sleep performance score during treatment

Abstract

A sleep performance score can be generated for an individual receiving respiratory therapy. Sensor data can be obtained from one or more sensors when a user is sleeping and using a respiratory therapy system. The sensor data can be used to determine one or more usage variables associated with use of the respiratory therapy system, as well as sleep stage information indicative of sleep stages experienced by the user while sleeping. The sleep performance score can be calculated using the one or more usage variables and the sleep stage information. In some cases, the sleep stage information can be used to apply a weight to one, some, or all of the one or more usage variables. The sleep performance score can be indicative of compliance, efficacy, quality, and / or general use of the respiratory therapy system, taking into account the relationship between sleep stages and use of the respiratory therapy system.

Description

[0001] Cross-reference to related applications

[0002] This application claims the benefit of U.S. Provisional Patent Application No. 63 / 107,935, filed on October 30, 2020, entitled “Sleep Performance Scoring During Therapy,” the disclosure of which is incorporated herein by reference in its entirety. Technical Field

[0003] This disclosure generally relates to the treatment of sleep conditions, and more specifically to providing a useful measure for scoring sleep performance during the treatment of sleep conditions. Background Technology

[0004] 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-induced hyperventilation 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 may occur during sleep, such as snoring, apnea, hypopnea, restless legs, sleep disturbances, apnea, increased heart rate, dyspnea, asthma attacks, seizures, convulsions, or any combination thereof. Individuals with such sleep-related breathing disorders are often treated with one or more medical devices to improve sleep and reduce the likelihood of these events occurring during sleep. An example of such a device is a respiratory therapy system that provides positive airway pressure ventilation to the individual, although other devices may also be used. Meaningful metrics regarding the use of such devices are needed, such as monitoring adherence, increasing user engagement, and monitoring the efficacy of treatment. Summary of the Invention

[0005] Some aspects of this disclosure include a method for scoring sleep performance, the method comprising: receiving sensor data from one or more sensors associated with sleep periods of a user using a respiratory therapy system; determining one or more usage variables associated with the use of the respiratory therapy system from the received sensor data; determining sleep stage information associated with the sleep periods from the received sensor data; and calculating a sleep performance score for the sleep periods using the determined one or more usage variables and the sleep stage information.

[0006] Some aspects of this disclosure include a system comprising: a control system including one or more processors; and a memory having machine-readable instructions stored thereon; wherein the control system is coupled to the memory, and the method described above is implemented when the machine-executable instructions in the memory are executed by at least one of the one or more processors of the control system.

[0007] Some aspects of this disclosure include a system for scoring sleep performance, the system including a control system configured to implement the methods described above.

[0008] Some aspects of this disclosure include a computer program product comprising instructions that, when executed by a computer, cause the computer to perform the methods disclosed above. In some cases, the computer program product is a non-transitory computer-readable medium. Attached Figure Description

[0009] This specification refers to the following figures, in which the same reference numerals are used in different figures to illustrate the same or similar parts.

[0010] Figure 1 This is a functional block diagram of a system for scoring sleep performance according to certain aspects of this disclosure.

[0011] Figure 2 Based on certain aspects of this disclosure Figure 1 A perspective of the system, users, and bed partners.

[0012] Figure 3 An example timeline of sleep periods according to certain aspects of this disclosure is illustrated.

[0013] Figure 4 The illustrations depict certain aspects of this disclosure. Figure 3 Example sleep graphs associated with different sleep periods.

[0014] Figure 5 It is illustrated in relation to certain aspects of this disclosure. Figure 4 The sleep graph is associated with a chart that uses variables.

[0015] Figure 6 It is a flowchart depicting a process for scoring sleep performance according to certain aspects of this disclosure.

[0016] Figure 7 It is a flowchart depicting a process for scoring sleep performance using an adaptation phase, according to certain aspects of this disclosure.

[0017] Figure 8 It is a diagram illustrating the progress of a user through an adaptation phase according to certain aspects of this disclosure.

[0018] While this disclosure allows for various modifications and alternatives, specific implementations and embodiments have been illustrated by example in the accompanying drawings and will be described in detail herein. However, it should be understood that this disclosure is not intended to be limited to the specific forms disclosed; rather, it will cover all modifications, equivalents, and alternatives falling within the spirit and scope of this disclosure as defined by the appended claims. Detailed Implementation

[0019] Certain aspects and features of this disclosure relate to systems and methods for generating sleep performance scores for individuals using a respiratory therapy system (e.g., using the respiratory therapy system to provide respiratory therapy during a sleep period). When a user engages in a sleep period and uses the respiratory therapy system, the system can acquire sensor data from one or more sensors. The sensor data can be used to determine one or more usage variables associated with the use of the respiratory therapy system, as well as sleep stage information indicating the sleep stage and / or the sleep state experienced by the user during the sleep period (e.g., awake or asleep). 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. Taking into account the relationship between sleep stages and the use of the respiratory therapy system, the sleep performance score can be used to indicate adherence, efficacy, quality, and / or general use of the respiratory therapy system.

[0020] Certain aspects of this disclosure can be used to generate sleep performance scores associated with sleep periods of a user receiving respiratory therapy. The respiratory therapy can be applied using a respiratory therapy device, such as a breathing device that supplies pressurized air to a user via a catheter and user interface. While receiving respiratory therapy, the user can participate in sleep periods during which sensor data can be collected from one or more sensors, such as sensors in the respiratory therapy device, sensors in the user's device (e.g., a smartphone), sensors in an activity tracker (e.g., a wearable activity tracker), or other sensors located within, above, or around the user (e.g., implantable devices, clothing-integrated sensors, mattress-integrated sensors, wall-mounted or ceiling-mounted sensors, etc.). Data collected from one or more sensors can be used to determine one or more usage variables and sleep stage information associated with the use of the respiratory therapy system. Sensor data can be used to determine other variables and / or information.

[0021] Usage variables associated with the use of a respiratory therapy system can include any suitable variables relating to how the user uses the system. Examples of suitable usage variables include usage time (e.g., the duration of a user's use of the respiratory therapy system); seal quality variables (e.g., an indication of the quality of the seal between the user and the user interface); leakage flow variables (e.g., an indication of accidental leakage flow, such as leakage through a poor seal or mouth breathing when wearing a nasal pillow-type user interface); event information (e.g., an indication of events detected occurring during sleep periods, such as the apnea-hypopnea index (AHI)); user interface compliance information (e.g., an indication of detected user interface transition events, such as putting on or removing the user interface); multiple treatment sub-periods within a sleep period (e.g., multiple separate blocks of continuous use of the respiratory therapy system); and user interface pressure. Other usage variables may be used. Statistical summaries of one or more usage variables (e.g., mean, maximum, minimum, count, etc.) may be used as one or more additional usage variables. One or more usage variables can include any suitable combination of usage variables.

[0022] Determining the usage variable may include processing sensor data to identify one or more values ​​associated with the usage variable. The one or more values ​​may be a measurement or calculated score associated with the usage variable. For example, a seal quality variable may be a measurement of leakage flow rate (e.g., in L / min) or a seal quality score (e.g., 18 out of 20). Determining the usage variable may include determining a single value or multiple values ​​(e.g., timestamped values). For example, in some cases, determining a seal quality variable may include determining a single value representing the overall (e.g., average) seal quality over the entire sleep period (e.g., 18 out of 20). However, in some cases, determining a seal quality variable may include determining a set of timestamped values ​​representing seal quality changing over time (e.g., 18 at 10:00:00 PM, 18.1 at 10:00:05 PM, 18.2 at 10:00:10 PM, etc.) on a scale of 0 to 20, such as data that can be charted to depict seal quality over the entire duration.

[0023] Sleep stage information can include information indicating the sleep stages a user experiences during a sleep period. Examples of sleep stages include wakefulness, rapid eye movement (REM) sleep, light sleep, and deep sleep. Sensor data can be processed to determine the time a user enters and exits each sleep stage. In some cases, determining sleep stage information may include determining the total duration a user spends in each sleep stage. In an example 8-hour sleep period, sleep stage information may indicate 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, determining sleep stage information may include generating timestamped data indicating the user's sleep stages at various times throughout the sleep period, such as data that can be plotted to generate a sleep graph of the user's sleep period.

