Sleep performance scoring during treatment

Abstract

A sleep performance score can be generated for an individual receiving respiratory treatment. Sensor data can be obtained from one or more sensors while the user is sleeping and using a respiratory treatment system. The sensor data can be used to determine one or more usage variables associated with use of the respiratory treatment system and sleep stage information indicative of the sleep stages the user experiences during sleep. A sleep performance score can be calculated using one or more usage variables and sleep stage information. In some cases, the sleep stage information can be used to apply weights to one, some, or all of the one or more usage variables. Considering the relationship between sleep stages and use of the respiratory treatment system, the sleep performance score can be indicative of compliance, effectiveness, quality, and / or general use of the respiratory treatment system.

Description

Technical Field

[0001] (Cross - reference to Related Applications) This application claims the benefit of U.S. Provisional Patent Application No. 63 / 107,935, filed October 30, 2020, entitled "Sleep Performance Scoring During Treatment", the disclosure of which is incorporated herein by reference in its entirety.

[0002] This disclosure generally relates to the treatment of sleep states, and more specifically to providing useful metrics for scoring sleep performance during the treatment of sleep states.

Background Art

[0003] Many individuals suffer from sleep - related and / or breathing disorders, such as periodic limb movement disorder (PLMD), restless legs syndrome (RLS), sleep - disordered breathing (SDB), obstructive sleep apnea (OSA), Cheyne - Stokes respiration (CSR), respiratory insufficiency, obesity hypoventilation syndrome (OHS), chronic obstructive pulmonary disease (COPD), neuromuscular disease (NMD), chest wall disorders, and insomnia. Sleep - related breathing disorders can be associated with one or more events that can occur during sleep, such as snoring, apnea, hypoventilation, restless legs syndrome, sleep disruptions, choking, increased heart rate, dyspnea, asthma attacks, epilepsy, epileptic seizures, or any combination thereof. Individuals suffering from such sleep - related respiratory diseases are typically treated using one or more medical devices to improve sleep and reduce the likelihood of events occurring during sleep. An example of such a device is a respiratory therapy system that can provide positive airway pressure to an individual, although other devices can also be used. It is necessary to provide meaningful measurements regarding the use of such devices, such as compliance monitoring, increased user engagement, and monitoring of treatment effectiveness.

Summary of the Invention

Means for Solving the Problems

[0004] Some aspects of the present disclosure include receiving sensor data from one or more sensors associated with a user's sleep session using a respiratory therapy system; determining, from the received sensor data, one or more usage variables associated with the use of the respiratory therapy system; determining, from the received sensor data, sleep stage information associated with the sleep session; and calculating a sleep performance score for the sleep session based at least in part on the determined one or more usage variables and the sleep stage information.

[0005] Some aspects of the present disclosure include a control system including one or more processors and a memory storing machine-readable instructions, wherein the control system is coupled to the memory and the machine-executable instructions in the memory, when executed by at least one of the one or more processors of the control system, cause the above method to be implemented.

[0006] Some aspects of the present disclosure include a system for scoring sleep performance, the system including a control system configured to implement the above method.

[0007] Some aspects of the present disclosure include a computer program product including instructions that, when executed by a computer, cause the computer to execute the above method. In some cases, the computer program product is a non-transitory computer-readable medium. This specification refers to the following drawings, and the use of the same reference numerals in different drawings is intended to indicate the same or similar components.

Brief Description of the Drawings

[0008] [Figure 1] FIG. 22 is a functional block diagram of a system for scoring sleep performance according to some aspects of the present disclosure. [Figure 2] FIG. 25 is a perspective view of the system, user, and co-sleeper of FIG. X according to an aspect of the present disclosure. [Figure 3] The following are illustrative timelines of sleep sessions according to several aspects of this disclosure. [Figure 4] The following are exemplary sleep progression diagrams associated with the sleep session in Figure 3, according to several aspects of this disclosure. [Figure 5] This chart shows the variables used in relation to the sleep progression diagram in Figure 4, according to several aspects of this disclosure. [Figure 6] This is a flowchart of a process for scoring sleep performance according to one aspect of the present disclosure. [Figure 7] This flowchart shows a process for scoring sleep performance using adaptive stages, according to some aspects of this disclosure. [Figure 8] This chart shows the user's progress through several adaptation stages according to various aspects of this disclosure. [Modes for carrying out the invention]

[0009] While this disclosure is susceptible to various modifications and alternative forms, specific implementations are shown as examples in the accompanying drawings and will be described in detail herein. However, this disclosure is not intended to be limited to any particular form disclosed, and conversely, it should be understood that this disclosure covers all modifications, equivalents, and alternatives that fall within the spirit and scope of this disclosure as limited by the accompanying claims. Certain aspects and features of this disclosure relate to a system and method for generating an individual sleep performance score using a respiratory therapy system (e.g., using a respiratory therapy system to provide respiratory therapy during a sleep session). The system can acquire sensor data from one or more sensors when a user enters a sleep session and uses the respiratory therapy system. The sensor data may be used to determine one or more use variables associated with the use of the respiratory therapy system, and sleep stage information indicating the sleep stages and / or sleep states (e.g., wakefulness or sleep) that the user experiences during the sleep session. A sleep performance score may be calculated using one or more use variables and sleep stage information. In some cases, sleep stage information may be used to apply weights to one, some, or all of the one or more use variables. Given the relationship between sleep stages and the use of the respiratory therapy system, the sleep performance score may be used to indicate compliance, effectiveness, quality, and / or general use of the respiratory therapy system.

[0010] Aspects of this disclosure may be used to generate a sleep performance score associated with a sleep session of a user receiving respiratory therapy. Respiratory therapy can be applied using a respiratory therapy device, such as a respiratory device that supplies pressurized air to the user via a conduit and user interface. While receiving respiratory therapy, the user can participate in a sleep session, during which sensor data can be collected from one or more sensors, such as sensors in the respiratory therapy device, sensors in a user device (e.g., a smartphone), sensors in an activity tracker (e.g., a wearable activity tracker), or other sensors placed inside, above, or around the user (e.g., implantable devices, clothing-integrated sensors, mattress-integrated sensors, wall-mounted sensors, or ceiling-mounted sensors). Data collected from one or more sensors may be used to determine one or more use variables and sleep stage information associated with the use of the respiratory therapy system. Sensor data may be used to determine other variables and / or information.

[0011] Use variables associated with the use of a respiratory therapy system may include any appropriate variables related to how the user uses the respiratory therapy system. Examples of appropriate use variables include usage time (e.g., duration of use by the user of the respiratory therapy system), seal quality variables (e.g., indication of seal quality between the user and the user interface), leak flow rate variables (e.g., indication of unintentional leak flow rate, such as leaks due to poor quality seals or mouth breathing when wearing a nasal pillow-type user interface), event information (e.g., indication of detected events that occurred during a sleep session, such as the apnea-hypopnea index (AHI)), user interface compliance information (e.g., indication of detected user interface transition events, such as putting on or removing the user interface), and pressure on the user interface for multiple therapy subsessions within a sleep session (e.g., multiple independent blocks of continuous use of the respiratory therapy system). Other use variables may also be used. Statistical summaries (mean, maximum, minimum, count, etc.) of one or more use variables may be used as one or more additional use variables. One or more use variables may include any appropriate combination of use variables.

[0012] The step of determining the variables to use may include processing sensor data to identify one or more values ​​associated with the variables to use. These one or more values ​​may be measured or calculated scores associated with the use of the variables. For example, a seal quality variable may be a measured value of leak flow rate (e.g., L / min) or a seal quality score (e.g., 18 out of 20). The step of determining the variables to use may include determining a single value or multiple values ​​(e.g., timestamp values). For example, in some cases, the step of determining the seal quality variable may include determining a single value (e.g., 18 out of 20) that represents the overall (e.g., average) seal quality for the entire sleep session. However, in some cases, the step of determining the seal quality variable may include determining a set of timestamp values ​​that represent the change in seal quality over time (e.g., in the range of 0 to 20, such as 18 at 10:00:00 pm, 18.1 at 10:00:05 pm, 18.2 at 10:00:10 pm), which may, for example, chart the data on seal quality over the entire duration.

[0013] Sleep stage information may include information indicating the sleep stages experienced by the user during a sleep session. Examples of sleep stages include wakefulness, rapid eye movement (REM) sleep, light sleep, and deep sleep. Sensor data may be processed to determine when the user enters and ends different sleep stages. In some cases, the step of determining sleep stage information may include determining the total duration spent by the user in each sleep stage. In an exemplary 8-hour sleep session, the sleep stage information may show a total of 21 minutes of wakefulness, 101 minutes of REM sleep, 267 minutes of light sleep, and 91 minutes of deep sleep. However, in some cases, the step of determining sleep stage information may include generating timestamp data indicating the user's sleep stages at different times throughout the sleep session, for example, data that can be charted to generate a sleep progression diagram of the user's sleep session.

[0014] While usage variables indicate the use of a respiratory therapy system, scores based solely on usage variables may not be as informative or useful as scores based on usage variables and sleep stage information. For example, tracking the total amount of time a user spends using a respiratory therapy device during a sleep session may be beneficial. Generally, the longer the usage time, the better. Using a respiratory therapy device only a few hours before sleep may not be desirable. Therefore, it is useful to provide users with a score that increases (e.g., improves) as the user spends more time using the respiratory therapy device. This can be done based solely on a simple score of the usage variable, where longer usage times result in higher values ​​and shorter usage times result in lower values. While such simple scores can be used to encourage users to use respiratory therapy devices for longer periods, they may also lead to undesirable behaviors or fail to reflect important details about how the respiratory therapy device is actually used. For example, a user may obtain a high simple score by using the respiratory therapy device for longer periods before sleep, but this high value may be counterproductive as it does not necessarily reflect the substantial benefit the user gains from using the respiratory therapy device while awake. However, a sleep performance score calculated using variables and sleep stage information can provide more information and useful scores. Apnea and hypopnea events are more common during REM sleep (e.g., due to decreased genioglossal muscle tone) and more harmful during REM and deep sleep (e.g., due to interruptions in REM sleep, negative impacts on spatial memory, and / or reduced deep sleep duration). Therefore, tracking the amount of time respiratory therapy devices are used during REM sleep and / or deep sleep may be more useful. In addition to tracking total usage time, the time spent using respiratory therapy devices during a particular sleep stage (e.g., REM sleep or deep sleep) can be emphasized (e.g., given more weight) than the time spent using respiratory therapy devices during other sleep stages (e.g., wakefulness or light sleep). Consequently, longer usage of respiratory therapy devices before bedtime may not significantly or at all increase sleep performance scores.However, if the same user uses a respiratory therapy device to achieve longer periods of REM sleep, their sleep performance score may increase significantly.

[0015] Similarly, while the detection of apnea or hypopnea events may be information to track and a useful variable, a proliferation of (e.g., obvious) events detected during wakefulness may be a false positive and can be ignored, and a proliferation of events detected during REM sleep may indicate that the respiratory therapy device is not providing adequate respiratory therapy. Therefore, detected events associated with the REM sleep phase may be more significant than detected events associated with other sleep phases (e.g., wakefulness).

[0016] Similarly, seal quality variables or leak rates are tracked information and can be useful variables. Since poor seals or unintended leaks increase the risk of apnea and hypoventilation, changes in seal quality or decreased leak rates may indicate this risk. Therefore, when an event could have a substantially harmful impact (e.g., REM sleep disruption, adverse effects on spatial memory, and / or reduced deep sleep volume), the prevalence of poor seals or unintended leaks during REM sleep may be more important than the prevalence of poor seals or unintended leaks during wakefulness. Thus, low seal quality variables or low leak rates associated with REM sleep stages may be given more weight than low seal quality variables or low leak rates associated with wakefulness. Furthermore, poor seals or unintended leaks associated with light sleep can be harmful because they pose a risk to the user experience. Also, users may be more aware during light sleep, which may affect user compliance. For example, an undesirable seal during light sleep may be perceived by the user, be uncomfortable to the user, and cause the user to remove the user interface. Therefore, low seal quality variables or low leak rates associated with light sleep stages may be given more weight than low seal quality variables or low leak rates associated with deep sleep stages.

[0017] Therefore, sleep performance scores based on use variables and sleep stage information are particularly useful and can provide information. The step of calculating such a sleep performance score may include the use of other data in addition to the use of use variables and sleep stage information. In some cases, the step of calculating the sleep performance score may include the step of applying weight values ​​to each use variable, the weight values ​​may be adjusted (or generated) based at least in part on sleep stage information and / or at least in part on other use variables. In some cases, the weight values ​​may be adjusted (or generated) based at least in part on sleep-related parameters such as total bedtime, total sleep duration, sleep latency, wake-up parameters, sleep efficiency, fragmentation index, or any combination thereof.

[0018] For example, if the sleep performance score is calculated based on a single value (e.g., a use variable which is a score related to the average or the entire sleep session), the use variable may include usage time (U), seal quality variable (Q), event information (E), and user interface compliance information (C), and the sleep performance score (Score) can be calculated according to the following equation.

number

[0019] For example, the first night, when a relatively large amount of time is spent in REM sleep, may have a higher χ3 value than the nights when a relatively small amount of time is spent in REM sleep. Such changes in the weighting of χ3 can highlight the significant impact on the sleep performance score because events that occur when a user is experiencing REM sleep in an alternative way may be more harmful than events that occur when they are not experiencing REM sleep in an alternative way. Other examples can also be used.

[0020] In another example, the first night, with a relatively high seal quality variable, may have a higher χ4 than the night with a relatively low seal quality variable. Such a change in χ4 weight can highlight that on nights with poor seal quality, the user is more likely to remove and rearrange the user interface, and therefore should not have as much impact on the overall sleep performance score as nights with good seal quality where the user removes the user interface for other reasons.

