System and method for estimating subjective comfort level - Patents.com

Abstract

A method for predicting a subjective comfort level of a user of a respiratory treatment system is disclosed as follows: data associated with a user of the respiratory treatment system during a treatment session is received; at least one parameter associated with the user is determined based at least in part on a first portion of the received data; a comfort score is determined based at least in part on the determined at least one parameter; the comfort score is indicative of a subjective comfort level of the user of the respiratory treatment system during at least a portion of the treatment session.

Description

[Technical Field]

[0001] (CROSS-REFERENCE TO RELATED APPLICATIONS) This application claims priority to and benefit of U.S. Provisional Patent Application No. 63 / 143,381, filed January 29, 2021, which is incorporated herein by reference in its entirety.

[0002] The present disclosure relates generally to respiratory treatment systems, and more particularly to systems and methods for estimating a subjective comfort level of a user of a respiratory treatment system. [Background technology]

[0003] Various systems exist to provide relief to users experiencing sleep apnea and related breathing disorders. There is a range of breathing disorders that affect users. Some disorders are characterized by specific events (e.g., apnea, hypopnea, hyperpnea, or any combination thereof). Examples of breathing disorders include periodic limb movement disorder (PLMD), obstructive sleep apnea (OSA), Cheyne-Stokes respiration (CSR), respiratory insufficiency, obesity hyperventilation syndrome (OHS), chronic obstructive pulmonary disease (COPD), neuromuscular diseases (NMD), and chest wall disease. Summary of the Invention [Problem to be solved by the invention]

[0004] Respiratory therapy systems can improve a user's sleep quality. However, if the initial user experience is poor due to discomfort, the user is likely to abandon therapy in search of a better sleep experience, sacrificing sleep quality. Because the user is not receiving quality sleep and / or appropriate treatment for sleep-disordered breathing, delaying treatment can lead to a worsening quality of life. Therefore, there is a need for a system and method for estimating a user's subjective comfort level of a respiratory therapy system. The present disclosure aims to solve these and other problems. [Means for solving the problem]

[0005] According to some embodiments of the present disclosure, a method for estimating a subjective comfort level of a user of a respiratory treatment system is disclosed as follows: Data related to a user of the respiratory treatment system is received during a treatment session; At least one parameter related to the user is determined based at least in part on a first portion of the received data; A comfort score is determined based at least in part on the determined at least one parameter; The comfort score is indicative of a subjective comfort level of the user of the respiratory treatment system during at least a portion of the treatment session.

[0006] According to some embodiments of the present disclosure, a system includes a control system and a memory. The control system includes one or more processors. Machine-readable instructions are stored in the memory. The control system is coupled to the memory. When the machine-executable instructions in the memory are executed by at least one of the one or more processors of the control system, any of the methods disclosed herein are performed.

[0007] According to some embodiments of the present disclosure, a system for estimating a subjective comfort level of a user of a respiratory treatment system is disclosed as follows: The system includes a control system configured to perform any of the methods disclosed herein.

[0008] According to some embodiments of the present disclosure, there is provided a computer program product comprising instructions that, when executed by a computer, cause the computer to perform any of the methods disclosed herein. In some embodiments, the computer program product is a non-transitory computer-readable medium.

[0009] According to some embodiments of the present disclosure, a system includes a control system and a memory. The control system includes one or more processors. The memory stores machine-readable instructions. The control system is coupled to the memory and configured to implement a method for estimating a subjective comfort level of a user of a respiratory treatment system. The method includes receiving data related to a user of the respiratory treatment system during a treatment session. The method also includes determining at least one parameter related to the user based at least in part on a first portion of the received data. The method further includes determining a comfort score indicative of a subjective comfort level of the user of the respiratory treatment system during at least a portion of the treatment session based at least in part on the determined at least one parameter.

[0010] The foregoing and additional aspects and implementations of the present disclosure will become apparent to those skilled in the art upon consideration of the detailed description of various embodiments and / or implementations in conjunction with the accompanying drawings, a brief description of which is provided.

[0011] The foregoing and other advantages of the present disclosure will become apparent from a reading of the following detailed description and a review of the accompanying drawings. [Brief explanation of the drawings]

[0012] [Figure 1] FIG. 1 is a functional block diagram of a system for estimating a subjective comfort level of a user of a respiratory treatment system, according to some embodiments of the present disclosure. [Figure 2] FIG. 2 is a perspective view of at least a portion of the system of FIG. 1, a user wearing a full face mask, and a bed partner, according to some embodiments of the present disclosure. [Figure 3] 1 shows a flowchart of a method for estimating a subjective comfort level of a user of a respiratory treatment system, according to some embodiments of the present disclosure. DETAILED DESCRIPTION OF THE INVENTION

[0013] While the present disclosure is susceptible to various modifications and alternative forms, specific implementations and embodiments thereof have been shown by way of example in the accompanying drawings and will be described in detail herein. It is to be understood, however, that the disclosure is not intended to be limited to the particular forms disclosed, but on the contrary, the disclosure is to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the disclosure as defined by the appended claims.

[0014] The present disclosure will be described with reference to the accompanying drawings, wherein the same reference numerals are used throughout the accompanying drawings to represent like or equivalent elements. The drawings are not to scale and are provided solely for the purpose of illustrating the present invention. Several aspects of the present disclosure will be described below with reference to exemplary applications.

[0015] Various systems exist to provide relief to users experiencing sleep apnea and related breathing disorders. There is a range of breathing disorders that affect users. Examples of breathing disorders include periodic limb movement disorder (PLMD), obstructive sleep apnea (OSA), Cheyne-Stokes respiration (CSR), respiratory insufficiency, obesity hyperventilation syndrome (OHS), chronic obstructive pulmonary disease (COPD), neuromuscular disorders (NMD), and chest wall disorders. These and other disorders are characterized by specific events that occur while an individual is sleeping (e.g., snoring, apnea, hypoventilation, restless legs, sleep disturbances, choking, increased heart rate, difficulty breathing, asthma attacks, epileptic seizures, sudden illness attacks, or any combination thereof).

[0016] The apnea-hypopnea index (AHI) is an indicator of the severity of sleep apnea during sleep. The AHI is calculated by dividing the number of apnea and / or hypopnea events experienced by the user during a sleep session by the total number of sleep minutes during the sleep session. For example, an event may be an apnea lasting at least 10 seconds. An AHI of less than 5 is considered normal. An AHI of 5 or greater but less than 15 is considered to indicate mild sleep apnea. An AHI of 15 or greater but less than 30 is considered to indicate moderate sleep apnea. An AHI of 30 or greater is considered to indicate severe sleep apnea. In children, an AHI of 1 or greater is considered abnormal. If the AHI is normal, or if the AHI is normal or mild, sleep apnea can be considered "controlled." The AHI can also be used in combination with oxygen desaturation levels to indicate the severity of obstructive sleep apnea. An AHI calculated based on the apnea and / or hypopnea events experienced by the user during sleep and respiratory therapy is referred to as a "residual" AHI.

[0017] To alleviate some of these sleep-related and / or breathing disorders, users may be prescribed breathing devices or systems. For example, continuous positive airway pressure (CPAP) machines may be used to increase air pressure in the breathing device user's throat and prevent the airway from closing and / or narrowing during sleep. Thus, the goal of treatment is to reduce the AHI, ideally to a normal (or other acceptable) level, and improve the user's quality of sleep.

[0018] While these breathing devices or systems can improve a user's sleep quality, they can also reduce the user's comfort. For example, when trying to sleep, a user may experience discomfort from forced air into their airway. Other discomforts may be related to the noise of the system, sweating or stickiness caused by or associated with the mask, or the discomfort and perceived claustrophobia associated with wearing the mask. After a period of sleep, the user may experience belching, stomach bloating, stomach distension, painful bloating, and pain due to aerobic airflow. Aerobic airflow occurs when air enters the esophagus and enters the stomach rather than the airways and lungs as expected. Other discomforts after sleep include dryness of the nose, throat, and eyes. This can be due to high airflow caused by leaks or uncomfortable airflow due to a wet condition.

[0019] While subjective feedback from the user can be used to identify patient discomfort, objective measures can also be used alone or in combination with subjective feedback to identify discomfort. For example, detection of blinking or facial muscle movements due to physiological responses to air expelled by a poor mask seal can be used to estimate discomfort due to unintentional leaks, which may dry or otherwise irritate the eyes.

[0020] The discomfort experienced by those prescribed the use of a respiratory therapy system is typically more pronounced the first time the user uses the respiratory therapy system for sleep therapy. If the initial user experience is poor due to poor treatment comfort, the user is more likely to abandon treatment or sacrifice treatment and / or sleep quality in pursuit of better treatment and / or sleep comfort. Delaying treatment can lead to a worsening quality of life because the user is not receiving quality sleep and / or appropriate treatment for sleep-disordered breathing.

[0021] In some cases, the user's experience may not be fully reflected in the current treatment score. For example, myAir TMThe score (ResMed's proprietary treatment score) is calculated based on duration of use, user interface sealing / leakage, number of events per hour, and whether the mask is on or off. Furthermore, the comfort experience is highly subjective to the user. Female users may have a lower AHI but experience more personal discomfort than male users. In particular, male users may experience fewer events during non-eye movement periods but more events during eye movement periods. Female users with a relatively lower AHI compared to male users may exhibit similar symptoms to male users, but their AHI may be much higher if their REM sleep is particularly severely disrupted by apneic events. As further described herein, the comfort score can be used to determine a treatment and / or sleep score, for example, as a separate component of the treatment and / or sleep score, or by weighting or adjusting one or more components of the treatment and / or sleep score based on the comfort score.

[0022] Although comfort is a subjective sensation, it can have objective characteristics. This disclosure relates to systems and methods for estimating a user's subjective comfort level (e.g., outputting a comfort score) based at least in part on those objective characteristics. If a user knows that their comfort score is low, abnormal, or potentially below a threshold, they may be motivated and guided to improve it.

[0023] 1, a functional block diagram of a system 100 for estimating a subjective comfort level of a user of a respiratory treatment system is shown. System 100 includes a comfort score 102, a control system 110, a storage device 114, an electronic interface 119, one or more sensors 130, and one or more user devices 170. In some embodiments, system 100 also optionally includes a respiratory treatment system 120, a blood pressure device 182, an active tracker 190, or any combination thereof.

[0024] The comfort score 102 determines a user's comfort score based on at least the comfort parameters 104 (e.g., calculated and / or derived from comfort-related parameters) and, optionally, based on a base weight value 106. The comfort score indicates a subjective comfort level of a user of the respiratory treatment system. The comfort parameters 104 are determined based on data, such as data collected by sensors 130 and / or input by the user. The base weight value 106 is a modifier applied to a comfort parameter depending on the importance of the particular parameter, the impact of the particular parameter on the user's comfort level (or its relative impact compared to one or more other parameters), or the correlation of the particular parameter with the user's comfort level (or its relative correlation compared to one or more other parameters). That is, the comfort score is a function of both the comfort parameters 104 and the base weight value 106. The comfort score can be any useful expression or value, such as a number, a word, a text string, a character, a symbol, or a machine-readable code string.

[0025] The control system 110 includes one or more processors 112 (hereinafter referred to as processors 112). The control system 110 is generally used to control (e.g., operate) various components of the system 100 and / or analyze data acquired and / or generated by the components of the system 100. The processor 112 may be a general-purpose processor or a special-purpose processor or microprocessor. While one processor 112 is shown in FIG. 1 , the control system 110 may include any suitable number of processors (e.g., one processor, two processors, five processors, ten processors, etc.), which may be located within a single housing or may be separate from one another. The control system 110 may be coupled to and / or located within the housing of the user device 170, the active tracker 190, and / or one or more sensors 130, for example. The control system 110 may be centralized (within one such housing) or distributed (within two or more physically distinct such housings). In this embodiment including two or more housings containing control system 110, such housings may be located adjacent to and / or distant from one another.

[0026] The storage device 114 stores machine-readable instructions executable by the processor 112 of the control system 110. The storage device 114 may be any suitable computer-readable storage device or medium, such as a random or serial access memory device, a hard disk drive, a solid-state drive, a flash memory device, or the like. While one storage device 114 is shown in FIG. 1 , the system 100 may include any suitable number of storage devices 114 (e.g., one memory device, two memory devices, five memory devices, ten memory devices, etc.). The storage device 114 may be coupled to and / or located within the housing of the respiratory apparatus 122, within the housing of the user device 170, within the housing of the active tracker 190, within the housing of one or more sensors 130, or any combination thereof. Like the control system 110, the storage device 114 may be centralized (within one such enclosure) or distributed (within two or more such physically distinct enclosures).

[0027] In some embodiments, the storage device 114 (FIG. 1) stores a user profile associated with the user, which can be implemented as the comfort parameters 104 used to determine the comfort score. The user profile can include, for example, demographic information associated with the user, biometric information associated with the user, medical information associated with the user, self-reported user feedback, sleep parameters associated with the user (e.g., sleep-related parameters recorded from one or more sleep sessions), or any combination thereof. The demographic information can include, for example, information indicative of the user's age, the user's gender, the user's race, the user's ethnicity, the user's geographic location, the user's travel history, relationship status, whether the user has one or more pets, whether the user has family, a family history of a health condition, the user's employment status, the user's education status, the user's socioeconomic status, or any combination thereof. The medical information can include, for example, information indicative of one or more medical conditions associated with the user, the user's medication use, or both. The medical information data can also include Multiple Sleep Latency Test (MSLT) test results or scores and / or Pittsburgh Sleep Quality Index (PSQI) scores or values. The medical information data may include results from one or more of a multi-lead sleep map (PSG) test, CPAP titration, or home sleep testing (HST), respiratory therapy system settings from one or more sleep sessions, sleep-related respiratory events from one or more sleep sessions, or any combination thereof. The self-reported user feedback may include information indicating a self-reported subjective therapy score (e.g., poor, average, excellent), a self-reported user's subjective stress level, a self-reported user's subjective fatigue level, a self-reported user's subjective health status, a life event recently experienced by the user, or any combination thereof. The user profile information may be updated at any time, such as daily (e.g., between sleep sessions), weekly, monthly, or yearly.In some embodiments, storage device 114 stores media content that can be displayed on display device 128 and / or display device 172 .