[0024] While variables are used to indicate the use of a respiratory therapy system, scores based solely on usage variables may not be as informative and useful as scores based on both usage variables and sleep stage information. For example, tracking the total amount of time a user uses the respiratory therapy device during sleep periods can be informative and useful. Generally, the more time used, the better. Using the respiratory therapy device only during the first two hours of sleep periods may be undesirable. Therefore, providing users with scores that increase (e.g., improve) as the user uses the respiratory therapy device longer can be useful. Simple scores based solely on usage variables simply indicate higher values ​​for longer use and lower values ​​for shorter use. While such simple scores are useful for encouraging users to use the respiratory therapy device for longer periods, they may also induce undesirable behavior or fail to reflect important details about how the respiratory therapy device is actually used. For example, a user could achieve a high simple score by simply using the respiratory therapy device for a longer period before falling asleep, although such a high score may be counterproductive because it does not necessarily reflect any substantial benefit the user receives from using the respiratory therapy device while awake. However, sleep performance scores calculated based on usage variables and sleep stage information provide more informative and useful scores. Because apnea and hypopnea events may be more prevalent during REM sleep (e.g., due to reduced pitch in the genioglossus muscle of the tongue) and more detrimental during REM and deep sleep (e.g., due to the likelihood of disrupting REM sleep, negatively impacting spatial memory, and / or reducing the amount of deep sleep), tracking the amount of time a respiratory therapy device is used during REM sleep and / or deep sleep may be more useful. Therefore, in addition to tracking total usage time, the amount of time a respiratory therapy device is used in certain sleep stages (e.g., REM sleep or deep sleep) can be emphasized (e.g., more strongly weighted) compared to the amount of time used in other sleep stages (e.g., wakefulness or light sleep). Thus, even if a user uses a respiratory therapy device for a prolonged period before falling asleep, sleep performance scores may not increase significantly or at all. However, if the same user uses a respiratory therapy device for a prolonged period during REM sleep, sleep performance scores may increase significantly.

[0025] Similarly, the detection of apnea or hypopnea events can be an informative and useful variable for tracking, but the prevalence of events detected during wakefulness (e.g., obvious) can be a false detection that can be ignored, while the prevalence of events detected during REM sleep can indicate that the respiratory therapy device is not providing adequate respiratory therapy. Therefore, events detected that are associated with the REM sleep stage can be emphasized compared to events detected that are associated with other sleep stages (such as wakefulness).

[0026] Similarly, seal quality variables or leakage flow can be informative and useful variables for tracking. Since poor sealing and accidental leakage increase the risk of apnea or hypopnea events, a decrease in seal quality variables or leakage flow can indicate the risk of such events occurring. Therefore, the prevalence of poor sealing or accidental leakage during REM sleep may be more significant than during wakefulness when the event is likely to have substantially harmful effects (e.g., disrupting REM sleep, negatively impacting spatial memory, and / or reducing the amount of deep sleep). Thus, low seal quality variables or low leakage flow associated with REM sleep stages can be emphasized compared to low seal quality variables or low leakage flow associated with wakefulness. Additionally, poor sealing and accidental leakage associated with light sleep can be harmful due to the risk to user experience, as users may be more conscious during light sleep, which can affect user compliance. For example, poor sealing during light sleep may be noticeable to the user and uncomfortable, and may lead the user to remove the user interface. Therefore, the low sealing quality variable or low leakage flow rate associated with the light sleep stage can be emphasized compared with the low sealing quality variable or low leakage flow rate associated with the deep sleep stage.

[0027] Therefore, sleep performance scores based on usage variables and sleep stage information can be particularly useful and informative. In addition to using usage variables and sleep stage information, calculating such sleep performance scores may also involve using other data. In some cases, calculating a sleep performance score may include applying weighted values ​​to each of the usage variables, adjusting (or generating) the weighted values ​​based at least in part on sleep stage information and / or at least in part on another usage variable. In some cases, the weighted values ​​may be adjusted (or generated) based at least in part on sleep-related parameters, such as total time in bed, total sleep time, sleep latency, sleep-wake parameters, sleep efficiency, segmentation index, or any combination thereof.

[0028] In instances where a sleep performance score is calculated based on usage variables that are each a single value (e.g., an average or score associated with the entire sleep period), the usage variables may include usage time (U), sealing quality variables (Q), event information (E), and user interface compliance information (C), and the sleep performance score (score) can be calculated according to the following equation.

[0029] Score = χ1U + χ2Q + χ3E + χ4C

[0030] In this example, χ1 is a weighted value associated with usage time, χ2 is a weighted value associated with sealing quality, χ3 is a weighted value associated with event information, and χ4 is a weighted value associated with interface compliance information. Each of these weighted values ​​can be determined based on sleep stage information, other usage variables, or combinations thereof. Therefore, the weighted values ​​can be adjusted according to the time spent in different sleep stages.

[0031] In one instance, the χ3 score for the first night with a relatively high amount of REM sleep can be higher than for the first night with a relatively low amount of REM sleep. Such weighted variations in χ3 can emphasize that events occurring when the user is also having good REM sleep may be more detrimental than events occurring when the user is also not having good REM sleep, and therefore have a greater impact on sleep performance scores. Other examples can be used.

[0032] In another instance, χ4 can be higher on the first night with a relatively high seal quality variable than on a night with a relatively low seal quality variable. Such weighted changes in χ4 may emphasize that on nights with poor seal quality, users may be more likely to remove the user interface to reposition it, and therefore the impact on the overall sleep performance score should not be as significant as on nights with good seal quality and when users are removing the user interface for other reasons.

[0033] In another instance of calculating a sleep performance score based on a usage variable as a time-related value (e.g., a timestamped value in the course of a sleep period), the usage variable can be a function of time and can include usage time (U(t)), sealing quality variable (Q(t)), event information (E(t)), and user interface compliance information (C(t)), and the sleep performance score (score) can be calculated according to the following equation.

[0034] Rating=χ1U(t)+χ2Q(t)+χ3E(t)+χ4C(t)

[0035] In this example, χ1 is a weighted value associated with usage time, χ2 is a weighted value associated with sealing quality, χ3 is a weighted value associated with event information, and χ4 is a weighted value associated with interface compliance information.

[0036] In another instance of calculating a sleep performance score based on a usage variable as a time-related value (e.g., a timestamped value in the course of a sleep period), the usage variable can be a function of time and can include usage time (U(t)), sealing quality variable (Q(t)), event information (E(t)), and user interface compliance information (C(t)), and the sleep performance score (score) can be calculated according to the following equation.

[0037] Rating=χ1(t)U(t)+χ2(t)Q(t)+χ3(t)E(t)+χ4(t)C(t)

[0038] In this example, the weights are time-dependent, where χ1(t) is a weight associated with usage time, χ2(t) is a weight associated with sealing quality, χ3(t) is a weight associated with event information, and χ4(t) is a weight associated with interface compliance information.

[0039] In another example, sleep performance scores can be calculated based on segmented usage variables. Using sleep stage information, usage variables can be segmented by sleep stage. For instance, total usage time (U) can be segmented into usage time segments, including usage time during wakefulness (Uw). W ), usage time during REM sleep (U R Usage time during light sleep (U L ) and usage time during deep sleep (U D Similar segmentation can be performed on any usage variable (e.g., seal quality segment, air leak segment, detected event segment, user interface compliance segment). While multiple usage variables can be segmented to calculate a sleep performance score, in instances with only a single usage variable as usage time, the sleep performance score (score) can be calculated according to the following equation.

[0040] Rating = χ 1W U W +χ 1R U R +χ 1L U L +χ 1D U D

[0041] In this example, χ 1W It is a weighted value associated with the usage time during the awakening period, χ 1R It is a weighted value associated with usage time during REM sleep, χ² 1L It is a weighted value associated with usage time during light sleep, and χ² 1D These are weighted values ​​associated with usage time during deep sleep. In some cases, the usage variables and / or weighted values ​​described above can be time-dependent.