[0021] In another example where the sleep performance score is calculated based on a time-dependent usage variable (e.g., a timestamp value during a sleep session), the usage variable may be a function of time and may include usage time (U(t)), seal quality variable (Q(t)), event information (E(t)), and user interface compliance information (C(t)), and the sleep performance score (Score) may be calculated according to the following formula:

number

[0022] In another example where the sleep performance score is calculated based on a time-dependent usage variable (e.g., a timestamp value during a sleep session), the usage variable may be a function of time and may include usage time (U(t)), seal quality variable (Q(t)), event information (E(t)), and user interface compliance information (C(t)), and the sleep performance score (Score) may be calculated according to the following formula:

number

[0023] In another example, the sleep performance score may be calculated based on divided usage variables. Sleep stage information can be used to divide the variables by sleep stage. For example, sleep stage information can be used to divide the total usage time (U) and the usage time while awake (U). W ), usage time during REM sleep (U R ), usage time during light sleep (U L ), usage time during deep sleep (U D The usage time can be divided into segments consisting of the following: ) A similar division can be performed for any usage variable (e.g., seal quality segment, air leak segment, detected event segment, user interface compliance segment). The sleep performance score may be calculated using multiple divided usage variables, but in the usage time example where there is only one usage variable, it may be calculated according to the following formula (Score).

number

[0024] In an example of using the variables separately, apnea events are more common during REM sleep due to reduced genioglossus muscle tension in the tongue. Therefore, when the user is in REM sleep, respiratory therapy may be more important than when the user is awake or in light sleep. Thus, χ 1R is χ 1W , χ 1L Appropriate weight values (e.g., such that a specific duration of using a respiratory therapy device during REM sleep results in a greater score increase than the same duration of using the respiratory therapy device during wakefulness or light sleep, where the score increase is smaller) can be set so that it becomes larger than.

[0025] In yet another example, the sleep performance score may be calculated based on usage variables of segments divided based on another usage variable. For example, user interface compliance information (C) can be divided into user interface compliance segments that include user interface compliance information when the seal quality variable is low (C L ) and user interface compliance information when the seal quality variable is high (C H ). A similar division can be performed for any usage variable. The sleep performance score can be calculated using a plurality of divided usage variables. However, in an example having only a single usage variable as user interface compliance information, the sleep performance score (Score) can be calculated according to the following formula.

Equation

[0026] Various schemes for applying weight values ​​to variables used to determine a sleep performance score are disclosed above, including by referring to the equations given above. In some cases, the sleep performance score can be calculated by combining one, some, or all of the various schemes described herein. For example, in some cases, sleep performance scoring may include the step of applying weight values ​​based on sleep stage information to a used variable and the step of applying weight values ​​based on another used variable to the used variable. In another example, the sleep performance score may be calculated by applying weight values ​​based on sleep stage information to the first used variable without applying weight values ​​(or neutral weight values) to the second used variable.

[0027] As used herein, the step of applying a weight value to each used variable is intended to include a step of applying a weight value to fewer used variables than all used variables combined, in which case any used variable to which no weight value is applied is considered to have a neutral weight value (e.g., 1.0x or 100%) to which it is applied. For example, applying a weight value of 0.75x to only the first used variable of a set of four used variables and not applying a weight value to the other used variables is equivalent to applying a weight value of 0.75x to the first used variable and a weight value of 1.0x to the remaining used variables.

[0028] In some cases, the weight values ​​described herein may be static weight values ​​stored in system-accessible memory used to calculate the sleep performance score. For example, the weight value for time spent during REM sleep will always be 1.25 (or 125%). However, in some cases, the weight values ​​may be dynamic, such as a function of some data (e.g., another using a variable or sleep stage information) or the output of a machine learning algorithm (e.g., a deep neural network) trained to output weight values ​​from input data (e.g., sensor data, a variable or sleep stage information) to achieve an accurate sleep performance score (e.g., an objectively accurate score or a subjectively accurate score).

[0029] In some cases, a sleep quality score can be determined. The sleep quality score may represent the quality of sleep experienced by the user during a sleep session. For example, a sleep session with many awakenings or interruptions may have a low sleep quality score, while a sleep session with many awakenings or interruptions may have a high sleep quality score.

[0030] In some cases, the sleep quality score may be based on subjective feedback (e.g., feedback from the user's subjective feeling of rest after a sleep session), on objective data, or a combination of both. Subjective feedback may include the user's rating of their sleep sessions and / or PROMS (Proven Results Measures of Patient Reports) data collected from the user by the healthcare provider. In some cases, subjective feedback may include subjective reasons why the user feels that way about the quality of their sleep and / or the quality of the treatment they receive. These reasons may be remembered and optionally displayed in relation to the sleep quality score and / or sleep performance score.

[0031] In some cases, a sleep quality score can be used to calculate a sleep performance score. In some cases, the sleep quality score may be part of the sleep performance score. In some cases, the sleep quality score can be used to determine weight values ​​applied to different components of the sleep performance score (e.g., weight values ​​applied to one or more usage variables). In some cases, for example, when subjective feedback is collected, the subjective feedback may be used to directly modify one or more components of the sleep performance score or the sleep performance score itself by incorporating modification values ​​(e.g., directly adding) in place of or in addition to the influence weight values. The modification values ​​may be pre-set values ​​selected based on the subjective feedback (e.g., "5" for positive feedback, "-5" for negative feedback) or variable values ​​based on the subjective feedback.

[0032] For example, a sleep quality score or its components can be objectively determined based on, for instance, sleep stage information. For example, the time spent in different sleep stages can be used to determine the sleep quality score. Additionally or alternatively, sleep stage patterns (e.g., sleep structure) can be used to determine the sleep quality score. Sleep stage information can be divided into sleep stage segments (e.g., the total time spent in each sleep stage during a sleep session, or the duration of each consecutive sleep stage that occurred during a sleep session), each indicating the time spent in that stage. In some cases, the time spent in each sleep stage may be weighted based on, for example, a usage variable. For instance, the sleep quality score can be calculated using weighted values ​​such that the time spent in a certain sleep stage has a greater impact on the sleep quality score when the user interface seal is above a threshold than the time spent in a certain sleep stage when the user interface seal is below a threshold.

[0033] The sleep quality score may be at least partially based on physiological data about the user, such as i) respiratory rate, ii) heart rate, iii) heart rate variability, iv) exercise data, v) electroencephalogram data, vi) blood oxygen saturation data, vii) respiratory rate variability, viiii) respiratory depth, ix) tidal volume data, x) inspiratory amplitude data, xi) expiratory amplitude data, xii) expiratory volume data, xiv) inspiratory-to-expiratory ratio data, xvi) sweating data, xvi) temperature data, xvii) pulse wave propagation time data, xvii) blood pressure data, xix) location data, xx) posture data, xxi) blood glucose data, or xxi) any combination of i to xxi.

[0034] In some cases, sleep stage information (and / or alternatively, other variables) can be used to remove data from certain variables or to discount the data in other ways. For example, if event information indicates an event that occurred at 2:01:43 AM, but sleep stage information indicates the user was not asleep at that time, the detected event may be removed from the event information variable or otherwise ignored.

[0035] The sleep performance score can be displayed to the user by a display device on a respiratory therapy device, a display device on a user device (e.g., a smartphone), or any other appropriate method. Displaying the sleep performance score may include displaying the overall sleep performance score and displaying one or more component scores that make up the overall sleep performance score. Component scores may be based on individual or combined scores for each variable used, as well as sleep stage information and / or sleep quality scores. The component scores that make up the sleep performance score may also be graphically represented when displaying the sleep performance score.

[0036] When displaying a sleep performance score, component scores may be displayed that are broken down and / or categorized according to the level of contribution each component score makes to the sleep performance score. For example, if usage time and event information during REM sleep are highly weighted, but user interface compliance information during wakefulness or light sleep is less weighted, displaying the sleep performance score may include indicating that usage time and event information during REM sleep are important components of the sleep performance score for a sleep session, and that user interface compliance information during wakefulness or light sleep is not important.

[0037] In some cases, displaying a sleep performance score may include displaying component scores (e.g., contributions to the sleep performance score) of one or more use variables that have been broken down (e.g., classified) by sleep stage information and / or classified. For example, a set of four component scores (e.g., bins) may be displayed for a use time variable, including scores for use time during wakefulness, use time during REM sleep, use time during light sleep, and use time during deep sleep. It should be understood that each component score may be a score calculated by applying weight values ​​using the variables, as described herein regarding the calculation of the overall sleep performance score.

[0038] A sleep performance score can be used as an objective measure of a user's sleep sessions. In some cases, the sleep performance score may be limited to the portion of a user's sleep session in which respiratory therapy is used. The sleep performance score can provide users with information to monitor, maintain, or encourage compliance (e.g., whether the use of a respiratory therapy device is desirable or prescribed). In some cases, the sleep performance score can provide healthcare providers, facilities, and / or healthcare-related companies (e.g., healthcare insurers) with information on the compliance and effectiveness of users using respiratory therapy devices during sleep. The sleep performance score may also be used to provide an objective measure for research purposes.

[0039] Sleep performance scores may also be used to influence or adjust parameters related to the future use of a user's respiratory therapy system or another person's respiratory therapy system. Such influences or adjustments may be manual (e.g., the user switches the user interface) or automatic (e.g., the respiratory therapy device automatically changes the air pressure supplied during use). In one example, after recording one or more sleep performance scores (e.g., for one or more sleep sessions), one or more parameters of the respiratory therapy system may be adjusted, and then one or more additional sleep performance scores may be measured (e.g., for one or more additional sleep sessions). The additional sleep performance scores can then be compared to the original sleep performance scores to determine whether the adjustments were beneficial. If the adjustments were unhelpful, they could be reversed. If the adjustments were beneficial, they could be reserved for future use or further adjustments. In some cases, data associated with changes in sleep performance scores associated with one or more adjustments of the respiratory therapy system may be sent to a server (e.g., a cloud-based or internet-accessible server). Such data may be used to manufacture future respiratory therapy systems and / or to be accessed from existing respiratory therapy systems to improve respiratory therapy.

[0040] In some cases, sleep performance scores and / or sleep quality pain can be used to identify one or more use variables that a user will tolerate, even if they are out of range. In some cases, out-of-range use variables may be identified in addition to the calculation of sleep performance scores (and / or sleep quality scores). Identifying out-of-range use variables may include determining that the value of a use variable is outside a desired threshold range (e.g., below the threshold, above the threshold, or between two thresholds). Out-of-range use variables may be variables whose overall value exceeds a desired threshold range (e.g., the count of events detected in an event information variable is higher than the threshold number of events), variables whose value exceeds a desired threshold range over a period of time (e.g., a seal quality variable that is below the threshold during the threshold duration of the total time during a sleep session), or variables whose score exceeds a desired threshold range (e.g., a pre-weighted score or post-weighted score such as a component score). In some cases, if the sleep performance score (and / or sleep quality score) for a single sleep session or multiple sleep sessions (e.g., at least a certain number of sleep session thresholds or consecutive sleep session thresholds) is higher than the threshold amount, and one or more specific use variables are out of range, it can be determined that a given out-of-range use variable is still an acceptable use variable. In this case, the acceptable use may not be considered very important to overall sleep performance, sleep quality, and / or the effect of respiratory therapy.

[0041] For example, poor seal quality may be a problem that should be improved (e.g., by replacing the user interface), but if a particular user achieves a high sleep performance score (and / or sleep quality score) despite poor seal quality (e.g., a seal quality variable below a threshold), the respiratory therapy system may consider seal quality to be an acceptable variable. Once seal quality is considered an acceptable variable, the system may choose not to notify the user of changes to the user interface, may reduce one or more weight values ​​associated with the seal quality variable, may make one or more adjustments to the respiratory therapy system, may perform other actions associated with the seal quality variable, or any combination thereof.

[0042] These illustrative examples are presented to introduce the reader to the general subject matter discussed herein and are not intended to limit the scope of the concepts disclosed. The following sections describe various additional features and examples with reference to the accompanying drawings, where the same numbers represent the same components and directional descriptions are used to describe exemplary implementations, but, like the exemplary implementations, are not applied to limit this disclosure. Elements included in these drawings may not be scaled.

[0043] Referring to Figure 1, several implementations of the System 100 of the present disclosure are shown. The System 100 includes a control system 110, a memory device 114, an electronic interface 119, a respiratory therapy system 120, one or more sensors 130, one or more user devices 170, one or more light sources 180, and one or more activity trackers 190.

[0044] The control system 110 includes one or more processors 112 (hereinafter referred to as processor 112). The control system 110 is generally used to control (e.g., operate) various components of system 100 and / or analyze data acquired and / or generated by the components of system 100. The processors 112 may be general-purpose or special-purpose processors or microprocessors. Although one processor 112 is shown in Figure 1, the control system 110 may include any appropriate number of processors (e.g., one processor, two processors, five processors, ten processors, etc.) which may be located in a single housing or separated from one another. The control system 110 may be coupled and / or located in, for example, the housing of the user device 170, part of the breathing system 120 (e.g., housing), and / or the housing of one or more sensors 130. The control system 110 can be centralized (in one such housing) or distributed (in two or more physically separate such housings). In such an implementation configuration, which includes two or more enclosures housing the control system 110, such enclosures may be located close to and / or far apart from one another.

[0045] The storage device 114 stores machine-readable instructions that can be executed 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-access memory device or serial-access memory device, a hard drive, a solid-state drive, or a flash memory device. Although one storage device 114 is shown in Figure 1, the system 100 may include any suitable number of storage devices 114 (e.g., one storage device, two storage devices, five storage devices, ten storage devices, etc.). The storage device 114 may be coupled to and / or located inside the housing of the breathing device 122, the housing of the user device 170, the housing of one or more sensors 130, or any combination thereof. Like the control system 110, the storage device 114 may be centralized (in one such housing), distributed (in two or more physically different such housings), or distributed (in two or more physically different such housings).