[0028] The electronic interface 119 is configured to receive data (e.g., physiological data, flow rate data, pressure data, motion data, acoustic data, etc.) from one or more sensors 130, which may be stored in the storage device 114 and / or analyzed by the processor 112 of the control system 110. The received data, such as physiological data, flow rate data, pressure data, motion data, acoustic data, etc., may be used to determine and / or calculate comfort parameters 104 to determine a comfort score. The electronic interface 119 may communicate with the one or more sensors 130 using a wired or wireless connection (e.g., using an RF communication protocol, a Wi-Fi communication protocol, a Bluetooth® communication protocol, an IR communication protocol, via a cellular network, or via any other optical communication protocol, etc.). The electronic interface 119 may include an antenna, a receiver (e.g., an RF receiver), a transmitter (e.g., an RF transmitter), a transceiver, or any combination thereof. Electronic interface 119 may also include one or more processors and / or one or more storage devices the same as or similar to processor 112 and storage device 114 described herein. In some embodiments, electronic interface 119 is coupled to or integrated with user device 170. In other embodiments, electronic interface 119 is coupled to control system 110 and / or storage device 114 or integrated with control system 110 or storage device 114 (e.g., within a housing).

[0029] Respiratory treatment system 120 may include a respiratory pressure therapy (RPT) device 122 (referred to herein as respiratory device 122 or respiratory treatment device), a user interface 124, a conduit 126 (also referred to as tubing or air circuit), a display 128, a humidification tank 129, a reservoir 180, or any combination thereof. In some embodiments, control system 110, memory device 114, display 128, one or more sensors 130, and humidification tank 129 are part of respiratory device 122. Respiratory pressure therapy refers to the delivery of air to a user's airway entrance at a controlled target pressure nominally positive relative to the atmosphere (as opposed to negative pressure therapy, e.g., tank-type ventilators or chest pads) throughout the user's respiratory cycle. Respiratory treatment system 120 is generally used to treat individuals with one or more sleep-related breathing disorders (e.g., obstructive sleep apnea, central sleep apnea, or mixed sleep apnea).

[0030] Respiratory apparatus 122 is generally used to generate pressurized air delivered to a user (e.g., using one or more motors driving one or more compressors). In some embodiments, respiratory apparatus 122 generates a continuously constant air pressure delivered to a user. In other embodiments, respiratory apparatus 122 generates two or more predetermined pressures (e.g., a first predetermined air pressure and a second predetermined air pressure). In other embodiments, respiratory apparatus 122 is configured to generate a variety of different air pressures within a predetermined range. For example, respiratory apparatus 122 can 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. Respiratory apparatus 122 can also deliver pressurized air at a predetermined flow rate, for example, between about 20 L / min and about 150 L / min, while maintaining a positive pressure (relative to ambient pressure).

[0031] The user interface 124 engages a portion of the user's face and delivers pressurized air from the breathing apparatus 122 to the user's airway to help prevent narrowing and / or collapse of the airway while sleeping. This may also increase the user's oxygen intake while sleeping. Generally, the user interface 124 engages the user's face such that the pressurized air is delivered to the user's airway through the user's mouth, nose, or both the user's mouth and nose. Together, the breathing apparatus 122, the user interface 124, and the conduit 126 form an air passageway fluidly coupled to the user's airway. The pressurized air also increases the user's oxygen intake while sleeping.

[0032] Depending on the treatment being applied, the user interface 124 may, for example, form a seal with an area or portion of the user's face to facilitate delivery of gas at a pressure varying sufficiently relative to ambient pressure to achieve treatment at a positive pressure of about 10 cm HO relative to ambient pressure. For other forms of treatment, such as oxygen delivery, the user interface may not include a seal sufficient to facilitate delivery of the gas supply to the gas passageway at a positive pressure of about 10 cm HO.

[0033] As shown in FIG. 2, in some embodiments, the user interface 124 is or includes a face mask (e.g., a full-face mask) that covers the user's nose and mouth. Alternatively, in some embodiments, the user interface 124 is a nose mask cover that supplies air to the user's nose or a nasal pillow mask that supplies air directly to the user's nostrils. The user interface 124 may include a number of straps (e.g., including hook-and-ring fasteners) for positioning and / or stabilizing the interface on a portion of the user (e.g., face) and a form-retaining liner (e.g., silicone, plastic, foam, etc.) that facilitates providing an airtight seal between the user interface 124 and the user. The user interface 124 may also include one or more vents for allowing carbon dioxide and other gases exhaled by the user 210 to escape. In other embodiments, the user interface 124 includes a mouthpiece (e.g., a night-protection mouthpiece molded to fit the user's teeth, a mandibular reset device, etc.).

[0034] The conduit 126 (also called an air circuit or duct) allows air flow between two components of the respiratory treatment system 120 (e.g., the respiratory device 122 and the user interface 124). In some embodiments, there may be separate branches of the conduit for inhalation and exhalation. In other embodiments, a single limb conduit is used for inhalation and exhalation.

[0035] One or more of the breathing apparatus 122, the user interface 124, the conduit 126, the display device 128, and the humidification tank 129 may include one or more sensors (e.g., a pressure sensor, a flow rate sensor, a humidity sensor, a temperature sensor, or more generally, any other sensor 130 described herein) that may be used, for example, to measure the air pressure and / or flow rate of the pressurized air supplied by the breathing apparatus 122.

[0036] The display device 128 is typically used to display images, including still images, video images, or both, and / or information about the respiratory apparatus 122. For example, the display device 128 may provide information about the status of the respiratory apparatus 122 (e.g., whether the respiratory apparatus 122 is open or closed, the pressure of the air being delivered by the respiratory apparatus 122, the temperature of the gas being delivered by the respiratory apparatus 122, etc.) and / or other information (e.g., a sleep score and / or a treatment score) (myAir TM and U.S. Patent Publication No. 2017 / 0311879, each of which is incorporated herein by reference), the current date and time, user personal information 210, etc. In some embodiments, display device 128 functions 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, an OLED display, an LCD display, etc. The input interface may be, for example, a touchscreen or touch sensor board, a mouse, a keyboard, or any sensor system configured to sense input by a human user interacting with breathing apparatus 122.

[0037] The humidification tank 129 is coupled to or integrated with the breathing apparatus 122. The humidification tank 129 includes a water reservoir that can be used to humidify the pressurized air delivered from the breather apparatus 122. The breathing apparatus 122 can include a heater that heats the water in the humidification tank 129 to humidify the pressurized air delivered to the user. Additionally, in some embodiments, the conduit 126 can further include a heating element (e.g., coupled to and / or embedded in the conduit 126) that heats the pressurized air delivered to the user. The humidification tank 129 can be fluidly connected to a water vapor inlet of the air passage and deliver water vapor to the air passage via the water vapor inlet, or can be in line with the air passage and formed as part of the air passage itself. In other embodiments, the breathing apparatus 122 or the conduit 126 can include a waterless humidifier. The waterless humidifier can incorporate a sensor that interfaces with other sensors located elsewhere in the system 100.

[0038] In some embodiments, system 100 may be used to transfer at least a portion of a substance from container 180 to a user's airway based at least in part on physiological data, sleep-related parameters, other data or information, or any combination thereof. Generally, altering the partial delivery of a substance to the airway may include (i) initiating partial delivery of a substance to the airway, (ii) terminating partial delivery of a substance to the airway, (iii) altering the amount of partial delivery of a substance to the airway, (iv) altering a temporal characteristic of the partial delivery of a substance to the airway, (v) altering a quantitative characteristic of the partial delivery of a substance to the airway, (vi) altering any parameter associated with the partial delivery of a substance to the airway, or (vii) any combination of (i)-(vi).

[0039] Varying the time characteristics of this partial material transport into the air passageway can be independent of varying the material transport rate, starting and / or ending at different times, continuing for different time periods, varying the time distribution or characteristics of transport, varying the time distribution and amount distribution, etc. Independent time and amount variations ensure that the mass of material per release can be varied in addition to varying the frequency of material release. In this manner, various combinations of release frequency and amount can be achieved (e.g., higher frequency but lower release amount, higher frequency and higher amount, lower frequency and higher amount, lower frequency and lower amount, etc.). Other variations for the transport of this partial material into the air passageway can also be utilized.

[0040] The respiratory treatment system 120 can be used, for example, as an artificial respiration device or as a positive airway pressure (PAP) system, such as a continuous positive airway pressure (CPAP) system, an automatic positive airway pressure (APAP), a bi-level or variable positive airway pressure system (BPAP or VPAP), or any combination thereof. A CPAP system delivers a predetermined air pressure (e.g., determined by a sleep physician) to a user. An APAP system automatically varies the air pressure delivered to a user, for example, based on respiratory data associated with the user. A BPAP or VPAP system is configured to deliver a first predetermined pressure (e.g., inspiratory port positive pressure or IPAP) and a second predetermined pressure (e.g., expiratory port positive pressure or EPAP) that is lower than the first predetermined pressure.

[0041] Referring to Figure 2, a portion of the system 100 (Figure 1) is shown according to some embodiments. A user 210 and a bed partner 220 of the respiratory treatment system 120 are positioned in a bed 230 and lying on a mattress 232. Although a motion sensor 138, a blood pressure device 182, and an active tracker 190 are shown, any one or more of the sensors 130 may be used to generate or monitor comfort parameters 104 during a treatment, sleep, and / or rest session of the user 210.

[0042] The user interface 124 is a face mask (e.g., a full-face mask) that covers the nose and mouth of the user 210. Alternatively, the user interface 124 may be a nose cover that supplies air to the nose of the user 210, or a nasal pillow mask that delivers air directly to the nostrils of the user 210. The user interface 124 may include a number of straps (e.g., including hook-and-ring fasteners) for positioning and / or stabilizing the interface on a portion of the user 210 (e.g., the face) and a shape-retaining liner (e.g., silicone, plastic, foam, etc.) that facilitates providing an airtight seal between the user interface 124 and the user 210. The user interface 124 may also include one or more vents for allowing carbon dioxide and other gases exhaled by the user 210 to escape. In other embodiments, the user interface 124 is a mouthpiece (e.g., a night-protection mouthpiece shaped to fit the user's teeth, a mandibular reset device, etc.) for directing pressurized air into the user's 210's oral cavity.

[0043] User interface 124 is fluidly coupled and / or connected to breathing apparatus 122 via conduit 126. Additionally, breathing apparatus 122 delivers pressurized air to user 210 via conduit 126 and user interface 124, increasing air pressure in the throat of user 210 and thereby helping to prevent the airway from closing and / or narrowing during sleep. Breathing apparatus 122 may be positioned on a side table 240 directly adjacent to bed 230, as shown in FIG. 2, or more generally on any surface or structure typically adjacent to bed 230 and / or user 210.

[0044] Typically, a user prescribed to use respiratory treatment system 120 tends to experience better quality sleep and less fatigue during the daytime after using respiratory treatment system 120 while sleeping (especially if the user suffers from sleep apnea or other sleep-related disorders) compared to not using respiratory treatment system 120. For example, user 210 may have obstructive sleep apnea and rely on user interface 124 (e.g., a full-face mask) to deliver pressurized air from breathing device 122 via conduit 126. Breathing device 122 may be a continuous positive airway pressure (CPAP) machine that increases air pressure in the user's 210 throat to prevent the airway from closing and / or narrowing during sleep. A person with sleep apnea may experience a narrowing or collapse of their airway during sleep, reducing oxygen intake and waking them up or otherwise disrupting their sleep. The CPAP machine prevents the airway from narrowing or collapsing, thereby minimizing their arousal or disturbance from reduced oxygen intake. If the breathing apparatus 122 attempts to maintain one or more medically prescribed air pressures during sleep, the user may experience sleep discomfort.

[0045] Returning to FIG. 1 , the one or more sensors 130 of the system 100 include a pressure sensor 132, a flow sensor 134, a temperature sensor 136, a motion sensor 138, a microphone 140, a speaker 142, a radio frequency (RF) receiver 146, an RF transmitter 148, a camera 150, an infrared sensor 152, a photovoltaic plethysmogram (PPG) sensor 154, an electrocardiogram (ECG) sensor 156, an electroencephalogram (EEG) sensor 158, a capacitance sensor 160, a force sensor 162, a strain gauge sensor 164, an electromyogram (EMG) sensor 166, an oxygen sensor 168, an analyte sensor 174, a humidity sensor 176, a light detection and ranging (LiDAR) sensor 178, an electrodermal sensor, an accelerometer, an electrooculogram (EOG) sensor, an optical sensor, a moisture sensor, an air quality sensor, or any combination thereof. Generally, each of the one or more sensors 130 is configured to output sensor data that is received and stored in the storage device 114 or one or more other storage devices.

[0046] Although the one or more sensors 130 are shown and described as including each of a pressure sensor 132, a flow sensor 134, a temperature sensor 136, a motion sensor 138, a microphone 140, a speaker 142, an RF receiver 146, an RF transmitter 148, a camera 150, an infrared sensor 152, a photovoltaic plethysmogram (PPG) sensor 154, an electrocardiogram (ECG) sensor 156, an electroencephalogram (EEG) sensor 158, a capacitance sensor 160, a force sensor 162, a strain gauge sensor 164, an electromyogram (EMG) sensor 166, an oxygen sensor 168, an analyte sensor 174, a humidity sensor 176, and a light detection and ranging (LiDAR) sensor 178, more generally, the one or more sensors 130 may include any combination and any number of each sensor described and / or illustrated herein.