[0042] In one instance of using variables in a segmented approach, because apnea events are more prevalent in REM sleep due to pitch reduction caused by the genioglossus muscle in the tongue, breathing therapy may be more important when the user is in REM sleep than when the user is awake or in light sleep. Therefore, weighting values ​​can be appropriately set, where χ² 1R Greater than χ1W and χ 1L (For example, a larger score increase is given for using a breathing therapy device for a certain duration during REM sleep, while a smaller score increase is given for using a breathing therapy device for the same duration during wakefulness or light sleep).

[0043] In another example, sleep performance scores can be calculated based on segmented usage variables, which are segmented based on another usage variable. For instance, user interface compliance information (C) can be segmented into user interface compliance fragments using a sealing quality variable, including user interface compliance information (C) when the sealing quality variable is low. L ) and user interface compliance information when the sealing quality variable is high (C H Similar segmentation can be performed on any usage variable. While multiple segmented usage variables can be used to calculate a sleep performance score, in a single instance of a usage variable that serves as user interface compliance information, the sleep performance score (rating) can be calculated according to the following equation.

[0044] Rating = χ 1L C L + 1H C H

[0045] In this example, χ 1L It is a weighted value associated with user interface compliance when the seal quality variable is low (e.g., below a threshold), and χ² 1H This is a weighted value associated with user interface compliance when the seal quality variable is high (e.g., equal to or above a threshold). In some cases, the above usage variables and / or weighted values ​​may be time-dependent. In the example above, χ² 1L Can be compared to χ 1H Smaller, so as to emphasize that user interface changes detected when the seal quality variable is low (e.g., possibly instructing the user to manipulate the user interface to improve seal quality) should not affect the overall sleep performance score as much as user interface changes detected when the seal quality variable is high (e.g., possibly undesirable user interface changes).

[0046] The foregoing discloses various schemes for applying weighted values ​​to usage variables to determine sleep performance scores, such as referring to the equations presented above. In some cases, one, some, or all of the various schemes described herein can be combined to calculate a sleep performance score. For example, in some cases, a sleep performance score may include applying a weighted value based on sleep stage information to a usage variable and applying a weighted value based on another usage variable. In another instance, a sleep performance score may be calculated by applying a weighted value based on sleep stage information to a first usage variable without applying a weighted value (or a neutral weighted value) to a second usage variable.

[0047] As used herein, applying a weighted value to each of the usage variables is intended to include applying a weighted value to fewer than all usage variables, in which case any usage variable to which no weighted value is applied may be considered to have a neutral weighted value applied to it (e.g., 1.0x or 100%). For example, applying a 0.75x weighted value only to the first usage variable in a set of four usage variables without applying any weighted values ​​to the other usage variables is equivalent to applying a 0.75x weighted value to the first usage variable and a 1.0x weighted value to the remaining usage variables.

[0048] In some cases, the weighting values ​​described herein can be static weighting values ​​stored in system-accessible memory for calculating sleep performance scores. For example, the weighting value for usage time during REM sleep can always be 1.25x (or 125%). However, in other cases, the weighting values ​​can be dynamic, such as a function of certain data (e.g., another usage variable or sleep stage information) or the output of a machine learning algorithm (e.g., a deep neural network) trained to output weighting values ​​from input data (e.g., sensor data, usage variables, or sleep stage information) to achieve an accurate sleep performance score (e.g., an objectively accurate or subjectively accurate score).

[0049] In some cases, a sleep quality score can be determined. A sleep quality score is an indicator of the quality of sleep a user experiences during a sleep period. For example, sleep periods with frequent awakenings or interruptions may have a low sleep quality score, while sleep periods with fewer awakenings or interruptions may have a high sleep quality score.

[0050] In some cases, sleep quality scores may be based on subjective feedback (e.g., feedback from the user's subjective feeling about rest after a specified sleep period), objective data, or a combination of both. Subjective feedback may include user ratings of sleep periods and / or PROMS (Patient Reported Outcomes Measure) data collected from the user by the healthcare provider. In some cases, subjective feedback may include subjective reasons why the user feels this way about their sleep quality and / or the quality of the treatment they receive. Such reasons may be stored in association with sleep quality scores and / or sleep performance scores and may optionally be presented.

[0051] In some cases, sleep quality scores can be used to calculate sleep performance scores. In some cases, sleep quality scores can be components of sleep performance scores. In some cases, sleep quality scores can be used to determine weighted values ​​to be applied to the different components of the sleep performance score (e.g., weighted values ​​applied to one or more used variables). In some cases, such as when collecting subjective feedback, subjective feedback can be used to directly modify one or more components of the sleep performance score or the sleep performance score itself, such as by incorporating (e.g., directly adding) modified values ​​in place of influencing weighted values ​​or by incorporating modified values ​​in addition to influencing weighted values. Modified values ​​can be preset values ​​selected based on subjective feedback (e.g., "5" for positive feedback or "-5" for negative feedback), or they can be variable values ​​based on subjective feedback.

[0052] In one instance, such as based on sleep stage information, a sleep quality score or its components can be objectively determined. In another instance, the time spent in different sleep stages can be used to determine the sleep quality score. Additionally or alternatively, patterns of sleep stages (e.g., sleep structure) can be used to determine the sleep quality score. Sleep stage information can be segmented into sleep stage segments representing the time spent in each sleep stage (e.g., the total time spent in each sleep stage during a sleep period or the duration of each of the consecutive sleep stages occurring during a sleep period). In some cases, such as based on the use of variables, the time spent in each sleep stage is weighted. For example, a weighted value can be used to calculate the sleep quality score such that the time spent in certain sleep stages when the user interface seal is above a threshold has a greater impact on the sleep quality score than the time spent in certain sleep stages when the user interface seal is below a threshold.

[0053] In some cases, sleep quality scores may be based at least in part on physiological data associated with the user, such as i) respiratory rate; ii) heart rate; iii) heart rate variability; iv) movement data; v) electroencephalogram (EEG) data; vi) blood oxygen saturation data; vii) respiratory rate variability; viii) respiratory depth; ix) tidal volume data; x) inspiratory amplitude data; xi) expiratory amplitude data; xii) inspiratory volume data; xiii) expiratory volume data; xiv) inspiratory-expiratory ratio data; xv) sweating data; xvi) temperature data; xvii) pulse wave transit time data; xviii) blood pressure data; xix) location data; xx) posture data; xxi) blood glucose level data; or any combination of xxii)i to xxi.

[0054] In some cases, sleep stage information (and / or optionally, usage variables) can be used to remove data from a specific usage variable or otherwise ignore it. For example, if event information indicates an event that occurred at 2:01:43 AM, but sleep stage information indicates that the user was not asleep at that time, the detected event can be removed from the event information usage variable or otherwise ignored.

[0055] Sleep performance scores can be presented to the user in any suitable manner, such as via a display on a respiratory therapy device, a display on a user device (e.g., a smartphone), etc. Presentation of sleep performance scores may include presenting a total sleep performance score, as well as presenting one or more component scores that constitute the overall sleep performance score. Component scores may be based on individual or combined scores using each of the variables, as well as sleep stage information and / or sleep quality scores. In some cases, presenting sleep performance scores may include presenting a graphical representation of the component scores that constitute the overall sleep performance score.

[0056] In some cases, presenting a sleep performance score may include presenting component scores that decompose and / or categorize the sleep performance score by their contribution level to the overall score. In some cases, this decomposition or categorization may be associated with weighting values ​​used to calculate the sleep performance score. In one instance, if usage time during REM sleep and event information during REM sleep are highly weighted, but user interface compliance information during wakefulness or light sleep is lightly weighted, presenting a sleep performance score may include indicating that usage time during REM sleep and event information during REM sleep are significant components of the sleep performance score for that sleep period, optionally indicating that user interface compliance information during wakefulness or light sleep is less significant.