[0046] In some implementations, the memory device 114 (Figure 1) stores a user profile associated with the user. The user profile may include, for example, demographic information associated with the user, biometric information associated with the user, medical information associated with the user, self-reported user feedback, sleep parameters associated with the user (e.g., sleep-related parameters recorded from one or more early sleep sessions), or any combination thereof. Demographic information may include, for example, information indicating the user's age, gender, race, geographical location, relationship status, family history of insomnia, employment status, education level, socioeconomic status, or any combination thereof. Medical information may include, for example, information indicating one or more medical conditions associated with the user, the amount of medication used by the user, or both. Medical information data may further include the results or scores of the Multiple Sleep Latency Test (MSLT) and / or the scores or values ​​of the Pittsburgh Sleep Quality Index (PSQI). Self-reported user feedback may include information indicating the user's self-reported subjective sleep score (e.g., poor, average, good), the user's self-reported subjective stress level, the user's self-reported subjective fatigue level, the user's self-reported subjective health status, life events the user has recently experienced, or any combination thereof.

[0047] The electronic interface 119 is configured to receive data (e.g., physiological data and / or audio data) from one or more sensors 130, which can be stored in a storage device 114 and / or analyzed by a processor 112 of the control system 110. The electronic interface 119 can communicate with one or more sensors 130 using wired or wireless connections (e.g., via a cellular network using RF communication protocols, WiFi communication protocols, Bluetooth® communication protocols, etc.). The electronic interface 119 may include an antenna, a receiver (e.g., an RF receiver), a transmitter (e.g., an RF transmitter), a transceiver, or any combination thereof. The electronic interface 119 may also include another processor and / or another storage device that is identical or similar to the processor 112 and storage device 114 described herein. In some implementations, the electronic interface 119 is coupled to or integrated with a user device 170. In some other implementations, the electronic interface 119 is coupled to the control system 110 and / or the storage device 114, or integrated with the control system 110 and / or the storage device 114 (for example, within the enclosure).

[0048] As described above, in some implementations, system 100 optionally includes a respiratory system 120 (also called a respiratory therapy system). The respiratory system 120 may include a respiratory pressure therapy device 122 (hereinafter referred to as respiratory device 122), a user interface 124, a conduit 126 (also referred to as a tube or air circuit), a display device 128, a humidifier tank 129, or any combination thereof. In some implementations, one or more of the control system 110, memory device 114, display device 128, sensor 130, and humidifier tank 129 are part of the respiratory device 122. Respiratory pressure therapy means supplying air to the user's airway inlet at a controlled target pressure positive to atmospheric pressure throughout the user's respiratory cycle (in contrast to negative pressure therapy such as tank ventilators or chest rests). The respiratory system 120 is commonly used to treat individuals suffering from one or more sleep-related respiratory disorders (e.g., obstructive sleep apnea, central sleep apnea, or mixed sleep apnea).

[0049] The breathing device 122 is generally used to generate pressurized air delivered to a user (for example, using one or more motors that drive one or more compressors). In some implementations, the breathing device 122 generates a continuous, constant air pressure delivered to the user. In other implementations, the breathing device 122 generates two or more predetermined pressures (for example, a first predetermined air pressure and a second predetermined air pressure). In yet another implementation, the breathing device 122 is configured to generate a variety of different air pressures within a predetermined range. For example, the breathing device 122 can deliver at least about 6 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. The breathing device 122 can also deliver pressurized air at a predetermined flow rate between, for example, about -20 L / min and about 150 L / min while maintaining positive pressure (relative to ambient pressure).

[0050] The user interface 124 engages with a portion of the user's face and delivers pressurized air from the breathing device 122 to the user's airway, helping to prevent airway narrowing and / or obstruction during sleep. This can also increase the user's oxygen intake during sleep. Depending on the therapy applied, the user interface 124 can, for example, form a seal with an area or portion of the user's face to facilitate the delivery of gas at a pressure sufficiently different from the ambient pressure, e.g., a positive pressure of approximately 10 cm H2O relative to the ambient pressure, in order to activate the therapy. For other forms of therapy, such as oxygen delivery, the user interface may not include a seal sufficient to facilitate the delivery of gas to the gas passage at a positive pressure of approximately 10 cm H2O.

[0051] As shown in Figure 2, in some implementations, the user interface 124 is a mask that covers the user's nose and mouth. Alternatively, the user interface 124 may be a nasal mask 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 also include multiple straps (e.g., including hook-and-loop fasteners) and conformal cushions (e.g., silicone, plastic, foam, etc.) for positioning and / or stabilizing the interface on part of the user (e.g., face) to facilitate an airtight seal between the user interface 124 and the user. In some examples, the user interface 124 may be a tube-up mask, in which the mask straps are configured to function as conduits for delivering pressurized air to the face mask or nasal mask. The user interface 124 may further include one or more vents that allow carbon dioxide and other gases exhaled by the user 210 to escape. In some other implementations, the user interface 124 may include a mouthpiece (e.g., a night protection mouthpiece molded to fit the user's teeth, a mandibular orthodontic device, etc.).

[0052] The conduit 126 (also called an air circuit or tube) allows air to flow between two components of the breathing system 120, such as the breathing device 122 and the user interface 124. In some implementations, this conduit may have separate branch tubes for inhalation and exhalation. In some other implementations, a single limb conduit is used for both inhalation and exhalation. One or more of the breathing device 122, user interface 124, conduit 126, display device 128, and humidification tank 129 may house one or more sensors (e.g., pressure sensors, flow sensors, or more generally, any of the other sensors 130 described herein). These one or more sensors may be used, for example, to measure the air pressure and / or flow rate of pressurized air supplied by the breathing device 122.

[0053] The display device 128 is typically used to display images, including still images, video images, or both, and / or information about the respiratory device 122. For example, the display device 128 can provide information about the status of the respiratory device 122 (e.g., whether the respiratory device 122 is on / off, the pressure of the air expelled by the respiratory device 122, the temperature of the air expelled by the respiratory device 122, etc.) and / or other information (e.g., sleep performance score, sleep score or treatment score (e.g., myAir® score), current date / time, personal information of user 210, etc.). In some implementations, the display device 128 functions as a human-machine interface (HMI) including a graphical user interface (GUI) configured to display images as an input interface. The display device 128 may be an LED display, OLED display, LCD display, etc. The input interface may be, for example, a touchscreen or contact-sensing substrate, a mouse, a keyboard, or any sensor system configured to detect input made by a human user interacting with the respiratory device 122.

[0054] The humidification tank 129 is connected to or integrated with the breathing device 122. The humidification tank 129 also includes a water reservoir that can be used to humidify the pressurized air delivered from the breathing device 122. <636> The breathing device 122 may include a heater for heating the water in the humidification tank 129 to humidify the pressurized air provided to the user.<!--636--> Furthermore, in some implementations, the conduit 126 may also include a heating element (connected to or embedded in the conduit 126) that heats the pressurized air delivered to the user.

[0055] The respiratory system 120 can be used, for example, as a ventilator, or as a positive airway pressure (PAP) system such as a continuous positive airway pressure (CPAP) system, an automated positive airway pressure (APAP) system, a two-level or variable positive airway pressure (BPAP or VPAP) system, or any combination thereof. A CPAP system transmits a predetermined pressure (e.g., determined by a sleep physician) to the user. An APAP system automatically changes the air pressure delivered to the user based, for example, on respiratory data associated with the user. A BPAP or VPAP system is configured to deliver a first predetermined pressure (e.g., positive inspiratory port pressure or IPAP) and a second predetermined pressure lower than the first predetermined pressure (e.g., positive expiratory port pressure or EPAP).

[0056] Referring to Figure 2, some aspects of system 100 (Figure 1) relating to several implementation configurations are shown. The user 210 and bedmate 220 of the respiratory system 120 are located in bed 230 and lying on mattress 232. A user interface 124 (e.g., a full-face mask) may be worn by the user 210 during a sleep session. The user interface 124 is fluidically connected to and / or connected to the respiratory device 122 via a conduit 126. The respiratory device 122 then delivers pressurized air to the user 210 via the conduit 126 and user interface 124, increasing the air pressure in the user's throat to help prevent airway closure and / or narrowing during sleep. The respiratory device 122 may be placed on a nightstand 240 directly adjacent to the bed 230 as shown in Figure 2, or more generally, on any surface or structure substantially adjacent to the bed 230 and / or the user 210.

[0057] Referring also to Figure 1, one or more sensors 130 of system 100 include a pressure sensor 132, a flow velocity sensor 134, a temperature sensor 136, a motion sensor 138, a microphone 140, a speaker 142, a radio frequency (RF) receiver 146, an RF transmitter 148, a camera 150, an infrared sensor 152, a photoelectric (PPG) sensor 154, an electrocardiogram (ECG) sensor 156, an electroencephalogram (EEG) sensor 158, a capacitance sensor 160, a force sensor 162, a strain gauge sensor 164, an electromyogram (EMG) sensor 166, an oxygen sensor 168, an analyte sensor 174, a humidity sensor 176, a laser radar sensor 178, or any combination thereof. Generally, each of the one or more sensors 130 is configured to output sensor data received and stored by a storage device 114 or one or more other storage devices.

[0058] One or more sensors 130 are illustrated and described as including each of the following: pressure sensor 132, flow sensor 134, temperature sensor 136, motion sensor 138, microphone 140, speaker 142, RF receiver 146, RF transmitter 148, camera 150, infrared sensor 152, photoelectric (PPG) sensor 154, electrocardiogram (ECG) sensor 156, electroencephalogram (EEG) sensor 158, capacitance sensor 160, force sensor 162, strain gauge sensor 164, electromyogram (EMG) sensor 166, oxygen sensor 168, specimen sensor 174, moisture sensor 176, and LiDAR sensor 178, but more generally, one or more sensors 130 may include any combination and any number of each sensor described and / or illustrated herein.

[0059] One or more sensors 130 can be used, for example, to generate physiological data, audio data, or both. Physiological data generated by one or more sensors 130 can be used by the control system 110 to determine a sleep-wake signal associated with the user during a sleep session, and one or more sleep-related parameters. The sleep-wake signal can indicate one or more sleep states, including wakefulness, relaxed wakefulness, micro-wakefulness, rapid eye movement (REM) stages, the first non-REM stage (often referred to as "N1"), the second non-REM stage (often referred to as "N2"), the third non-REM stage (often referred to as "N3"), or any combination thereof. N1 and N2 can be considered light sleep stages, and N3 the deep sleep stage. The sleep-wake signal can also be time-stamped to indicate the time the user entered bed, the time the user got out of bed, the time the user attempted to fall asleep, etc. For example, the sleep-wake signal may be measured by the sensors 130 during a sleep session at a predetermined sampling rate, such as 1 sample / second, 1 sample / 30 seconds, or 1 sample / minute. One or more sleep-related parameters that can be determined for a user during a sleep session based on sleep-wake signals include total bedtime, total sleep duration, sleep latency, wake-up parameters, sleep efficiency, fragmentation index, or any combination thereof.

[0060] Physiological and / or audio data generated by one or more sensors 130 may also be used to determine respiratory signals associated with the user during a sleep session. Respiratory signals typically indicate the user's breathing or respiration during a sleep session. Respiratory signals may indicate, for example, respiratory rate, respiratory rate variability, inspiratory amplitude, expiratory amplitude, inspiratory-to-expiratory ratio, number of events per hour, event pattern, pressure setting of the breathing device 122, or any combination thereof. Events may include snoring, apnea, central apnea, obstructive apnea, mixed apnea, respiratory deprivation, mask leak (e.g., from user interface 124), restless legs syndrome, sleep disturbance, suffocation, increased heart rate, dyspnea, asthma attack, epilepsy, epileptic seizure, or any combination thereof.

[0061] The pressure sensor 132 outputs pressure data that can be stored in the memory device 114 and / or analyzed by the processor 112 of the control system 110. In some implementations, the pressure sensor 132 is an air pressure sensor (e.g., a barometric pressure sensor) that generates sensor data indicating the user's breathing (e.g., inhalation and / or exhalation) and / or ambient pressure in the breathing system 120. In such implementations, the pressure sensor 132 may be coupled to the breathing device 122 or incorporated within the breathing device 122. The pressure sensor 132 may be, for example, a capacitive sensor, an electromagnetic sensor, a piezoelectric sensor, a strain sensor, an optical sensor, a potential sensor, or any combination thereof. In one example, the pressure sensor 132 can be used to determine the user's blood pressure.

[0062] The flow sensor 134 outputs flow data that can be stored in the memory device 114 and / or analyzed by the processor 112 of the control system 110. In some implementations, the flow sensor 134 is used to determine the airflow rate from the breathing device 122, the airflow rate through the conduit 126, the airflow rate through the user interface 124, or any combination thereof. In such implementations, the flow sensor 134 can be connected to or integrated with the breathing device 122, the user interface 124, or the conduit 126. The flow sensor 134 may be a mass flow sensor, such as a rotary flow meter (e.g., a Hall effect flow meter), a turbine flow meter, an orifice flow meter, an ultrasonic flow meter, a hot-wire sensor, an eddy current sensor, a membrane sensor, or any combination thereof.

[0063] The temperature sensor 136 outputs temperature data that can be stored in the memory device 114 and / or analyzed by the processor 112 of the control system 110. In some implementations, the temperature sensor 136 generates temperature data indicating the user 210's core body temperature (Figure 2), the user 210's skin temperature, the temperature of the air flowing from and / or through the conduit 126, the 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.