[0047] Data from room environmental sensors can also be incorporated into a comfort score, such as temperature (e.g., too hot, too cold), humidity (e.g., too high, too low), pollution levels (e.g., CO2 and / or particle amounts and / or concentrations below or above threshold), light levels (e.g., too bright, not using blackout blinds, too much blue light before bed), and sound levels (e.g., above threshold, source type, sleep interruption-related, partner's snoring) throughout the sleep session. These can be obtained by sensors on the respiratory therapy device, a smartphone (e.g., connected via Bluetooth or the Internet), or a separate sensor (e.g., connected to a home automation system). Air quality sensors can also detect other types of room pollution that can cause allergies, such as pets or dust mites, and indicate that the room may benefit from an air filter to improve user comfort.

[0048] Data from the user's health (physical and / or mental) status can also be combined. For example, some detected changes in comfort may not be solely related to changes in the treatment device (or selected device application) or the room / bed environment. Changes in comfort may also be health-related (e.g., changes due to the onset or offset of a disease, such as a respiratory disorder, and / or changes due to changes in an underlying condition, such as a co-morbid chronic disease).

[0049] For example, PPG data from a PPG sensor 154 (e.g., a mask, helmet, patch, wristwatch, finger probe, ring, inside / in the ear, etc.) can be used to estimate changes in heart rate, pulse rate (PR), blood pressure, SpO2, and peripheral arterial tension. Blood oxygen levels can be referenced to PAP therapy to ensure there are no unexpected drops, and if / when therapy is turned off (e.g., by removing the mask) can be referenced to monitor residual respiratory events (e.g., apnea). These PPG data can estimate the likelihood of daytime headaches and, in addition to simple obstructive events, can recommend changes to PAP therapy, such as blood flow restriction procedures. These PPG data can also be used to examine inflammatory responses. Headaches can also be caused by pressure settings that are too high and can benefit from lowering the pressure or changing to an EPR setting.

[0050] Peripheral arterial tension (PAT) sensing devices can utilize a PPG probe attached to a fingertip. The PPG probe uses optical technology to measure changes in blood volume in tissue microvascular beds. As described above, PPG measurements are used to derive changes in arterial oxygen saturation (SpO2), pulse rate (PR), and peripheral arterial tension, which are then used to detect respiratory events. Peripheral arterial tension is the tension in peripheral arterial smooth muscle tissue. Increased peripheral arterial muscle tension reduces arterial diameter, decreases perfusion, and reduces pulsatile blood volume in peripheral tissues. The decrease in pulsatile blood volume in peripheral tissues is captured as a decrease in the swing of the PPG signal between systole and diastole. A PAT signal can be derived from the PPG signal from a PPG sensor, such as by the method described in WO 2021 / 260190, incorporated herein by reference. A PPG-derived signal, referred to as a PAT signal, can be derived by trending this decrease in pulsatile blood volume.

[0051] The PAT sensor device analyzed the simultaneous occurrence of a decrease in SpO2, a sudden increase in pulse rate, and an increase in peripheral arterial tension. During a respiratory (e.g., apnea) event, a decrease in airflow leads to a decrease in oxygen delivery to the lungs from baseline, resulting in a significant decrease in SpO2. As the respiratory event nears its end, a surge in sympathetic nervous system activity arouses the patient, leading to a return of ventilation, an increase in pulse rate, and the release of noradrenaline into the bloodstream. Consequently, noradrenaline binds to α-adrenergic receptors innervating the smooth muscle tissue of the digital arteries, causing a sudden vasoconstriction and a synchronous increase in PAT picked up by the PPG sensor. Such PPG sensors can be implemented to detect respiratory events, for example, by detecting signal features indicative of apnea and / or hypopnea events in peripheral arterial tonometry signals, which can be derived from the sensor's PPG signal. The PPG signal can also be processed to extract respiratory movement-related information, which can determine different types of respiratory events.

[0052] Photographs taken with the camera and / or facial maps drawn with radar or laser radar can be used to inspect the tightness of the mask (e.g., too tight or too tight) and / or any marks on the face or head (e.g., abrasions or skin irritation—indicating that the patient interface deviates from expected normal skin color variations). Skin irritation may result in the recommendation of a different type of mask (comprising a similar form factor but with a different shape for a more comfortable seal on the face), a different size mask, or a different type of cushioning. The camera 150 can also be used to inspect any facial hair and its possible interaction with the mask seal.

[0053] Sleep quality analysis based on sensor processing can be used to screen for insomnia (including hyperarousal as determined by an increase in a person's body temperature and / or heart rate). The system described herein can correlate detected discomfort factors with insomnia, sleep maintenance, or acute insomnia, such as insomnia attacks resulting from waking earlier than expected or anticipated.

[0054] As described herein, system 100 may be used to generate data (e.g., physiological data, flow rate data, pressure data, movement data, acoustic data, etc.) generally associated with a user (e.g., a user of respiratory therapy system 120 shown in FIG. 2 ) during a sleep session. The generated data may be analyzed to generate one or more sleep-related parameters, which may include any parameter, measurement, etc. associated with the user during a sleep session. The one or more sleep-related parameters that may be determined for user 210 during a sleep session may include, for example, an apnea-hypopnea index (AHI) score, a sleep score, a flow signal, a respiratory signal, a breathing rate, an inspiratory amplitude, an expiratory amplitude, an inspiratory-expiratory ratio, a number of events per time, an event pattern, a stage, a pressure setting of respiratory device 122, a heart rate, a heart rate variability, movement of user 210, a temperature, an EEG activity, an EMG activity, wake-ups, snoring, choking, coughing, whistling, gasping, or any combination thereof.

[0055] The one or more sensors 130 may be used to generate, for example, physiological data, flow rate data, pressure data, motion data, acoustic data, etc. In some embodiments, the control system 110 may use the data generated by the one or more sensors 130, which are comfort parameters 104, to determine the sleep duration and sleep quality of the user 210. For example, a sleep-wake signal and one or more sleep-related parameters associated with the user 210 during a sleep session. The sleep-wake signal may indicate one or more sleep states, including sleep, wakefulness, relaxed wakefulness, micro-arousal, or different sleep stages, such as a rapid eye movement (REM) stage, a first non-REM stage (commonly referred to as "N1"), a second non-REM stage, a third non-REM stage, or any combination thereof. For example, each of which is incorporated herein by reference in its entirety. Methods for determining sleep states and / or sleep stages from physiological data generated by one or more sensors (e.g., sensor 130) are described, for example, in WO 2014 / 047310, U.S. Patent Publication Nos. 2014 / 0088373, WO 2017 / 132726, WO 2019 / 122413, WO 2019 / 222414, and U.S. Patent Publication No. 2020 / 0383580, each of which is incorporated herein by reference in its entirety.

[0056] The sleep-wake signal may also include time stamps to determine when the user got into bed, when the user left bed, when the user attempted to fall asleep, etc. The sleep-wake signal may be measured by one or more sensors 130 during the sleep session at a predetermined sampling rate, e.g., one sample per second, one sample per 30 seconds, one sample per minute, etc. In some embodiments, the sleep-wake signal may also be indicative of a respiratory signal, a respiratory rate, an inspiratory amplitude, an expiratory amplitude, an inspiratory-expiratory ratio, a number of events per time, an event pattern, a pressure setting of the respiratory device 122, or any combination thereof, during the sleep session.

[0057] These events may include snoring, apnea, central apnea, obstructive apnea, mixed apnea, shortness of breath, mouth leak, mask leak (e.g., from the user interface 124), restless legs, sleep disturbances, choking, increased heart rate, changes in heart rate, difficulty breathing, asthma attack, epileptic seizure, sudden illness attack, fever, coughing, sneezing, snoring, the appearance of an asthma, cold, or flu-like illness, or any combination thereof. In some embodiments, mouth leak may include a continuous mouth leak or a valve-like mouth leak (i.e., a change in the duration of a breath) where a user's lips suddenly open during exhalation, typically using a nasal / nasal pillow mask. Mouth leak can cause dry mouth, bad breath, and is commonly referred to as a "sandpaper nozzle." Events may include carbon dioxide (CO2) accumulation in the user interface 124 or other parts of the respiratory therapy system 120, which may be uncomfortable for the user. Carbon dioxide (CO2) levels may be detected, for example, by one or more CO2 sensors in the user interface 124 and / or other parts of the respiratory treatment system 120.

[0058] One or more sleep-related parameters that may be determined for a user based on the sleep-wake signal during a sleep session include, for example, a sleep quality metric such as total time in bed, total sleep time, sleep onset latency, wake-up parameters after sleep onset, sleep efficiency, fragmentation index, or any combination thereof.

[0059] Data generated by one or more sensors 130 (e.g., physiological data, flow rate data, pressure data, motion data, acoustic data, etc.) can also be used to determine a respiratory signal associated with the user during a sleep session. The respiratory signal typically indicates the user's breathing or respiration during a sleep session. The respiratory signal can indicate, for example, breathing rate, breathing rate variability, inspiratory amplitude, expiratory amplitude, inspiratory-expiratory ratio, number of events per hour, event patterns, pressure settings of the respiratory device 122, or any combination thereof. The events can include snoring, apnea, central apnea, obstructive apnea, mixed apnea, hypopnea, mouth leak, mask leak (e.g., from the user interface 124), restless legs, sleep disorder, choking, heart rate acceleration, dyspnea, asthma attack, epileptic attack, sudden illness attack, or any combination thereof. As discussed herein, one or more parameters associated with sleep and / or respiration can indicate one or more comfort-related parameters from which a comfort score can be derived.

[0060] Generally, a sleep session includes any time after the user 210 lies or sits in bed 230 (or another area or object in which they intend to sleep), and / or after they turn on the breathing apparatus 122, and / or after they put on the user interface 124. Thus, a sleep session can include the following periods: (i) when the user 210 is using the CPAP system but before the user 210 attempts to fall asleep (e.g., when the user 210 is lying in bed 230 reading a book); (ii) when the user 210 attempts to fall asleep but is still awake; (iii) when the user 210 is in light sleep (also known as phases 1 and 2 of non-REM sleep (NREM)); (iv) when the user 210 is in deep sleep (phase 3, also known as slow wave sleep, SWS, or NREM sleep); (v) when the user 210 is in REM sleep; (vi) when the user 210 periodically awakens during light, deep, or REM sleep; or (vii) when the user 210 awakens and does not return to sleep.

[0061] A sleep session is generally defined to end when the user 210 removes the user interface 124, turns off the respiratory apparatus 122, and / or gets out of bed 230. In some embodiments, a sleep session may include additional periods or may be limited to some of the periods disclosed above. For example, a sleep session may be defined to include the period from when the respiratory apparatus 122 begins to supply pressurized air to the airway or user 210 until the respiratory apparatus 122 stops supplying pressurized air to the airway of the user 210, and to include some or all of the time points while the user 210 is asleep or awake.

[0062] Pressure sensor 132 outputs pressure data that can be stored in memory 114 and / or analyzed by processor 112 of control system 110. In some embodiments, pressure sensor 132 is an air pressure sensor (e.g., an atmospheric pressure sensor) that generates sensor data indicative of a user's breath (e.g., inhalation and / or exhalation) and / or environmental pressure of respiratory treatment system 120. In such embodiments, pressure sensor 132 may be coupled to or incorporated within respiratory apparatus 122, user interface 124, or conduit 126. Pressure sensor 132 may be used to determine air pressure within respiratory apparatus 122, conduit 126, user interface 124, or conduit 126, or any combination thereof. Pressure sensor 132 may be, for example, a capacitance sensor, an electromagnetic sensor, an inductance sensor, a resistance sensor, a piezoelectric sensor, a strain sensor, an optical sensor, an electric potential sensor, or any combination thereof. In one example, pressure sensor 132 may be used to determine a user's blood pressure.

[0063] The flow rate sensor 134 outputs flow data that may be stored in the memory device 114 and / or analyzed by the processor 112 of the control system 110. In some embodiments, the flow rate sensor 134 is used to determine the airflow rate from the respiratory apparatus 122, the airflow rate through the conduit 126, the airflow rate through the user interface 124, or any combination thereof. In such embodiments, the flow rate sensor 134 may be coupled to or integrated with the respiratory apparatus 122, the user interface 124, or the conduit 126. The flow rate sensor 134 may be a mass flow rate 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.

[0064] The flow sensor 134 may be used to generate flow data associated with a user 210 (FIG. 2) of the respiratory apparatus 122 during a sleep session. Examples of flow sensors (e.g., flow sensor 134) are described in WO 2012 / 012835 and U.S. Patent No. 10,328,219, each of which is incorporated herein by reference in its entirety. In some embodiments, the flow sensor 134 is configured to measure exhaust gas flow (e.g., intentional “leak”), unintentional leak (e.g., mouth leak and / or mask leak), patient flow (e.g., air inflow and / or air outflow into and / or from the lungs), or any combination thereof. In some embodiments, the flow sensor may be analyzed to determine the user's cardiogenic oscillations.

[0065] The temperature sensor 136 outputs temperature data that may be stored in the memory device 114 and / or analyzed by the processor 112 of the control system 110. In some embodiments, the temperature sensor 136 generates temperature data indicative of the core body temperature of the user 210 ( FIG. 2 ), the skin temperature of the user 210, the temperature of the air flowing from the respiratory device 122 and / or through the conduit 126, the air temperature within the user interface 124, the ambient temperature, or any combination thereof. The temperature sensor 136 may be, for example, a thermocouple sensor, a thermistor sensor, a silicon bandgap temperature sensor or semiconductor-based sensor, a resistance temperature detector, or any combination thereof.

