 of the control system 110. Memory device 114 can be any suitable computer-readable storage device or medium, such as random or serial access storage devices, hard disk drives, solid-state drives, flash memory devices, etc. Although Figure 1 A memory device 114 is shown, but system 100 may include any suitable number of memory devices 114 (e.g., one memory device, two memory devices, five memory devices, ten memory devices, etc.). The memory device 114 may be coupled to and / or located within the housing of the breathing device 122, the housing of the external device 170, the housing of one or more sensors 130, or any combination thereof. Similar to control system 110, the memory device 114 may be centralized (within one such housing) or distributed (within two or more physically different such housings).

[0033] In some implementations, memory device 114 ( Figure 1The system stores user profiles associated with each user. User profiles may include, for example, user-associated demographic information, user-associated biostatistics, user-associated medical information, self-reported user feedback, user-associated sleep parameters (e.g., sleep-related parameters recorded from one or more earlier sleep periods), or any combination thereof. Demographic information may include, for example, information indicating the user's age, gender, ethnicity, 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, medication use, or both. Medical information data may further include Multisleep Latency Test (MSLT) results or scores and / or Pittsburgh Sleep Quality Index (PSQI) scores or values. Self-reported user feedback may include information indicating self-reported subjective sleep scores (e.g., poor, average, excellent), user's self-reported subjective stress levels, user's self-reported subjective fatigue levels, user's self-reported subjective health status, recent life events experienced by the user, or any combination thereof.

[0034] Electronic interface 119 is configured to receive data (e.g., physiological data and / or audio data) from one or more sensors 130, such that the data can be stored in memory device 114 and / or analyzed by processor 112 of control system 110. 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, via cellular networks, etc.). 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. Electronic interface 119 may also include one or more processors and / or one or more memory devices that are the same as or similar to processor 112 and memory device 114 described herein. In some implementations, electronic interface 119 is coupled to or integrated into external device 170. In other implementations, electronic interface 119 is coupled to or integrated into control system 110 and / or memory device 114 (e.g., within a housing).

[0035] The respiratory system 120 (also referred to as a respiratory therapy system) includes a respiratory pressure therapy device 122 (also referred to herein as respiratory device 122), a user interface 124, a catheter 126 (also referred to as a tube or air circuit), a display device 128, and a humidifier 129. In some implementations, a control system 110, a memory device 114, a display device 128, one or more sensors 130, and a humidifier 129 are part of the respiratory device 122. Respiratory pressure therapy refers to supplying air to the user's airway inlet at a controlled target pressure that is nominally positive relative to the atmosphere throughout the user's respiratory cycle (e.g., the opposite of negative pressure therapy in a canister ventilator or tubing ventilator). The respiratory system 120 is typically used to treat individuals suffering from one or more sleep-related breathing disorders (e.g., obstructive sleep apnea, central sleep apnea, or mixed sleep apnea).

[0036] Breathing device 122 is typically used to generate pressurized air to be delivered to a user (e.g., using one or more motors driving one or more compressors). In some implementations, breathing device 122 generates a continuous, constant air pressure that is delivered to the user. In other implementations, breathing device 122 generates two or more predetermined pressures (e.g., a first predetermined air pressure and a second predetermined air pressure). In still other implementations, breathing device 122 is configured to generate a variety of different air pressures within a predetermined range. For example, breathing device 122 may deliver at least about 6 cm H2O, at least about 10 cm H2O, at least about 20 cm H2O, between about 6 cm H2O and about 10 cm H2O, between about 7 cm H2O and about 12 cm H2O, etc. Breathing device 122 may also deliver pressurized air at predetermined flow rates, for example, between about -20 L / min and about 150 L / min, while maintaining positive pressure (relative to ambient pressure).

[0037] User interface 124 engages with a portion of the user's face and delivers pressurized air from breathing device 122 to the user's airway to help prevent airway narrowing and / or collapse during sleep. This can also increase the user's oxygen intake during sleep. Depending on the treatment to be applied, user interface 124 may form a seal with, for example, an area or portion of the user's face, facilitating the delivery of gas at a pressure sufficiently different from ambient pressure (e.g., a positive pressure of approximately 10 cm H2O relative to ambient pressure) to achieve the treatment. For other forms of treatment, such as oxygen delivery, the user interface may not include a seal sufficient to facilitate the delivery of a gas supply at a positive pressure of approximately 10 cm H2O to the airway.

[0038] like Figure 2As shown, in some implementations, user interface 124 is a mask covering the user's nose and mouth. Alternatively, user interface 124 may be a nasal mask that delivers air to the user's nose or a nasal pillow mask that delivers air directly to the user's nostrils. User interface 124 may include multiple straps (e.g., including hook and loop fasteners) for positioning and / or stabilizing the interface on a part of the user (e.g., the face) and conformal cushioning pads (e.g., silicone, plastic, foam, etc.) to help provide an airtight seal between user interface 124 and the user. User interface 124 may also include one or more vents for allowing carbon dioxide and other gases exhaled by user 210 to escape. In other implementations, user interface 124 is a mouthpiece for directing pressurized air into the user's mouth (e.g., a night-protective mouthpiece molded to conform to the user's teeth, a jaw repositioning device, etc.).

[0039] The conduit 126 (also referred to as an air circuit or tube) allows air to flow between two components of the respiratory system 120, such as the breathing device 122 and the user interface 124. In some implementations, there may be separate branches for the inspiratory and expiratory conduits. In other implementations, a single-branch air conduit is used for both inspiratory and expiratory breathing.

[0040] One or more of the breathing device 122, user interface 124, tubing 126, display device 128, and humidifier 129 may include one or more sensors (e.g., pressure sensor, flow sensor, or any other sensor 130 described more generally herein). These one or more sensors may be used, for example, to measure the air pressure and / or flow rate of the pressurized air supplied by the breathing device 122.

[0041] Display device 128 is typically used to display images, including still images, video images, or both, and / or information about breathing apparatus 122. For example, display device 128 may provide information about the status of breathing apparatus 122 (e.g., whether breathing apparatus 122 is on / off, the pressure of the air delivered by breathing apparatus 122, the temperature of the air delivered by breathing apparatus 122, etc.) and / or other information (e.g., sleep score (also known as myAir score), current date / time, user 210's personal information, etc.). In some implementations, display device 128 acts as a human-machine interface (HMI) including a graphical user interface (GUI) configured to display images as an input interface. Display device 128 may be an LED display, OLED display, LCD display, etc. The input interface may be, for example, a touchscreen or touch-sensitive substrate, a mouse, a keyboard, or any sensor system configured to sense input made by a human user interacting with breathing apparatus 122.

[0042] The humidifier 129 is connected to or integrated into the breathing apparatus 122 and includes a water reservoir for humidifying pressurized air delivered from the breathing apparatus 122. The breathing apparatus 122 may include a heater to heat the water in the humidifier 129 to humidify the pressurized air supplied to the user. Additionally, in some implementations, the conduit 126 may include a heating element (e.g., coupled to and / or embedded in the conduit 126) that heats the pressurized air delivered to the user.

[0043] The respiratory system 120 can be used as, for example, a ventilator or a positive airway pressure (PAP) system, such as a continuous positive airway pressure (CPAP) system, an automated positive airway pressure (APAP) system, a bilevel or variable positive airway pressure (BPAP or VPAP) system, or any combination thereof. A CPAP system delivers a predetermined pressure (e.g., determined by a sleep physician) to the user. An APAP system automatically changes the pressure delivered to the user based on, for example, breathing data relevant to 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).

[0044] Reference Figure 2 The system 100 is shown according to some implementation methods. Figure 1 Part of the breathing system 120. The user 210 and bed partner 220 are located in the bed 230 and lying on the mattress 232. A user interface 124 (e.g., a full-face mask) can be worn by the user 210 during sleep. The user interface 124 is fluidly coupled and / or connected to the breathing device 122 via a conduit 126. The breathing device 122, in turn, delivers pressurized air to the user 210 via the conduit 126 and the user interface 124 to increase the air pressure in the user 210's throat, thereby helping to prevent airway closure and / or narrowing during sleep. The breathing device 122 may be positioned such as... Figure 2 The bedside table 240 shown is directly adjacent to the bed 230, or more generally, is positioned on any surface or structure that is typically adjacent to the bed 230 and / or the user 210.

[0045] See again Figure 1The system 100 includes one or more sensors 130, such as 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 photoplethysmography (PPG) sensor 154, an electrocardiogram (ECG) sensor 156, an EEG sensor 158, a capacitance sensor 160, a force sensor 162, a strain gauge sensor 164, an electromyography (EMG) sensor 166, an oxygen sensor 168, an analyte sensor 174, a moisture sensor 176, a lidar sensor 178, or any combination thereof. Typically, 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.

[0046] Although one or more sensors 130 are shown 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, photoplethysmography (PPG) sensor 154, electrocardiogram (ECG) sensor 156, EEG sensor 158, capacitance sensor 160, force sensor 162, strain gauge sensor 164, electromyography (EMG) sensor 166, oxygen sensor 168, analyte sensor 174, moisture sensor 176, and lidar sensor 178, more generally, one or more sensors 130 may include any combination and any number of each of the sensors described and / or shown herein.

[0047] One or more sensors 130 can be used to generate, for example, physiological data, audio data, or both. The control system 110 can use the physiological data generated by the one or more sensors 130 to determine sleep-wake signals and one or more sleep-related parameters associated with the user during a sleep period. Sleep-wake signals can indicate one or more sleep states, including wakefulness, relaxed wakefulness, micro-wakefulness, rapid eye movement (REM) stage, first non-REM stage (commonly referred to as "N1"), second non-REM stage (commonly referred to as "N2"), third non-REM stage (commonly referred to as "N3"), or any combination thereof. Sleep-wake signals can also be timestamped to indicate the time the user entered bed, the time the user left bed, the time the user attempted to fall asleep, etc. Sleep-wake signals can be measured by the sensors 130 during a sleep period at a predetermined sampling rate, such as one sample per second, one sample every 30 seconds, or one sample per minute. Examples of one or more sleep-related parameters that can be determined for a user based on sleep-wake signals during a sleep period include total time in bed, total sleep time, sleep onset wait time, sleep wakefulness parameters, sleep efficiency, segmentation index, or any combination thereof.