[0064] The microphone 140 outputs audio data that can be stored in the memory device 114 and / or analyzed by the processor 112 of the control system 110. The audio data generated by the microphone 140 can be reproduced as one or more sounds during a sleep session (e.g., sounds from user 210). The audio data from the microphone 140 can also be used (e.g., using the control system 110) to identify events experienced by the user during a sleep session, as will be described in more detail herein. The microphone 140 may be coupled to the breathing device 122, user interface 124, conduit 126, or user device 170, or integrated into the breathing device 122, user interface 124, conduit 126, or user device 170.

[0065] Speaker 142 outputs sound waves that can be heard by a user of system 100 (e.g., user 210 in Figure 2). Speaker 142 can be used, for example, as an alarm clock or to play a warning or message to user 210 (e.g., in response to an event). In some implementations, speaker 142 may be used to transmit audio data generated by microphone 140 to the user. Speaker 142 can be connected to or integrated with the breathing device 122, user interface 124, conduit 126, or user device 170.

[0066] The microphone 140 and speaker 142 can be used as separate devices. In some implementations, the microphone 140 and speaker 142 may be combined with an acoustic sensor 141, as described, for example, in WO 2018 / 050913, all of which are incorporated hereby by reference. In such implementations, the speaker 142 generates or emits sound waves at predetermined intervals, and the microphone 140 detects reflections of the sound waves emitted from the speaker 142. The sound waves generated or emitted by the speaker 142 have frequencies inaudible to the human ear (e.g., less than 20 Hz or more than about 18 kHz) so as not to disturb the sleep of the user 210 or the user's bedmate 220 (Figure 2). The control system 110 can determine the location of user 210 (Figure 2) and / or one or more sleep-related parameters described herein, at least in part, based on data from microphone 140 and / or speaker 142.

[0067] In some implementations, the sensor 130 includes (i) a first microphone which is the same as or similar to microphone 140 and integrated into acoustic sensor 141, and (ii) a second microphone which is the same as or similar to microphone 140 but is independent of and separate from the first microphone integrated into acoustic sensor 141.

[0068] The RF transmitter 148 generates and / or emits radio waves having a predetermined frequency and / or amplitude (e.g., within the high frequency band, within the low frequency band, long wave signal, short wave signal, etc.). The RF receiver 146 detects the reflection of the radio waves transmitted from the RF transmitter 148, and this data can be analyzed by the control system 110 to determine the location of the user 210 (Figure 2) and / or one or more sleep-related parameters described herein. The RF receiver (RF receiver 146 and RF transmitter 148 or another RF pair) may also be used for wireless communication between the control system 110, the respiratory device 122, one or more sensors 130, the user device 170, or any combination thereof. Although the RF receiver 146 and RF transmitter 148 are shown as separate elements in Figure 1, in some implementations the RF receiver 146 and RF transmitter 148 are combined as part of an RF sensor 147. In such implementations, the RF sensor 147 may also include a control circuit. The specific format of the RF communication may be Wi-Fi, Bluetooth (registered trademark), or other similar technologies.

[0069] In some implementations, the RF sensor 147 is part of a mesh system. An example of a mesh system is a WiFi mesh system which may include mesh nodes, mesh routers, and mesh gateways, each of which may be mobile / movable or fixed. In such an implementation, the WiFi mesh system includes WiFi routers and / or WiFi controllers, each containing an RF sensor identical or similar to the RF sensor 147, as well as one or more satellites (e.g., access points). The WiFi routers and satellites communicate with each other continuously using WiFi signals. The WiFi mesh system can be used to generate motion data based on changes in the WiFi signal between the routers and satellites (e.g., differences in received signal strength) due to the movement of objects or people partially interfering with the signal. This motion data may represent exercise, respiration, heart rate, walking, falls, behavior, or any combination thereof.

[0070] The camera 150 outputs image data that can be reproduced as one or more images (e.g., still images, moving images, thermal images, or a combination thereof) that can be stored in the memory 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. For example, the image data from the camera 150 can be used to identify the user's location, determine the time when the user 210 goes to bed 230 (Figure 2), and determine the time when the user 210 gets out of bed 230.

[0071] The infrared (IR) sensor 152 outputs infrared image data that can be reproduced as one or more infrared images (e.g., still images, moving images, or both) which can be stored in the memory device 114. Using the infrared data from the IR sensor 152, one or more sleep-related parameters during a sleep session can be determined, including the user 210's temperature and / or movement. The IR sensor 152 can also be used in combination with the camera 150 to measure the user 210's presence, position, and / or movement. The IR sensor 152 can detect infrared light with wavelengths between approximately 700 nm and 1 mm, for example, while the camera 150 can detect visible light with wavelengths between approximately 380 nm and 740 nm.

[0072] The PPG sensor 154 outputs physiological data associated with user 210 (Figure 2), which can be used to determine one or more sleep-related parameters, such as heart rate, heart rate variability, cardiac cycle, respiratory rate, inspiratory amplitude, expiratory amplitude, inspiratory-expiratory ratio, estimated blood pressure parameters, or any combination thereof. The PPG sensor 154 can be worn by user 210 and embedded in clothing and / or fabric worn by user 210, or embedded and / or connected to user interface 124 and / or its associated headgear (e.g., straps).

[0073] The ECG sensor 156 outputs physiological data associated with the electrical activity of the user 210's heart. In some implementations, the ECG sensor 156 includes one or more electrodes placed on or around a portion of the user 210 during a sleep session. The physiological data from the ECG sensor 156 can be used to determine, for example, one or more sleep-related parameters as described herein.

[0074] The EEG sensor 158 outputs physiological data associated with the electrical activity of the user 210's brain. In some implementations, the EEG sensor 158 includes one or more electrodes placed on or around the user 210's scalp during a sleep session. The physiological data from the EEG sensor 158 can be used, for example, to determine the user 210's sleep state at any given time during a sleep session. In some implementations, the EEG sensor 158 can be integrated into the user interface 124 and / or its associated headgear (e.g., a strap).

[0075] The capacitance sensor 160, force sensor 162, and strain gauge sensor 164 output data that can be stored in the memory device 114 and used by the control system 110 to determine one or more of the sleep-related parameters described herein. The EMG sensor 166 outputs physiological data related to the electrical activity produced by one or more muscles. The oxygen sensor 168 outputs oxygen data indicating the oxygen concentration of the gas (e.g., in the conduit 126 or in the user interface 124). The oxygen sensor 168 may be, for example, an ultrasonic oxygen sensor, an electro-oxygen sensor, a chemical oxygen sensor, an optical oxygen sensor, or any combination thereof. In some implementations, one or more sensors 130 also include a galvanic skin response (GSR) sensor, a blood flow sensor, a respiration sensor, a pulse sensor, a blood pressure sensor, an oxygen measurement sensor, or any combination thereof.

[0076] The sample sensor 174 can be used to detect the presence of a sample in the exhaled breath of the user 210. The data output by the sample sensor 174 is stored in the storage device 114 and can be used by the control system 110 to determine the identity and concentration of any sample in the breath of the user 210. In some implementations, the analyte sensor 174 is positioned near the mouth of the user 210 to detect analytes in the breath exhaled from the user 210's mouth. For example, if the user interface 124 is a mask that covers the nose and mouth of the user 210, the sample sensor 174 can be positioned inside the mask to monitor the user 210's mouth breathing. In some other implementations, for example, if the user interface 124 is a nasal mask or nasal pillow mask, the analyte sensor 174 can be positioned near the nose of the user 210 to detect analytes in the breath exhaled through the user's nose. In yet another implementation, if the user interface 124 is a nasal mask or nasal pillow mask, the sample sensor 174 can be positioned near the mouth of the user 210. In this implementation, the sample sensor 174 can be used to detect whether air is inadvertently leaking from the user 210's mouth. In some implementations, the sample sensor 174 is a volatile organic compound (VOC) sensor that can be used to detect carbonaceous chemicals or carbonaceous compounds. In some implementations, the sample 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 placed near the user 210's mouth or inside the mask (in implementations where the user interface 124 is the mask), the control system 110 can use this data as an indication that the user 210 is breathing through their mouth.

[0077] The humidity sensor 176 outputs data that can be stored in the memory device 114 and used by the control system 110. The moisture sensor 176 can be used to detect moisture in various areas surrounding the user (e.g., inside the conduit 126 or user interface 124, near the user 210's face, near the connection between the conduit 126 and the user interface 124, near the connection between the conduit 126 and the breathing device 122, etc.). Therefore, in some implementations, the humidity sensor 176 may be coupled to or integrated with the user interface 124 or conduit 126 to monitor the humidity of the pressurized air from the breathing device 122. In some other implementations, the humidity sensor 176 is placed near any area where the humidity level needs to be monitored. The moisture sensor 176 can also be used to monitor the ambient environment surrounding the user 210, for example, the humidity of the air in the user's bedroom.

[0078] The LiDAR (Light Detection and Ranging) sensor 178 can be used for depth sensing. Such optical sensors (e.g., laser sensors) can be used to detect objects and create three-dimensional (3D) maps of the surrounding environment, such as living spaces. LiDAR generally uses pulsed lasers to measure time of flight. LiDAR is also called 3D laser scanning. In one use case of such a sensor, a stationary or mobile device (such as a smartphone) having the LiDAR sensor 166 can measure and map an area more than 5 meters away from the sensor. LiDAR data can be fused with point cloud data estimated by, for example, an electromagnetic RADAR sensor. The LiDAR sensor 178 can also automatically create geofences for a RADAR system by using artificial intelligence (AI) to detect and classify features in space that may pose problems for the RADAR system, such as glass windows (which may be highly reflective to RADAR). LiDAR can also be used to estimate a person's height, as well as changes in height that occur when a person sits down, falls down, etc. LiDAR can be used to form a 3D mesh representation of the environment. In further applications, LiDAR can reflect radio waves off solid surfaces (e.g., radio-transparent materials) to enable the classification of different types of obstacles.

[0079] As shown individually in Figure 1, any combination of one or more sensors 130 may be integrated and / or coupled to any one or more components of system 100, including the breathing device 122, user interface 124, conduit 126, humidification tank 129, control system 110, user device 170, or any combination thereof. For example, the microphone 140 and speaker 142 are integrated and / or coupled to the user device 170, and the pressure sensor 130 and / or flow sensor 132 are integrated and / or coupled to the breathing device 122. In some implementations, at least one of the one or more sensors 130 is not coupled to the breathing device 122, control system 110, or user device 170, and is typically positioned adjacent to the user 210 during a sleep session (e.g., positioned on or in contact with part of the user 210, worn by the user 210, coupled to or positioned on a nightstand, coupled to a mattress, coupled to the ceiling, etc.).

[0080] For example, as shown in Figure 2, one or more of the sensors 130 may be located in a first position 250a on the nightstand 240, close to the bed 230 and the user 210. Alternatively, one or more sensors 130 may be located in a second position 250B on and / or within the mattress 232 (e.g., the sensors are coupled to and / or integrated within the mattress 232). Furthermore, one or more sensors 130 may be located in a third position 250C on the bed 230 (e.g., a second sensor 140 may be coupled to and / or integrated into the bed board, footboard, or other position on the frame of the bed 230). One or more of the sensors 130 may be located in a fourth position 250d on a wall or ceiling generally adjacent to the bed 230 and / or the user 210. One or more of the sensors 130 may be located in a fifth position such that they are coupled to and / or located on and / or within the housing of the breathing device 122 of the breathing system 120. Furthermore, one or more sensors 130 may be positioned at the sixth position 250F so that the sensors are coupled to and / or positioned on the user 210 (for example, the sensors are embedded in or coupled to the fabric or clothing worn by the user 210 during the sleep session). More generally, one or more sensors 130 can be positioned at any suitable location relative to the user 210 so that the sensors 140 can generate physiological data associated with the user 210 and / or the person sharing the bed 220 during one or more sleep sessions.

[0081] The user device 170 (Figure 1) includes a display device 172. The user device 170 may be a mobile device such as a smartphone, tablet, or laptop. Alternatively, the user device 170 may be an external sensing system, a television (e.g., a smart TV), or another smart home device (e.g., a smart speaker such as Google Home®, Amazon Echo®, or Alexa®). In some implementations, the user device is a wearable device (e.g., a smartwatch). The display device 172 is generally used to display images, including still images, moving images, or both. In some implementations, the display device 172 functions as a human-machine interface (HMI) including a graphical user interface (GUI) and an input interface configured to display images. The display device 172 may be an LED display, an OLED display, an LCD display, etc. The input interface may be a touchscreen or touch-sensor board, a mouse, a keyboard, or any sensor system configured to sense input made by a human user interacting with the user device 170. In some implementations, one or more user devices may be used by and / or included in system 100.

[0082] The light source 180 is commonly used to emit light having intensity and wavelength (e.g., color). For example, the light source 180 can emit light with wavelengths between approximately 380 nm and approximately 700 nm (e.g., wavelengths in the visible light spectrum). The light source 180 may include, for example, one or more light-emitting diodes, one or more organic light-emitting diodes, light bulbs, lamps, incandescent bulbs, CFL bulbs, halogen bulbs, or any combination thereof. In some implementations, the intensity and / or wavelength (e.g., color) of the light emitted from the light source 180 may be modified by the control system 110. The light source 180 can also emit light in a predetermined emission pattern such as continuous emission, pulsed emission, periodic emission of different intensities (e.g., including emission periods in which the intensity gradually increases and then decreases), or any combination thereof. The light emitted from the light source 180 may be directly observed by the user, or it may be reflected or refracted before reaching the user. In some implementations, the light source 180 includes one or more light pipes.