[0057] In some cases, presenting a sleep performance score may include presenting component scores (e.g., contributions to the sleep performance score) of one or more use variables that are decomposed (e.g., binned) and / or categorized by sleep stage information. For example, a set of four component scores (e.g., bins) of the use time variable may be presented, including a score for use time during wakefulness, a score for use time during REM sleep, a score for use time during light sleep, and a score for use time during deep sleep. It should be understood that each of the component scores may be a score calculated by applying weighted values ​​to the use variable, as described herein with reference to the calculation of the total sleep performance score.

[0058] Sleep performance scores can be used as an objective measure of a user's sleep duration. In some cases, sleep performance scores can be limited to a portion of a user's sleep duration when using respiratory therapy. Sleep performance scores can provide information to users to help monitor, maintain, and / or encourage adherence (e.g., use of respiratory therapy devices as needed or prescribed). In some cases, sleep performance scores can provide healthcare providers, facilities, and / or healthcare-related companies (e.g., healthcare insurance providers) with information about user adherence and efficacy when using respiratory therapy devices during sleep. In some cases, sleep performance scores can be used to provide an objective measure for research purposes.

[0059] In some cases, sleep performance scores can be used to influence or adjust parameters associated with future use of a user's respiratory therapy system or another respiratory therapy system. This influence or adjustment can be manual (e.g., a user switching user interfaces) or automatic (e.g., the respiratory therapy device automatically changing the air pressure supplied during use). In one instance, after recording one or more sleep performance scores (e.g., one or more sleep periods), one or more additional sleep performance scores (e.g., one or more additional sleep periods) can be measured after adjusting one or more parameters of the respiratory therapy system. The additional sleep performance scores can then be compared to the original sleep performance scores to determine if the adjustment was beneficial. If the adjustment was not beneficial, it can be reversed. If the adjustment was beneficial, it can be retained for further use or further adjustments. In some cases, data associated with changes in sleep performance scores related to one or more adjustments to the respiratory therapy system can be sent to a server (e.g., a cloud-based or internet-accessible server). Such data can be used to produce future respiratory therapy systems and / or accessed by existing respiratory therapy systems to improve respiratory therapy.

[0060] In some cases, sleep performance scores and / or sleep quality scores can be used to identify one or more use variables that a user tolerates, even if they are out of range. In some cases, in addition to calculating sleep performance scores (and / or sleep quality scores), out-of-range use variables can be identified. Identifying out-of-range use variables can include determining that the value of a use variable falls outside the expected threshold range (e.g., below the threshold, above the threshold, or between two thresholds). Out-of-range use variables can be use variables whose total value exceeds the expected threshold range (e.g., the count of the number of events detected in an event information variable is higher than the threshold number of events), use variables whose value exceeds the expected threshold range for a duration (e.g., a sealed quality variable with a value below the threshold for the total duration during a sleep period), or use variables whose scores exceed the expected threshold range (e.g., pre-weighted or post-weighted scores, such as component scores). In some cases, if a sleep performance score (and / or sleep quality score) is higher than the threshold amount, and one or more specific use variables for a single sleep period or multiple sleep periods (e.g., at least the threshold number of sleep periods or the threshold number of consecutive sleep periods) are out of range, it can be determined that a given out-of-range use variable can still be a tolerable use variable. In such cases, it can be assumed that the tolerance for the use of variables is not very important for overall sleep performance, sleep quality, and / or the effectiveness of respiratory therapy.

[0061] In one instance, while poor seal quality is often a problem that should be remedied (e.g., by replacing the user interface), the respiratory therapy system may consider seal quality a tolerable variable 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). Once seal quality is considered a tolerable variable, the system may choose not to notify the user of changes to the user interface, may reduce one or more weights associated with the seal quality variable, may make one or more adjustments to the respiratory therapy system, may take other actions related to the seal quality variable, or any combination thereof.

[0062] These illustrative examples are given to introduce the reader to the general topics discussed herein, and are not intended to limit the scope of the disclosed concepts. Various additional features and examples are described below with reference to the accompanying drawings, in which like reference numerals indicate like elements, and directional descriptions are used to describe illustrative embodiments, but are similar to the illustrative embodiments and should not be used to limit this disclosure. Elements included in the illustrations herein may be drawn out of scale.

[0063] refer to Figure 1The illustration shows a system 100 according to some embodiments of the present disclosure. 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.

[0064] The control system 110 includes one or more processors 112 (hereinafter referred to as processor 112). The control system 110 is typically used to control (e.g., actuate) various components of the system 100 and / or analyze data acquired and / or generated by the components of the system 100. The processor 112 may be a general-purpose or special-purpose processor or a microprocessor. Although in Figure 1 A processor 112 is shown, but the control system 110 may include any suitable number of processors (e.g., one processor, two processors, five processors, ten processors, etc.), which may be located in a single housing or positioned remotely from each other. The control system 110 may be coupled to and / or located within the housing of, for example, the housing of user equipment 170, a portion of respiratory system 120 (e.g., a housing), and / or one or more of sensors 130. The control system 110 may be centralized (within one such housing) or distributed (within two or more physically distinct such housings). In such embodiments including two or more housings containing the control system 110, such housings may be positioned close to and / or far from each other.

[0065] Memory device 114 stores machine-readable instructions executable by processor 112 of control system 110. Memory device 114 can be any suitable computer-readable storage device or medium, such as, for example, random or serial access memory devices, hard disk drives, solid-state drives, flash memory devices, etc. Although Figure 1 A memory device 114 is shown, but system 100 may include any suitable number of memory devices 114 (e.g., one memory device, two memory devices, five memory devices, ten memory devices, etc.). Memory devices 114 may be coupled to and / or located within the housing of the breathing device 122, the housing of the user device 170, the housing of one or more of the sensors 130, or any combination thereof. Similar to control system 110, memory devices 114 may be centralized (within one such housing) or distributed (within two or more physically different such housings).

[0066] In some embodiments, memory device 114 ( Figure 1This system stores user profiles associated with each user. User profiles may include, for example, user-associated demographic information, user-associated biostatistics, user-associated medical information, self-reported user feedback, user-associated sleep parameters (e.g., sleep-related parameters recorded from one or more earlier sleep periods), or any combination thereof. Demographic information may include, for example, information indicating the user's age, gender, ethnicity, geographic location, relationship status, family history of insomnia, employment status, education status, socioeconomic status, or any combination thereof. Medical information may include, for example, information indicating one or more medical conditions associated with the user, medication use, or both. Medical information data may also include multiple sleep wait time test (MSLT) results or scores and / or Pittsburgh Sleep Quality Index (PSQI) scores or values. Self-reported user feedback may include information indicating self-reported subjective sleep scores (e.g., poor, average, excellent), user's self-reported subjective stress levels, user's self-reported subjective fatigue levels, user's self-reported subjective health status, recent life events experienced by the user, or any combination thereof.

[0067] Electronic interface 119 is configured to receive data (e.g., physiological data and / or audio data) from one or more sensors 130, such that the data can be stored in memory device 114 and / or analyzed by processor 112 of control system 110. Electronic interface 119 can communicate with one or more sensors 130 using wired or wireless connections (e.g., using RF communication protocols, WiFi communication protocols, Bluetooth communication protocols, via cellular networks, etc.). Electronic interface 119 may include an antenna, a receiver (e.g., an RF receiver), a transmitter (e.g., an RF transmitter), a transceiver, or any combination thereof. Electronic interface 119 may also include one or more processors and / or one or more memory devices that are the same as or similar to processor 112 and memory device 114 described herein. In some embodiments, electronic interface 119 is coupled to or integrated into user equipment 170. In other implementations, electronic interface 119 is coupled to or integrated with control system 110 and / or memory device 114 (e.g., in a housing).