[0048] Physiological and / or audio data generated by one or more sensors 130 can also be used to determine respiratory signals associated with the user during sleep periods. Respiratory signals typically represent the user's breathing during sleep periods. Respiratory signals can indicate, for example, respiratory rate, respiratory rate variability, inspiratory amplitude, expiratory amplitude, inspiratory-expiratory ratio, number of events per hour, event pattern, pressure setting of breathing device 122, or any combination thereof. Events may include snoring, sleep apnea, central sleep apnea, obstructive sleep apnea, mixed sleep apnea, hypopnea, mask leakage (e.g., from user interface 124), restless legs, sleep disturbance, choking, increased heart rate, difficulty breathing, asthma attack, seizure, epilepsy, or any combination thereof.

[0049] Pressure sensor 132 outputs pressure data that can be stored in memory device 114 and / or analyzed by processor 112 of control system 110. In some implementations, pressure sensor 132 is an air pressure sensor (e.g., an atmospheric pressure sensor) that generates sensor data indicating the breathing (e.g., inhalation and / or exhalation) and / or ambient pressure of the user of respiratory system 120. In such implementations, pressure sensor 132 can be coupled to or integrated into respiratory device 122. Pressure sensor 132 can be, for example, a capacitive sensor, an electromagnetic sensor, a piezoelectric sensor, a strain gauge sensor, an optical sensor, a potentiometric sensor, or any combination thereof. In one example, pressure sensor 132 can be used to determine the user's blood pressure.

[0050] The flow sensor 134 outputs flow data that can be stored in the memory device 114 and / or analyzed by the processor 112 of the control system 110. In some implementations, the flow sensor 134 is used to determine the airflow from the breathing device 122, the airflow through the conduit 126, the airflow through the user interface 124, or any combination thereof. In this implementation, the flow sensor 134 can be coupled to or integrated into the breathing device 122, the user interface 124, or the conduit 126. The flow sensor 134 can 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 any combination thereof.

[0051] Temperature sensor 136 outputs temperature data that can be stored in memory device 114 and / or analyzed by processor 112 of control system 110. In some implementations, temperature sensor 136 generates instructions for user 210 ( Figure 2 Temperature data may include core body temperature, user 210 skin temperature, temperature of air flowing from breathing device 122 and / or through conduit 126, temperature in user interface 124, ambient temperature, or any combination thereof. Temperature sensor 136 may be, for example, a thermocouple sensor, a thermistor sensor, a silicon bandgap temperature sensor or a semiconductor-based sensor, a resistance temperature detector, or any combination thereof.

[0052] The microphone 140 outputs audio data that can be stored in memory device 114 and / or analyzed by processor 112 of control system 110. The audio data generated by microphone 140 can be reproduced as one or more sounds (e.g., a sound from user 210) during sleep periods. The audio data from microphone 140 can also be used to identify (e.g., using control system 110) events experienced by the user during sleep periods, as described further in detail herein. Microphone 140 can be coupled to or integrated into breathing device 122, using interface 124, catheter 126, or external device 170.

[0053] The speaker 142 outputs to the user of the system 100 (e.g., Figure 2 The speaker 142 can be used as, for example, an alarm clock or to play alarms or messages to the user 210 (e.g., in response to an event). In some implementations, the speaker 142 can be used to transmit audio data generated by the microphone 140 to the user. The speaker 142 can be coupled to or integrated into the breathing device 122, the user interface 124, the catheter 126, or the external device 170.

[0054] Microphone 140 and speaker 142 can be used as separate devices. In some implementations, microphone 140 and speaker 142 can be combined into acoustic sensor 141, as described, for example, in WO2018 / 050913, which is incorporated herein by reference in its entirety. In this implementation, speaker 142 generates or emits sound waves at predetermined intervals, and microphone 140 detects reflections of emitted sound waves from speaker 142. The sound waves generated or emitted by speaker 142 have frequencies inaudible to the human ear (e.g., below 20 Hz or above about 18 kHz) so as not to disturb the sleep of user 210 or bed partner 220. Figure 2 Based at least in part on data from microphone 140 and / or speaker 142, control system 110 can determine user 210 ( Figure 2 The location of the sleep and / or one or more sleep-related parameters described herein.

[0055] In some embodiments, sensor 130 includes (i) a first microphone that is the same as or similar to microphone 140 and is integrated in acoustic sensor 141; and (ii) a second microphone that is the same as or similar to microphone 140 but is separate from and different from the first microphone integrated in acoustic sensor 141.

[0056] RF transmitter 148 generates and / or transmits radio waves with a predetermined frequency and / or predetermined amplitude (e.g., in the high-frequency band, in the low-frequency band, long-wave signal, short-wave signal, etc.). RF receiver 146 detects the reflection of the radio waves emitted from RF transmitter 148, and this data can be analyzed by control system 110 to determine user 210 (…). Figure 2 The location of the device and / or one or more of the sleep-related parameters described herein. An RF receiver (RF receiver 146 and RF transmitter 148 or another RF pair) may also be used for wireless communication between the control system 110, the breathing device 122, one or more sensors 130, external devices 170, or any combination thereof. While RF receiver 146 and RF transmitter 148 are in... Figure 1 While shown as separate and distinct components, in some implementations, the RF receiver 146 and the RF transmitter 148 are combined as part of the RF sensor 147. In some such implementations, the RF sensor 147 includes control circuitry. The specific format of the RF communication may be WiFi, Bluetooth, etc.

[0057] In some implementations, the RF sensor 147 is part of a mesh system. An example of a mesh system is a WiFi mesh system, which may include mesh nodes, mesh routers, and mesh gateways, each of which may be mobile / movable or fixed. In such an implementation, the WiFi mesh system includes WiFi routers and / or WiFi controllers and one or more satellites (e.g., access points), each satellite including the same or similar RF sensor as the RF sensor 147. The WiFi routers and satellites communicate continuously with each other using WiFi signals. The WiFi mesh system can be used to generate motion data based on variations in the WiFi signals between the routers and satellites (e.g., differences in received signal strength), variations caused by moving objects or people partially obstructing the signal. The motion data may indicate movement, breathing, heart rate, gait, falls, behavior, etc., or any combination thereof.

[0058] Camera 150 outputs image data that can be reproduced as one or more images (e.g., still images, video images, thermal images, or combinations thereof) that can be stored in memory device 114. Image data from camera 150 can be used by control system 110 to determine one or more sleep-related parameters of the present invention. For example, image data from camera 150 can be used to identify the user's location and determine when user 210 is in bed 230. Figure 2 The time of departure of user 210 from bed 230, and the time of departure of user 210 from bed 230.

[0059] 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 memory device 114. The infrared data from IR sensor 152 can be used to determine one or more sleep-related parameters during a sleep period, including the user 210's temperature and / or the user 210's movement. IR sensor 152 can also be used in conjunction with camera 150 when measuring the presence, location, and / or movement of user 210. For example, IR sensor 152 can detect infrared light with wavelengths between about 700 nm and about 1 mm, while camera 150 can detect visible light with wavelengths between about 380 nm and about 740 nm.

[0060] PPG sensor 154 output and user 210 ( Figure 2 The associated physiological data 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-expiratory ratio, estimated blood pressure parameters, or any combination thereof. The PPG sensor 154 can be worn by the user 210, embedded in clothing and / or fabric worn by the user 210, embedded in and / or connected to the user interface 124 and / or its associated helmet (e.g., straps, etc.).

[0061] ECG sensor 156 outputs physiological data associated with the electrical activity of the heart of user 210. In some implementations, ECG sensor 156 includes one or more electrodes located above or around a portion of user 210 during sleep periods. The physiological data from ECG sensor 156 can be used, for example, to determine one or more sleep-related parameters as described herein.

[0062] EEG sensor 158 outputs physiological data associated with the electrical activity of the user 210's brain. In some embodiments, EEG sensor 158 includes one or more electrodes positioned on or around the user 210's scalp during sleep periods. Physiological data from EEG sensor 158 can be used, for example, to determine the user 210's sleep state at any given time during a sleep period. In some implementations, EEG sensor 158 may be integrated into user interface 124 and / or an associated helmet (e.g., a strap, etc.).

[0063] The capacitive sensor 160, force sensor 162, and strain gauge sensor 164 output data that can be stored in memory device 114 and used by control system 110 to determine one or more of the sleep-related parameters described herein. EMG sensor 166 outputs physiological data related to electrical activity generated by one or more muscles. Oxygen sensor 168 outputs oxygen data indicating the oxygen concentration of a gas (e.g., in conduit 126 or at user interface 124). Oxygen sensor 168 can be, for example, an ultrasonic oxygen sensor, an electro-oxygen sensor, a chemical oxygen sensor, an optical oxygen sensor, or any combination thereof. In some embodiments, one or more sensors 130 further include a ground-skin response (GSR) sensor, a blood flow sensor, a respiration sensor, a pulse sensor, a blood pressure sensor, a blood oxygen sensor, or any combination thereof.

[0064] Analyte sensor 174 can be used to detect the presence of analytes in the exhaled breath of user 210. Data output from analyte sensor 174 can be stored in memory device 114 and used by control system 110 to determine the identity and concentration of any analytes in the breath of user 210. In some implementations, analyte sensor 174 is located near the mouth of user 210 to detect analytes in the breath exhaled from the mouth of user 210. For example, when user interface 124 is a mask covering the nose and mouth of user 210, analyte sensor 174 can be located inside the mask to monitor mouth breathing of user 210. In other implementations, such as when user interface 124 is a nasal mask or nasal pillow mask, analyte sensor 174 can be positioned near the nose of user 210 to detect analytes in the breath exhaled through the nose of user 210. In other implementations, when user interface 124 is a nasal mask or nasal pillow mask, analyte sensor 174 can be located near the mouth of user 210. In this implementation, the analyte sensor 174 can be used to detect whether any air is unintentionally leaking from the mouth of user 210. In some implementations, the analyte sensor 174 is a volatile organic compound (VOC) sensor that can be used to detect carbon-based chemicals or compounds. In some embodiments, the analyte sensor 174 can also be used to detect whether user 210 is breathing through their nose or mouth. For example, if the presence of an analyte is detected by data output from the analyte sensor 174 located near the mouth of user 210 or inside a mask (in the implementation where user interface 124 is a mask), the control system 110 can use that data as an indication that user 210 is breathing through their mouth.

[0065] Moisture sensor 176 outputs data that can be stored in storage device 114 and used by control system 110. Moisture sensor 176 can be used to detect humidity in various areas surrounding the user (e.g., inside conduit 126 or user interface 124, near the user 210's face, near the connection between conduit 126 and user interface 124, near the connection between conduit 126 and breathing device 122, etc.). Therefore, in some implementations, moisture sensor 176 can be coupled to or integrated into user interface 124 or conduit 126 to monitor the humidity of pressurized air from breathing device 122. In other implementations, moisture sensor 176 is placed near any area where humidity levels need to be monitored. Moisture sensor 176 can also be used to monitor the humidity of the surrounding environment around user 210, such as the air in a bedroom.