[0083] In some implementations, the light source 180 is physically coupled to or integrated with the respiratory therapy system 120. For example, the light source 180 may be physically coupled to or integrated with the respiratory device 122, the user interface 124, the conduit 126, the display device 128, or any combination thereof. In some implementations, the light source 180 is physically coupled to or integrated with the user device 170. In some other implementations, the light source 180 is separated and distinct from the respiratory therapy system 120, the user device 170, and the activity tracker 190. In such implementations, the light source 180 can be located at the user 210 (Figure 2), for example, on a nightstand 240, a bed 230, other furniture, a wall, a ceiling, etc.

[0084] The activity tracker 190 is generally used to help generate physiological data for determining activity measurements relevant to the user. Activity measurements may include, for example, steps taken, distance traveled, steps climbed, duration of physical activity, type of physical activity, intensity of physical activity, time spent standing, respiratory rate, mean respiratory rate, resting respiratory rate, maximum respiratory rate, respiratory rate variability, heart rate, mean heart rate, resting heart rate, maximum heart rate, heart rate variability, calories burned, blood oxygen saturation, skin electrical activity (also called skin electrical conductivity or skin electroreactivity), or any combination thereof. The activity tracker 190 may include, for example, one or more sensors 130 as described herein, such as a motion sensor 138 (e.g., one or more accelerometers and / or gyroscopes), a PPG sensor 154 and / or an ECG sensor 156.

[0085] In some implementations, the activity tracker 190 is a wearable device that the user can wear, such as a smartwatch, wristband, ring, or patch. For example, referring to Figure 2, the activity tracker 190 is worn on the wrist of user 210. The activity tracker 190 can also be coupled to or integrated into one or more garments worn by the user. Alternatively, the activity tracker 190 may be coupled to the user device 170 or integrated within the user device 170 (e.g., within the same housing). More generally, the activity tracker 190 may be communicatively coupled to the control system 110, memory 114, respiratory system 120, and / or the user device 170, or physically integrated within them (e.g., within the housing).

[0086] Meanwhile, the control system 110 and the memory device 114 are described and shown in Figure 1. In some implementations, the control system 110 and / or the memory device 114 are integrated into the user device 170 and / or the breathing device 122. Alternatively, in some implementations, the control system 110 or a part of it (e.g., the processor 112) may reside in the cloud (e.g., embedded in a server, embedded in an IoT (Internet of Things) device (e.g., smart TV, smart thermostat, smart electrical appliance, smart lighting, etc.), connected to the cloud, and processed by an edge cloud), or in one or more servers (e.g., a remote server, a local server, or any combination thereof).

[0087] Although System 100 is shown to include all of the above components, according to the implementations of this disclosure, the system for generating physiological data and determining suggested notifications or actions for the user may include more or fewer components. For example, a first alternative system includes a control system 110, a memory device 114, and at least one of one or more sensors 130. Another example is a second alternative system including a control system 110, a memory device 114, at least one of one or more sensors 130, and a user device 170. Yet another example is a third alternative system including a control system 110, a memory device 114, a breathing system 120, at least one of one or more sensors 130, and a user device 170. Thus, various systems may be formed using any part of the components illustrated and described herein and / or in combination with one or more other components.

[0088] As used herein, a sleep session can be defined in various ways, for example, based on an initial start time and an end time. Referring to Figure 3, an exemplary timeline 301 for a sleep session is shown. Timeline 301 is based on bedtime (t 就床 ), sleep onset time (tGTS ), initial sleep time (t 初期 ), first minute awakening MA1 and second minute awakening MA2, wake-up time (t 目覚 ) and wake-up time (t 起床 ) includes.

[0089] In some implementations, a sleep session is defined as the duration of a user's sleep. In such implementations, a sleep session has a start time and an end time, and the user remains awake until the end time. In other words, time the user is awake is not included in the sleep session. From this first definition of a sleep session, if a user wakes up and falls asleep multiple times during the night, each sleep period separated by those periods of wakefulness constitutes a sleep session.

[0090] Alternatively, in some implementations, a sleep session has a start time and an end time, and during the sleep session, the user may wake up without the sleep session ending, as long as the continuous duration of the user's wakefulness falls below an awakening duration threshold. The awakening duration threshold can be defined as a percentage of the sleep session. The awakening duration threshold may be, for example, about 20 percent of the sleep session, about 15 percent of the sleep session duration, about 10 percent of the sleep session duration, about 5 percent of the sleep session duration, about 2 percent of the sleep session duration, or any other arbitrary threshold percentage. In some implementations, the awakening duration threshold is defined as a fixed amount of time, such as about 1 hour, about 30 minutes, about 15 minutes, about 10 minutes, about 5 minutes, about 2 minutes, or any other arbitrary amount of time.

[0091] In some implementations, a sleep session is defined as the total time from when the user first goes to bed at night until when they last get out of bed the following morning. In other words, a sleep session can be defined as the time that begins at a first time (e.g., 10:00 p.m.) on a first date that can be called the current night (e.g., Monday, January 6, 2020) when the user first goes to bed with the intention of sleeping (e.g., not when the user first intends to watch TV or use their smartphone before going to sleep), and ends at a second time (e.g., 7:00 a.m.) on a second date that can be called the following morning (e.g., Tuesday, January 7, 2020) when the user first gets up with the intention of not going back to sleep the following morning.

[0092] In some implementations, users can manually define the start of a sleep session and / or manually end a sleep session. For example, a user can select a user-selectable element displayed on the display device 172 of the user device 170 (Figure 1) (e.g., by clicking or tapping) to manually start or end a sleep session. bedtime t 就床 This is associated with the time when the user first goes to bed (for example, bed 230 in Figure 2) before going to sleep (for example, when lying down or sitting in bed). 就床 Based on the bedtime threshold duration, it is possible to distinguish between the time a user goes to bed to sleep and the time a user goes to bed for other reasons (e.g., watching TV). For example, the bedtime threshold duration could be at least about 10 minutes, at least about 20 minutes, at least about 30 minutes, at least about 45 minutes, at least about 1 hour, at least about 2 hours, etc. Here, in relation to the bed, the bedtime t 就床 Although it is stated, more generally, bedtime t 就床 This could also be the time the user first entered their sleeping position (e.g., sofa, chair, sleeping bag, etc.).

[0093] The time to fall asleep (GTS) is when the user goes to bed (t 就床This is associated with the time when the user first attempts to fall asleep after getting into bed. For example, after getting into bed, the user may engage in one or more activities to relax (e.g., reading, watching TV, listening to music, using user device 170, etc.) before attempting to sleep. 睡眠 ) is the time when the user first fell asleep. For example, initial sleep time (t 睡眠 This could also be the time when the user first entered the first non-REM sleep phase.

[0094] wake up time t 目覚 This is the time associated with when the user wakes up without falling back asleep (unlike, for example, when the user wakes up in the middle of the night and falls back asleep). After initially falling asleep, the user may experience one of several short, unconscious micro-awakenings (e.g., micro-awakening MA1, MA2) of varying durations (e.g., 5 seconds, 10 seconds, 30 seconds, 1 minute, etc.). The user's wake-up time t 目覚 Conversely, the user returns to sleep after each minor awakening in MA1 and MA2. Similarly, the user may experience one or more conscious awakenings (e.g., awakening A) after initially falling asleep (e.g., getting up to go to the toilet, caring for a child or pet, sleepwalking, etc.). However, the user will fall back asleep after awakening A. Therefore, the wake-up time t 目覚 This can be defined, for example, based on the wake-threshold duration (e.g., the duration during which the user is awake for 15 minutes or more, 20 minutes or more, 30 minutes or more, 1 hour or more, etc.).

[0095] Similarly, wake-up time t 起床 This is associated with the time when a user gets out of bed and is absent from bed with the intention of ending a sleep session (for example, getting up at night to go to the toilet, caring for a child or pet, or sleepwalking). In other words, wake-up time t 起床 This is the time when the user last left bed without returning to bed until the next sleep session (e.g., the next night). Therefore, the wake-up time t 起床This can be defined, for example, based on the wake-up threshold duration (e.g., when the user is away from bed for 15 minutes or more, 20 minutes or more, 30 minutes or more, 1 hour or more, etc.). The second and subsequent bedtimes t 就床 The wake-up threshold can also be defined based on the duration of the wake-up threshold (for example, if the user has been out of bed for 4 hours, 6 hours, 8 hours, or 12 hours or more).

[0096] As mentioned above, the user first t 就床 from the last t 起床 Between then and the last wake time t, there is a possibility of waking up and getting out of bed at least once during the night. In some implementations, the last wake time t 目覚 and / or last wake-up time t 起床 This is identified or determined based on a predetermined threshold duration of time following an event (e.g., falling asleep or getting out of bed). Such a threshold duration may be customized for the user. For a standard user who goes to bed at night and wakes up and gets out of bed in the morning, any time between approximately 12 and 18 hours (when the user wakes up (t 目覚 ) or wake up (t 起床 ) From the user to bed (t 就床 ), falling asleep (t GTS ) or sleep (t 睡眠 A threshold period of up to ) can be used. For users with longer sleep durations, a shorter threshold period (e.g., between approximately 8 and 14 hours) can be used. The threshold period may be initially selected and / or adjusted later based on a system that monitors the user's sleep behavior.

[0097] Total sleep time (TIB) refers to the time spent in bed. 就床 From wake time 起床This is the duration up to t. Total sleep time (TST) is the duration from the initial sleep time to the wake time, excluding conscious and unconscious wakefulness and / or minute wakefulness during that time. Generally, total sleep time (TST) is shorter than total bedtime (TIB) (e.g., 1 minute shorter, 10 minutes shorter, 1 hour shorter, etc.). For example, referring to time axis 301 in Figure 3, total sleep time (TST) is from the initial sleep time t 睡眠 and alarm time t 目覚 Although it spans between these periods, the durations of the first micro-awakening MA1, the second micro-awakening MA2, and awakening A are excluded. As illustrated, in this example, total sleep time (TST) is shorter than total time spent asleep (TIB).

[0098] In some implementations, total sleep time (TST) may be defined as total continuous sleep time (PTST). In such implementations, total continuous sleep time excludes a predetermined initial portion or period of the first non-REM phase (e.g., light sleep phase). For example, this predetermined initial portion could be approximately 30 seconds to 20 minutes, approximately 1 minute to 10 minutes, or approximately 3 minutes to 5 minutes. Total continuous sleep time is a measure of continuous sleep and smooths the sleep-wake sleep progression. For example, when a user first falls asleep, they may enter the first non-REM phase for a very short time (e.g., approximately 30 seconds), then return to the wakefulness phase for a short time (e.g., 1 minute), and then return to the first non-REM phase. In this example, total continuous sleep time excludes the first instance of the first non-REM phase (e.g., approximately 30 seconds).

[0099] In some implementations, the sleep session is defined by the time of going to bed (t 就床 It starts with ) and wake-up time (t 起床 A sleep session is defined as ending at t 睡眠 It starts at ) and wake-up time (t 目覚A sleep session is defined as ending at t GTS It starts at ) and wake-up time (t 目覚 It is defined as ending at the time of sleep onset (t). In some implementations, a sleep session is defined as ending at the time of sleep onset (t). GTS It starts with ) and wake-up time (t 起床 It is defined as ending at bedtime (t). In some implementations, a sleep session is defined as ending at bedtime (t). 就床 It starts at ) and wake-up time (t 目覚 It is defined as ending at the initial sleep time (t). In some implementations, a sleep session is defined as ending at the initial sleep time (t). 睡眠 It starts with ) and wake-up time (t 起床 It is defined as ending in ).

[0100] Referring to Figure 4, exemplary sleep progression diagrams 400 corresponding to timeline 400 (Figure 4) in several implementation forms are shown. As shown in the figure, the sleep progression diagram 400 includes a sleep-wake signal 401, an arousal stage axis 410, a REM stage axis 420, a light sleep stage axis 430, and a deep sleep stage axis 440. The intersections between the sleep-wake signal 401 and one of the axes 410, 420, 430, and 440 indicate any predetermined sleep stage during a sleep session.

[0101] The sleep-wake signal 401 can be generated based on physiological data associated with the user (e.g., generated by one or more of the sensors 130 (Figure 1) described herein). The sleep-wake signal can indicate one or more sleep states or stages, including wakefulness, relaxed wakefulness, micro-wakefulness, REM stage, first non-REM stage, second non-REM stage, third non-REM stage, or any combination thereof. In some implementations, one or more of the first non-REM stage, second non-REM stage, and third non-REM stage may be grouped together and classified into a light sleep stage or a deep sleep stage. For example, the light sleep stage may include the first non-REM stage, and the deep sleep stage may include the second and third non-REM stages. As shown in Figure 4, the hypnogram 400 includes a light sleep stage axis 430 and a deep sleep stage axis 440, but in some implementations, the hypnogram 400 may include axes for the first non-REM stage, the second non-REM stage, and the third non-REM stage, respectively. In other implementations, the sleep-wake signal can represent respiratory signals, respiratory rate, inspiratory amplitude, expiratory amplitude, inspiratory-expiratory ratio, number of events per hour, event pattern, or any combination thereof. Information describing the sleep-wake signal can be stored in the memory device 114. The sleep progression diagram 400 can be used to determine one or more sleep-related parameters, such as sleep latency (SOL), wakefulness during the night (WASO), sleep efficiency (SE), sleep fragmentation index, sleep block, or any combination thereof.