[0068] As described above, in some embodiments, system 100 may optionally include a respiratory system 120 (also referred to as a respiratory therapy system). The respiratory system 120 may include a respiratory pressure therapy device 122 (referred to herein as 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 129, or any combination thereof. In some embodiments, a control system 110, a memory device 114, a display device 128, one or more sensors 130, and a humidifier 129 are part of the respiratory device 122. Respiratory pressure therapy refers to the application of a controlled target pressure of air to the inlet of a user's airway throughout the user's respiratory cycle, which is nominally positive relative to atmosphere (e.g., the opposite of negative pressure therapy such as a canister ventilator or a chest tube). The respiratory system 120 is typically used to treat individuals suffering from one or more sleep-related breathing disorders (e.g., obstructive sleep apnea, central sleep apnea, or mixed sleep apnea).

[0069] Breathing device 122 is typically used to generate pressurized air to be delivered to a user (e.g., using one or more motors driving one or more compressors). In some embodiments, breathing device 122 generates a continuous, constant air pressure that is delivered to the user. In other embodiments, breathing device 122 generates two or more predetermined pressures (e.g., a first predetermined air pressure and a second predetermined air pressure). In other embodiments, breathing device 122 is configured to generate a variety of different air pressures within a predetermined range. For example, breathing device 122 may deliver at least about 6 cm H2O, at least about 10 cm H2O, at least about 20 cm H2O, about 6 cm H2O to about 10 cm H2O, about 7 cm H2O to about 12 cm H2O, etc. Breathing device 122 may also deliver pressurized air at, for example, a predetermined flow rate from about -20 L / min to about 150 L / min while maintaining positive pressure (relative to ambient pressure).

[0070] User interface 124 engages with a portion of the user's face and delivers pressurized air from breathing device 122 to the user's airway to help prevent airway narrowing and / or collapse during sleep. This can also increase the user's oxygen uptake during sleep. Depending on the treatment to be applied, user interface 124 may, 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 ambient pressure, for example, at a positive pressure of about 10 cm H2O relative to ambient pressure to achieve the treatment. For other forms of treatment, such as oxygen delivery, the user interface may not include a seal sufficient to facilitate the delivery of a gas supply at a positive pressure of about 10 cm H2O to the airway.

[0071] like Figure 2As shown, in some embodiments, 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 delivers air to the user's nose or a nasal pillow mask that delivers air directly to the user's nostrils. The user interface 124 may include multiple straps (e.g., including hook-and-loop fasteners) for positioning and / or stabilizing the interface on a portion of the user (e.g., the face) and conformal cushioning pads (e.g., silicone, plastic, foam, etc.) to facilitate an airtight seal between the user interface 124 and the user. In some instances, the user interface 124 may be a tubular mask in which the straps of the mask are configured to function as conduits for delivering pressurized air to the mask or nasal mask. The user interface 124 may also include one or more vents for the escape of carbon dioxide and other gases exhaled by the user 210. In other embodiments, the user interface 124 may include a mouthpiece (e.g., a night-time protective mouthpiece molded to conform to the user's teeth, a jaw repositioning device, etc.).

[0072] The conduit 126 (also referred to as an air circuit or tube) allows air to flow between two components of the respiratory system 120, such as between the breathing device 122 and the user interface 124. In some embodiments, there may be separate conduit branches for inhalation and exhalation. In other embodiments, a single branch conduit is used for both inhalation and exhalation.

[0073] One or more of the breathing device 122, user interface 124, tubing 126, display device 128, and humidifier 129 may include one or more sensors (e.g., pressure sensor, flow sensor, or any 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 the pressurized air supplied by the breathing device 122.

[0074] Display device 128 is typically used to display images including still images, video images, or both, and / or information about breathing device 122. For example, display device 128 may provide information about the status of breathing device 122 (e.g., whether breathing device 122 is on / off, the pressure of the air delivered by breathing device 122, the temperature of the air delivered by breathing device 122, etc.) and / or other information (e.g., sleep performance score, sleep rating, or treatment score such as myAir). TM(e.g., ratings), current date / time, user 210's personal information, etc.). In some embodiments, the display device 128 acts 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, an OLED display, an LCD display, etc. The input interface may be, for example, a touchscreen or touch-sensitive substrate, a mouse, a keyboard, or any sensor system configured to sense input made by a human user interacting with the breathing device 122.

[0075] The humidification tank 129 is coupled to or integrated into the breathing apparatus 122 and includes a water reservoir for humidifying pressurized air delivered from the breathing apparatus 122. The breathing apparatus 122 may include a heater to heat the water in the humidification tank 129 to humidify the pressurized air supplied to the user. Additionally, in some embodiments, the conduit 126 may also include a heating element (e.g., coupled to and / or embedded in the conduit 126) that heats the pressurized air delivered to the user.

[0076] The respiratory system 120 can be used as, for example, a ventilator or a positive airway pressure (PAP) system, such as a continuous positive airway pressure (CPAP) system, an automated positive airway pressure (APAP) system, a bilevel or variable positive airway pressure (BPAP or VPAP) system, or any combination thereof. A CPAP system delivers a predetermined air pressure to the user (e.g., determined by a sleep physician). An APAP system automatically changes the air pressure delivered to the user based on, for example, respiratory data associated with the user. A BPAP or VPAP system is configured to deliver a first predetermined pressure (e.g., inspiratory positive airway pressure or IPAP) and a second predetermined pressure below the first predetermined pressure (e.g., expiratory positive airway pressure or EPAP).

[0077] refer to Figure 2 The illustration shows a system 100 according to some embodiments. Figure 1 Part of the breathing system 120. The user 210 and bed partner 220 are located in the bed 230 and lying on the mattress 232. A user interface 124 (e.g., a full-face mask) can be worn by the user 210 during sleep. The user interface 124 is fluidly connected and / or connected to the breathing device 122 via a conduit 126. The breathing device 122, in turn, delivers pressurized air to the user 210 via the conduit 126 and the user interface 124 to increase the air pressure in the user 210's throat, thereby helping to prevent airway closure and / or narrowing during sleep. The breathing device 122 can be positioned such as... Figure 2 The bedside table 240 shown is directly adjacent to the bed 230, or more generally, is positioned on any surface or structure that is typically adjacent to the bed 230 and / or the user 210.

[0078] Return to reference Figure 1 The system 100 includes one or more sensors 130, such as a pressure sensor 132, a flow sensor 134, a temperature sensor 136, a motion sensor 138, a microphone 140, a speaker 142, a radio frequency (RF) receiver 146, an RF transmitter 148, a camera 150, an infrared sensor 152, a photoplethysmography (PPG) sensor 154, an electrocardiogram (ECG) sensor 156, an electroencephalogram (EEG) sensor 158, a capacitance sensor 160, a force sensor 162, a strain gauge sensor 164, an electromyography (EMG) sensor 166, an oxygen sensor 168, an analyte sensor 174, a humidity sensor 176, a LiDAR sensor 178, or any combination thereof. Typically, each of the one or more sensors 130 is configured to output sensor data that is received and stored in a memory device 114 or one or more other memory devices.

[0079] While one or more sensors 130 are shown and described as including each of the following: pressure sensor 132, flow sensor 134, temperature sensor 136, motion sensor 138, microphone 140, speaker 142, RF receiver 146, RF transmitter 148, camera 150, infrared sensor 152, photoplethysmography (PPG) sensor 154, electrocardiogram (ECG) sensor 156, electroencephalogram (EEG) sensor 158, capacitance sensor 160, force sensor 162, strain gauge sensor 164, electromyography (EMG) sensor 166, oxygen sensor 168, analyte sensor 174, humidity sensor 176, and LiDAR sensor 178, more generally, one or more sensors 130 may include any combination and any number of each of the sensors described and / or shown herein.