[0066] The optical detection and ranging (LiDAR) sensor 178 can be used for depth sensing. This type of optical sensor (e.g., a laser sensor) can be used to detect objects and construct a three-dimensional (3D) map of the surrounding environment (e.g., a living space). LiDAR typically utilizes pulsed lasers for time-of-flight measurements. LiDAR is also known as 3D laser scanning. In an example using this sensor, a fixed or mobile device (such as a smartphone) with LiDAR sensor 166 can measure and map an area extending 5 meters or more from the sensor. For example, LiDAR data can be fused with point cloud data estimated by an electromagnetic radar (RADAR) sensor. LiDAR sensor 178 can also use artificial intelligence (AI) to automatically geofence radar systems by detecting and classifying features in space that may cause problems for the radar system, such as glass windows (which may be highly reflective to radar). For example, LiDAR can also be used to provide an estimate of a person's height, and how that height changes when the person sits down or falls. LiDAR can be used to form a 3D mesh representation of the environment. In further applications, lidar can reflect radio waves off solid surfaces (e.g., transmissive materials) to allow for the classification of different types of obstacles.

[0067] Although Figure 1 While shown separately, any combination of one or more sensors 130 may be integrated into and / or coupled to any one or more components of system 100, including breathing device 122, user interface 124, catheter 126, humidifier 129, control system 110, external device 170, or any combination thereof. For example, microphone 140 and speaker 142 are integrated into and / or coupled to external device 170, and pressure sensor 132 and / or flow sensor 134 are integrated into and / or coupled to breathing device 122. In some implementations, at least one of the one or more sensors 130 is not coupled to breathing device 122, control system 110, or external device 170, and is typically positioned near or in contact with user 210 during sleep periods (e.g., positioned on or in contact with a portion of user 210, worn by user 210, coupled to or positioned on a bedside table, coupled to a mattress, coupled to a ceiling, etc.).

[0068] For example, such as Figure 2As shown, one or more sensors 130 may be located at a first position 250A on a bedside table 240 near the bed 230 and user 210. Alternatively, one or more sensors 130 may be located at a second position 250B on and / or within the mattress 232 (e.g., sensors are attached to and / or integrated into the mattress 232). Furthermore, one or more sensors 130 may be located at a third position 250C on the bed 230 (e.g., auxiliary sensors are attached to and / or integrated into other locations on the headboard, footboard, or frame of the bed 230). One or more sensors 130 may also be located at a fourth position 250D on a wall or ceiling, typically adjacent to the bed 230 and / or user 210. One or more sensors 130 may also be located at a fifth position 250E, such that one or more sensors 130 are attached to and / or positioned on and / or within the housing of the breathing device 122 of the breathing system 120. Furthermore, one or more sensors 130 may be located at a sixth position 250F, such that the sensors are coupled to and / or positioned on the user 210 (e.g., during sleep periods, the sensors are embedded in or coupled to fabric or clothing worn by the user 210). More generally, one or more sensors 130 may be positioned relative to the user 210 at any suitable location, such that the sensors can generate physiological data associated with the user 210 and / or bed partner 220 during one or more sleep periods.

[0069] Return to reference Figure 1 External device 170 includes a processor, memory, and display device 172. External device 170 may be, for example, a mobile device such as a smartphone, tablet, or laptop. The processor is the same as or similar to the processor 112 of control system 110. Similarly, the memory is the same as or similar to the memory device 114 of control system 110. Display device 172 is typically used to display images including still images, video images, or both. In some implementations, display device 172 acts as a human-machine interface (HMI) including a graphical user interface (GUI), which is configured to display images and provide input. Display device 172 may be an LED display, OLED display, LCD display, etc. Input interface may be, for example, a touchscreen or touch-sensitive substrate, a mouse, a keyboard, or any sensor system configured to sense input made by a human user interacting with external device 170.

[0070] Although the control system 110 and the memory device 114 are in Figure 1While described and shown as separate and distinct components of system 100, in some implementations, control system 110 and / or memory device 114 are integrated into external device 170 and / or breathing device 122. Alternatively, in some implementations, control system 110 or a portion thereof (e.g., 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, subjected to edge cloud processing, etc.), or in one or more servers (e.g., remote server, local server, etc., or any combination thereof).

[0071] While system 100 is shown as including all of the components described above, more or fewer components may be included in a system for generating physiological data and determining recommended sleep-related parameters. For example, a first alternative system includes a control system 110, a respiratory system 120, and at least one of one or more sensors 130. As another example, a second alternative system includes a respiratory system 120, multiple or one or more sensors 130, and an external device 170. As yet another example, a third alternative system includes a control system 110 and multiple or one or more auxiliary sensors. Therefore, various systems for determining sleep-related parameters associated with sleep periods can be formed using any part or multiple parts of the components shown and described herein and / or in combination with one or more other components.

[0072] In many cases, the user of the respiratory system 120 will only wear the user interface 124 for a portion of their sleep period (e.g., 1 hour, 2 hours, 4 hours, 6 hours, etc., of an eight-hour sleep period). For example, a user may initially fall asleep while wearing the user interface 124 mask, wake up and remove the user interface 124, and then fall back asleep. In other cases, the user may not wear the user interface 124 at all during a sleep period (e.g., the user uses the respiratory system 120 only every other day). When the user does not actively use the respiratory system 120, any sensors in the respiratory system 120 cannot obtain physiological data (e.g., for determining sleep-related parameters or generating reports indicating sleep quality). In other words, failure to use the respiratory system 120 for the entire sleep period creates gaps (or no physiological data at all) in the physiological data for that sleep period, leaving the user, their therapist, and other stakeholders (e.g., family members, bed partners, etc.) without sleep quality indications (or lack thereof) when the user does not adhere to their prescribed use of the respiratory system 120. Typically, without the ventilator system 120, a user's sleep quality will be worse than when using the system as prescribed. Identifying and quantifying this difference in sleep quality is useful for encouraging and / or motivating users to utilize the system and adhere to their prescribed usage.

[0073] The same or similar types of physiological data obtained using sensors physically coupled to or integrated into the respiratory system 120 can also be generated or obtained using independent external sensors (e.g., sensors integrated into a separate and different mobile device from the respiratory system 120). Advantageously, this independent sensor can be used to record physiological data during sleep periods, even when the respiratory system 120 is not in use and / or powered off. However, due to variations in sensor instrumentation, the physiological data obtained or generated by the sensor may differ from the physiological data obtained or generated by the respiratory therapy system. For example, while a respiratory therapy system may use pressure sensors and / or flow sensors to determine a user's respiratory signal or rate, an external sensor may use a microphone to determine the respiratory rate. Due to these sensor variations, it is often difficult to make accurate comparisons between physiological data obtained by the respiratory therapy system and physiological data obtained by an independent sensor in order to determine sleep-related parameters and / or generate reports indicating sleep quality.

[0074] refer to Figure 3 This illustrates a method 300 for determining sleep-related parameters of a user during a sleep period. One or more steps of the method 300 described herein can be used with system 100 (…). Figure 1 and 2 This can be achieved through [the following].

[0075] Step 301 of method 300 includes generating or acquiring first physiological data associated with the user during a sleep period. The first physiological data is generated by a first or a first group of one or more sensors 130 described herein. For example, step 301 may include generating or acquiring the first physiological data using a barometric pressure sensor 132 and / or a flow sensor 134, the barometric pressure sensor and / or flow sensor being physically coupled to or integrated into a respiratory system 120. Figure 1 The first physiological data generated or acquired using the first sensor during step 301 may include any physiological data described herein. For example, the first physiological data may be generated from the breathing device 122, the user interface 124, the catheter 126, etc. Figure 1 The pressure (e.g., using pressure sensor 130), airflow (e.g., using flow sensor 134), or both are derived from measurements of air pressure (e.g., using pressure sensor 130), airflow (e.g., using flow sensor 134), or both within any combination thereof.

[0076] Step 302 of method 300 includes generating or acquiring second physiological data associated with the user during a sleep period. For example, step 302 may include generating or acquiring the second physiological data using a second or second group of sensors 130 that are separate from and different from the first sensor (step 301). The second physiological data generated or acquired using the second sensor during step 302 may be physiological data of a different type from the first physiological data (e.g., the first physiological data is derived from pressure and / or airflow, and the second physiological data is derived from measurements of the user's motion or sound). Typically, the generation of the second physiological data during step 302 and the generation of the first physiological data during step 301 are substantially simultaneous, such that the first physiological data (step 301) and the second physiological data (step 302) can be compared for at least a portion of the sleep period (when the user is using the respiratory system 120).

[0077] Unlike the first sensor used to generate the first physiological data during step 301 (e.g., pressure sensor 132 and / or flow sensor 134 coupled to or integrated into respiratory system 120), in some implementations, the sensor used to generate or obtain the second physiological data during step 302 is not coupled to or integrated into respiratory system 120. That is, the second sensor used for the second physiological data in step 302 is separate from and distinct from respiratory system 120. As described herein, the second sensor may be positioned as a separate, independent sensor (e.g., coupled to bed 230, mattress 232, bedside table 240, ceiling, etc.) substantially adjacent to user 210 or integrated into or coupled to external device 170. Alternatively, the second sensor used in step 302 may be coupled to the housing of respiratory device 122 of respiratory system 120. In some such alternative implementations, the second sensor used during step 302 does not measure the pressure or flow rate of pressurized air generated by respiratory system 120.

[0078] Method 300 ( Figure 3 Step 303 includes analyzing first physiological data (step 301) to determine a first set of sleep-related parameters of the user during the sleep period. For example, the first physiological data (step 301) may be stored in the control system 110. Figure 1The machine-readable instructions stored in the memory device 114 are executed by the processor 112 to analyze the first physiological data. The first set of sleep-related parameters may include, for example, a first sleep score, a first flow signal, a first respiratory signal, a first respiratory rate, a first inspiratory amplitude, a first expiratory amplitude, a first inspiratory-expiratory ratio, a first number of events per hour, a first average number of events per hour, a first event pattern, a first sleep state, a first pressure setting of the breathing device 122, a first heart rate, a first heart rate variability, a first movement of the user 210, or any combination thereof.