[0102] Sleep latency (SOL) is the time it takes to fall asleep (t GTS ) from the initial sleep time (t 睡眠Sleep latency is defined as the time until the user first attempts to fall asleep. In other words, sleep latency indicates the time it takes from the time the user first attempts to fall asleep until they actually fall asleep. In some implementations, sleep latency is defined as persistent sleep onset latency (PSOL). Persistent sleep latency differs from sleep latency in that it is defined as the duration from the time the user falls asleep until a given amount of sustained sleep. In some implementations, a given amount of sustained sleep may include, for example, a second non-REM phase, a third non-REM phase, and / or a REM phase including a state of wakefulness of 2 minutes or less, a first non-REM phase, and / or at least 10 minutes of sleep in the transition between them. In other words, persistent sleep latency requires, for example, a second non-REM phase, a third non-REM phase, and / or up to 8 minutes of sustained sleep in the REM phase. In other implementations, a predetermined amount of sustained sleep may include at least 10 minutes of sleep in a first non-REM phase, a second non-REM phase, a third non-REM phase, and / or a REM phase following the initial sleep time. In such implementations, the predetermined amount of sustained sleep may exclude all minute awakenings (for example, a 10-second minute awakening would not be followed by a resumption of sleep for 10 minutes).

[0103] Nocturnal awakening (WASO) is associated with the total duration of wakefulness a user experiences between the initial sleep time and the wake-up time. Therefore, nocturnal awakening includes short, minute awakenings during a sleep session, whether conscious or unconscious (e.g., minute awakenings MA1 and MA2 shown in Figure 4). In some implementations, nocturnal awakening (WASO) is defined as persistent nocturnal awakening (PWASO), which includes only the total duration of wakefulness having a predetermined length (e.g., 10 seconds or more, 30 seconds or more, 60 seconds or more, approximately 5 minutes or more, approximately 10 minutes or more, etc.).

[0104] Sleep efficiency (SE) is determined as the ratio of total bedtime (TIB) to total sleep time (TST). For example, if total bedtime is 8 hours and total sleep time is 7.5 hours, the sleep efficiency for that sleep session is 93.75%. Sleep efficiency indicates a user's sleep hygiene. For example, if a user goes to bed and spends time on other activities (e.g., watching television) before falling asleep, sleep efficiency decreases (e.g., the user is penalized). In some implementations, sleep efficiency (SE) can be calculated based on total bedtime (TIB) and the total time the user tries to fall asleep. In such implementations, the total time the user tries to fall asleep is defined as the duration from the time of falling asleep (GTS) to the time of waking up as described herein. For example, in an implementation where total sleep time is 8 hours (e.g., from 11 p.m. to 7 a.m.), the time of falling asleep is 10:45 p.m., and the time of waking up is 7:15 a.m., the sleep efficiency parameter is calculated as approximately 94%.

[0105] The fragmentation index is determined at least partially based on the number of awakenings during a sleep session. For example, if a user had two minor awakenings (e.g., minor awakenings MA1 and MA2 shown in Figure 4), the fragmentation index could be represented as 2. In some implementations, the fragmentation index is scaled between integers within a given range (e.g., between 0 and 10).

[0106] A sleep block is associated with the transition between any sleep stage (e.g., the first non-REM stage, the second non-REM stage, the third non-REM stage, and / or REM) and the wakefulness stage. For example, a sleep block can be calculated with a resolution of 30 seconds.

[0107] In some implementations, the systems and methods described herein generate or analyze a sleep time map including sleep-wake signals and determine the time of going to bed (t) based at least partially on the sleep-wake signals of the sleep time map. 就床 ), sleep onset time (t GTS ), initial sleep time (t 睡眠), one or more first minute awakenings (e.g., MA1 and MA2), wake time (t 目覚 ), wake-up time (t 起床 This may include determining or identifying, or any combination thereof.

[0108] In other implementations, one or more sensors 130 determine the time of going to bed (t 就床 ), sleep onset time (t GTS ), initial sleep time (t 睡眠 ), one or more first minute awakenings (e.g., MA1 and MA2), wake time (t 目覚 ), wake-up time (t 起床 ) or any combination thereof can be used to define a sleep session by determining or identifying these. For example, bedtime t 就床 This can be determined, for example, based on data generated by a motion sensor 138, a microphone 140, a camera 150, or any combination thereof. Sleep time can be determined, for example, based on data from the motion sensor 138 (e.g., data indicating that the user is not moving), data from the camera 150 (e.g., data indicating that the user is not moving and / or that the user has turned off the lights), data from the microphone 140 (e.g., data indicating that the user has turned off the TV), data from the user device 170 (e.g., data indicating that the user is no longer using the user device 170), data from the pressure sensor 132 and / or the flow sensor 134 (e.g., data indicating that the user has turned on the breathing device 122, data indicating that the user has put on the user interface 124, etc.), or any combination thereof.

[0109] Sleep progression diagram 400 depicts the REM phase gradually shortening as the sleep session progresses, but this is not always the case. In some cases, the duration of the REM phase gradually increases as the sleep session progresses (for example, the first REM phase is shorter than the last REM phase).

[0110] Figure 5 is a chart 500 showing several usage variables associated with the sleep progression chart of Figure 4, according to several aspects of this disclosure. Chart 500 can be associated with the sleep session of Figure 3. Chart 500 includes several usage variables, including usage time 514, events 516, and seal quality 518, determined during the process of the sleep session 502. Furthermore, user interface compliance usage variables may be determined and / or indicated based on detected user interface transformations, expressed as user interface transformation periods 506, 510 (e.g., gaps in other usage variables). In some cases, user interface compliance usage variables may be or include one or more mask on / off events (e.g., events indicating the putting on or taking off of a mask or user interface). In some cases, user interface compliance usage variables may track when the user interface was put on and / or taken off, and / or the number of times the user interface was put on and / or taken off.

[0111] The usage time 514 can represent the amount of time that the respiratory therapy system (e.g., respiratory therapy system 120 in Figure 1) uses to provide respiratory therapy to the user. As shown in Chart 500, a set of blocks 520 representing time blocks while the user is using the respiratory therapy system is drawn using time 514 during a sleep session. The respiratory therapy system is used, for example, during a first period 504, a second period 508, and a third period 512. Between the first period 504 and the second period 508, the user may temporarily stop using the respiratory therapy system (e.g., by removing and replacing the user interface), as identified by the user interface transition period 506. The start of the user interface transition period 506 may indicate a first user interface transition (e.g., removal of the user interface), and the end of the user interface transition period 506 may indicate a second user interface transition (e.g., attachment of the user interface). Similarly, a similar user interface transition period 510 is located between the second period 508 and the third period 512.

[0112] The variables used in event 516 can be represented as a set of timestamp values ​​(or simply timestamps), as shown by events 522 and 524 in chart 500. Events 522 and 524 may be apnea events, hypopnea events, or other events.

[0113] The variable used for seal quality 518 can be represented by a line 526 that shows the value associated with seal quality during a sleep session. In the example of seal quality 518, there are two instances 528, 530 of low seal quality, between which the timeline 526 falls below the threshold line 532. In some cases, user interface transition periods 506, 510 may be discounted for using the variable for seal quality 518, or they may illustrate an example of low seal quality.

[0114] Comparing the variables used in Chart 500 with the sleep stages depicted in Sleep Progress Diagram 400, the first period 504 is t 就床 It can be seen that this includes the time from to MA1. The user was using a respiratory therapy device during this time and presumably only temporarily removed it during MA1. During the first period 504, the user experiences four stages of light sleep, two stages of deep sleep, and one stage of REM sleep. During the first period 504, a low seal quality instance 528 is detected, which matches detected event 522. It is presumed that the low seal quality in instance 528 may have caused inadequate respiratory therapy, thereby enabling the occurrence of event 522. Event 522 can also be matched with the user temporarily dropping from a deep sleep stage to a light sleep stage.

[0115] The second period 508 represents the usage time from the end of MA1 to the start of MA2, and includes four stages of light sleep, two stages of deep sleep, and one stage of REM sleep. During the second period 508, the seal quality 518 is strong (line 526 is higher than the threshold line 542), and event 524 is detected. Comparing chart 500 with the sleep progression diagram 400, event 524 occurs at approximately the same time the user is in the REM sleep stage.

[0116] The third period 512 is from the end of MA2 to t 目覚 This indicates the duration of use up to a certain point, including one REM sleep phase, two light sleep phases, and one deep sleep phase. No events are detected during the third period 512, however, the third period 512 begins with an instance 530 of low seal quality occurring during the REM sleep phase.

[0117] Since event 524 occurs during the REM sleep phase and event 522 occurs during the deep sleep phase, the occurrence of event 524 can be given a higher weight than the occurrence of event 522. For example, event 524 can lower the overall sleep performance score more than event 522.

[0118] Since low-seal-quality instance 530 occurs during the REM sleep phase, and low-seal-quality instance 528 occurs during the light and deep sleep phases, the occurrence of low-seal-quality instance 530 can be weighted more highly than the occurrence of low-seal-quality instance 528. For example, low-seal-quality instance 530 can lower the overall sleep performance score more than low-seal-quality instance 528.

[0119] Chart 500 shows an exemplary visual representation using a set of variables that may be determined based on sensor data collected from one or more sensors (e.g., one or more sensors 130 in Figure 1). Other sets of variables may include one or more variables disclosed herein, or any combination of other similar variables associated with the use of a respiratory therapy system. Furthermore, any set of variables may be displayed, stored, and / or otherwise represented in any suitable format such as charts, numbers, spreadsheets, databases, data strings, or other formats.

[0120] Figure 6 is a flowchart of a process 600 for scoring sleep performance according to one embodiment of the present disclosure. The process 600 can be performed by any suitable system, such as the system 100 in Figure 1, which includes being performed by the processor 112 of the control system 110 in Figure 1. One, some, or all blocks of the process 600 can occur during a sleep session (e.g., a given sleep session or a subsequent sleep session in which the sleep performance score is being calculated), immediately after a sleep session, or at another time. In some cases, the process 600 is performed by a user device (e.g., a smartphone), such as the user device 170 in Figure 1.

[0121] In block 602, sensor data is received. The received sensor data may be collected from one or more sensors associated with the user's sleep session while the user is receiving respiratory therapy from a respiratory therapy system (e.g., respiratory therapy system 120 in Figure 1). Such one or more sensors (e.g., one or more sensors 130 in Figure 1) may include a set of sensors from the respiratory therapy system (e.g., pressure sensors and flow rate sensors) and / or a set of sensors from the user device (e.g., acoustic or RF sensors from a smartphone), although other sensors may also be available. In some cases, the sensor data may be preprocessed before block 602 receives the sensor data. In some cases, receiving the sensor data in block 602 may include preprocessing the sensor data to improve the ability to determine any desired use variables and / or sleep stage information later. In other cases, the sensor data may not be preprocessed.

[0122] In block 604, one or more use variables can be determined from sensor data. Determining one or more use variables may involve processing the sensor data (e.g., via equations, functions, or machine learning algorithms) to identify one or more values ​​of the one or more use variables. The one or more use variables may be any number or a suitable combination of use variables as disclosed herein. In some cases, the use variables determined in block 604 may be single-value use variables, such as the average leak rate, which can be expressed as a single numerical value, or the number of detected events, which can be expressed as a single numerical value. However, in some cases, the use variables determined in block 604 may be a set of values ​​that occur throughout the sleep session, such as a timestamp value or the timestamp itself. For example, a seal quality use variable may be expressed as a set of seal quality values ​​(e.g., 0 to 100%, 0 to 20 on a 20 scale, etc.) that are collected periodically (e.g., based on a sampling rate).

[0123] In block 606, sleep stage information can be determined. Determining sleep stage information may include processing sensor data to identify the user's sleep stages at different points in the overall sleep session, for example, identifying transitions between different sleep stages and the duration of time spent in each sleep stage. Time spent in a sleep stage may refer to the total time spent in all instances of a particular sleep stage (e.g., 90 minutes of REM sleep in total during the entire sleep session), or the time spent in individual instances of different sleep stages (e.g., 40 minutes of REM sleep, followed by 10 minutes of light sleep, followed by 5 minutes of wakefulness (e.g., mild wakefulness), followed by 30 minutes of light sleep, followed by 10 minutes of deep sleep, followed by 15 minutes of light sleep, and then another 20 minutes of REM sleep). In some cases, sleep stage information may include the total duration of the sleep session. In some cases, sleep stage information may include the duration between sleep stages, and / or one or more ratios between the duration between each sleep stage and the total duration of the sleep session.

[0124] In block 612, a sleep performance score can be calculated. The sleep performance score can be calculated using the use variables determined from block 604 and the sleep stage information determined from block 606. In some cases, calculating the sleep performance score may include calculating one or more component scores that can be combined to calculate the final sleep performance score. In some cases, component scores can be determined for one, some, or all of the use variables from block 604 and / or the sleep stage information determined in block 606.

[0125] In some cases, determining the sleep performance score in block 612 may include determining one or more weight values ​​in block 614 and applying one or more weight values ​​in block 616. The weight values ​​may be determined for any combination of use variables, sleep stage information, segment use variables, or segment sleep stage information. In some cases, determining the weight values ​​may include dividing a use variable into multiple use variable segments. These segments may be based on sleep stages and / or other use variables. For example, the use variable for use time may be divided based on sleep states, and the use variable for event information may be divided based on the use variable for seal quality.

[0126] Determining weight values ​​may involve accessing predefined weight values, calculating weight values, or receiving weight values ​​(for example, receiving weight values ​​from the output of a machine learning algorithm). In some cases, the determined weight values ​​may be neutral weight values, such as 1.0x or 100% weight values. In some cases, the determined weight values ​​may be incremental weight values, such as 1.5x or 150% weight values. In some cases, the determined weight values ​​may be reduced weight values, such as 0.5x or 50% weight values.

[0127] In some cases, the weight values ​​of the use variables may be determined based on sleep stage information from block 606 and / or other use variables from block 604. In some cases, determining the weight values ​​in block 614 may include determining a set of weight values ​​for a given use variable, for example, determining the weight values ​​for each combination of a given use variable and a sleep stage from sleep stage information and / or other use variables. For example, the weight values ​​determined for an event information use variable (e.g., a detected apnea or hypopnea event) may include determining 1) the weight values ​​of the event information use variable in combination with the wakefulness sleep stage, 2) the weight values ​​of the event information use variable in combination with the light sleep stage, 3) the weight values ​​of the event information use variable in combination with the deep sleep stage, and 4) the weight values ​​of the event information use variable in combination with the REM sleep stage.