[0066] The motion sensor 138 outputs motion data that can be stored in the memory device 114 and / or analyzed by the processor 112 of the control system 110. The motion sensor 138 may be used to detect the movement of the user 210 during a sleep session and / or to detect the movement of any component of the respiratory treatment system 120, such as the respiratory device 122, the user interface 124, or the conduit 126. The motion sensor 138 may include one or more inertial sensors, such as an accelerometer, a gyro, and a magnetometer. In some embodiments, the motion sensor 138 optionally or additionally generates one or more signals representative of the user's body movement, e.g., movement due to the user's breathing, from which signals representative of the user's sleep state or sleep stage can be obtained. In some embodiments, the motion data from the motion sensor 138 may be used in combination with additional data from other sensors 130 to determine the user's sleep state or sleep stage. In some embodiments, the motion data can be used to determine the user's position, body position, and / or changes in body position.

[0067] The microphone 140 outputs audio data that may be stored in the storage device 114 and / or analyzed by the processor 112 of the control system 110. The microphone 140 may be used to record audio (e.g., audio from the user 210) during a sleep session to determine (e.g., using the control system 110) one or more sleep-related parameters, which may include one or more events (e.g., respiratory events), as described in further detail herein. The microphone 140 may be coupled to or integrated into the respiratory device 122, the user interface 124, the conduit 126, or the user device 170. In some embodiments, the system 100 includes multiple microphones (e.g., two or more microphones and / or a microphone array with beamforming), and audio data generated by each of the multiple microphones is used to distinguish audio data generated by other microphones of the multiple microphones.

[0068] The speaker 142 outputs sound waves. In one or more embodiments, the sound waves may be audible to a user of the system 100 (e.g., user 210 of FIG. 2 ) or inaudible to a user of the system (e.g., ultrasound). The speaker 142 may function, for example, as an alarm clock or to play alarms or messages to the user 210 (e.g., in response to an identified body position and / or change in body position). In some embodiments, the speaker 142 may be used to transmit audio data generated by the microphone 140 to the user. The speaker 142 may be coupled to the respiratory apparatus 122, the user interface 124, the conduit 126, or the user device 170, or may be integrated into the respiratory apparatus 122, the user interface 124, the conduit 126, or the user device 170.

[0069] The microphone 140 and the speaker 142 can be used as separate devices. In some embodiments, the microphone 140 and the speaker 142 may be combined into an acoustic sensor 141 (e.g., a sonar sensor), for example, as described in WO2018 / 050913 and WO2020 / 104465, each of which is incorporated herein by reference in its entirety. In such embodiments, the speaker 142 generates or transmits sound waves at predetermined intervals and / or frequencies, and the microphone 140 detects reflections of the sound waves transmitted from the speaker 142. In one or more embodiments, the sound waves generated or transmitted by the speaker 142 can have a frequency that is inaudible to the human ear (e.g., below 20 Hz or above about 18 kHz) so as not to disturb the sleep of the user 210 or the bed partner 220 ( FIG. 2 ). Based at least in part on data from microphone 140 and / or speaker 142, control system 110 can determine the user's 210 (FIG. 2) position and / or one or more sleep-related parameters (e.g., identified body position and / or changes in body position) described herein, such as a breathing signal, breathing rate, inhalation amplitude, exhalation amplitude, inhalation-exhalation ratio, number of events per time, event pattern, sleep state, sleep stage, pressure setting of breathing device 122, or any combination thereof. In this case, sonar sensors can be understood to include active acoustic sensing, such as generating / transmitting ultrasonic or low-frequency ultrasonic sensing signals through the air (e.g., within a frequency range of approximately 17-23 kHz, 18-22 kHz, or 17-18 kHz). Such systems can be considered in combination with the above-mentioned WO2018 / 050913 and WO2020 / 104465.

[0070] In some embodiments, sensor 130 includes (i) a first microphone that is identical to or similar to microphone 140 and integrated into acoustic sensor 141, and (ii) a second microphone that is identical to or similar to microphone 140 and separate from the first microphone that is integrated into acoustic sensor 141.

[0071] The RF transmitter 148 generates and / or transmits radio waves having a predetermined frequency and / or amplitude (e.g., within a high frequency band, within a low frequency band, a long wave signal, a short wave signal, etc.). The RF receiver 146 detects reflections of the radio waves transmitted from the RF transmitter 148, and this data can be analyzed by the control system 110 to determine the location and / or body position of the user 210 ( FIG. 2 ) and / or one or more sleep-related parameters described herein. The RF receiver (RF receiver 146 and RF transmitter 148 or another RF pair) is also used for wireless communication between the control system 110, the respiratory device 122, one or more sensors 130, the user device 170, or any combination thereof. While the RF receiver 146 and the RF transmitter 148 are shown as separate and distinct elements in FIG. 1 , in some embodiments, the RF receiver 146 and the RF transmitter 148 are combined as part of an RF sensor 147, such as a radar sensor. In some such embodiments, the RF sensor 147 includes control circuitry. The specific format of the RF communication may be Wi-Fi, Bluetooth, etc.

[0072] In some embodiments, RF sensor 147 is part of a mesh system. One example of a mesh system is a Wi-Fi mesh system, which can include mesh nodes, mesh routers, and mesh gateways, each of which can be mobile / movable or fixed. In such embodiments, the Wi-Fi mesh system includes a Wi-Fi router and / or a Wi-Fi controller and one or more satellites (e.g., access points) that include RF sensors that are the same as or similar to RF sensor 147. The Wi-Fi router and satellites constantly communicate with each other using Wi-Fi signals. The Wi-Fi mesh system can be used to generate motion data based on changes in the Wi-Fi signal between the router and the satellite (e.g., differences in received signal strength) caused by moving objects or people partially blocking the signal. The motion data can indicate movement, respiration, heart rate, gait, falls, behavior, etc., or any combination thereof.

[0073] The camera 150 outputs image data playable as one or more images (e.g., still images, video images, thermal images, or any combination thereof) that can be stored in the storage device 114. The control system 110 can use the image data from the camera 150 to determine one or more sleep-related parameters described herein. The control system 110 can use the image data from the camera 150 to determine one or more sleep-related parameters described herein, such as one or more events (e.g., periodic limb movement or restless legs syndrome), a respiratory signal, a respiratory rate, an inhalation amplitude, an exhalation amplitude, an inhalation-exhalation ratio, a number of events per hour, an event pattern, a sleep state, a sleep stage, or any combination thereof. Additionally, the image data from the camera 150 can be used to identify the position and / or body position of the user, determine chest movement of the user 210, determine airflow at the mouth and / or nose of the user 210, determine the time the user 210 enters the bed 230, and determine the time the user 210 leaves the bed 230. The camera 150 may also be used to track eye movement, pupil dilation (when one or both eyes of the user 210 are open), blink rate, or any changes during REM sleep.

[0074] The infrared (IR) sensor 152 outputs infrared image data that can be played back as one or more infrared images (e.g., still images, video images, or both) that can be stored in the storage device 114. The infrared data from the IR sensor 152 can be used to determine one or more sleep-related parameters during a sleep session, including the temperature of the user 210 and / or the movement of the user 210. The IR sensor 152 can also be used in combination with the camera 150 when measuring the presence, location, and / or movement of the user 210. For example, the IR sensor 152 can detect infrared light having a wavelength between about 700 nm and about 1 mm, while the camera 150 can detect visible light having a wavelength between about 380 nm and about 740 nm.

[0075] The PPG sensor 154 outputs physiological data associated with the user 210 (FIG. 2), which can be used to determine one or more sleep-related parameters such as heart rate, heart rate pattern, heart rate variability, cardiac cycle, respiration 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 coupled to the user interface 124 and / or its associated helmet (e.g., straps, etc.).

[0076] The ECG sensor 156 outputs physiological data related to the electrical activity of the heart of the user 210. In some embodiments, the ECG sensor 156 includes one or more electrodes placed on or around a portion of the user 210 during a sleep session. The physiological data from the ECG sensor 156 may be used, for example, to determine one or more sleep-related parameters described herein.

[0077] The EEG sensor 158 outputs physiological data related to the electrical activity of the brain of the user 210. In some embodiments, the EEG sensor 158 includes one or more electrodes placed on or around the scalp of the user 210 during a sleep session. The physiological data from the EEG sensor 158 may be used, for example, to determine the sleep state or sleep stage of the user 210 at any given time during the sleep session. In some embodiments, the EEG sensor 158 may be integrated into the user interface 124 and / or an associated helmet (e.g., a strap, etc.).

[0078] The capacitance sensor 160, the force sensor 162, and the strain gauge sensor 164 output data that can be stored in the memory device 114 and used by the control system 110 to determine one or more sleep-related parameters described herein. The EMG sensor 166 outputs physiological data related to electrical activity generated by one or more muscles. The oxygen sensor 168 outputs oxygen data indicative of the oxygen concentration of a gas (e.g., the conduit 126 or the user interface 124). The oxygen sensor 168 may be, for example, an ultrasonic oxygen sensor, an electrical oxygen sensor, a chemical oxygen sensor, an optical oxygen sensor, or any combination thereof. In some embodiments, the one or more sensors 130 further include a electrodermal response (GSR) sensor, a blood flow sensor, a respiration sensor, a pulse sensor, a blood pressure sensor, a blood oximetry sensor, or any combination thereof.

[0079] The analyte sensor 174 can be used to detect the presence of an analyte in the exhaled breath of the user 210. Data output by the analyte sensor 174 can be stored in the memory device 114 and used by the control system 110 to determine the identity and concentration of any analytes in the breath of the user 210. In some embodiments, the analyte sensor 174 is positioned near the mouth of the user 210 to detect the analyte in the breath exhaled from the user's 210 mouth. For example, if the user interface 124 is a mask that covers the nose and mouth of the user 210, the analyte sensor 174 can be positioned within the mask to monitor the user's 210 mouth breathing. In other embodiments, for example, if the user interface 124 is a nasal mask or nasal pillows mask, the analyte sensor 174 can be positioned near the nose of the user 210 to detect the analyte in the breath exhaled through the user's nose. In other embodiments, if the user interface 124 is a nasal mask or nasal pillows mask, the analyte sensor 174 can be positioned near the mouth of the user 210. In some embodiments, the analyte sensor 174 can be used to detect whether there is an unintentional air leak through the mouth of the user 210. In some embodiments, 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 the user 210 is breathing through their nose or mouth. For example, if the presence of an analyte is detected by data output by the analyte sensor 174 positioned near the mouth of the user 210 or in a face mask (in embodiments in which the user interface 124 is a mask), the control system 110 can use this data as an indicator that the user 210 is breathing through their mouth.

[0080] The humidity sensor 176 outputs data that can be stored in the memory device 114 and used by the control system 110. The humidity sensor 176 can be used to detect humidity in various areas around the user (e.g., within the conduit 126 or the user interface 124, near the face of the user 210, the connection between the conduit 126 and the user interface 124, the connection between the conduit 126 and the respiratory apparatus 122, etc.). Thus, in some embodiments, the humidity sensor 176 can be located within the user interface 124 or the conduit 126 to monitor the humidity of the pressurized air from the respiratory apparatus 122. In other embodiments, the humidity sensor 176 can be located near any area where humidity levels need to be monitored. The humidity sensor 176 can also be used to monitor the humidity of the ambient environment around the user 210, for example, the humidity of the air in the user's 210's bedroom. The humidity sensor 176 can also be used to track the user's 210's biometric response to environmental changes.

[0081] One or more light detection and ranging (LiDAR) sensors 178 can be used for depth sensing. This type of optical sensor (e.g., a laser sensor) can be used to detect objects and build a three-dimensional (3D) map of a surrounding environment, such as a living space. Laser radar typically utilizes a pulsed laser to measure time of flight. Laser radar is also known as 3D laser scanning. In an example using such a sensor, a fixed or mobile device (e.g., a smartphone) with a LiDAR sensor 178 can measure and map an area more than 5 meters away from the sensor. For example, LiDAR data can be fused with point cloud data estimated by an electromagnetic radar sensor. The LiDAR sensor 178 can also automatically geofence a radar system by using artificial intelligence (AI) to detect and classify features in space that may cause problems for the radar system (e.g., window glass that can highly reflect radar). For example, LiDAR can be used to estimate a person's height and how their height changes when they sit or fall. LiDAR can be used to create a 3D mesh representation of the environment. In a further application, LiDAR can reflect off solid surfaces (e.g., radio-transparent materials) through which the radio waves pass, thereby enabling classification of different types of obstacles.

[0082] In some embodiments, the one or more sensors 130 further include a galvanic skin response (GSR) sensor, a blood flow sensor, a respiration sensor, a pulse sensor, a blood pressure sensor, an oxygen saturation sensor, a sonar sensor, a radar sensor, a blood glucose sensor, a color sensor, a pH sensor, an air quality sensor, a tilt sensor, a direction sensor, a rainwater sensor, a soil moisture sensor, a water flow sensor, an alcohol sensor, or any combination thereof.

[0083] 1 , any combination of one or more sensors 130 may be integrated into and / or coupled to any one or more components of system 100, including breathing apparatus 122, user interface 124, conduit 126, humidification tank 129, control system 110, user device 170, or any combination thereof. For example, acoustic sensor 141 and / or RF sensor 147 may be integrated into and / or coupled to user device 170. In such embodiments, in accordance with some aspects of the present disclosure, user device 170 can be considered an auxiliary device that generates additional or auxiliary data for use by system 100 (e.g., control system 110). In some embodiments, at least one of the one or more sensors 130 is not physically and / or communicatively coupled to the respiratory apparatus 122, the control system 110, or the user device 170, but is typically positioned near the user 210 during a sleep session (e.g., positioned on or in contact with a part of the user 210, worn by the user 210, coupled or positioned on a side table, coupled to a mattress, coupled to a ceiling, etc.).