[0079] Method 300 ( Figure 3 Step 304 includes analyzing the second physiological data (step 302) to determine a second set of sleep-related parameters of the user during the sleep period. For example, the second physiological data (step 302) may be stored in a memory device 114 of the control system 110, and the machine-readable instructions stored in the memory device 114 may be processed by the processor 112. Figure 1 The process is executed to analyze a second set of physiological data. This second set of sleep-related parameters may include, for example, a second sleep score, a second flow signal, a second respiratory signal, a second respiratory rate, a second inspiratory amplitude, a second expiratory amplitude, a second inspiratory-expiratory ratio, a second number of events per hour, a second average number of events per hour, a second event pattern, a second sleep state, a second pressure setting of the breathing device 122, a second heart rate, a second heart rate variability, a second movement of the user 210, or any combination thereof. Steps 303 and 304 of method 300 may be performed substantially simultaneously or sequentially (e.g., step 303 is completed before step 304 is initiated, step 304 is completed before step 303 is initiated, etc.).

[0080] Step 305 of method 300 includes calibrating a second sensor for generating second physiological data based at least in part on a first set of sleep-related parameters (step 303) and / or a second set of sleep-related parameters (step 304). Specifically, step 305 includes comparing the first set of sleep-related parameters (step 303) with the second set of sleep-related parameters (step 304). As described herein, because the first sensor (e.g., pressure sensor 132 and / or flow sensor 134) differs from the second sensor, variations may exist between the first physiological data (step 301) and the second physiological data (step 302), which in turn cause variations between the determined first set of sleep-related parameters (step 303) and the determined second set of sleep-related parameters (step 304) at any given time during the sleep period. For example, before calibrating the second sensor, the first set of sleep-related parameters (step 303) may include a first event pattern, a first average number of events per hour, a first respiratory signal, or any combination thereof, while the second set of sleep-related parameters (step 304) may include a second event pattern, a second average number of events per hour, a second respiratory signal, or any combination thereof that does not match one or more of the first event pattern, the first average number of events, or the first respiratory signal. As another example, noise in the data from the second sensor (step 302) may cause the control system 110 to determine, based on analysis of the second physiological data, that the user has experienced one of the events described herein, even if the user has not or has not actually experienced that event (e.g., based on analysis of the first physiological data).

[0081] In some implementations, step 305 includes modifying one or more parameters of the sensor, at least in part, based on a comparison between a first set of sleep-related parameters (step 303) and a second set of sleep-related parameters (step 304). Modifying one or more parameters of the second sensor affects the second physiological data generated by the second sensor during step 302. For example, modifying the parameters can help eliminate outlier data points and / or other noise in the recorded data. Analysis and / or modification of one or more parameters can include linear and nonlinear transformation operations, resampling, template matching, automatic and cross-correlation, morphological processing, etc. Furthermore, in some implementations, analysis and / or modification of one or more parameters can include characterization and adjustment, followed by recharacterization. Such characterization and adjustment can include reading physiological and / or other signals, or injecting a known signal that can be identified by both sensors (e.g., a first sensor and a second sensor) and used as a reference for different sensors. One or more sensor parameters of the second sensor that can be modified include frequency, phase, power, amplitude, intensity, modulation of the sensor signal, beam pattern, on / off state of one or more antennas of the sensor, beamforming, physical location of one or more antennas of the sensor, physical location of the sensor, spectral shape, or any combination thereof. In some implementations, one or more sensor parameters are automatically modified by the control system 110. In other implementations, the control system 110 causes instructions or other markers to be displayed (e.g., using display device 172 of external device 170, display device 128 of CPAP system 120, or both) to prompt the user to modify one or more parameters (e.g., physically reposition the sensor).

[0082] In some implementations, step 305 includes modifying the control system 110 when analyzing the second physiological data during step 304 based at least in part on a comparison between the first set of sleep-related parameters (step 303) and the second set of sleep-related parameters (step 304). Figure 1 The processor 112 executes one or more parameters of machine-readable instructions. Modifying the parameters of the machine-readable instructions adjusts how the second set of sleep-related parameters is determined during step 304. For example, if sleep-related parameters are determined based on at least two types of physiological data (e.g., motion data from motion sensor 138 and image data from camera 150), the parameters of the machine-readable instructions stored in memory device 114 can be modified to assign different weights to each type of physiological data. In some implementations, step 305 includes modifying one or more parameters of the second sensor and one or more parameters of the machine-readable instructions stored in memory device 114 of the control system 110.

[0083] Steps 301-305 of method 300 may be repeated once or multiple times until one or more parameters in the first set of sleep-related parameters (step 303) match, substantially match, or correspond to one or more parameters in the second set of sleep-related parameters (step 304) (e.g., the second set of sleep-related parameters are within a predetermined standard deviation from the first set of sleep-related parameters). For example, in a first iteration of method 300 where the first set of sleep-related parameters (step 303) does not match the second set of sleep-related parameters (step 304), the secondary sensor is calibrated during step 305 (e.g., by modifying one or more parameters of the sensor, modifying one or more parameters of the machine-readable instruction, or both). Steps 301-304 may be repeated in a second iteration of method 300. If, during the second iteration, the first set of sleep-related parameters (step 303) still does not sufficiently match the second set of sleep-related parameters (step 304), then step 305 includes recalibrating the second sensor by modifying the same parameters modified during the first iteration of method 300, or by modifying different parameters of the second sensor and / or the machine-readable instruction, or both. In this way, method 300 can be repeated multiple times (e.g., 5 times, 20 times, 50 times, 200 times, 1,000 times, etc.) at predetermined intervals (e.g., every 0.01 seconds, every 0.1 seconds, every 2 seconds, every 10 seconds, every 60 seconds, every 5 minutes, every 30 minutes, every 60 minutes, etc.) until the calibration performed during step 305 matches or corresponds to the first set of sleep-related parameters (step 303) with the second set of sleep-related parameters (step 304) in subsequent iterations.

[0084] In some implementations, method 300 includes using a machine learning algorithm to calibrate an auxiliary sensor during step 305. In such implementations, information indicating (i) modifications to one or more parameters of the second sensor and / or machine-readable instructions and (ii) a comparison between a first set of sleep-related parameters (step 303) and a second set of sleep-related parameters (step 304) is stored in the control system 110. Figure 1 The information is stored in the memory device 114. This information can be used by a machine learning algorithm during multiple iterations of method 300 to assist in calibrating the second sensor by, for example, determining which modifications or combinations of modifications to one or more parameters have a corresponding impact on the differences between the first set of sleep-related parameters (step 303) and the second set of sleep-related parameters (step 304). If the machine learning algorithm determines that certain sensor or machine-readable instruction parameters or parameter modifications do not affect the differences in the multiple sets of sleep-related parameters, these parameters are no longer modified in subsequent iterations of method 300. Therefore, using a machine learning algorithm can reduce the number of iterations of method 300 required to calibrate the second sensor (parameter modifications during step 305).

[0085] Once the second sensor has been calibrated using method 300, system 100 can proceed to determine the second sleep-related parameters (step 304), even including the respiratory system 120 with the first sensor. Figure 1 The first set of physiological data is no longer generated, for example, via pressure sensor 132 and / or flow sensor 134 (step 301). Advantageously, because the second sensor has been calibrated using method 300, the second set of sleep-related parameters will continue to provide information indicating sleep periods, which is the same as or similar to the first set of sleep-related parameters obtained using respiratory system 120. For example, if user 210 ( Figure 2 If the user interface 124 is worn during the first part of the sleep period, but is subsequently removed during the second part of the sleep period, the system 100 continues to determine the sleep-related parameters of the user 210, even if the user 210 does not use the respiratory system 120 throughout the sleep period.

[0086] refer to Figure 4 This illustrates a method 400 for generating reports associated with sleep periods. One or more steps of the method 400 described herein can be used with system 100 (…). Figure 1 This can be achieved through [the following].

[0087] Step 401 of method 400 includes generating or acquiring first physiological data associated with the user during a sleep period when a user interface (e.g., user interface 124) engages with the user. Step 401 is similar to method 300. Figure 3 Step 301, because first physiological data can be obtained using one or more first sensors (e.g., pressure sensor 132 and / or flow sensor 134, which are integrated and / or coupled to the breathing device 122). Step 401 is related to step 301. Figure 3 The difference is that the user interface 124 of the respiratory system 120 interacts with the user 210 only during sleep periods. Figure 2 First physiological data is generated or obtained during the engagement. Therefore, for the portion of the sleep period when the user interface 124 is not engaged with the user 210, first physiological data is not generated or obtained.

[0088] In some implementations, step 401 includes using the same or different sensors as those used to generate the first physiological data (step 401) or the second physiological data (step 402) to determine whether the user interface 124 is engaged with the user 210. For example, if the pressure sensor 132 and / or the flow sensor 134 detects a lack of air pressure and / or airflow to the user interface 124, this could indicate that the user interface 124 is not engaged with the user 210. Figure 2The fact that the user interface 124 engages with the user 210. As another example, the mask 124 may include one or more sensors that can be used to determine whether the user interface 124 engages with the user 210. In another example, object recognition algorithms, facial recognition algorithms, or both can be used to analyze data from camera 150. Figure 1 Image data is used to determine whether user interface 124 is engaged with user 210.

[0089] Step 402 of method 400 includes generating or acquiring second physiological data associated with the user during sleep periods, both when the user interface is engaged with the user and when the user interface is not engaged with the user. Step 402 is similar to method 300. Figure 3 Step 302, because the second physiological data can be obtained from system 100 ( Figure 1 At least one of the sensors 130 is used to obtain the information, which is separate from and different from sensors connected to or integrated into the respiratory system 120 (e.g., sensors connected to or integrated into external device 170). The difference between step 402 and step 401, except for the use of different sensors not integrated into system 120, is that step 402 is performed regardless of whether the user interface 124 is in contact with user 210 during all or part of the sleep period. Figure 2 Both combinations yielded second physiological data.

[0090] Step 403 of method 400 includes analyzing first physiological data (step 401), second physiological data (step 402), or both, to determine a first set of sleep-related parameters during a first portion of a sleep period in which the user interface engages with the user. For example, the first physiological data (step 401) and / or the second physiological data (step 402) may be stored in control system 110 ( Figure 1 The machine-readable instructions stored in the memory device 114 are executed by the processor 112 to analyze the first and / or second physiological data. The first set of sleep-related parameters determined during step 403 are generally consistent with the method 300 described herein. Figure 3 The first set of sleep-related parameters in step 303 is the same or similar, but the difference is that the first set of sleep-related parameters determined during step 403 is limited to user interface 124 and user 210 ( Figure 2 The first part of the sleep period that is connected.