[0080] One or more sensors 130 can be used to generate, for example, physiological data, audio data, or both. The control system 110 can use the physiological data generated by the one or more sensors 130 to determine sleep-wake signals and one or more sleep-related parameters associated with the user during sleep periods. Sleep-wake signals can indicate one or more sleep states, including wakefulness, relaxed wakefulness, micro-awakeness, rapid eye movement (REM) stage, first non-REM stage (commonly referred to as "N1"), second non-REM stage (commonly referred to as "N2"), third non-REM stage (commonly referred to as "N3"), or any combination thereof. N1 and N2 can be considered light sleep stages, while N3 can be considered a deep sleep stage. The sleep-wake signals can also be timestamped to indicate the time the user enters the bed, the time the user leaves the bed, the time the user attempts to fall asleep, etc. Sleep-wake signals can be measured by the sensors 130 during sleep periods at a predetermined sampling rate, such as one sample per second, one sample every 30 seconds, one sample per minute, etc. Examples of one or more sleep-related parameters that can be determined for a user during a sleep period based on sleep-wake signals include total bed rest time, total sleep time, sleep latency, sleep-wake parameters, sleep efficiency, segmentation index, or any combination thereof.

[0081] Physiological and / or audio data generated by one or more sensors 130 can also be used to determine respiratory signals associated with the user during sleep periods. Respiratory signals typically indicate the user's respiration / breathing during sleep periods. Respiratory signals can indicate, for example, respiratory rate, respiratory rate variability, inspiratory amplitude, expiratory amplitude, inspiratory-expiratory ratio, number of events per hour, event pattern, pressure setting of breathing device 122, or any combination thereof. Events may include snoring, apnea, central apnea, obstructive apnea, mixed apnea, hypopnea, mask leakage (e.g., from user interface 124), restless legs, sleep disturbance, choking, increased heart rate, dyspnea, asthma attack, seizure, convulsions, or any combination thereof.

[0082] Pressure sensor 132 outputs pressure data that can be stored in memory device 114 and / or analyzed by processor 112 of control system 110. In some embodiments, pressure sensor 132 is an air pressure sensor (e.g., an atmospheric pressure sensor) that generates sensor data indicating the breathing (e.g., inhalation and / or exhalation) and / or ambient pressure of the user of respiratory system 120. In such embodiments, pressure sensor 132 can be coupled to or integrated into respiratory device 122. Pressure sensor 132 can be, for example, a capacitive sensor, an electromagnetic sensor, a piezoelectric sensor, a strain gauge sensor, an optical sensor, a potentiometric sensor, or any combination thereof. In one example, pressure sensor 132 can be used to determine the user's blood pressure.

[0083] 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 embodiments, 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 embodiments, the flow sensor 134 may be coupled to or integrated into the breathing device 122, the user interface 124, or the conduit 126. The flow sensor 134 may be a mass flow sensor, such as, for example, a rotary flow meter (e.g., a Hall effect flow meter), a turbine flow meter, an orifice plate flow meter, an ultrasonic flow meter, a hot-wire sensor, an eddy current sensor, a membrane sensor, or any combination thereof.

[0084] Temperature sensor 136 outputs temperature data that can be stored in memory device 114 and / or analyzed by processor 112 of control system 110. In some embodiments, temperature sensor 136 generates instructions for user 210 ( Figure 2 Temperature data including core body temperature, user 210 skin temperature, temperature of air flowing from breathing device 122 and / or flowing through conduit 126, temperature in user interface 124, ambient temperature, or any combination thereof. Temperature sensor 136 may be, for example, a thermocouple sensor, a thermistor sensor, a silicon bandgap temperature sensor or a semiconductor-based sensor, a resistance temperature detector, or any combination thereof.

[0085] The microphone 140 output can be stored in memory device 114 and / or analyzed by processor 112 of control system 110. The audio data generated by microphone 140 can be reproduced as one or more sounds (e.g., a sound from user 210) during sleep periods. The audio data from microphone 140 can also be used to identify (e.g., using control system 110) events experienced by the user during sleep periods, as described further in detail herein. Microphone 140 can be coupled to or integrated into breathing device 122, user interface 124, catheter 126, or user device 170.

[0086] The speaker 142 output can be controlled by a user of system 100 (e.g., Figure 2 The speaker 142 can be used as, for example, an alarm clock or to play alarms or messages to the user 210 (e.g., in response to an event). In some embodiments, the speaker 142 can be used to transmit audio data generated by the microphone 140 to the user. The speaker 142 can be coupled to or integrated into the breathing device 122, user interface 124, conduit 126, or user equipment 170.

[0087] Microphone 140 and speaker 142 can be used as separate devices. In some embodiments, microphone 140 and speaker 142 can be combined into acoustic sensor 141, as described, for example, in WO 2018 / 050913, which is incorporated herein by reference in its entirety. In such embodiments, speaker 142 generates or emits sound waves at predetermined intervals, and microphone 140 detects reflections of emitted sound waves from speaker 142. The sound waves generated or emitted by speaker 142 have frequencies inaudible to the human ear (e.g., below 20 Hz or above about 18 kHz) so as not to disturb the sleep of user 210 or bed partner 220. Figure 2 Based at least in part on data from microphone 140 and / or speaker 142, control system 110 can determine user 210 ( Figure 2 The location of the sleep and / or one or more of the sleep-related parameters described herein.

[0088] In some embodiments, sensor 130 includes (i) a first microphone that is the same as or similar to microphone 140 and is integrated in acoustic sensor 141; and (ii) a second microphone that is the same as or similar to microphone 140 but is separate from and different from the first microphone integrated in acoustic sensor 141.

[0089] RF transmitter 148 generates and / or transmits radio waves with a predetermined frequency and / or predetermined amplitude (e.g., in the high-frequency band, in the low-frequency band, long-wave signal, short-wave signal, etc.). RF receiver 146 detects the reflection of the radio waves emitted from RF transmitter 148, and this data can be analyzed by control system 110 to determine the user 210 ( Figure 2 The location of the RF receiver (RF receiver 146 and RF transmitter 148 or another RF pair) and / or one or more of the sleep-related parameters described herein. The RF receiver may also be used for wireless communication between the control system 110, the breathing device 122, one or more sensors 130, the user equipment 170, or any combination thereof. While the RF receiver 146 and RF transmitter 148 are in... Figure 1 While shown as separate and distinct components, in some embodiments, the RF receiver 146 and the RF transmitter 148 are combined as part of the RF sensor 147. In some such embodiments, the RF sensor 147 includes control circuitry. The specific format of the RF communication may be WiFi, Bluetooth, etc.

[0090] In some implementations, 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 implementations, the WiFi mesh system includes WiFi routers and / or WiFi controllers and one or more satellites (e.g., access points), each of which includes an RF sensor identical or similar to RF sensor 147. 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 router and the satellite (e.g., differences in received signal strength), caused by a moving object or person partially blocking the signal. The motion data may indicate movement, breathing, heart rate, gait, falls, behavior, etc., or any combination thereof.

[0091] Camera 150 outputs image data that can be reproduced as one or more images (e.g., still images, video images, thermal images, or combinations thereof) that can be stored in memory device 114. Image data from camera 150 can be used by control system 110 to determine one or more sleep-related parameters as described herein. For example, image data from camera 150 can be used to identify the user's location, determine if user 210 has gone to bed 230 (…). Figure 2 The time of ), and the time of determining when user 210 got out of bed 230.

[0092] The output of infrared (IR) sensor 152 is reproducible as infrared image data (e.g., still images, video images, or both) that can be stored in memory device 114. Infrared data from IR sensor 152 can be used to determine one or more sleep-related parameters during a sleep period, including the temperature of user 210 and / or the movement of user 210. IR sensor 152 can also be used in conjunction with camera 150 when measuring the presence, location, and / or movement of user 210. For example, IR sensor 152 can detect infrared light with wavelengths from about 700 nm to about 1 mm, while camera 150 can detect visible light with wavelengths from about 380 nm to about 740 nm.

[0093] PPG sensor 154 output and user 210 ( Figure 2 The associated physiological data can be used to determine one or more sleep-related parameters, such as heart rate, heart rate variability, cardiac cycle, respiratory rate, inspiratory amplitude, expiratory amplitude, inspiratory-expiratory ratio, estimated blood pressure parameters, or any combination thereof. The PPG sensor 154 can be worn by the user 210, embedded in clothing and / or fabric worn by the user 210, embedded in and / or connected to the user interface 124 and / or its associated head-mounted device (e.g., a strap, etc.).