[0084] Data from one or more sensors 130 may be analyzed to determine a respiratory signal, a respiratory rate, a respiratory pattern, a respiratory amplitude, an expiratory amplitude, an inspiratory-expiratory ratio, the occurrence of one or more events, the number of events per hour, an event pattern, a sleep state, a sleep stage, an apnea-hypopnea index (AHI), or any combination thereof. The one or more events may include snoring, apnea, central apnea, obstructive apnea, mixed apnea and hypopnea, an intentional mask leak, an unintentional mask leak, a mouth leak, coughing, restless legs, a sleep disorder, choking, heart rate acceleration, dyspnea, an asthma attack, an epileptic seizure, a sudden illness attack, an elevated blood pressure, or any combination thereof. While many of these sleep-related parameters are physiological, some sleep-related parameters may be considered non-physiological. Non-physiological parameters may also include operating parameters of the respiratory therapy system, including flow rate, pressure, humidity of the pressurized air, motor speed, etc. Other types of physiological and non-physiological parameters may also be determined based on data from one or more sensors 130 or based on other types of data.

[0085] User device 170 (FIG. 1) includes a display device 172. User device 170 may be a mobile device, such as a smartphone, tablet, game console, smartwatch, laptop, etc. Alternatively, user device 170 may be an external sensing system, a television (e.g., a smart TV), or other smart home device (e.g., Google Home TM , Google Nest TM , Amazon Echo TM , Amazon Echo Show TM , Alexa TMThe display device 172 may be a display, a touch-sensitive device, a keyboard, a mouse, a keyboard, a keyboard-enabled device, or a smart speaker. In some embodiments, the user device is a wearable device (e.g., a smart watch). The display device 172 is typically used to display images, including still images, video images, or both. In some embodiments, the display device 172 functions as a human interface (HMI) that includes a graphical user interface (GUI) configured to display images and an input interface. The display device 172 may be an LED display, an OLED display, an LCD display, or the like. The input interface may be, for example, a touchscreen or touch-sensitive substrate, a mouse, a keyboard, or any sensor system configured to sense input by a human user interacting with the user device 170. In some embodiments, one or more user devices may be used and / or included by the system 100.

[0086] The blood pressure device 182 is generally used to assist in generating physiological data for determining one or more blood pressure measurements associated with a user. The blood pressure device 182 may include at least one of the one or more sensors 130 to measure, for example, a systolic blood pressure component and / or a diastolic blood pressure component.

[0087] In some embodiments, blood pressure device 182 is a blood pressure monitor that includes an inflatable cuff that can be worn by the user and a pressure sensor (e.g., pressure sensor 132 described herein). For example, as shown in the example of FIG. 2, blood pressure device 182 can be worn on the upper arm of user 210. In this embodiment, where blood pressure device 182 is a blood pressure monitor, blood pressure device 182 further includes a pump (e.g., a manually operated light bulb) for inflating the cuff. In some embodiments, blood pressure device 182 is coupled to respiratory device 122 of respiratory treatment system 120, which delivers pressurized air to inflate the cuff. More generally, blood pressure device 182 may be communicatively coupled to and / or physically integrated within (e.g., within a housing) control system 110, memory 114, respiratory treatment system 120, user device 170, and / or activity tracker 190.

[0088] The activity tracker 190 is generally used to assist in generating physiological data for determining activity measures related to a user. Activity measurements can include, for example, number of steps, distance traveled, number of steps climbed, duration of physical activity, type of physical activity, intensity of physical activity, time spent standing, respiration rate, average respiration rate, resting respiration rate, maximum respiration rate, respiration rate variability, heart rate, average heart rate, resting heart rate, maximum heart rate, heart rate variability, number of calories burned, blood oxygen saturation level (SpO2), electrodermal activity (also called skin conductance or galvanic skin response), user position, user posture, or any combination thereof. The activity tracker 190 includes one or more sensors 130 described herein, such as a motion sensor 138 (e.g., one or more accelerometers and / or gyros), a PPG sensor 154, and / or an ECG sensor 156.

[0089] In some embodiments, active tracker 190 is a wearable device that can be worn by a user, such as a smartwatch, wristband, ring, or patch. For example, referring to FIG. 2 , active tracker 190 is worn on the wrist of user 210. Active tracker 190 can also be coupled to or integrated with one or more articles of clothing worn by the user. Alternatively, active tracker 190 can be coupled to user device 170 or integrated within user device 170 (e.g., within the same housing). More generally, active tracker 190 can be communicatively coupled to or physically integrated within (e.g., within the housing of) control system 110, storage device 114, respiratory treatment system 120, and / or user device 170 and / or blood pressure device 182.

[0090] 1 as separate and distinct components of system 100, in some embodiments control system 110 and / or storage 114 are integrated into user device 170 and / or respiratory device 122. Alternatively, in some embodiments control system 110 or portions thereof (e.g., processor 112) may be located in the cloud (e.g., embedded in a server, embedded in an Internet of Things (IoT) device, connected to the cloud, processed by an edge cloud, etc.), on one or more servers (e.g., remote servers, local servers, etc.), or any combination thereof.

[0091] Although system 100 is shown as including all of the above components, according to embodiments of the present disclosure, a system for analyzing data associated with a user's use of respiratory treatment system 120 may include more or fewer components. For example, a first alternative system includes control system 110, storage device 114, and at least one of one or more sensors 130. As another example, a second alternative system includes control system 110, storage device 114, at least one of one or more sensors 130, user device 170, and blood pressure device 182 and / or activity tracker 190. As yet another example, a third alternative system includes control system 110, storage device 114, respiratory treatment system 120, at least one of one or more sensors 130, activity tracker 190, and user device 170. As another example, a fourth alternative system includes control system 110, memory device 114, respiratory treatment system 120, at least one of one or more sensors 130, user device 170, blood pressure device 182, and / or activity tracker 190. Accordingly, various systems may be formed using any portion of the components illustrated and described herein and / or in combination with one or more other components.

[0092] Referring to FIG. 3 , a method 300 for estimating a subjective comfort level of a user of a respiratory treatment system is disclosed. An initial estimate of the subjective comfort level may be based on objective data, and the estimate may subsequently be updated based on additional objective and / or subjective data. A system implementing method 300 can estimate a user's possible subjective comfort level. For example, in some embodiments, the system is a fully personalized system that can approximate the actual experience of a user (e.g., user 210 of FIG. 2 ) and, optionally, the user's bed partner (e.g., bed partner 220 of FIG. 2 ) by analyzing objective data. Optionally, the system can also solicit subjective data (e.g., responses to questions or questionnaires (e.g., based on the Epworth Sleepiness Scale) or other provision of cues or hints) to augment and / or confirm the objective estimate. Such subjective data (e.g., subjective feedback) can be obtained from a third party, such as user 210 and / or bed partner 220 or user 210's caregiver. In other embodiments, the system may not use or require subjective feedback from the user (and any bed partner or caregiver, etc.), and instead directly or indirectly effect changes to seek to increase the estimated comfort score (e.g., by automatically adjusting one or more parameters of the system, such as the pressure setting of the respiratory apparatus 122, room environment settings, etc., or by providing suggestions to the healthcare provider (HCP) / home medical equipment provider (HME) to make changes, such as a different mask type or size for the respiratory apparatus 122, or a different pressure setting / profile).

[0093] In contrast to traditional sleep or treatment scores, the comfort score described herein can be determined to represent a user's actual subjective comfort level. For example, a user's treatment score may be very good (i.e., indicating that the user received a good level of treatment), but the user may be awake some / most of the night, or may have used treatment for the required amount of time but slept less than required. The comfort score described herein may include data from sleep analysis, leakage type, bed partner influence or impact, room environmental parameters, and previous treatment / sleep sessions, and thus more closely represent the user's perception of comfort and thus better influence changes to increase this comfort level during the treatment / sleep session and / or for future treatment / sleep sessions. The comfort score may be combined with a sleep score, treatment score, or a combined sleep and treatment score to provide a more accurate representation of the user's experience during a sleep or treatment session.

[0094] In step 310, data related to a user of the respiratory treatment system is received. The received data can be associated with a sleep session and / or a treatment session using the respiratory treatment system. The respiratory treatment system can include one or more components of respiratory treatment system 120 (FIG. 1) and / or a mandibular repositioning device configured to adjust the position of the user's mandible. The data can be received from the respiratory treatment system, one or more sensors (e.g., sensor 130 of system 100), a user device (e.g., user device 170 of system 100), a blood pressure device (e.g., blood pressure device 182 of system 100), an activity tracker (activity tracker 190 of system 100), or any combination thereof.

[0095] The data received in step 310 may include acoustic data, pressure data, flow rate data, humidity data, motion data (e.g., including user motion data), physiological data, user input data, or any combination thereof. The acoustic data may include noise associated with the user (e.g., noise associated with breathing), noise associated with the user's environment (e.g., noise from traffic, noise from a television, etc.), or both. The pressure and flow rate data may be derived from the respiratory treatment system 120 and the pressure sensor 132 and flow rate sensor 134 included therein and indicate the airflow characteristics of the pressurized air generated by the respiratory treatment system 120 and the effect of the user's breathing on that airflow. The humidity data may include a humidification level associated with a humidifier in the respiratory treatment system. Additionally or alternatively, the humidity data may include the presence and / or amount of liquid (e.g., a leak) in the conduits and / or user interface. The motion data may be generated by a motion sensor 138 (e.g., a radar or sonar sensor described herein, a body-worn accelerometer, etc.) and may indicate the motion of the user 210, bed partner 220, etc. The physiological data may include breath alcohol data, blood alcohol data, blood pressure data, blood glucose data, blood oxygen data (e.g., SPO2 level data), airway congestion data, airway obstruction data, body temperature data, heart rate data, respiration data, sleep stage data, CO2 level data, or any combination thereof. The user input data may include information indicative of a subjective comfort level, a subjective stress level of the user, a subjective fatigue level of the user, a subjective health state of the user, a life event recently experienced by the user, objective health data associated with the user, or any combination thereof.

[0096] In some embodiments, congestion (ranging from acute congestion attacks, chronic congestion, conditions such as allergic rhinitis) can be detected using microphones to detect olfactory sounds, sensors to detect nasal drip, facial pain or pressure, or by using a full face mask to estimate a change from nose to mouth breathing. Additionally or alternatively, in some embodiments, lung compliance may be tracked based on processing of respiratory device signals, for example, to estimate lung compliance, i.e., the ability of the lungs to inflate, stretch and expand in terms of static and dynamic components.

[0097] In some embodiments, the objective health data associated with the user includes demographic and / or medical information of the user, such as the user's age, gender, race, geographic location, the user's relationship status, the user's employment status, the user's education status, the user's socioeconomic status, one or more medical conditions associated with the user, the user's medication use, or any combination thereof.

[0098] At least one parameter associated with the user is determined at step 310 based at least in part on the first portion of the received data. For example, at step 320, a first parameter associated with the user is determined. At step 322, a second parameter associated with the user is determined. The at least one parameter may include a leak type (e.g., mouth leak, unintentional user interface leak, unintentional user interface leak, and / or conduit leak), a leak amount (e.g., total leak amount and / or leak amount for each leak type), a leak duration (e.g., total leak time and / or leak time for each leak type), a duration of use, an event type, a number of event types, a body position, a change in body position, multiple changes in body position, a motion change, multiple motion changes, a residual snoring duration, a residual score (e.g., a residual AHI score and / or a residual snoring score), and / or a residual snore score. a), residual snoring severity, number of misses, number of awakenings (e.g., awakenings), changes in sleep architecture, anxiety measures (e.g., number of body position changes, number of times the user gets in and out of bed, time required for the user to fall asleep, percentage of time awake while the user is asleep, etc.), number of pressure changes, heart rate, heart rate variability, number of heart rate changes, heart rate variability, respiration rate, respiration rate changes, multiple changes in respiration rate, respiration rate variability, pain location, pain severity, humidification effectiveness level, or other physiological parameters indicative of physiological characteristics relevant to the user, or any combination thereof. Compared to anxiety measures, anxiety is a useful indicator of discomfort. Such discomfort may be due to the user's sleep environment, therapy and / or therapy device, or a medical condition (possibly a complication) such as PLMD, COPD, etc. PLMD may interfere with the user's sleep, therapy, or both and may negatively impact the user's comfort score. PLMD can be detected, for example, by means described in US 2018 / 0353138 A1, which is incorporated herein by reference in its entirety. COPD typically negatively impacts a user's respiratory function, disrupting their sleep, their treatment, or both, and negatively impacting their comfort score. Other conditions (comorbidities) such as high blood pressure, diabetes, insomnia, and excessive arousal may also impact a user's comfort.Events or deterioration associated with these conditions can be detected by appropriate sensors (e.g., a blood pressure monitor can detect high blood pressure, a glucose monitor can detect blood sugar levels associated with diabetes, a sleep monitor (e.g., a sleep tracker based on radar or sonar technology described herein) can detect insomnia, hyperarousal, etc.) and / or can be reported by the user, a caregiver, or other third party.

[0099] For example, body position can relate to comfort. Some people with positional apnea may find it more comfortable to sleep on their side for most of the night because the pressure may be lower. Conversely, if a patient has a history of back pain, being able to sleep on their back for longer periods of time and experiencing a higher average pressure may increase their overall comfort.