[0091] Step 404 of method 400 includes analyzing second physiological data (step 402) to determine a second set of sleep-related parameters during a second portion of a sleep period when the user interface is not engaged with the user. For example, the second physiological data (step 402) may be stored in control system 110. Figure 1The machine-readable instructions stored in the memory device 114 are executed by the processor 112 to analyze the second physiological data. Because the first set of sleep-related parameters is limited to the first portion of the sleep period without engaging the user interface, the first physiological data is not used to determine the second set of sleep-related parameters during step 404.

[0092] The first part of the sleep period (step 403) and / or the second part of the sleep period (step 404) may include continuous time periods or a combination of discontinuous time periods during the sleep period. For example, if user 210 ( Figure 2 User interface 124 is worn for the first 3 hours of the sleep period, removed for the next 2 hours, and then reopened for the last 2 hours of the sleep period (a total of 7 hours). The first part comprises 5 hours, and the second part comprises 2 hours. In some implementations, either the first or second part includes the entire sleep period (e.g., user 210 wears user interface 124 for the entire sleep period, or does not wear user interface 124 at all during the sleep period).

[0093] Step 405 of method 400 includes generating a report associated with a first set of sleep-related parameters (step 403) and a second set of sleep-related parameters (step 404). For example, control system 110 ( Figure 1 The memory device 114 may include machine-readable instructions executable by the processor 112 to analyze a first set of sleep-related parameters (step 403) and a second set of sleep-related parameters (step 404) to generate a report. The generated reports for sleep periods may be stored in the memory device 114 of the control system 110, displayed on the display device 172 of the external device 170 and / or the display device 128 of the respiratory system 120 after a sleep period (e.g., when the user wakes up), transmitted to a third party (e.g., a therapist, CPAP device technician), or any combination thereof. Storing the generated reports in the memory device 114 allows the system 100 to compare reports over multiple sleep periods to identify trends and provide recommendations and / or predictions, as further described herein.

[0094] In some implementations, the report generated during step 405 may include a comparison of at least a portion of a first set of sleep-related parameters (step 403) with at least a portion of a second set of sleep-related parameters (step 404). In other implementations, the report may indicate the sleep quality of user 210 during a first portion of a sleep period in which user interface 124 is engaged with user 210, and the sleep quality of user 210 during a second portion of a sleep period in which user interface 124 is not engaged with user. As described herein, typically, a user will experience lower quality sleep when user interface 124 is not used compared to sleep quality when user interface 124 is used. The report may encourage adherence to prescribed use of respiratory system 120 by quantifying the difference in sleep quality and communicating it to the user, or provide an indication that prescribed use of respiratory system 120 is no longer necessary.

[0095] For example, the report may include a sleep score or metric indicating the sleep quality of the first and second parts of a sleep period. Typically, a sleep score may be expressed as a number between 0 and 100 (e.g., an integer), where a score of 100 indicates high-quality sleep, a score of 0 indicates low-quality sleep, and scores from 1 to 99 indicate varying quality within that range. The sleep score may be determined based on, for example, the number or type of events during the sleep period, the duration of the sleep period, the duration of one or more sleep states during the sleep period (e.g., the relative duration of REM sleep and / or non-REM sleep), the amount of movement by the user during the sleep period, any other sleep-related parameters and / or events described herein, or any combination thereof. The sleep score may also be displayed or transmitted to the user 210 in a manner that illustrates how the use or non-use of the respiratory system 120 affects the user's sleep score.

[0096] In some implementations, the report generated during step 405 may include a sleep compliance score measure based on the prescribed use of the respiratory system 120 by user 210. Information indicating the prescribed use of the respiratory system 120 may be stored in the control system 110. Figure 1The sleep compliance score measure indicates the degree of adherence of user 210 to prescribed use during sleep periods. The sleep compliance score measure can be expressed as a number between 0 and 100 (e.g., an integer), a percentage, or using other labels (e.g., poor, moderate, good, excellent, etc.). For example, if it is assumed that user 210 wears mask 124 for at least 80% of sleep periods and user interface 124 for 50% of sleep periods, the sleep compliance score measure could be expressed as 62.5. The sleep compliance score measure can help motivate or encourage user 210 to adhere to their prescribed use of respiratory system 120 and / or provide information to their therapist. Further details regarding sleep compliance score metrics (e.g., treatment quality indicators) are described in US Application No. 15,520,663, filed April 20, 2017 and published November 2, 2017, as US2017 / 0311879, which is incorporated herein by reference in its entirety.

[0097] As described above, in some implementations, the generated report includes a sleep score or metric indicating sleep quality. In such implementations, the generated report may also include recommendations on adjusting one or more of the aforementioned sleep habits to help improve the quality of the sleep score. For example, recommendations may instruct the user to modify their bedtime and increase the duration of their sleep period to improve their sleep score. The report may also include a predicted quantitative improvement in the user's sleep score or quality metric corresponding to the implementation of the recommended adjustments for one or more sleep habits. For example, if the sleep score in the report indicates low-quality sleep, the report may recommend increasing the duration of the sleep period (e.g., from 5 hours to 7 hours) and increasing the amount of time the user wears the mask 124 (e.g., from 50% of the sleep period to at least 90% of the sleep period) and predict a quantitative improvement in the sleep score (e.g., from a score of 50 to a score of 90). The quantitative prediction may be determined based on, for example, a previously generated report stored in a memory device 114 of the control system 110.

[0098] In some implementations, a predicted quantitative improvement in sleep score or quality metrics can be determined even if the user 210 has not yet used the CPAP system 120. After an individual is diagnosed with sleep-related breathing disorders and prescribed a respiratory therapy system, it typically takes weeks or even months for the individual to access the system. In some cases, the individual may not follow up during this delay or change their perception of using the respiratory system (e.g., the user may see images of the respiratory therapy system and perceive it as too invasive or difficult to use). Using sensors (e.g., integrated into external device 170) during sleep periods to generate secondary physiological data allows system 100 to determine sleep scores, and if the user 210 intends to use the respiratory system 120 to help motivate, encourage, or incentivize the user 210 to follow the prescription and obtain and use the respiratory system 120, a predicted quantitative improvement in sleep score quality is generated.

[0099] In some implementations, the report generated during step 405 may include information about the respiratory system 120 ( Figure 1 The report may include recommendations on the use of the breathing device 122 and / or recommendations on adjusting the user's sleep habits. For example, the generated report may include recommendations on modifying the user's bedtime, wake-up time, duration of sleep periods, amount of time the user wears the user interface 124 during sleep periods (e.g., at least 33% of the sleep period duration, at least 66% of the sleep period, at least 75% of the sleep period, 90% of the sleep period, etc.) or any combination thereof. Recommendations on the use of the breathing device 122 with the CPAP system 120 may also include recommendations on using different breathing systems (e.g., a CPAP system capable of delivering higher pressure) or different breathing system components (e.g., different user interface types).

[0100] The report generated during step 405 can also be used to confirm whether the respiratory system 120 is improving sleep quality based on a comparison between the first set of sleep-related parameters (step 303) and the second set of sleep-related parameters (step 304). For example, if the report indicates little or no difference between the first set of sleep-related parameters (step 303) and the second set of sleep-related parameters (step 304), this can indicate that the user 210 no longer needs the respiratory system 120. The comparisons in the report can also be used to help identify other factors that may negatively impact the user's sleep that are not being treated by the respiratory system 120 (e.g., undiagnosed health conditions, environmental conditions, etc.).

[0101] Method 400 ( Figure 4Step 406 includes modifying one or more parameters of the breathing device 122 of the respiratory system 120 based on first physiological data (step 401), second physiological data (step 402), the generated report (step 405), or any combination thereof. Step 406 may include, for example, modifying the ramp time of the breathing device 122, modifying the pressure setting during a sleep period, modifying the pressure setting in response to determining that the user has woken up from a sleep period, or any combination thereof.

[0102] Step 407 of Method 400 is the same as that of Method 300. Figure 3 Step 305 is the same as or similar to step 402, and includes calibrating the second sensor used to generate or obtain the second physiological data during step 402. Step 407 may include modifying one or more parameters of the second sensor, modifying one or more parameters of machine-readable instructions stored in memory device 114 and executed by processor 112 of control system 110, or both.

[0103] Although method 400 is described as obtaining information from user 210 ( Figure 2 The first and second physiological data associated with the bed partner 220 (steps 401 and 402), but in some implementations, method 400 may also include generating or obtaining data associated with the bed partner 220 during sleep periods. Figure 2 The third physiological data associated with the bed partner 220. In this implementation, one or more auxiliary sensors and / or the respiratory system 120 can be used to generate or obtain third physiological data for the bed partner 220, which can be stored in the memory device 114 of the control system 110. The sensor used to generate or obtain the third physiological data can be the same sensor used to generate or obtain the second physiological data during step 402, or a different sensor.

[0104] In an implementation that includes third physiological data associated with bed partner 220, the report generated during step 405 may include a sleep quality score or metric indicating the sleep quality of bed partner 220 during a sleep period. Even if bed partner 220 does not suffer from any sleep-related breathing disturbances, bed partner 220 may have a lower sleep score than user 210 of respiratory system 120. For example, snoring from user 210 may disrupt bed partner 220's sleep. As another example, air leakage from user interface 124 worn by user 210 may cause noise that may disturb bed partner 220's sleep. Therefore, the report generated during step 405 may include recommendations for adjusting one or more sleep habits of user 210 of respiratory system 120 to help improve bed partner 220's sleep quality metric. For example, the report may include recommendations to increase or decrease the average amount of time user 210 uses respiratory device 122 per sleep period. The report may also include a predicted quantitative improvement in bed partner 220's sleep quality metric corresponding to user 210 implementing the recommended adjustments to one or more sleep habits.

[0105] In some implementations, the generated report may also include recommendations regarding the user interface 124 worn by user 210. For example, the report may recommend different mask sizes to help create a better fit or interface between the mask and user 210, or recommend different mask types (e.g., nose mask, full face mask, cradle mask, etc.).

[0106] refer to Figure 5 This illustrates a method 500 for training a machine learning algorithm. One or more steps of the method 500 described herein can be used with system 100 (…). Figure 1 This can be achieved through [the following].

[0107] Step 501 of method 500 includes generating or acquiring ventilator data associated with the user during a sleep period using a breathing device when the user interface is engaged with the user. Step 501 is similar to method 400. Figure 4 Step 401, because one or more auxiliary sensors 130 of the system 100 integrated in the breathing device 122 of the breathing system 120 can be used. Figure 1 One of them is used to obtain the first physiological data. For example, when user interface 124 is not in contact with user 210 ( Figure 2 When engaged, the pressure sensor 132 and / or flow sensor 134 in the breathing device 122 do not generate or acquire first physiological data. Similar to method 400 ( Figure 4 In some implementations, step 501 of step 401 includes determining whether user interface 124 is engaged with user 210.