[0128] In some cases, determining the weight values ​​of a given usage variable in block 604 may involve applying another usage function (e.g., a time-dependent usage variable) to the function. For example, the weight values ​​of a given usage variable may be a proportional or inverse proportional function of another usage variable.

[0129] In some cases, determining weight values ​​may involve accessing a database of weight values. In some cases, accessing a database of weight values ​​may involve using information associated with the user (e.g., physiological information and / or demographic information) to select one or more weight values ​​from the database. For example, information associated with the user may be used to determine the group to which the user belongs (e.g., based on age range, gender information, geographical location, etc.), and then one or more weight values ​​associated with the determined group may be selected. In some cases, health information (e.g., professional diagnoses, self-report diagnoses, and / or health-related measurements) may be used to determine one or more weight values.

[0130] Applying weight values ​​in block 616 may include applying one or more weight values ​​to one or more usage variables and / or sleep stage information. Applying weight values ​​may include using the weight values ​​to calculate component scores using the variables and / or sub-component scores using the partition variables. In some cases, applying weight values ​​may include multiplying the weight values ​​by the usage variables (or segment usage variables or other such values). In some cases, applying weight values ​​in block 616 may include multiple weight values ​​for a given usage variable or usage variable segment. For example, a usage variable segment that is a usage time segment during REM sleep may have a first applied weight value that is a weight value specifically calculated and / or selected for the usage time segment during REM sleep, and a second applied weight value that is a weight value calculated and / or selected globally for the usage variables and / or sleep stages. For example, the first weight value may be based on a preset weight value, and the second weight value may be based on user information.

[0131] In some cases, the calculation of the sleep performance score in block 612 may be performed in a different manner, utilizing the variables used determined from block 604 and the sleep stage information from block 606.

[0132] In block 618, the sleep performance score may be displayed to, for example, a user of a respiratory therapy system, a caregiver, or other entity. Displaying the sleep performance score may include displaying the sleep performance score in an easily understandable manner, such as, for example, a number (e.g., a number from 0 to 100), a percentage (e.g., a percentage from 0% to 100%), a color-coded indicator, a graphic indicator (e.g., a bar or circular gauge filled according to the sleep performance score), or other such methods.

[0133] In some cases, displaying the sleep performance score in block 618 may further include displaying additional information, for example, by default and / or when a trigger action (e.g., button press) is received. In some cases, the additional information may include one or more component scores or sub-component scores. In some cases, the additional information may include a sleep progression chart of sleep stage information. In some cases, the additional information may include a summary of sleep stage information and / or a summary of one or more component scores or sub-component scores. In some cases, the additional information may include an indicator of how much a component score or sub-component score contributes to the sleep performance score. In some cases, the additional information may include suggestions for adjusting the respiratory therapy system to improve the sleep performance score. For example, these suggestions may include instructions to change the user interface or adjust settings on the respiratory therapy device. In some cases, the additional information may include trend data showing the trend of sleep performance scores for a given sleep session and many previous sleep sessions.

[0134] In some options, out-of-range usage variables may be determined in block 608. Out-of-range usage variables may be determined based on sensor data received from block 602. In block 604, determining out-of-range usage variables can be separated from and / or part of the determination of usage variables, and may include identifying whether the value of a given usage variable exceeds a threshold range (e.g., below a threshold level, above a threshold level, and / or between a lower threshold level and a higher threshold level).

[0135] In the optional block 610, out-of-range use variables can be identified as acceptable use variables based on the sleep performance score calculated from block 612 and the out-of-range use variables determined from block 608. If the sleep performance score is higher than the threshold, the out-of-range use variable can be identified as an acceptable use variable. Thus, even though a given use variable is outside the desired range, the sleep performance score indicates a good sleep session with respiratory therapy (e.g., a high-quality sleep session and / or a sleep session in which respiratory therapy was used efficiently and / or effectively). In some cases, identifying an out-of-range use variable as an acceptable use variable in block 610 may further include indicating that the out-of-range use variable is an acceptable use variable (e.g., indicating that a given use variable is appropriately acceptable).

[0136] In some cases, once a usage variable is identified as a permitted usage variable, determining future instances of the weight values ​​in block 614 may include determining adjusted weight values ​​for any usage variables identified as permitted usage variables. The adjusted weight values ​​can emphasize how well the usage variables can withstand the impact on the sleep performance score. For example, if a user is well tolerant of a decrease in seal quality, the calculation of future sleep performance scores may apply a lower weight value to the seal quality variable.

[0137] The blocks of process 600 can be executed in any suitable order, including executing several blocks simultaneously. For example, the calculation of the sleep performance score in block 612 may be performed at the same time as determining the use variables that are out of range. In another example, determining the sleep stage information can be done after deciding which variables to use. Furthermore, although process 600 is described in several blocks, one, some, or all of the blocks of process 600 may be removed and / or replaced with other blocks. In addition, in some cases, process 600 may include additional blocks not shown in Figure 6. For example, in some cases, calculating the sleep performance score in block 612 may further include determining the sleep quality score, as disclosed in more detail herein.

[0138] Figure 7 is a flowchart of a process 700 for scoring sleep performance using adaptive stages, according to some aspects of the present disclosure. The process 700 can be performed by any suitable system, such as system 100 in Figure 1, including being performed by the processor 112 of the control system 110 in Figure 1. One, some, or all blocks of process 700 can occur during a sleep session (e.g., a given sleep session or a subsequent sleep session in which the sleep performance score is being calculated), immediately after a sleep session, or at another time. In some cases, the process 700 can be performed by a user device (e.g., a smartphone), such as user device 170 in Figure 1. In some cases, some or all of the process 700 can be performed as part of the calculation of the sleep performance score, as described with reference to block 612 in Figure 6.

[0139] Process 700 includes determining an adaptation stage in block 708, calculating a sleep performance score using the adaptation stage in block 710, and / or displaying the adaptation stage in block 708. Each adaptation stage can modify how the sleep performance score is calculated in other ways and / or how the user is encouraged to achieve a specific goal. Each adaptation stage may have a different purpose. For example, an early adaptation stage may be designed to encourage the user to sleep while using the treatment, and an intermediate adaptation stage may be designed to encourage the user to get longer sleep while using the treatment. A late adaptation stage may be designed to encourage the user to get good overall sleep while using the treatment. Additionally, an adaptation maintenance stage may be designed to encourage the user to maintain good overall sleep while using the treatment.

[0140] Possible adaptation stages can be established in a sequence, for example, starting with the initial adaptation stage, progressing through the intermediate adaptation stage, the late adaptation stage, and then the adaptation maintenance stage. For descriptive purposes, the adaptation stages can be described vertically, starting from the initial adaptation stage at the bottom and progressing upwards until reaching the adaptation maintenance stage at the top. Any number of adaptation stages can be used, e.g., two, three, four, or more than four. In some cases, discontinuous adaptation stages may be used. For example, a set of possible adaptation stages may include starting with the initial adaptation stage and ending with the adaptation maintenance stage, but may have many different potential intermediate adaptation stages that can be used depending on the user's situation. In such an example, the user may start from the initial adaptation stage and progress through the treatment time intermediate adaptation stage, followed by the total sleep time intermediate adaptation stage, followed by the late adaptation stage, followed by the adaptation maintenance stage. While it is desirable for the user to progress through the adaptation stages sequentially, in some cases, for example, if certain usage variables and / or sleep stage information indicate that the user's sleep is deteriorating or not sufficiently improving, or if it indicates that the user is not receiving treatment, the user may return to a previous adaptation stage.

[0141] In some cases, determining the adaptation stage in block 702 may include using information received in block 702. In block 702, one or more usage variables and / or sleep state information are received. Receiving usage variables may include determining usage variables as described with reference to block 604 in Figure 6, for example. Receiving sleep state information may include determining sleep state information as described with reference to block 606 in Figure 6, for example. Using the information received in block 702, the adaptation stage may be determined in block 708, at least in part, based on the use of variables and / or sleep stage information. For example, in some cases, the adaptation stage may be based on whether the user reached sleep latency below a threshold time. A user who has achieved a long sleep latency may be placed in an early adaptation stage until they can achieve a short sleep latency. Any usage variable information and / or sleep stage information can be used to determine the adaptation stage. In some cases, the determination of the adaptation stage may be based on achieving one or more desired thresholds for one or more use variables within a threshold duration (e.g., achieving an average leakage flow rate below the threshold within at least 120 minutes or at least 50% of a sleep session).

[0142] In some cases, the adaptation stage is determined in block 708, at least in part, based on the historical use variables and / or historical sleep stage information accessed in block 704. The historical data received in block 704 may be use variables and / or sleep stage information associated with one or more sleep sessions prior to the current sleep session, and may be historical data associated with, for example, a set number of days in the past (e.g., the past 7 days or the past 30 days), the number of past sleep sessions during which treatment was used, etc. Which adaptation stage is used can be determined by analyzing the historical use variables and / or historical sleep stage information (e.g., by identifying whether one or more use variables or sleep stages reached or exceeded the threshold within the threshold duration). For example, if the number of sleep sessions with available data falls below the threshold number (e.g., only two nights' worth of data is available), the default adaptation stage (e.g., early adaptation stage) can be used. If historical data indicates that a sleep latency of 30 minutes or less was achieved for at least three consecutive days, the adaptation stage can be determined as a different adaptation stage (e.g., intermediate adaptation stage). Similarly, if the user's sleep latency is displayed as 30 minutes or longer for at least three consecutive days, the adaptation stage can be determined as a different adaptation stage that emphasizes sleep latency (e.g., early adaptation stage).

[0143] In some cases, the determination of the adaptation stage in block 708 may be based at least in part on one or more historical adaptation stages received in block 706. Receiving a historical adaptation stage in block 706 may include receiving the current adaptation stage (e.g., the last adaptation stage determined for the user). Based on the current and / or historical data received in blocks 702 and / or 704, respectively, a decision can be made in block 708 to maintain the current adaptation stage or to move the user to a new adaptation stage (e.g., step down from a mid-term adaptation stage to a late-term adaptation stage, or from a mid-term adaptation stage to an early-term adaptation stage). Thus, in some cases, determining the adaptation stage 708 may include i) using a default (e.g., early) adaptation stage, ii) moving from the current adaptation stage to the next adaptation stage in sequence, or iii) moving from the current adaptation stage to a previous adaptation stage in sequence.

[0144] In some cases, determining an adaptation stage in block 708 may include determining an adaptation score. The adaptation score may be based on one or more expected values ​​using one or more variables and / or specific sleep stage information. The adaptation score may increase as the user approaches the expected values. When the user reaches or exceeds the expected values, the adaptation score may reach or exceed a threshold score indicating the next adaptation stage in the order to be performed. In some cases, each adaptation stage includes its own set of expected values ​​for one or more variables and / or specific sleep stage information. For example, in the early adaptation stage, the adaptation score may be based on the user's sleep latency and mean leak flow rate. The adaptation score increases as the sleep latency and mean leak flow rate decrease. When the user reaches a sufficiently low sleep latency (e.g., less than 30 minutes) and a sufficiently low mean leak flow rate (e.g., no leak or to an acceptable leak level), the adaptation score may reach or exceed a threshold score required to transition to a new adaptation stage (e.g., an intermediate adaptation stage). In the mid-term adaptation phase, the adaptation score may be based not only on treatment time but also on the user's sleep onset latency and total sleep duration. In the late adaptation phase, the adaptation score may be based on the user's sleep onset latency, total sleep duration, duration of different sleep phases, cardiac / respiratory rate during sleep, and other use variables associated with the treatment. In the maintenance adaptation phase, the adaptation score may be based on the same or similar use variables and sleep phase information as in the late adaptation phase, but with different weights given to the use variables and sleep phase information.

[0145] In some cases, the determined adaptive stage in block 710 is used for calculating the sleep performance score. Calculating the sleep performance score in block 710 may be the same as or similar to calculating the sleep performance score in block 612 of Figure 1, except that adaptive stages are used. In some cases, calculating the sleep performance score may include modifying the sleep performance score based on the adaptive stage determined in block 708, for example, by directly modifying the score based on the adaptive stage, or by modifying the weight values ​​for the sleep performance score based on the determined adaptive stage.

[0146] For example, in block 712, a set of weight values ​​can be determined based at least in part on the determined adaptive stage. Since each determined adaptive stage can highlight different aspects of sleep and / or sleep therapy, different adaptive stages can have different sets of associated weight values. For example, an early adaptive stage may be associated with a first set of weight values ​​that highlight sleep latency and / or mean leakage flow, a mid-stage adaptive stage may be associated with a second set of weight values ​​that highlight sleep latency, total sleep time, and therapy time. A late adaptive stage may be associated with a third set of weight values ​​that highlight sleep latency, total sleep time, time in one or more selective sleep stages (e.g., time in REM sleep and time in deep sleep), heart rate, respiratory rate, and / or other use variables, and maintaining an adaptive stage may be associated with a fourth set of weight values ​​designed to encourage maintaining a sleep quality score above a threshold sleep quality score. Determining weight values ​​in block 712 can also consider the manner in which weight values ​​are determined associated with block 614 in Figure 6.

[0147] After determining the weight values ​​in block 712, the sleep performance score can be calculated by applying the weight values ​​in block 714. Applying the weight values ​​in block 714 can be the same as or similar to applying the weight values ​​in block 616 in Figure 6.

[0148] Therefore, in some cases, each adaptation stage may influence how the sleep performance score is calculated when the user is in that adaptation stage.