[0094] ECG sensor 156 outputs physiological data associated with the electrical activity of the heart of user 210. In some embodiments, ECG sensor 156 includes one or more electrodes positioned on or around a portion of user 210 during sleep periods. The physiological data from ECG sensor 156 can be used, for example, to determine one or more sleep-related parameters as described herein.

[0095] EEG sensor 158 outputs physiological data associated with the electrical activity of the user 210's brain. In some embodiments, EEG sensor 158 includes one or more electrodes positioned on or around the user 210's scalp during sleep periods. The physiological data from EEG sensor 158 can be used, for example, to determine the user 210's sleep state at any given time during a sleep period. In some embodiments, EEG sensor 158 may be integrated into user interface 124 and / or an associated headgear (e.g., a fixation splint, etc.).

[0096] The outputs of capacitive sensor 160, force sensor 162, and strain gauge sensor 164 can be stored in memory device 114 and used by control system 110 to determine one or more of the sleep-related parameters described herein. EMG sensor 166 outputs physiological data associated with electrical activity generated by one or more muscles. Oxygen sensor 168 outputs oxygen data indicating the oxygen concentration of a gas (e.g., in conduit 126 or at user interface 124). Oxygen sensor 168 can be, for example, an ultrasonic oxygen sensor, an electro-oxygen sensor, a chemical oxygen sensor, an optical oxygen sensor, or any combination thereof. In some embodiments, one or more sensors 130 also include a ground-skin response (GSR) sensor, a blood flow sensor, a respiration sensor, a pulse sensor, a blood pressure sensor, a blood oxygenation sensor, or any combination thereof.

[0097] Analyte sensor 174 can be used to detect the presence of analytes in the exhaled breath of user 210. Data output from analyte sensor 174 can be stored in memory device 114 and used by control system 110 to determine the identity and concentration of any analyte in the breath of user 210. In some embodiments, analyte sensor 174 is positioned near the mouth of user 210 to detect analytes in the breath exhaled from the mouth of user 210. For example, when user interface 124 is a mask covering the nose and mouth of user 210, analyte sensor 174 can be positioned inside the mask to monitor mouth breathing of user 210. In other embodiments, such as when user interface 124 is a nasal mask or nasal bolus mask, analyte sensor 174 can be positioned near the nose of user 210 to detect analytes in the breath exhaled through the nose of user 210. In other embodiments, when user interface 124 is a nasal mask or nasal bolus mask, analyte sensor 174 can be positioned near the mouth of user 210. In this embodiment, the analyte sensor 174 can be used to detect whether any air is unintentionally leaking from the mouth of user 210. In some embodiments, the analyte sensor 174 is a volatile organic compound (VOC) sensor that can be used to detect carbon-based chemicals or compounds. In some embodiments, the analyte sensor 174 can also be used to detect whether user 210 is breathing through their nose or mouth. For example, if the presence of an analyte is detected by data output from the analyte sensor 174 located near the mouth of user 210 or inside a mask (in the implementation where user interface 124 is a mask), the control system 110 can use that data as an indication that user 210 is breathing through their mouth.

[0098] The humidity sensor 176 outputs data that can be stored in the memory device 114 and used by the control system 110. The humidity sensor 176 can be used to detect humidity in various areas around the user (e.g., within the conduit 126 or user interface 124, near the user 210's face, near the connection between the conduit 126 and user interface 124, near the connection between the conduit 126 and breathing device 122, etc.). Therefore, in some embodiments, the humidity sensor 176 can be coupled to or integrated into the user interface 124 or conduit 126 to monitor the humidity of pressurized air from breathing device 122. In other embodiments, the humidity sensor 176 is placed near any area where the humidity level needs to be monitored. The humidity sensor 176 can also be used to monitor the humidity of the surrounding environment around the user 210, such as the air in a bedroom.

[0099] The Light Detection and Ranging (LiDAR) sensor 178 can be used for depth sensing. This type of optical sensor (e.g., a laser sensor) can be used to detect objects and construct a three-dimensional (3D) map of the surrounding environment, such as a living space. LiDAR typically utilizes pulsed lasers for time-of-flight measurements. LiDAR is also known as 3D laser scanning. In examples of such sensor use, a fixed or mobile device (such as a smartphone) with a LiDAR sensor 166 can measure and map an area extending 5 meters or more from the sensor. For example, LiDAR data can be fused with point cloud data estimated by an electromagnetic RADAR sensor. The LiDAR sensor 178 can also use artificial intelligence (AI) to automatically geofence the RADAR system by detecting and classifying features in the space that may cause problems for the RADAR system, such as glass windows (which may be highly reflective of RADAR). For example, LiDAR can also be used to provide an estimate of a person's height, and how that height changes when the person sits down or falls. LiDAR can be used to form a 3D mesh representation of the environment. In further applications, LiDAR can reflect radio waves away from solid surfaces (e.g., semi-transparent materials), allowing for the classification of different types of obstacles.

[0100] Although Figure 1While shown separately, any combination of one or more sensors 130 may be integrated into or coupled to any one or more components of system 100, including breathing device 122, user interface 124, catheter 126, humidifier 129, control system 110, user device 170, or any combination thereof. For example, microphone 140 and speaker 142 are integrated into and / or coupled to user device 170, and pressure sensor 130 and / or flow sensor 132 are integrated into and / or coupled to breathing device 122. In some embodiments, at least one of the one or more sensors 130 is not coupled to breathing device 122, control system 110, or user device 170, and is positioned substantially adjacent to user 210 during sleep periods (e.g., positioned on or in contact with a portion of user 210, worn by user 210, coupled to or positioned on a bedside table, coupled to a mattress, coupled to a ceiling, etc.).

[0101] For example, such as Figure 2 As shown, one or more sensors 130 may be located at a first position 250A on a bedside table 240 adjacent to the bed 230 and the user 210. Alternatively, one or more sensors 130 may be located at a second position 250B on and / or within the mattress 232 (e.g., sensors are attached to and / or integrated into the mattress 232). Furthermore, one or more sensors 130 may be located at a third position 250C on the bed 230 (e.g., auxiliary sensors 140 are attached to and / or integrated into the headboard, footboard, or other location on the frame of the bed 230). One or more sensors 130 may also be located at a fourth position 250D on a wall or ceiling, typically adjacent to the bed 230 and / or the user 210. One or more sensors 130 may also be located at a fifth position, such that one or more sensors 130 are attached to and / or positioned on and / or inside the housing of the breathing device 122 of the breathing system 120. Furthermore, one or more sensors 130 may be located at a sixth position 250F, such that the sensors are coupled to and / or positioned on the user 210 (e.g., the sensors are embedded in or coupled to fabric or clothing worn by the user 210 during sleep periods). More typically, one or more sensors 130 may be positioned at any suitable location relative to the user 210, such that the sensor 140 may generate physiological data associated with the user 210 and / or bed partner 220 during one or more sleep periods.

[0102] User equipment 170 ( Figure 1The system 100 includes a display device 172. User equipment 170 may be, for example, a mobile device such as a smartphone, tablet, laptop, etc. Alternatively, user equipment 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, Alexa, etc.). In some embodiments, the user equipment is a wearable device (e.g., a smartwatch). Display device 172 is typically used to display images including still images, video images, or both. In some embodiments, display device 172 acts as a human-machine interface (HMI) including a graphical user interface (GUI) configured to display images and an input interface. Display device 172 may be an LED display, OLED display, LCD display, etc. The input interface may be, for example, a touchscreen or touch-sensitive substrate, a mouse, a keyboard, or any sensor system configured to sense input made by a human user interacting with user equipment 170. In some embodiments, system 100 may use and / or include one or more user devices.