[0108] Step 502 of method 500 includes generating or acquiring user-associated sensor data during sleep periods, both when the user interface is engaged with the user and when the user interface is not engaged with the user. Step 502 is similar to method 400. Figure 4 Step 402, because the second physiological data can be obtained from system 100 ( Figure 1 One or more different sensors 130 are obtained, which are separate from and different from sensors connected to or integrated into the respiratory system 120 (e.g., sensors connected to or integrated into external devices 170).

[0109] Step 503 of method 500 includes accumulating ventilator data and sensor data. The accumulated ventilator data includes first physiological data currently generated or acquired during step 501 (hereinafter, current ventilator data) and previously recorded first physiological data from previous iterations of method 500 (hereinafter, historical ventilator data). Similarly, the accumulated sensor data includes second physiological data currently generated or acquired during step 502 (hereinafter, current sensor data) and previously recorded second physiological data from previous iterations of method 500 (hereinafter, historical sensor data). Historical ventilator data and historical sensor data can be accumulated over multiple sleep periods and can be stored in the control system 110. Figure 1 The historical ventilator data and / or historical sensor data may be stored in the memory device 114 indefinitely or for a predetermined period of time (e.g., one week, one month, six months, one year, three years, etc.) and then automatically deleted.

[0110] Step 504 of method 500 includes training a machine learning algorithm (MLA) using historical ventilator data and historical sensor data accumulated during step 503. The MLA is trained such that it receives current ventilator data (step 501) and current sensor data (step 502) as input and determines a predicted activity level, a predicted reaction time measurement, a predicted subjective sleepiness, or any combination thereof as output. That is, the MLA is trained using historical ventilator data and historical sensor data as a training dataset. Historical ventilator data and historical sensor data can be continuously accumulated or updated (step 503) to update the MLA's training dataset. The MLA can be, for example, a deep learning algorithm or a neural network, and can be stored as machine-readable instructions in the memory device 114 of the control system 110, which can be executed by the processor 112.

[0111] Step 505 of method 500 includes determining the predicted activity level that the user will experience during a predetermined time period following a sleep period. After training the MLA using historical ventilator and sensor data during step 504, the MLA may receive current ventilator data (step 501) and / or current sensor data (step 502) as input and determine the predicted activity level for the predetermined time period as output. The predetermined time period may be between approximately 0.1 hours and approximately 24 hours after the end of the sleep period, between approximately 3 hours and approximately 16 hours after the end of the sleep period, between approximately 8 hours and approximately 14 hours after the end of the sleep period, between approximately 12 hours after the end of the sleep period, between approximately 24 hours and approximately 1 week after the end of the sleep period, between approximately 12 hours and approximately 1 month after the end of the sleep period, etc.

[0112] When user 210 uses the respiratory therapy system as directed, typically, user 210 will experience better quality sleep, resulting in more activity throughout the day (e.g., steps), lower resting heart rate, lower heart rate variability, weight loss (e.g., by burning more calories), improved diet (e.g., consuming fewer calories), fewer headaches, or any combination thereof. The predicted activity level determined using the trained MLA during step 505 (step 504) may include predicted steps, predicted calories burned, predicted resting heart rate, predicted heart rate variability, predicted number of headaches, predicted weight loss, or any combination thereof during a predetermined time period (e.g., 12 hours after the end of the sleep period). The predicted activity level determined during step 505 may be displayed or transmitted to user 210 using the display device 172 of the mobile device, the display device 128 of the respiratory system 120, or both. Transmitting the determined predicted activity level to user 210 may help encourage or motivate user 210 to use the respiratory system 120 as directed.

[0113] Step 506 of method 500 includes using a trained MLA to determine a predictive measure of the user's reaction time to a standardized test after a sleep period during a predetermined time period (step 504). Generally, individuals experiencing higher quality sleep will be more alert and less drowsy than those experiencing lower quality sleep. Reaction time is measured or assessed using standardized tests such as click reaction time test, tap reaction time test, speed test, recognition test, resolution test, processing test, decoding test, reaction stick test, light board reaction test, or any combination thereof to measure or assess the speed at which an individual responds to stimuli. The predicted reaction time determined during step 506 can be displayed or transmitted to the user 210 using the display device 172 of the mobile device, the display device 128 of the respiratory system 120, or both. Transmitting the determined predicted reaction time to the user 210 can help encourage or motivate the user 210 to adhere to their prescribed use of the respiratory system 120.

[0114] Step 507 of method 500 includes using a trained MLA to determine a predicted subjective sleepiness score that the user will experience after the sleep period (step 504). The subjective sleepiness score is based on the user 210's subjective feeling after the sleep period and can be represented as a number between 0 and 100 (e.g., an integer) or using other markers (e.g., extremely or very sleepy, moderately sleepy, neutral, not sleepy, etc.). The predicted subjective sleepiness score determined during step 507 can be displayed or transmitted to the user 210 using the display device 172 of the mobile device, the display device 128 of the respiratory system 120, or both. Transmitting the predicted subjective sleepiness score to the user 210 can help encourage or motivate the user 210 to comply with their prescribed use of the respiratory system 120.

[0115] Step 508 of method 500 includes displaying a prompt requesting topical feedback from the user regarding the user's subjective feelings. The user's subjective feelings can be expressed using, for example, descriptive terms (e.g., poor, average, neutral, good, excellent, drowsy, tired, restful, alert, etc.), numbers (e.g., integers between 0 and 10, where 10 represents the highest subjective feeling and 0 represents the lowest subjective feeling), or both. An external device 170 can be used ( Figure 1 The prompt can be displayed on the display device 172 of the respiratory system 120, the display device 128 of the respiratory system 120, or both. The prompt typically instructs the user 210 to provide topical feedback and may be displayed or visually transmitted to the user 210 as alphanumeric text and / or other markings. Alternatively, the prompt may be audibly transmitted to the user 210 (e.g., using a speaker that is the same as or similar to speaker 142 and is connected to or integrated into external device 170).

[0116] Similarly, topic feedback from user 210 can be received by display device 172 of external device 170. For example, the requested topic feedback can be provided by selecting (e.g., clicking or tapping) one or more markers displayed on display device 172, by entering alphanumeric text (e.g., using a touch keyboard displayed on display device 172), by using speech-to-text (e.g., using a microphone that is the same as or similar to microphone 140 and is coupled to or integrated into external device 170), or any combination thereof.

[0117] The MLA described herein can be trained during step 504 using the topic feedback provided in response to the prompt during step 508. Object feedback provided during multiple sleep periods can be stored in the control system 110. Figure 1In the memory device 114, as historical object feedback, the MLA is trained in the same or similar manner as the historical ventilator data and historical sensor data described above. The MLA can be trained to determine a quality of life metric (e.g., represented as a numerical value or descriptive text), which is a combination of the sleep score or metric described herein and the topical feedback provided during step 508.

[0118] While methods 300, 400, and 500 are described herein as collecting physiological data during sleep periods, more generally, these methods can be implemented (e.g., using system 100) to generate or acquire the same or similar physiological data while the user is awake throughout the day. In such implementations, sensors for generating physiological data can be coupled to or integrated into an external device 170, which is typically located or situated near the user 210 for at least a portion of the day (e.g., in the user 210's pocket). Physiological data generated or acquired for the user outside of sleep periods can be compared with physiological data generated or acquired during sleep periods, or with previously recorded physiological data generated or acquired outside of sleep periods. These comparisons can be used, for example, in method 500 ( Figure 5 During step 504, the MLA is trained to predict activity levels (step 505), predict a measure of reaction time (step 506), predict a subjective sleep score (step 507), or any combination thereof.

[0119] As used here, sleep periods can be defined in various ways based on, for example, initial start and end times. (See reference) Figure 6 An exemplary timeline 600 for a sleep period is shown. Timeline 600 includes bedtime (t... 入床 ), sleep onset time (t) GTS ), initial sleep time (t) 睡 ), First micro-awakening MA1 and second micro-awakening MA2, awakening time (t) 醒 ) and wake-up time (t 起床 ).

[0120] As used herein, sleep periods can be defined in several ways. For example, a sleep period can be defined by an initial start time and an end time. In some implementations, a sleep period is the duration of a user's sleep; that is, a sleep period has a start time and an end time, and the user does not wake up until the end time during the sleep period. In other words, any period during which the user is awake is not included in a sleep period. According to this first definition of a sleep period, if a user wakes up and falls asleep multiple times during the same night, each sleep interval separated by the wake-up intervals is a sleep period.

[0121] Alternatively, in some implementations, sleep periods have start and end times, and during a sleep period, the user can remain awake as long as the continuous duration of wakefulness is below a wakefulness duration threshold, without the sleep period ending. The wakefulness duration threshold can be defined as a percentage of the sleep period. For example, the wakefulness duration threshold can be approximately 20 percent, 15 percent, 10 percent, 5 percent, 2 percent, or any other threshold percentage. In some implementations, the wakefulness duration threshold is defined as a fixed amount of time, such as approximately one hour, approximately thirty minutes, approximately fifteen minutes, approximately ten minutes, approximately five minutes, approximately two minutes, or any other amount of time.

[0122] In some implementations, a sleep period is defined as the entire time between the time a user first goes to bed at night and the time the user last leaves bed the following morning. In other words, a sleep period can be defined as the time period that begins at a first time (e.g., 10:00 PM) on a first date (e.g., Monday, September 7, 2020), which can be referred to as the current night, when the user first enters bed intending to go to sleep (e.g., if the user does not intend to watch TV or use their smartphone before going to sleep), and ends at a second time (e.g., 7:00 AM) on a second date (e.g., Tuesday, September 8, 2020), which can be referred to as the following morning, when the user first leaves bed with the intention of not returning to sleep the following morning.

[0123] In some implementations, users can manually define the start and / or end of sleep periods. For example, a user can select (e.g., by clicking or tapping) an external device 170 ( Figure 1 One or more user-selectable elements are displayed on the display device 172 to manually initiate or terminate a sleep period.