[0100] In some embodiments, the event types include snoring, apnea, central apnea, obstructive apnea, mixed apnea, shortness of breath, residual apnea event, cough, restless legs, sleep disorder, choking, increased heart rate, difficulty breathing, asthma attack, epileptic seizure, sudden illness attack, elevated blood pressure, or any combination thereof. Thus, the system can acquire, collect, and process biometric and behavioral data from multiple data sources for the patient (user) and optionally the user's bed partner.

[0101] In step 330, a comfort score is determined based at least in part on the determined at least one parameter (e.g., the first parameter received in step 320 and / or the second parameter received in step 322). The comfort score determined in step 330 can indicate a subjective comfort level of a user of the respiratory treatment system during at least a portion of the treatment session. In some embodiments, in step 380, a first weighted value for the first parameter and a second weighted value for the second parameter are determined. In some embodiments, the initial weights (e.g., used to determine the first and second weighted values) are default weights learned and / or estimated from the user and / or multiple non-users (e.g., similar demographics). For example, an initial baseline set of weighting values ​​(e.g., base weighting values ​​106 of system 100) can be pre-trained and / or updated based on access to a cloud service to connect to the most recently learned initial weighting values ​​of the comfort parameters. Additionally or alternatively, in some embodiments, the initial weights are calculated based on a look-up table (e.g., Table 2). Groups of users and / or non-users may be selected based on the user's own demographic information and / or personal preferences regarding sleep and / or treatment comfort, the identification and / or severity of the user's sleep / breathing-related disorder, the user's treatment device and / or treatment prescription, or any combination thereof.

[0102] For example, some objective health data may have a greater impact on a user's subjective comfort than other objective health data and therefore may be assigned a higher initial weight. For example, chronic pain (e.g., an initial weight of 8 on a scale of 0-10) may have a higher initial weight than dry skin (e.g., an initial weight of 3 on a scale of 0-10) because chronic pain impacts a user's comfort during a therapy session or sleep session more than dry skin.

[0103] In step 382, ​​the first weighting value and / or the second weighting value may be modified based at least in part on the data related to the user of the respiratory treatment system received in step 310. Then, in step 330, a comfort score is determined based at least in part on the modified first weighting value and the modified second weighting value from step 382.

[0104] An initial training sequence can be performed by collecting objective data from one or more sensors (e.g., on or connected to a respiratory treatment device, such as a PAP device) related to system performance and / or user biometrics, and / or from the room environment and / or user demographic details, and by collecting synchronous data from the user via questions / surveys, responses to hints or other stimuli, and / or PREMS (Patient Reported Experience Measures). These data can then be grouped together (e.g., allowed to learn) to train the AI ​​system to create a baseline system with initial parameters. This can then be refined through supervised or unsupervised learning, such as improving estimates of possible subjective comfort scores.

[0105] In some embodiments, the weightings may be modified at least in part based on changes in parameters related to sleep stages and / or movement. Additionally or alternatively, in some embodiments, weighting values ​​are initially assigned to each parameter. In some such embodiments, the weighting values ​​are determined using initial weights for each parameter from a lookup table (e.g., Table 2). In other such embodiments, the weighting values ​​for each parameter are determined based on pre-learned data related to the user of the system or other users, such as user groups (e.g., based on demographic data and / or health data classifications). In some embodiments, the weighting values ​​are updated based on access to a cloud service to connect to the most recently learned initial weighting values ​​for the comfort parameters. The algorithm then learns which parameters (e.g., sleep quality, treatment effect, and / or treatment duration) most impact the user's comfort.

[0106] Standardizing a user's comfort score allows for reporting, acting on, and / or combining the comfort score with a sleep / treatment score. For example, a user's comfort score may be interpreted (e.g., standardized) based on the user's demographics, medical history, respiratory device 122, and / or respiratory device settings. For example, a user's comfort score may be standardized based on the user's type of respiratory treatment system, which may be selected from a continuous positive airway pressure (CPAP) system, an automatic positive airway pressure (APAP) system, a bilateral positive airway pressure (BPAP) system, a variable positive airway pressure (VPAP) system, and a ventilator. In one example, a relatively low comfort score may be expected for a relatively young user, and this score may be normalized and interpreted as "good," "acceptable," or the like. Conversely, for an older user, the same comfort score may be unexpected, and the score may be normalized (or not) and interpreted as "poor," "unacceptable (requires intervention)," or the like. For example, the standardized comfort score may be output along with (e.g., alone or as part of) a therapy score and / or a sleep score determined based at least in part on received data associated with a user of the respiratory treatment system. Further, the therapy score and / or the sleep score may be determined based at least in part on (i) the received data associated with a user of the respiratory treatment system and (ii) the standardized comfort score.

[0107] In another example, the system can adjust / update the comfort score determination over time as it learns which parameters or combinations of parameters affect the user's comfort. This can be achieved, for example, by collecting data on the user's subjective comfort level after a night of sleep and / or treatment and correlating it with objective data / parameters collected (detected) during the corresponding sleep and / or treatment session. In this way, objectively determined parameters can be correlated to an individual user's subjective comfort experience. For example, an objectively determined parameter such as dry mouth (e.g., due to mouth breathing during respiratory therapy via a nasal mask) may be uncomfortable for one user but not another due to each user's different subjective experience. In a related example, the system can use the objective data / parameters collected during the sleep and / or treatment session to select targeted questions / surveys for the user or PREMS (Patient Reported Experience Measure) prompts. Using the previous example, if a nasal mask user's dry mouth caused by mouth breathing is objectively determined, the subjective data sought from the user can at least relate to the dry mouth.

[0108] In an additional or alternative example, data related to one or more objectively determined parameters can be collected over time, such as over multiple sleep and / or therapy sessions, and the baseline and / or threshold levels of each determined parameter. This can be accomplished, for example, by comparing the objectively determined parameters determined for one or more respiratory therapy sessions with the same parameters determined for one or more sleep sessions (or portions of sleep sessions) without using the respiratory therapy system. Additionally or alternatively, subjective feedback from these sessions can be correlated with the objectively determined parameters. Additionally or alternatively, objective data from different sources (e.g., sensors) can be correlated to help identify the incidence and causes of user discomfort. For example, objectively determined dry mouth / mouth breathing can be correlated with objectively determined room and / or airflow humidity levels, objectively determined mask type, etc. This correlation can then inform recommendations in step 350 and / or adjustments in step 352.

[0109] In some embodiments, the system may be configured to generate messages / outputs to the user (and / or their bed partner) regarding improving comfort, such as the sleep advice described in WO 2015 / 006364 and U.S. Patent No. 10,376,670, each of which is incorporated herein by reference in its entirety. For example, if the system establishes an understanding of the user's respiratory treatment device (e.g., PAP device) use, sleep patterns, and biometric patterns, e.g., through sleep correlation analysis of sensor signals and questionnaires, the system can provide customized, personalized advice to help improve the user's sleep and / or comfort by using a "recommendation engine." In some such embodiments, diagnostic functionality may be included in the recommendation engine to help identify other sleep issues that may connect the user to other products to treat sleep-related health issues (e.g., changing PAP settings, changing room environment, changing medication, etc.). Recommendations generated by the system's one or more processors may be designed to inform the user of the benefits of good sleep habits, optimal sleep environment conditions, and / or daily activities that are conducive to sleep. This provides reliable and insightful information to aid the user's sleep and participate in the overall system. The system may implement learning classifiers, such as those using Bayesian methods and / or decision trees, to customize advice based on the user's individual patterns, local groups of system users, or global groups. Additionally, in some embodiments, the user may be prompted to respond to electronic queries embedded in the received task / suggestion nugget. User responses may guide / track the path of the decision tree content. For example, in some embodiments, each parameter may be ordered, with higher weighting given to parameters that most impact the user's comfort level.

[0110] At step 340, one or more operational settings of the respiratory treatment system are altered based at least in part on the comfort score determined at step 330. In some embodiments, altering the one or more operational settings of the respiratory treatment system at step 340 includes adjusting a pressure setting of a respiratory treatment device of the respiratory treatment system, adjusting a humidification setting of a humidifier of the respiratory treatment system, or both. For example, in some embodiments, adjusting a pressure setting of a respiratory treatment device of the respiratory treatment system includes adjusting a blower motor within the respiratory treatment device.

[0111] In step 350, a recommendation is provided to the user based at least in part on the comfort score determined in step 330. The recommendation may be provided to the user via a display device of the respiratory treatment system, a user device (e.g., audio and / or visual instructions via the user's smartphone), a speaker (e.g., audio instructions via a smart speaker), or any combination thereof. Additionally or alternatively, in some embodiments, the recommendation may be provided (first, simultaneously, subsequently, or simply) to a caregiver (e.g., physician), HME, etc. The caregiver (e.g., physician), HME, etc., then communicates the recommendation to the user and / or acts on it (e.g., contacting the user or the user's caregiver, providing a new user interface, initiating a new treatment prescription, initiating an adjusted treatment prescription, etc.).

[0112] Additionally or alternatively, in some embodiments, a recommendation is made to investigate (e.g., consult a doctor / caregiver, perform a medical examination, troubleshoot, etc.), initiate, or modify treatment related to one or more medical conditions (e.g., comorbidities) of the user, as these conditions may be undertreated (e.g., without professional care). Such one or more medical conditions may include PLMD, COPD, diabetes, high blood pressure, insomnia, hyperarousal, etc., which may affect the user's comfort and be reflected in the comfort score, as described above. Additionally or alternatively, in some embodiments, the recommendation may include mapping one or more parameters (based on the comfort score) to a comfort score or multiple comfort scores, such as multiple comfort scores determined periodically during sleep and / or therapy sessions. Such parameters may include one or more of arousals, blood oxygenation (e.g., SpO2) fluctuations, apnea events, leak events, etc. In this way, the user, caregiver, or other third party may better understand the cause of the user's discomfort and help determine ways to increase the user's comfort.

[0113] In some embodiments, the recommendation provided in step 350 is to recommend (i) adjusting the pressure setting of the respiratory treatment device, (ii) adjusting the humidification setting of a humidifier connected to the respiratory treatment device, (iii) a mask type for the respiratory treatment system, (iv) a sleeping position for the user, (v) a chin strap for the user, (vi) a nose pad for the user, or (vii) a combination thereof.

[0114] Additionally or alternatively, in step 352, an automatic adjustment may be made based at least in part on the comfort score determined in step 330 and / or the recommendation provided in step 350. For example, in a smart room, the room temperature may be automatically lowered based at least in part on the comfort score determined in step 330. As another example, the humidity in the room may be automatically increased based at least in part on the comfort score determined in step 330. Thus, the determined comfort score may be used as part of a process or system for automatically adjusting treatment settings, such as (i) automatically adjusting a pressure setting on a respiratory treatment device associated with pressurized air delivered to a user's airway, (ii) automatically adjusting a humidification setting on a humidifier coupled to a respiratory treatment device configured to introduce moisture into the pressurized air delivered to a user's airway, or (iii) including both (i) and (ii). Additionally or alternatively, the determined comfort score may be used as part of a process or system for automatically adjusting treatment in response to one or more medical conditions (e.g., comorbidities) of the user. Such one or more medical conditions may include PLMD, COPD, diabetes, high blood pressure, insomnia, hyperarousal, etc., which, as described above, may affect the user's comfort and be reflected in the comfort score.

[0115] The comfort scores described herein can help determine adjustments (recommended, manual, or automatically applied) of settings in the respiratory therapy system 120, such as flow and pressure settings of the respiratory device 122, and / or automatically adjust settings (e.g., Autoset in the ResMed respiratory therapy system). TMThe comfort score can be used to inform other comfort-related settings of the respiratory therapy system, such as the comfort score (feature) or expedited pressure reduction (EPR), which maintains the user's airflow pressure during inspiration and reduces it during expiration, making it easier and more comfortable for the user to exhale. The comfort score can be used to determine such setting adjustments in real time and / or pre-treatment / post-treatment to dynamically adjust settings based on any changes in the user's comfort score that may occur during or between sleep / therapy sessions. In this way, the user's treatment comfort, which can change during or across multiple sleep / therapy sessions, can be monitored and settings adjusted as needed to maintain or improve the user's comfort. Therapy settings can be adjusted periodically throughout a therapy session (e.g., repeatedly every second, minute, or hour) based on the comfort score determined periodically throughout the therapy session.

[0116] In some embodiments, in step 350, an alternative user interface (e.g., an alternative mask) is recommended. Additionally or alternatively, in some embodiments, the alternative user interface is automatically processed (e.g., automatically ordered from the HME) and delivered to the user. This may be replacing the worn mask with a similar replacement, or replacing it with a different type of mask selected for greater comfort (e.g., moving from a full face to a nasal (or pillow) mask, or vice versa). In some embodiments, the method also provides for recommending and / or automatically processing conduit replacements (e.g., moving from an unheated conduit to a heated conduit, moving to a conduit of a different diameter or length, moving to a replaced waterless humidification module or element, or moving to a portion of the user interface such as a replaced silicone or foam element).

[0117] The comfort score can help determine whether a user is likely to abandon or not properly continue treatment. A user's comfort score can be determined and compared to a predetermined threshold. If the user's comfort score is lower than the threshold (or higher than the threshold, depending on the nature of the threshold), or if the number of comfort scores calculated over a predetermined number of sleep / treatment sessions is lower than (or higher than) the threshold, intervention can be recommended to the user, a caregiver, or another party. Thus, the comfort score can be used to input risk management tools or systems for monitoring users based on risks such as health risks, compliance risks, etc. Based at least in part on the determined comfort score or a trend in the determined comfort score, the comfort score can help predict whether a user will (i) not adhere to a treatment plan (e.g., a treatment plan prescribed by a doctor), (ii) abandon the treatment plan, or (iii) both (i) and (ii). For example, a comfort score above (or below) a predetermined threshold, or a (negative) trend in comfort scores from multiple treatment sessions, can indicate that the user is likely to abandon / not adhere to treatment due to unacceptable discomfort. In other examples, the comfort score can be input into an instructional tool or system, and instructional instructions or materials can be informed (e.g., selected) by, for example, the user's comfort score (e.g., multiple historical comfort scores). Based on the comfort score, the instructional system can provide instruction to the user regarding the user's sleep hygiene, sleep environment, therapeutic devices, etc., or any combination thereof.