[0149] Furthermore, instead of calculating the sleep performance score in block 710, adaptation stage information can be displayed in block 716, for example, by displaying it to the user or a third party monitoring the user (e.g., a caregiver or healthcare provider). Displaying adaptation stage information may include i) displaying which adaptation stage the user is in (e.g., "Intermediate adaptation stage!"), ii) displaying the adaptation score (e.g., "78%" or "78 / 100" or "78"), iii) displaying suggestions related to the adaptation stage (e.g., for the early adaptation stage, "Please continue treatment as long as possible tonight," for the late adaptation or adaptation maintenance stage, "You're doing well. Remember to clean the pipes every week"), iv) displaying the requirements for moving to the next adaptation stage (e.g., "You've been asleep within 30 minutes for the past 5 nights. You'll move to the next stage in two more nights"), or v) any combination of i-iv.

[0150] In some cases, the adaptation stage information is displayed in block 716 only if the adaptation stage determined in block 708 is different from the previous adaptation stage (for example, for a transition from the initial adaptation stage to the intermediate adaptation stage, it might say, "Congratulations! You're doing well wearing the treatment device while you sleep," or for a transition from the intermediate adaptation stage to the initial adaptation stage, it might say, "It looks like your treatment device has been leaking for the past few nights. Let's try to fix that").

[0151] The blocks of process 700 can be executed in any suitable order, including executing several blocks simultaneously. Furthermore, although process 700 is described in several blocks, one, some, or all of the blocks of process 700 may be removed and / or replaced with other blocks. In addition, in some cases, process 700 may include additional blocks not shown in Figure 7.

[0152] Figure 8 is Figure 800, which shows the progress of a user through the adaptation phase according to several aspects of this disclosure. Figure 800 shows four adaptation phases, including an initial phase 804, an intermediate phase 806, an advanced phase 808, and a maintenance phase 810. The adaptation phases shown in Figure 800 may be the adaptation phases determined and utilized with respect to process 700 in Figure 7.

[0153] Line 802 represents the user's current adaptation stage over time. The time axis indicates that the user has participated in multiple sleep sessions over a period of several days (e.g., over 30 or 60 days).

[0154] On day 812, the user can begin treatment. Once treatment is initiated, the user may be automatically placed in the initial stage 804.

[0155] On day 814, the user may have achieved the quality sleep performance associated with the initial stage 804 (e.g., sleep latency of less than 30 minutes, below the threshold level, or no sleep leakage), and the user may move on to the intermediate stage 806.

[0156] However, on day 816, the user may have experienced more than one day of sleep disturbance (e.g., an unacceptably high level of leakage), which would cause the user to return to the initial stage 804. By day 818, the user would again have achieved a sufficient number of days of high-quality sleep performance, moving the user to the intermediate stage 806.

[0157] On day 820, the user achieves several days of new high-quality sleep performance associated with intermediate stage 806 (e.g., sleep latency below the threshold level, total sleep duration above the threshold duration, treatment duration above the threshold duration), thereby allowing the user to progress to late stage 808.

[0158] On day 822, the user may have achieved several days of new high-quality sleep performance associated with late stage 808, allowing them to transition to maintenance stage 810. The user can then remain in maintenance stage 810. In some cases, the use of the adaptive stage can be completely discontinued after the user has remained in maintenance stage 810 for a threshold duration.

[0159] In some cases, users may regress to previous adaptation stages, such as the transition from intermediate stage 806 to early stage 804 on day 816, but this is not always the case. In some cases, users can establish an adaptation stage and progress through it sequentially. In this case, even if sleep performance is poor, the user may remain in the same stage until they qualify to advance to the next stage (for example, the user may remain in intermediate stage 806 from day 814 to day 816 and day 818, and will remain in the same stage until they qualify to advance to late stage 808 on day 820).

[0160] The foregoing description of the implementations, including the illustrated implementations, is provided for illustrative and explanatory purposes only and is not intended to outline or limit the exact implementations disclosed. Many modifications can be made to the disclosed implementations in accordance with the disclosure herein without departing from the spirit or scope of this disclosure, but many of these modifications, changes, and uses will be obvious to those skilled in the art. Therefore, the breadth and scope of this disclosure should not be limited by any of the implementations described above.

[0161] While some aspects of this disclosure have been described and illustrated in relation to one or more implementations, equivalent changes and modifications will occur or will be known to those skilled in the art after reading and understanding this specification and the drawings. Furthermore, certain features of one aspect of this disclosure may be disclosed in relation to only one of several implementations, but such features can be combined with one or more other features of other implementations that are desirable and advantageous for any given or particular application. One or more components, embodiments, steps, or any part thereof from any one or more of the following claims 1 to 35 can be combined with one or more components, embodiments, steps, or any part thereof from any one or more of the other claims 1 to 35 to form one or more additional implementations and / or claims of the present disclosure.

Claims

1. A method for scoring sleep performance, performed by a control system including one or more processors, wherein the control system The steps include receiving sensor data from one or more sensors associated with the sleep session of a user using a respiratory therapy system, The steps include determining one or more usage variables associated with the use of the respiratory therapy system from the received sensor data, A step of determining sleep stage information associated with the sleep session from the received sensor data, wherein the sleep stage information indicates the duration spent in multiple sleep stages. The process includes the step of calculating a sleep performance score for the sleep session based at least partially on one or more of the determined usage variables and the sleep stage information, The step of calculating the sleep performance score is performed for each of the one or more variables used, Based at least partially on the sleep stage information, each of the variables used is divided into a plurality of segments, and each of the plurality of segments is associated with one of the plurality of sleep stages. For each of the aforementioned multiple sleep stages, determine the weight values ​​of the variables used. To each of the aforementioned segments, apply the usage variable weight values ​​associated with each sleep stage associated with each segment. Methods that include...

2. The one or more variables used are, i) Usage time indicating the duration for which the respiratory therapy system is used during the sleep session, ii) A seal quality variable indicating the quality of the seal between the user and the user interface of the respiratory therapy system during use of the respiratory therapy system, iii) Event information indicating multiple detected events that occurred during the sleep session, iv) User interface compliance information associated with the number of detected user interface transition events in which the user interface was attached or removed during the sleep session, v) The average leakage flow rate of the sleep session, vi) Multiple therapeutic subsessions during the aforementioned sleep session, vii) The average pressure of the user interface during the sleep session, viiii) A statistical summary of one or more of the variables used in i) to vii), ix) Any combination of i) to viiii) above, The method according to claim 1, comprising one or more of the variables used.

3. The method according to claim 2, wherein the event information indicates the number of apnea-hypopnea events detected during the sleep session.

4. The step of calculating the aforementioned sleep performance score is: The steps include determining a weight value for each of the one or more variables used, at least partially based on the sleep stage information, The method according to any one of claims 1 to 3, comprising the step of applying a weight value associated with the variable to each of the one or more variables used.

5. The method according to any one of claims 1 to 4, further comprising the step of determining a sleep quality score associated with the sleep session, wherein the step of determining the sleep quality score is at least in part based on the sleep stage information.

6. The step of determining the aforementioned sleep quality score is: The steps include dividing the sleep stage information into sleep stage segments based at least partially on one or more of the aforementioned variables, For each of the aforementioned sleep stage segments, the steps include determining weight values ​​based on the variables used, Within each sleep stage segment, the steps include applying weight values ​​to each sleep stage within the sleep stage segment based on the respective variables used in each sleep stage segment, The method according to claim 5, including the method described in claim 5.

7. The method according to claim 5, wherein the step of receiving the sensor data includes the step of receiving physiological data associated with the user, and the step of determining the sleep quality score is at least partially based on the received physiological data.

8. The method according to claim 7, wherein the physiological data includes i) respiratory rate, ii) heart rate, iii) heart rate variability, iv) exercise data, v) electroencephalogram data, vi) blood oxygen saturation data, vii) respiratory rate variability, viiii) respiratory depth, ix) tidal volume data, x) inspiratory amplitude data, xi) expiratory amplitude data, xii) inspiratory volume data, iiii) expiratory volume data, xiv) inspiratory-expiratory ratio data, xv) sweating data, xvi) temperature data, xvii) pulse wave propagation time data, xviiii) blood pressure data, xix) position data, xx) posture data, xxi) blood glucose level data, or xxi) any combination of i) to xxi).

9. The method according to any one of claims 5 to 8, wherein the step of calculating a sleep performance score for the sleep session is at least partially based on the sleep quality score.

10. The method according to claim 9, wherein the step of calculating the sleep performance score based at least in part on the sleep quality score includes the step of applying one or more sleep quality score-based weight values ​​to one or more of the determined use variables based at least in part on the sleep quality score.

11. The method according to any one of claims 5 to 10, further comprising the step of receiving user feedback associated with the sleep session, wherein the step of calculating the sleep performance score based at least in part on the sleep quality score includes the step of applying one or more user feedback-based weight values ​​to the sleep quality score, based at least in part on the user feedback.

12. The method according to any one of claims 1 to 11, further comprising the step of receiving user feedback associated with the sleep session, wherein the step of calculating the sleep performance score based at least in part on one or more determined use variables includes the step of applying one or more user feedback-based weight values ​​to the one or more determined use variables, at least in part on the user feedback.

13. The steps include receiving user feedback associated with the sleep session, The steps include determining a correction value based at least partially on the received user feedback, The method according to any one of claims 1 to 12, further comprising the step of updating the sleep performance score by incorporating the correction value into the sleep performance score.

14. The method according to any one of claims 1 to 13, wherein the one or more variables used include a first variable and a second variable, and the step of calculating the sleep performance score based at least partially on the one or more variables determined includes the step of applying weight values ​​to the first variable based at least partially on the second variable.

15. The step of applying the weight values ​​to the first variable based at least partially on the second variable is: The steps include identifying a plurality of ranges associated with the second variable used, The steps of dividing the first use variable into a plurality of first use variable segments, each associated with one of a plurality of ranges associated with the second use variable, based at least in part on the second use variable, The steps include determining range weight values ​​for each of the aforementioned multiple ranges, The method according to claim 14, comprising the step of applying to each of the plurality of first variable use segments the range weight values ​​associated with each of the plurality of ranges associated with each of the first variable use segments.

16. The step of calculating the sleep performance score based at least partially on the determined one or more usage variables and sleep stage information is as follows: A step of identifying a period during the sleep session in which the user is not sleeping, based at least in part on the sleep stage information, The method according to any one of claims 1 to 15, further comprising the step of deleting the detected event from event information that occurred when the user was not sleeping.

17. If the out-of-range variable is outside the desired threshold range, the step is to identify the out-of-range variable among the one or more variables used. The steps include identifying the aforementioned sleep performance score as exceeding the sleep performance threshold, The method according to any one of claims 1 to 16, comprising the step of displaying an indication that a variable used outside the identified range is permitted.

18. The method according to any one of claims 1 to 17, further comprising the step of displaying the sleep performance score after the completion of the sleep session.

19. The method according to claim 18, wherein the step of displaying the sleep performance score includes a step of displaying the total contribution to the sleep performance for each of the one or more variables used, and the step of displaying the total contribution for a given variable among the one or more variables used includes a step of displaying a plurality of sub-contributions binned by the sleep stage for the given variable used.

20. The method according to any one of claims 1 to 19, wherein the step of calculating the sleep performance score for the sleep session includes the step of calculating the sleep performance score for only a portion of the sleep session that coincides with the use of the respiratory therapy system.

21. The steps include determining the adaptive stage associated with the sleep session, A step of determining an adaptive base weight value for each of the one or more variables used, at least partially based on the adaptation stage, The method according to any one of claims 1 to 20, comprising the step of applying an adaptive base weight value associated with each of the one or more variables used.

22. The step of determining the adaptation stage is: (i) One or more historical usage variables associated with one or more historical sleep sessions of the user, (ii) Historical sleep stage information associated with one or more historical sleep sessions of the user, or (iii) A step of accessing both (i) and (ii), (i) Identifying the adaptive stage based at least partially on one or more historical usage variables, (ii) the historical sleep stage information, or (iii) both (i) and (ii), The method according to claim 21, including the following:

23. The method according to any one of claims 21 or 22, further comprising the step of accessing a history adaptation stage associated with the user, wherein the step of determining the adaptation stage is at least partially based on the history adaptation stage.

24. The step of determining the adaptation stage is: (i) a step of calculating an adaptive score based at least partially on one or more variables used, (ii) sleep stage information, or (iii) both (i) and (ii), The method according to any one of claims 21 to 23, comprising the step of determining that the adaptation score exceeds a threshold score associated with the adaptation stage.

25. The method according to any one of claims 21 to 24, wherein the step of determining the adaptation stage includes the step of selecting an adaptation stage from a set of possible adaptation stages, the set of possible adaptation stages includes: i) an early adaptation stage in which the adaptation base weights are a first set of weights that emphasize sleep latency; ii) a mid-stage adaptation stage in which the adaptation base weights are a second set of weights that emphasize total sleep duration; iii) a late-stage adaptation stage in which the adaptation base weights are a third set of weights that emphasize duration in one or more sleep stages; or iv) any combination of i to iii.

26. The method according to claim 25, wherein the set of possible adaptation stages further includes an adaptation maintenance stage, the adaptation-based weight values ​​being a fourth set of weight values ​​associated with maintaining the sleep quality score at or above a threshold sleep quality score.

27. It is a system, A control system including one or more processors, Includes a memory for storing machine-readable instructions, The control system is coupled to a memory, and when a machine-executable instruction in the memory is executed by at least one of the one or more processors of the control system, the method according to any one of claims 1 to 26 is carried out.

28. A system for scoring sleep performance, comprising a control system configured to carry out the method described in any one of claims 1 to 26.

29. A computer program product that, when executed by a computer, includes instructions causing the computer to perform the method according to any one of claims 1 to 26.

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

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