[0124] Reference Figure 6 Bedtime t 入床 Before the user falls asleep (e.g., when the user lies down or sits in bed), the user initially gets into bed (e.g., Figure 2 The time of bed admission (230) is associated with the bed's time. Bed admission time t can be identified based on the bed threshold duration. 入床 This distinguishes between when a user goes to bed for sleep and when they go to bed for other reasons (e.g., watching television). For example, the bed threshold duration could be at least approximately 10 minutes, at least approximately 20 minutes, at least approximately 30 minutes, at least approximately 45 minutes, at least approximately 1 hour, at least approximately 2 hours, etc. While this document describes bedtime t... 入床 But more generally, bedtime t入床 This can refer to the time when a user initially enters any location intended for sleeping (e.g., sofa, chair, sleeping bag, etc.).

[0125] Time to fall asleep (GTS) and the time it takes for a user to first try to fall asleep after getting into bed (t) 入床 This is related to [the concept of sleep duration]. For example, after going to bed, a user can engage in one or more activities to relax before attempting to sleep (e.g., reading, watching TV, listening to music, using external devices, etc.). Initial sleep time (t) 睡 ) is the time when a user initially falls asleep. For example, initial sleep time (t) 睡 This could be the time when the user initially enters the first non-REM sleep stage.

[0126] Awakening Time t 醒 This is the time associated with when a user wakes up without returning to sleep (e.g., the opposite of when a user wakes up at night and returns to sleep). A user may experience one of several unconscious micro-awakenings (e.g., micro-awakenings MA1 and MA2) with short durations (e.g., 5 seconds, 10 seconds, 30 seconds, 1 minute, etc.) after initially falling asleep. This is related to the wakefulness time t. 醒 Conversely, the user returns to sleep after each of the micro-awakenings MA1 and MA2. Similarly, the user may have one or more conscious awakenings (e.g., awakening A) after initial sleep onset (e.g., waking up to go to the bathroom, caring for a child or pet, sleepwalking, etc.). However, the user returns to sleep after awakening A. Therefore, the awakening time t 醒 It can be defined, for example, based on the duration of the arousal threshold (e.g., the user is aroused for at least 15 minutes, at least 20 minutes, at least 30 minutes, at least 1 hour, etc.).

[0127] Similarly, wake-up time t 起床 This is associated with the time a user leaves the bed and gets out of bed to end a sleep period (e.g., the opposite of a user getting up at night to go to the bathroom, care for a child or pet, or sleepwalk). In other words, wake-up time t 起床 This is the time a user last leaves bed and does not return until the next sleep period (e.g., the next night). Therefore, wake-up time t 起床 The bedtime t for the second subsequent sleep period can be defined, for example, based on the duration of the wake-up threshold (e.g., at least 15 minutes, at least 20 minutes, at least 30 minutes, at least 1 hour, etc.). Alternatively, the bedtime t for the second subsequent sleep period can be defined based on the duration of the wake-up threshold (e.g., at least 4 hours, at least 6 hours, at least 8 hours, at least 12 hours, etc.). 入床 time.

[0128] As above, in the initial t 入床 And the last t起床 During the night, a user can wake up and get out of bed more than once. In some implementations, the final wake-up time t 醒 and / or final wake-up time t 起床 It is identified or determined based on a predetermined threshold duration following an event (e.g., falling asleep or getting out of bed). This threshold duration can be customized for the user. For any time period after getting out of bed at night and then waking up and getting out of bed in the morning (when the user wakes up (t...)...) 醒 ) or get up (t 起床 ) and users going to bed (t 入床 ), fall asleep (t GTS ) or fall asleep (t 睡 For standard users (between approximately 8 and 14 hours), the threshold period can be used for approximately 12 to approximately 18 hours. For users who spend longer periods in bed, a shorter threshold period can be used (e.g., between approximately 8 and approximately 14 hours). The threshold period can be initially selected and / or adjusted later based on the system monitoring the user's sleep behavior.

[0129] Total time in bed (TIB) is the time to bed entry t 入床 and wake-up time t 起床 The duration between sleep and wake times. Total sleep time (TST) is the duration between initial sleep and wake times, excluding any conscious or unconscious awakenings and / or micro-awakenings in between. Typically, total sleep time (TST) will be shorter than total time in bed (TIB) (e.g., one minute shorter, ten minutes shorter, one hour shorter, etc.). For example, refer to... Figure 6 Timeline 600, Total Sleep Time (TST) spanning initial sleep time t 睡 and awakening time t 醒 The duration of sleep is between, but does not include, the duration of the first micro-awake MA1, the second micro-awake MA2, and awakening A. As shown in the figure, in this example, the total sleep time (TST) is shorter than the total time in bed (TIB).

[0130] In some implementations, Total Sleep Time (TST) can be defined as Persistent Total Sleep Time (PTST). In this implementation, Persistent Total Sleep Time excludes a predetermined initial portion or period of the first non-REM stage (e.g., a light sleep stage). For example, the predetermined initial portion could be between approximately 30 seconds and approximately 20 minutes, between approximately 1 minute and approximately 10 minutes, between approximately 3 minutes and approximately 5 minutes, etc. Persistent Total Sleep Time is a measure of sustained sleep and smooths the sleep-wake sleep graph. For example, when a user initially falls asleep, the user may be in the first non-REM stage for a very short time (e.g., approximately 30 seconds), then return to the wakeful stage for a very short time (e.g., one minute), and then return to the first non-REM stage. In this example, Persistent Total Sleep Time excludes the first instance of the first non-REM stage (e.g., approximately 30 seconds).

[0131] In some implementations, the sleep period is defined as the time from bedtime (t... 入床 ) start and at wake-up time (t 起床 The sleep period ends at the initial sleep time (t), meaning the sleep period is defined as the total time to bed (TIB). In some implementations, the sleep period is defined as the time to bed (t). 睡 ) begins and at the awakening time (t) 醒 End. In some implementations, the sleep period is defined as the total sleep time (TST). In some implementations, the sleep period is defined as the time from the start of sleep (t... GTS ) begins and at the awakening time (t) 醒 The sleep period ends at the time of falling asleep (t). In some implementations, the sleep period is defined as the time from falling asleep to falling asleep (t). GTS ) start and at wake-up time (t 起床 The sleep period ends at bedtime. In some implementations, the sleep period is defined as the time from bedtime (t...). 入床 ) begins and at the awakening time (t) 醒 The sleep period ends at the initial sleep time (t). In some implementations, the sleep period is defined as the time from the start of sleep (t). 醒 ) start and at wake-up time (t 起床 )Finish.

[0132] Reference Figure 7 This shows the corresponding timeline 600 according to some implementation methods. Figure 6 An exemplary sleep graph 700 is shown. As illustrated, the sleep graph 700 includes a sleep-wake signal 701, a wakefulness stage axis 710, a REM stage axis 720, a light sleep stage axis 730, and a deep sleep stage axis 740. The intersection of the sleep-wake signal 701 and one of axes 710-740 represents a sleep stage at any given time during a sleep period.

[0133] The sleep-wake signal 701 may be generated based on physiological data associated with the user (e.g., generated by one or more of the sensors 130 herein). The sleep-wake signal may indicate one or more sleep states, including wakefulness, relaxed wakefulness, micro-wakefulness, REM sleep, a first non-REM sleep stage, a second non-REM sleep stage, a third non-REM sleep stage, or any combination thereof. In some implementations, one or more of the first non-REM sleep stage, the second non-REM sleep stage, and the third non-REM sleep stage may be grouped together and categorized as light sleep stages or deep sleep stages. For example, light sleep stages may include the first non-REM sleep stage, while deep sleep stages may include the second and third non-REM sleep stages. Although in Figure 7 The sleep graph 700 shown includes a light sleep stage axis 730 and a deep sleep stage axis 740, but in some implementations, the sleep graph 700 may include axes for each of the first non-REM stage, the second non-REM stage, and the third non-REM stage. In other implementations, sleep-wake signals may also indicate respiratory signals, respiratory rate, inspiratory amplitude, expiratory amplitude, inspiratory-expiratory ratio, number of events per hour, event pattern, or any combination thereof. Information describing sleep-wake signals may be stored in memory device 114.

[0134] The sleep recorder 700 can be used to determine one or more sleep-related parameters, such as sleep onset wait time (SOL), wakefulness after sleep onset (WASO), sleep efficiency (SE), sleep segmentation index, sleep blockage, or any combination thereof.

[0135] Sleep onset wait time (SOL) is defined as the time to enter sleep (t). GTS ) and initial sleep time (t 睡The sleep start wait time (SOL) represents the time it takes for a user to actually fall asleep after their initial attempt to fall asleep. In some implementations, the sleep start wait time is defined as the continuous sleep start wait time (PSOL). The difference between PSOL and the initial sleep start wait time is that PSOL is defined as the duration between the time of falling asleep and a predetermined amount of continuous sleep. In some implementations, the predetermined amount of continuous sleep may include, for example, at least 10 minutes of sleep within a second non-REM stage, a third non-REM stage, and / or a REM stage (with no more than 2 minutes of wakefulness), and / or movement between the first non-REM stage. In other words, continuous sleep of up to, for example, 8 minutes within the second non-REM stage, the third non-REM stage, and / or the REM stage and / or the REM stage. In other implementations, the predetermined amount of continuous sleep may include at least 10 minutes of sleep within the first non-REM stage, the second non-REM stage, the third non-REM stage, and / or the REM stage after the initial sleep time. In this way, a predetermined amount of continuous sleep can exclude any micro-awakening (e.g., a ten-second micro-awakening does not restart the 10-minute session).

[0136] Post-sleep wake-onset (WASO) is associated with the total duration of a user's wakefulness between the initial sleep time and wake time. Therefore, WASO includes brief and micro-awake periods during the sleep period (e.g., Figure 4 The micro-awakenings MA1 and MA2 shown are either conscious or unconscious. In some implementations, a sleep-wake attack (WASO) is defined as a sustained sleep-wake attack (PWASO) that consists only of the total duration of arousal having a predetermined length (e.g., greater than 10 seconds, greater than 30 seconds, greater than 60 seconds, greater than about 5 minutes, greater than about 10 minutes, etc.).

[0137] Sleep efficiency (SE) is defined as the ratio of total time spent in bed (TIB) to total sleep time (TST). For example, if the total time spent in bed is 8 hours and the total sleep time is 7.5 hours, then the sleep efficiency for that sleep period is 93.75%. Sleep efficiency reflects a user's sleep hygiene. For example, if a user goes to bed before sleep and spends time engaging in other activities (e.g., watching television), sleep efficiency will decrease (e.g., the user is penalized). In some implementations, sleep efficiency (SE) can be calculated based on total time spent in bed (TIB) and the total time the user attempts to sleep. In such implementations, the total time the user attempts to sleep is defined as the duration between the time to fall asleep (GTS) and the wake-up time described here. For example, if the total sleep time is 8 hours (e.g., between 11 PM and 7 AM), the time to fall asleep is 10:45 AM, and the wake-up time is 7:15 AM, then in such an implementation, the sleep efficiency parameter is calculated to be approximately 94%.