[0118] In some embodiments, in step 360, a therapy score and / or sleep score are determined based at least in part on the data related to the user of the respiratory therapy system received in step 310. In step 370, an updated therapy score and / or sleep score may be generated based at least in part on the comfort score determined in step 330 and the therapy score determined in step 360. In some embodiments, the initial sleep score represents an analysis of the sleep pattern throughout the sleep session, but does not represent how refreshed the user feels after waking up. The comfort score may better represent how the user feels immediately after waking up and at a time (e.g., several minutes or hours) after waking up, when different levels of comfort may be present. To provide a more realistic sleep score, the comfort score can be used to increase or decrease the sleep score as needed. For example, if an initial sleep score of 92 / 100 is calculated for a user, but the comfort score for that sleep session is low, the sleep score may be adjusted based on the comfort score (e.g., by multiplying the initial sleep score by a coefficient calculated from the comfort score).

[0119] In some embodiments, determining the comfort score in step 330 includes comparing the determined at least one parameter to multiple historical values ​​of the same parameter, where the multiple historical values ​​of the same parameter are associated with multiple corresponding historical comfort levels. For example, in some such embodiments, the received data includes objective data related to the respiratory treatment system during a treatment session.

[0120] In some embodiments, the plurality of historical values ​​and the plurality of corresponding historical subjective comfort levels are obtained from stored data associated with (i) a user of the respiratory treatment system, (ii) one or more other users of one or more other respiratory treatment systems, or (iii) both (i) and (ii). For example, in some such embodiments, the plurality of corresponding historical subjective comfort levels are received, e.g., by user input, from (i) a user of the respiratory treatment system, (ii) one or more other users of one or more other respiratory treatment systems, or (iii) both (i) and (ii).

[0121] In some embodiments, the plurality of historical values ​​are associated with a plurality of corresponding historical subjective comfort levels by first receiving historical objective data and historical subjective comfort levels associated with a plurality of historical sleep sessions, a plurality of historical therapy sessions, or both. In some embodiments, the historical objective data is received from at least two different sensors and then associated with the historical subjective comfort levels. The received historical objective data is processed to extract the plurality of historical values. A historical subjective comfort level of the plurality of historical subjective comfort levels is associated with one or more historical values ​​of the plurality of historical values. In some such embodiments, the plurality of past sleep sessions is associated with non-use of the respiratory treatment system and the plurality of past therapy sessions is associated with use of the respiratory treatment system.

[0122] In some such embodiments, method 300 also includes determining a baseline level of the at least one parameter based, at least in part, on comparing (i) corresponding historical parameters extracted from the objective data received during the one or more historical sleep sessions and (ii) corresponding historical parameters extracted from the objective data received during the one or more historical therapy sessions.

[0123] In general, method 300 may be implemented using a control system having one or more processors and a memory that stores machine-readable instructions. The control system may be coupled to the memory, and method 300 may be performed when the machine-readable instructions are executed by at least one processor of the control system. Method 300 may also be implemented using a computer program product (e.g., a non-transitory computer-readable medium) that includes instructions that, when executed by the computer, cause a computer to perform the steps of method 300.

[0124] Although system 100 and method 300 have been described herein with reference to a single user, more generally, system 100 and method 300 may be used simultaneously with multiple users (e.g., 2 users, 5 users, 10 users, 20 users, etc.) For example, system 100 and method 300 may be used in a cloud monitoring setting.

[0125] Additionally or alternatively, in some embodiments, system 100 and / or method 300 may be used to monitor one or more users while using one or more respiratory systems (e.g., respiratory treatment systems described herein). For example, some such embodiments may send a notification to a monitoring device or person related to a subjective comfort level and / or comfort score associated with one or more users. One or more steps of method 300 may be used to determine the subjective comfort level and / or comfort score. Additionally or alternatively, in some embodiments, the subjective comfort level and / or comfort score are simply recorded. (Example)

[0126] 1, the comfort score is determined by the comfort score module 102 and is a function of the comfort parameters 104 and the base weight values ​​106. An example of the base weight values ​​106 is shown with reference to Table 1, which lists the base weight values ​​(on a scale of 0 to 10) and comfort thresholds for the user's heart rate and heart rate variability.

[0127] [Table 1]

[0128] There are many ways to calculate heart rate variability (HRV) from heartbeat intervals. These intervals are first estimated between detected or estimated fiducial points, either from cardiac "RR" intervals (RRI) or based on PPG or other optical sensors, with some form of interpolation applied to combat artifacts such as noise. For example, RRI in milliseconds can be used directly. Alternatively, in some embodiments, further processing can be applied in the time domain to obtain mean NN (the average RR interval at a given time—where "N" represents normal with unexpected beats removed), SDNN (the average RR interval over time), and RMSSD (the standard deviation of normal RR intervals). Frequency-domain metrics can include low frequency (LF—where 0.03–0.15 Hz is commonly used), high frequency (HF—where 0.15–0.40 Hz is commonly used), and the LF:HF ratio.

[0129] A parameter value is considered comfortable if it is between a low threshold and a high threshold. In this example, if the user's heart rate is between 40 beats per minute (bpm) and 100 bpm, these are considered comfort values ​​for heart rate. Similarly, if the user's heart rate variability (HRV) is between 50 and 70 ms, these are considered comfort values ​​for HRV. Comfort thresholds can be initially set based on a user profile associated with the user (e.g., demographic information, medical information, age, and gender) and / or additional data collected about the user (e.g., data received in step 310 of method 300). In some embodiments, only a low threshold exists, or only a high threshold exists. The initially set thresholds can be adjusted based on changes in the user profile.

[0130] In some embodiments, the heart rate data may be further analyzed. For example, the absolute heart rate for the night may be processed. As another example, a normalized heart rate may be processed based on values ​​from the entire night or from the previous nights. The change from the normalized value may better eliminate user-to-user variability, and the residuals may be used for trend analysis (e.g., determining whether a trend increases, remains the same, or decreases over a period of time) from wakefulness to sleep and between different sleep stages. In some embodiments, if a person is paced, a correction factor may be applied to the RR and HRV estimates.

[0131] In some embodiments, HRV may be estimated using spectral measurements, such as determining the LF / HF ratio. If the LF / HF ratio is used, it can be compared to the mean, trimmed mean, or median, rather than the RR interval measured in milliseconds as described herein. In some embodiments, HRV can be analyzed over shorter time scales, which may yield more subtle changes.

[0132] In some embodiments, HRV may be higher during NREM than REM, when both NREM and REM are higher than wakefulness. Therefore, given the "normal" sleep cycle variation, where more deep sleep is expected early in the night and more rapid eye movement activity later in the night, the system expects greater HRV during estimated deep sleep N3 (SWS). Maximum HRV may coincide with deepest sleep (continuous SWS), which occurs between 1:00 AM and 3:00 AM depending on bedtime. Additionally, HRV often increases during nighttime sleep because vagal (parasympathetic) balance is higher than sympathetic balance.

[0133] In some embodiments, the comfort score can be personalized by comparing daytime HRV estimates (e.g., from a smartwatch) with nighttime HRV estimates. The system checks whether there is an expected increase to indicate comfort and estimates the reason. Because daytime HRV (via RR interval analysis or spectral LF / HF analysis) and sleep can be circadian-based, the system uses a circadian model of sleepiness overlaid on daytime and nighttime sleepiness. In the presence of a chronic disease (e.g., cardiac disease, particularly after a myocardial infarction), the desired increase in HRV during sleep can be affected by the underlying disease, which may result in a low level of HRV difference (e.g., a user with a controlled disease may still be comfortable or uncomfortable). The system can then apply a correction factor, which then provides a more personalized comfort score for the user.

[0134] For example, if a user is very active, the example high heart rate comfort threshold in Table 1 can be lowered to 100 bpm or below. Conversely, a user who may have a new health problem, such as a hip fracture due to a fall, would need to have their high heart rate threshold moved upward. As another example, heart rate variability is typically higher in younger people, those with relatively good sleep, and those with low stress levels. Therefore, understanding the user's age, underlying health conditions, stress sources (chronic or acute), and estimated sleep quality can help estimate the user's subjective comfort level (which can result in short-term improvements in heart rate variability relative to this user's expected level).

[0135] In some embodiments, a long-short memory that learns patterns over time in conjunction with a recurrent neural network (RNN (artificial recurrent neural network) architecture) can be used to determine and / or modify the base weight values.

[0136] Table 1 shows base weight values ​​for short-term periods (e.g., 1, 2, 3 days) and long-term periods (e.g., 10 days, 1 month, 6 months). In some embodiments, these are further categorized according to the time trend of the comfort parameter. For example, in some such examples, a "+" represents a positive or good trend and a "-" represents a negative or bad trend. The "Stable-Good" category indicates that the trend is stable and within the comfort threshold. The "Stable-Bad" category indicates that the trend is stable, but the parameter or combination of parameters exceeds the comfort threshold.

[0137] A positive or favorable trend indication typically indicates that the value associated with a parameter over a given time period is trending in a desired direction. Conversely, a negative trend typically indicates that the value associated with a parameter is trending away from a desired direction over a given time period. For example, for a heart rate variability parameter, the trend indication may use a sign with a slope that maps the change in the value associated with the parameter over time (e.g., over one sleep session or multiple sleep sessions). For example, a positive slope represents a positive trend indicator, and a negative slope represents a negative trend indicator. For some other comfort parameters, the trend indication may use the opposite sign of the slope. For example, if the comfort parameter is blood pressure, the relationship is reversed. That is, a decrease in blood pressure typically indicates a positive trend indicator, and an increase in blood pressure typically indicates a negative trend indicator.

[0138] The trend display helps determine (1) whether it is a temporary comfort issue (e.g., requiring a small adjustment to pressure, humidity, etc.) or a chronic issue (e.g., requiring a new prescription or other medical attention), and (2) whether the user's baseline needs adjustment (e.g., the user may have a low HRV, which indicates low comfort in general, but the user may be quite comfortable because their baseline HRV has been low over time).

[0139] Other comfort parameters can be treated similarly to the heart rate and heart rate variability described above. In some embodiments, multiple data points associated with a comfort parameter, such as data points provided by one or more sensors 130, can be used to determine a trend indication for each comfort parameter. In some embodiments, the data sets for each comfort parameter are standardized. This standardization can simplify analysis and manipulation, for example, by allowing for the selection of a single meaningful upper slope threshold and a single meaningful lower slope threshold, and by having base weight values ​​of similar magnitude for all parameters.

[0140] Other categories of additional base weight values ​​and other categories used to determine the additional base weight values ​​can also be considered. For example, if the trend is positive but completely beyond the comfort threshold, a "positive-bad" category can be used. For a negative trend that completely exceeds the comfort threshold, a "negative-bad" category can be used.

[0141] Although only long and short periods are listed, other additional periods are also contemplated. For example, a very long period may be greater than a long period. The total number of days in a specified long-term period includes 1 to 30 days, but longer periods can be used. For example, data for determining comfort parameters can be collected for 30 days or more, 3 months or more, 6 months or more, 1 year or more, or several years or more (e.g., 2, 3, 4, 5 years or more).

[0142] Alternatively, in some embodiments, the trend indication for the first parameter is a stable-good trend indication in response to determining that (i) the slope of the fitting line is within a range of slope values ​​and (ii) the average value of at least two values ​​(e.g., the first value and the second value) of the first parameter is within a comfort threshold range. In some embodiments, at least one of the first value and the second value is within a range that indicates the trend is at a stable-good comfort threshold. In some embodiments, the first value and the second value are within a range that indicates the trend is at a stable-good comfort threshold.

[0143] Alternatively, in some embodiments, the trend indication for a first parameter of the plurality of parameters is a stable-to-bad trend indication in response to determining that (i) the slope of the fitting line is within a range of slope values ​​and (ii) the average value of at least the first value and the second value is outside a comfort threshold range. In some embodiments, at least one of the first value and the second value is outside a range indicating the trend is at a stable-to-bad comfort threshold. In some embodiments, at least the first value and the second value are outside a range indicating the trend is at a stable-to-bad comfort threshold.

[0144] In some embodiments, the comfort score is determined as the sum of a first product and a second product. The first product is the product of a second value of the first parameter and the determined base weight value associated with the first parameter. The second product is the product of a second value for the second parameter and the determined base weight value associated with the second parameter. Equation 1 is a mathematical formula for the function. CS=W1P1+W2P2(1) Wherein, CS is the comfort score, W1 is the determined base weight value of the first parameter P1, and W2 is the determined base weight value of the second parameter P2.

[0145] Equation 1 can be expanded to include additional parameters and expressed as a sum of products of all relevant parameters, as shown in Equation 2.

number

[0146] Table 2 provides a neutral adjustment scale of 0 to 10 for selected exemplary parameters, where 5 represents comfort, greater than 5 represents better comfort (e.g., comfort is greater than the comfort required for a comfortable night's sleep), and less than 5 represents worse comfort (e.g., comfort is lower than the comfort required for a comfortable night's sleep). Table 2 lists the sources and initial weights for sample parameters. As disclosed herein, in some embodiments, the initial weight for each parameter is looked up from Table 2. In some such embodiments, the weighting value for each parameter is determined based on prior learning data associated with the user of the system or other users (e.g., based on demographic data and / or health data classifications). For example, the initial weight for a first user may be different from the initial weight for a second user due to the demographic and / or health data of the first and second users, respectively.