[0138] The segmentation index is determined at least in part based on the number of awakenings during sleep periods. For example, if a user has two micro-awakes (e.g., Figure 7 As shown in the micro-awakenings MA1 and MA2, the segmentation index can be represented as 2. In some implementations, the segmentation index is scaled between a predetermined range of integers (e.g., between 0 and 10).

[0139] Sleep blocks are associated with the transition between any sleep stage (e.g., first non-REM stage, second non-REM stage, third non-REM stage, and / or REM stage) and the waking stage. Sleep blocks can be calculated at a resolution of, for example, 30 seconds.

[0140] In some implementations, the systems and methods described herein may include generating or analyzing a sleep map including sleep-wake signals to determine or identify bedtime (t) based at least in part on the sleep-wake signals of the sleep map. 入床 ), sleep onset time (t) GTS ), initial sleep time (t) 睡 ), one or more first micro-awakenings (e.g., MA1 and MA2), awakening time (t) 醒 ), wake-up time (t) 起床 ), or any combination thereof.

[0141] In other implementations, one or more of the sensors 130 can be used to determine or identify the bed entry time (t). 入床 ), sleep onset time (t) GTS ), initial sleep time (t) 睡 ), one or more first micro-awakenings (e.g., MA1 and MA2), awakening time (t) 醒 ), wake-up time (t)起床 (e.g., motion sensor 138, microphone 140, camera 150, or any combination thereof), which in turn defines the sleep period. For example, bedtime t can be determined based on data generated, for example, by motion sensor 138, microphone 140, camera 150, or any combination thereof. 入床 The time to fall asleep can be determined based on, for example, data from motion sensor 138 (e.g., data indicating that the user is not moving), data from camera 150 (e.g., data indicating that the user is not moving and / or that the user has turned off the lights), data from microphone 140 (e.g., data indicating that the TV is being turned off), data from external device 170 (e.g., data indicating that the user is no longer using external device 170), data from pressure sensor 132 and / or flow sensor 134 (e.g., data indicating that the user turns on breathing device 122, data indicating that the user wears user interface 124, etc.), or any combination thereof.

[0142] One or more elements or aspects or steps or any part thereof from one or more of the claims may be combined with one or more other elements or aspects or steps or any part thereof from other claims to form one or more additional implementations of the invention.

[0143] While the invention has been described with reference to one or more specific embodiments or implementations, those skilled in the art will recognize that many changes can be made thereto without departing from the spirit and scope of the invention. Each of these implementations and their obvious variations is considered to fall within the spirit and scope of the invention. Additional implementations of aspects of the invention are also contemplated that can combine any number of features from any of the implementations described herein.

Claims

1. A method comprising: When the user interface of the respiratory therapy system engages with the user, it receives ventilator data associated with the user during sleep periods. Sensor data associated with the user is received during the sleep period, both when the user interface is engaged with the user and when the user interface is not engaged with the user. The ventilator data and the sensor data are accumulated, wherein the ventilator data includes historical ventilator data and current ventilator data, and the sensor data includes historical sensor data and current sensor data; as well as The machine learning algorithm is trained using the historical ventilator data and the historical sensor data, such that the machine learning algorithm is configured to (i) receive the current ventilator data and the current sensor data as input, and (ii) determine the predicted activity level that the user will experience during a predetermined time period as output.

2. The method of claim 1, wherein the predetermined time period is between approximately 1 hour and approximately 12 hours after the end of the sleep period.

3. The method of claim 1 or 2, wherein the predicted activity level includes the predicted number of steps, the predicted number of calories burned, or both.

4. The method of any one of claims 1 to 2, wherein the machine learning algorithm is further trained to determine a predictive metric of the reaction time of a standardized test that the user will experience after the sleep period as output.

5. The method of any one of claims 1 to 2, wherein the machine learning algorithm is further trained to determine a predicted subjective sleepiness score that the user will experience after the sleep period as output.

6. The method of any one of claims 1 to 2, further comprising: Subjective feedback from the user is received after the sleep period, wherein training the machine learning algorithm includes training the machine learning algorithm using the subjective feedback from the user.

7. A system comprising: The user interface is configured to engage the user during sleep periods; A breathing device connected to the user interface via a catheter is configured to generate ventilator data associated with the user during the sleep period when the user interface is engaged with the user. A sensor is configured to generate sensor data associated with the user of the breathing device during the sleep period, both when the user interface is engaged with the user and when the user interface is not engaged with the user. Memory that stores machine-readable instructions; as well as The control system includes one or more processors configured to execute the machine-readable instructions to: Accumulate the ventilator data and the sensor data, wherein the ventilator data includes historical ventilator data and current ventilator data, and the sensor data includes historical sensor data and current sensor data; and The machine learning algorithm is trained using the historical ventilator data and the historical sensor data, such that the machine learning algorithm is configured to (i) receive the current ventilator data and the current sensor data as input, and (ii) determine the predicted activity level that the user will experience during a predetermined time period as output.

8. The system of claim 7, wherein the predetermined time period is between approximately 1 hour and approximately 12 hours after the end of the sleep period.

9. The system of claim 7 or 8, wherein the predicted activity level includes the predicted number of steps, the predicted number of calories burned, or both.

10. The system of any one of claims 7 to 8, wherein the control system is further configured to execute the machine-readable instructions to train the machine learning algorithm to determine a predictive metric of the reaction time of a standardized test that the user will undergo after the sleep period as output.

11. The system of any one of claims 7 to 8, wherein the control system is further configured to execute the machine-readable instructions to train the machine learning algorithm to determine, as output, a predicted subjective sleepiness score that the user will experience after the sleep period.

12. The system of any one of claims 7 to 8, wherein the control system is further configured to execute the machine-readable instructions such that a prompt is displayed on a display device, the prompt requesting the user's subjective feedback on the sleep period.

13. The system of claim 12, wherein training the machine learning algorithm includes training the machine learning algorithm with subjective feedback received from the user in response to the prompt.

14. A system comprising: The user interface is configured to engage the user during sleep periods; A breathing device connected to the user interface via a catheter is configured to generate first physiological data associated with the user during the sleep period when the user interface is engaged with the user. A sensor is configured to generate second physiological data associated with the user of the breathing device during the sleep period, both when the user interface is engaged with the user and when the user interface is not engaged with the user. Memory that stores machine-readable instructions; as well as The control system includes one or more processors configured to execute the machine-readable instructions to: Analyze the first physiological data, the second physiological data, or both to determine a first set of sleep-related parameters of the user during a first portion of the sleep period when the user interface is engaged with the user; The second physiological data is analyzed to determine a second set of sleep-related parameters for the user during a second portion of the sleep period when the user interface is not engaged with the user. as well as Generate a report associated with the first set of sleep-related parameters and the second set of sleep-related parameters, wherein the report includes a comparison of at least a portion of the first set of sleep-related parameters with at least a portion of the second set of sleep-related parameters.

15. The system of claim 14, wherein, The control system is further configured to execute the machine-readable instructions to modify one or more parameters of the breathing device, at least in part, based on the first physiological data, the second physiological data, the generated report, or any combination thereof.

16. The system of claim 15, wherein modifying the one or more parameters includes modifying the ramp time of the breathing device, modifying the pressure setting during the sleep period, modifying the pressure setting in response to determining that the user has woken up from the sleep period, or any combination thereof.

17. The system of any one of claims 15 to 16, wherein the report indicates the user's sleep quality during a first portion of the sleep period in which the mask is engaged with the user and the user's sleep quality during a second portion of the sleep period in which the mask is not engaged with the user.

18. The system of any one of claims 15 to 16, wherein the generated report includes recommendations regarding the use of the ventilator device.

19. The system of any one of claims 15 to 16, wherein the generated report includes suggestions for adjusting one or more sleep habits of the user.

20. The system of claim 19, wherein the suggestion to adjust one or more sleep habits of the user comprises: Suggestions for modifying (i) the time when the user goes to bed, (ii) the time when the user wakes up, (iii) the duration of the sleep period, (iv) the amount of time the user wears the mask during the sleep period, and (v) any combination thereof.

21. The system of any one of claims 15 to 16, wherein the report comprises: (i) a sleep quality metric indicating the user’s sleep quality during the sleep period, and (ii) a suggestion to adjust one or more of the user’s sleep habits to help improve the sleep quality metric.

22. The system of claim 21, wherein the report further comprises (iii) a predicted quantitative improvement in sleep quality metrics corresponding to the user’s implementation of the recommended adjustments to the one or more sleep habits.

23. The system of claim 22, wherein the recommended adjustments for the one or more sleep habits include recommending an increase in the average usage time of the breathing device per sleep period.

24. The system of any one of claims 15 to 16, wherein the sensor or another sensor is configured to generate third physiological data associated with a bed partner of the user of the breathing device during the sleep period, and the generated report includes a sleep quality metric representing the sleep quality of the bed partner during the sleep period.

25. The system of claim 24, wherein the report further includes recommendations to adjust one or more of the user's sleep habits to help improve the bed partner's sleep quality metrics.

26. The system of claim 25, wherein the report further comprises a predicted quantitative improvement in the bed partner’s sleep quality metrics corresponding to the user’s implementation of the suggested adjustments to the user’s one or more sleep habits.

27. The system of claim 24, wherein the suggested adjustments for the user’s one or more sleep habits include suggesting an increase in the average usage time of the breathing device per sleep period.

28. The system of any one of claims 15 to 16, wherein the control system is further configured to execute the machine-readable instructions to calibrate the sensor based at least on (i) the first physiological data generated during the first portion of the sleep period in which the user interface is engaged with the user and (ii) the second physiological data generated during the first portion of the sleep period in which the user interface is engaged with the user.

29. The system of claim 28, wherein calibrating the sensor includes modifying one or more parameters of the sensor.

30. The system of claim 28, wherein calibrating the sensor includes modifying one or more parameters of the machine-readable instructions.

31. The system of claim 29, wherein one or more parameters of the sensor include frequency, phase, power, amplitude, intensity, signal modulation of the sensor, beam pattern, on and off of one or more antennas of the sensor, beamforming, physical location of one or more antennas of the sensor, physical location of the sensor, or any combination thereof.

32. The system of any one of claims 15 to 16, wherein the sensor is coupled to an external device that is separate from and different from the breathing device.

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