[0147] [Table 2]

[0148] As those skilled in the art will readily appreciate, various aspects of the present disclosure can be performed by machine learning algorithms. In some examples, step 330 or step 360 of FIG. 3 may be performed by a supervised or unsupervised algorithm. For example, the system may utilize more basic machine learning tools, such as (1) decision trees (“DTs”), (2) Bayesian networks (“BNs”), (3) artificial neural networks (“ANNs”), or (4) support vector machines (“SVMs”). In other examples, deep learning algorithms or other more complex machine learning algorithms, such as convolutional neural networks (“CNNs”), recurrent neural networks (“RNNs”), or capsule networks (“CapsNets”), may be used.

[0149] DT is a classification diagram that matches input data with questions asked at successive steps in a decision tree. The DT program moves down "branches" of the tree based on the answers to the questions (e.g., first branch: Does the subjective data contain some input? Yes or No; second branch: Does the objective data contain a particular parameter? Yes or No, etc.).

[0150] A Bayesian network ("BN") is based on the likelihood that something is true based on a given argument and is modeled based on probability relationships. BNs are purely based on probability relationships that determine the likelihood of one variable based on one or another variable. For example, a BN can simulate relationships between objective data, subjective data, and any other information contemplated in this disclosure. In particular, if a user's past comfort level and certain parameters of the objective data are known, a BN can be used to calculate a current comfort score. This allows efficient BN algorithms to be used to make inferences based on input data.

[0151] An artificial neural network (ANN) is a computational model inspired by the central nervous system of animals. It maps inputs to outputs through a network of nodes. However, unlike BNs, in ANNs, the nodes do not necessarily represent real variables. Therefore, ANNs can have hidden layers of nodes that are not represented by variables known to the observer. ANNs are capable of pattern recognition. Their computational method makes it easy to understand the complex and unknown processes that may occur in the process of determining comfort based on various input data.

[0152] Support Vector Machines ("SVM") are a framework that utilizes machine learning statistics and vector spaces (a linear algebraic concept that represents the number of dimensions in a linear space) with some kind of marginal correlation structure. In some cases, they may determine a new coordinate system that can easily classify inputs into two classes. For example, SVMs can identify a line separating two sets of points that come from different event or parameter classifications.

[0153] Deep neural networks (DNNs) have been developed recently and are capable of modeling highly complex relationships with many variations. Over the past few decades, many researchers have proposed various DNN architectures to solve problems associated with algorithms such as ANNs. These types of DNNs include CNNs (Convolutional Neural Networks), RBMs (Restricted Boltzmann Machines), and LSTMs (Long Short-Term Memory). All of them are based on the theory of artificial neural networks. They show better performance by overcoming the backpropagation error decay problem associated with artificial neural networks.

[0154] Machine learning models require training data to identify the parameters of interest they are designed to detect. Machine learning models can be formed using a variety of methods, including, for example, applying randomly assigned initial weights to the network or applying gradient descent to a deep learning algorithm with backpropagation. In other examples, neural networks with one or two hidden layers can be used without being trained using this technique.

[0155] In some instances, the mark data or data representing specific user inputs can be used to train the machine learning model, while in other instances, the data is marked with results only, and various related data can be input to train the machine learning algorithm.

[0156] For example, various machine learning models can be used to input various data as disclosed herein to determine whether a particular comfort level is appropriate for the input data. In some examples, the input data is labeled by having experts in the field label relevant observations for a particular situation. Thus, inputting training data into the machine learning algorithm can distinguish between various data from healthy users and various data from users with various sleep disorders.

[0157] One or more elements, aspects or steps, or any portion thereof, from one or more of the following claims 1-47 may be combined with one or more elements, aspects or steps, or any portion thereof, from one or more other claims 1-47 to form one or more additional implementations and / or claims of the present disclosure.

[0158] While the present disclosure has been described with reference to one or more particular embodiments or implementations, those skilled in the art will recognize that many modifications are possible without departing from the spirit and scope of the present disclosure. Each of these embodiments and significant variations thereof is considered to fall within the spirit and scope of the present disclosure. It is also anticipated that additional implementations according to aspects of the present disclosure may combine any number of features from any of the implementations described herein.

Claims

1. 1. A method of operating a system for estimating a subjective comfort level of a user of a respiratory treatment system, comprising: receiving received data relating to the user of the respiratory treatment system during a treatment session; determining at least one parameter associated with the user based at least in part on a first portion of the received data; determining a comfort score indicative of the subjective comfort level of the user of the respiratory treatment system during at least a portion of the treatment session based at least in part on the determined at least one parameter; 4. A method of operation comprising:

2. The respiratory treatment system includes a respiratory treatment device configured to deliver pressurized air to the airway of the user, a mandibular repositioning device configured to adjust a position of the user's mandible, or both. The method of claim 1 .

3. the received data related to the user is received from the respiratory treatment system, a user device, a sensor, or any combination thereof; the user device comprises a smartphone, a tablet, a game console, a smartwatch, a laptop, a television, a smart home device, or any combination thereof; the sensor is coupled to the respiratory treatment system and / or the user device; A method according to claim 1 or claim 2.

4. the at least one parameter comprises a type of leak, an amount of leak, a duration of leak, a duration of use, a type of event, a number of types of events, a body position, a change in body position, a number of body position changes, a change in motion, a number of changes in motion, a duration of residual snoring, a residual score, a severity of residual snoring, a number of surges, a number of awakenings, a change in sleep architecture, an anxiety measure, a number of pressure changes, a heart rate, a heart rate change, a number of heart rate changes, a heart rate variability, a respiration rate, a respiration rate change, a number of respiration rate changes, a respiration rate variability, a pain location, a pain severity, a humidification effectiveness level, or any combination thereof; the residual score comprises a residual AHI score, a residual snoring score, or both; The type of leak may include a mouth leak, an unintentional user interface leak, an intentional user interface leak, a conduit leak, or any combination thereof; A method according to any one of claims 1 to 3.

5. 5. The method of claim 4, wherein the event type comprises snoring, apnea, central apnea, obstructive apnea, mixed apnea, shortness of breath, residual apnea event, coughing, restless legs, sleep disorder, choking, increased heart rate, difficulty breathing, asthma attack, epileptic attack, sudden sickness attack, elevated blood pressure, or any combination thereof.

6. The method of claim 1 , further comprising determining modifications to one or more operational settings of the respiratory treatment system based at least in part on the determined comfort score.

7. The method of claim 6 , wherein altering one or more operational settings of the respiratory treatment system comprises adjusting a pressure setting of a respiratory treatment device.

8. providing a recommendation to the user based at least in part on the determined comfort score; The provided recommendations include (i) adjusting a pressure setting of a respiratory treatment device, the pressure setting being related to the pressurized air delivered to the airway of the user; (ii) adjusting a humidification setting of a humidifier coupled to the respiratory treatment device, the humidifier being configured to introduce moisture into the pressurized air delivered to the airway of the user; (iii) recommending a mask type for the respiratory treatment system; (iv) recommending a sleep position for the user; (v) recommending a chin strap for the user; (vi) recommending a nasal cradle cushion for the user; or (vii) any combination thereof. A method according to any one of claims 1 to 7.

9. the received data includes acoustic data, pressure data, flow rate data, humidity data, motion data, physiological data, user input data, or any combination thereof; the physiological data includes breath alcohol data, blood alcohol data, blood pressure data, blood glucose data, blood oxygen data, airway congestion data, airway obstruction data, temperature data, heart rate data, respiration data, sleep stage data, CO2 level data, or any combination thereof; the user input data includes information indicative of a subjective comfort level, a subjective stress level of the user, a subjective fatigue level of the user, a subjective health state of the user, a life event recently experienced by the user, objective health data associated with the user, or any combination thereof; The objective health data associated with the user includes demographic and / or medical information of the user, such as the user's age, the user's gender, the user's race, the user's geographic location, the user's relationship status, the user's employment status, the user's education status, the user's socio-economic status, one or more medical conditions associated with the user, the user's medication use, or any combination thereof; A method according to any one of claims 1 to 8.

10. determining a first weighted value for a first one of the at least one parameter and a second weighted value for a second one of the at least one parameter, wherein a comfort score is determined based at least in part on the first weighted value and the second weighted value. A method according to any one of claims 1 to 9.

11. 11. The method of claim 10, further comprising modifying the first weighting value and / or the second weighting value based at least in part on a second portion of the received data associated with the user of the respiratory treatment system, and the comfort score is determined based at least in part on the modified first weighting value and the modified second weighting value.

12. The method of claim 1 , further comprising determining a treatment score and / or a sleep score based at least in part on the received data related to the user of the respiratory treatment system.

13. standardizing the comfort score based at least in part on demographic parameters of the user, a medical history of the user, a type of the respiratory treatment system or a component thereof, settings of the respiratory treatment system, or any combination thereof; the comfort score is standardized based at least in part on an age of the user, a sex of the user, a race of the user, one or more medical conditions associated with the user, medication use of the user, or any combination thereof; the comfort score is standardized based at least in part on the type of the respiratory treatment system, the type being selected from a continuous positive airway pressure (CPAP) system, an automatic positive airway pressure (APAP) system, a bi-level positive airway pressure (BPAP) system, a variable positive airway pressure (VPAP) system, and a mechanical ventilator; A method according to any one of claims 1 to 12.

14. outputting the standardized comfort score and / or sleep score based at least in part on the received data associated with the user of the respiratory treatment system; determining a treatment score and / or a sleep score based at least in part on (i) the received data related to the user of the respiratory treatment system and (ii) the standardized comfort score; 14. The method of claim 13.

15. 15. A method according to any of claims 1 to 14, further comprising generating an updated treatment score and / or sleep score based at least in part on the determined comfort score or the standardized comfort score.

16. 16. A method according to any one of claims 1 to 15, wherein determining the comfort score comprises comparing the determined at least one parameter to a plurality of historical values ​​of the same parameter associated with a plurality of corresponding historical subjective comfort levels.

17. 20. The method of claim 16, wherein the received data includes objective data related to the respiratory treatment system during the treatment session.

18. the plurality of historical values ​​and the plurality of corresponding historical subjective comfort levels are obtained from stored data associated with (i) the user of the respiratory treatment system, (ii) one or more other users of one or more other respiratory treatment systems, or (iii) both (i) and (ii); the plurality of corresponding historical subjective comfort levels are received, e.g., via user input, from (i) the user of the respiratory treatment system, (ii) the one or more other users of the one or more other respiratory treatment systems, or (iii) both (i) and (ii); 20. The method of claim 16.

19. The plurality of historical values ​​are associated with the plurality of corresponding historical subjective comfort levels in the following manner: receiving historical objective data and a plurality of historical subjective comfort levels associated with a plurality of historical sleep sessions, a plurality of historical therapy sessions, or both; processing the received historical objective data to extract a plurality of historical values; and a historical subjective comfort level of the plurality of historical subjective comfort levels is associated with one or more historical values ​​of the plurality of historical values; 20. The method of claim 16.

20. 20. The method of claim 19, wherein the plurality of historical sleep sessions is associated with not using a respiratory treatment system and the plurality of historical treatment sessions is associated with using a respiratory treatment system.

21. 21. The method of claim 20, further comprising determining a baseline level for the at least one parameter based at least in part on a comparison of (i) the corresponding historical parameter extracted from objective data received during one or more historical sleep sessions and (ii) the corresponding historical parameter extracted from objective data received during one or more historical therapy sessions.

22. 20. The method of claim 19, wherein the historical objective data is received from at least two different sensors and then correlated with the historical subjective comfort level.

23. 23. A method according to any one of claims 1 to 22, further comprising automatically adjusting treatment settings based on the determined comfort score.

24. 24. The method of claim 23, wherein automatically adjusting the treatment setting includes: (i) automatically adjusting a pressure setting of a respiratory treatment device associated with the pressurized air supplied to the user's airway; (ii) automatically adjusting a humidification setting of a humidifier connected to the respiratory treatment device, the humidifier configured to introduce moisture into the pressurized air supplied to the user's airway; or (iii) both (i) and (ii).

25. 24. The method of claim 23, wherein the treatment settings are adjusted periodically throughout the treatment session based on a comfort score determined periodically.

26. 24. The method of claim 23, wherein the treatment settings are adjusted based on (i) the determined comfort score from the treatment session, (ii) one or more determined comfort scores from one or more different treatment sessions, or (iii) both (i) and (ii).

27. 27. A method of operation as described in any of claims 1 to 26, further comprising predicting the likelihood that the user will (i) not adhere to the treatment plan, (ii) abandon the treatment plan, or (iii) both (i) and (ii) based at least in part on the determined comfort score or a trend in determined comfort scores.

28. 28. The method of claim 27, wherein predicting the likelihood comprises comparing the determined comfort score or a trend in determined comfort scores to a predetermined threshold.

29. A computer program product comprising instructions which, when executed by a computer, cause the computer to carry out the method of operation of any one of claims 1 to 28.

30. 1. A system comprising: a control system including one or more processors; and a memory storing machine-readable instructions; the control system is coupled to the memory and configured to implement a method for estimating a subjective comfort level of a user of a respiratory treatment system; The method comprises: receiving received data relating to the user of the respiratory treatment system during a treatment session; determining at least one parameter associated with the user based at least in part on a first portion of the received data; and determining a comfort score indicative of a subjective comfort level of the user of the respiratory treatment system during at least a portion of the treatment session based at least in part on the determined at least one parameter; Including, the system.

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