[0103] Light source 180 is typically used to emit light having intensity and wavelength (e.g., color). For example, light source 180 may emit light having wavelengths between about 380 nm and about 700 nm (e.g., wavelengths in the visible spectrum). Light source 180 may include, for example, one or more light-emitting diodes (LEDs), one or more organic light-emitting diodes (OLEDs), light bulbs, lamps, incandescent light bulbs, CFL bulbs, halogen bulbs, or any combination thereof. In some embodiments, the intensity and / or wavelength (e.g., color) of the light emitted from light source 180 may be modified by control system 110. Light source 180 may also emit light in predetermined emission modes, such as continuous emission, pulsed emission, periodic emission of varying intensities (e.g., including a light emission period with gradually increasing intensity followed by decreasing intensity), or any combination thereof. The light emitted from light source 180 may be directly viewed by a user, or alternatively reflected or refracted before reaching the user. In some embodiments, light source 180 includes one or more light guides.

[0104] In some embodiments, the light source 180 is physically coupled to or integrated into the respiratory therapy system 120. For example, the light source 180 may be physically coupled to or integrated into the respiratory device 122, user interface 124, catheter 126, display device 128, or any combination thereof. In some embodiments, the light source 180 is physically coupled to or integrated into the user device 170. In other embodiments, the light source 180 is separate and distinct from each of the respiratory therapy system 120, the user device 170, and the activity tracker 190. In such embodiments, the light source 180 may be located at the user 210 (… Figure 2For example, it can be positioned on bedside table 240, bed 230, other furniture, walls, ceilings, etc.

[0105] Activity tracker 190 is typically used to help generate physiological data for determining activity measurements associated with a user. Activity measurements may include, for example, steps, distance traveled, number of 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, electrical skin activity (also known as skin conductance or skin response), or any combination thereof. Activity tracker 190 includes one or more of the sensors 130 described herein, such as motion sensor 138 (e.g., one or more accelerometers and / or gyroscopes), PPG sensor 154, and / or ECG sensor 156.

[0106] In some implementations, the activity tracker 190 is a wearable device that can be worn by a user, such as a smartwatch, wristband, ring, or patch. For example, see reference... Figure 2 The activity tracker 190 is worn on the wrist of the user 210. The activity tracker 190 may also be attached to or integrated into clothing or garments worn by the user. Alternatively, the activity tracker 190 may be attached to or integrated into the user device 170 (e.g., within the same housing). More typically, the activity tracker 190 may be communicatively attached to or physically integrated into (e.g., within a housing) the control system 110, memory 114, breathing system 120, and / or user device 170.

[0107] Although the control system 110 and the memory device 114 are in Figure 1 While described and shown as separate and distinct components of system 100, in some embodiments, the control system 110 and / or memory device 114 are integrated into user equipment 170 and / or breathing device 122. Alternatively, in some embodiments, the control system 110 or a portion thereof (e.g., processor 112) may reside in the cloud (e.g., integrated into a server, integrated into an Internet of Things (IoT) device (e.g., smart TV, smart thermostat, smart home appliance, smart lighting, etc.), connected to the cloud, subjected to edge cloud processing, etc.), or reside in one or more servers (e.g., remote server, local server, etc., or any combination thereof).

[0108] While system 100 is shown as including all of the components described above, according to implementations of this disclosure, systems for generating physiological data and determining recommended notifications or actions for a 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. As another example, a second alternative system includes a control system 110, a memory device 114, at least one of one or more sensors 130, and a user device 170. As yet another example, a third alternative system includes a control system 110, a memory device 114, a respiratory system 120, at least one of one or more sensors 130, and a user device 170. Therefore, various systems can be formed using any part or multiple parts of the components shown and described herein and / or in combination with one or more other components.

[0109] As used in this article, sleep periods can be defined in several ways based on, for example, an initial start time and an end time. (See references.) Figure 3 The illustration shows an exemplary timeline 301 for a sleep period. Timeline 301 includes bedtime (t... 入床 ), time to fall asleep (t) GTS ), initial sleep time (t) 睡眠 ), first micro-awakening MA1 and second micro-awakening MA2, awakening time (t) 觉醒 ) and wake-up time (t 起床 ).

[0110] In some implementations, a sleep period is the duration during which a user falls asleep. In such implementations, a sleep period has a start time and an end time, and during the sleep period, the user does not wake up until the end time. That is, any period of time during which the user is awake is not included in a sleep period. According to this first definition of a sleep period, if a user wakes and falls asleep multiple times in the same night, each sleep interval separated by wakefulness intervals is a sleep period.

[0111] Alternatively, in some embodiments, the sleep period has a start time and an end time, and during the sleep period, the user can wake up as long as the continuous duration of wakefulness is less than a wakefulness duration threshold, without the sleep period ending. The wakefulness duration threshold can be defined as a percentage of the sleep period. The wakefulness duration threshold can be, for example, about 20% of the sleep period, about 15% of the sleep period duration, about 10% of the sleep period duration, about 5% of the sleep period duration, about 2% of the sleep period duration, etc., or any other threshold percentage. In some embodiments, the wakefulness duration threshold is defined as a fixed amount of time, such as about one hour, about thirty minutes, about fifteen minutes, about ten minutes, about five minutes, about two minutes, etc., or any other amount of time.

[0112] In some implementations, a sleep period is defined as the entire time between the time a user first goes to bed at night and the time the user last gets up the following morning. In other words, a sleep period can be defined as a time period that begins on the first date (e.g., Monday, January 6, 2020) when the user first goes to bed wanting to fall asleep (e.g., if the user does not intend to watch TV or use a smartphone before going to sleep), which can be referred to as the first time of the current night (e.g., 10:00 PM), and ends on the second date (e.g., Tuesday, January 7, 2020) when the user first gets out of bed not wanting to return to sleep the following morning, which can be referred to as the second time of the following morning (e.g., 7:00 AM).

[0113] In some implementations, the user can manually define the start and / or end of a sleep period. For example, the user can select (e.g., by clicking or tapping) on ​​the user device 170 ( Figure 1 The user-selectable elements displayed on the display device 172 allow users to manually initiate or terminate sleep periods.

[0114] Time to enter bed t 入床 Before the user falls asleep (e.g., when the user lies down or sits on the bed), they initially enter the bed (e.g., Figure 2 The time of going to bed (230) is associated with the bed time. Bedtime t can be identified based on the bed threshold duration. 入床 This is used to distinguish between the time a user goes to bed for sleep and the time a user goes to bed for other reasons (e.g., watching TV). For example, bed threshold duration could be at least approximately 10 minutes, at least approximately 20 minutes, at least approximately 30 minutes, at least approximately 45 minutes, at least approximately 1 hour, at least approximately 2 hours, etc. While this document refers to bedtime t in the context of "bed," the term "bed" is used to describe bedtime. 入床 But more often, bedtime t 入床 This can refer to the time a user initially enters any location (e.g., a recliner, chair, sleeping bag, etc.) to sleep.

[0115] Going to sleep (GTS) and the time the user goes to bed (t) 入床 This is related to the initial attempt to fall asleep. For example, after going to bed, a user can engage in one or more activities to relax before attempting to sleep (e.g., reading, watching TV, listening to music, using user device 170, etc.). Initial sleep time (t) 睡眠 ) is the time when the user initially falls asleep. For example, initial sleep time (t) 睡眠 This could be the time when the user initially enters the first non-REM sleep stage.

[0116] Awakening Time t觉醒 This is the time associated with when a user wakes up but does not return to sleep (e.g., the opposite of when a user wakes up in the middle of the night and returns to sleep). A user may experience one of several unconscious micro-awakenings (e.g., micro-awakenings MA1 and MA2) with short durations (e.g., 5 seconds, 10 seconds, 30 seconds, 1 minute, etc.) after initial sleep onset. This is related to the wakefulness time t. 觉醒 Conversely, the user returns to sleep after each of the micro-awakenings MA1 and MA2. Similarly, the user may have one or more conscious awakenings (e.g., awakening A) after initial sleep onset (e.g., getting up to go to the bathroom, caring for a child or pet, sleepwalking, etc.). However, the user returns to sleep after awakening A. Therefore, the wakefulness time t 觉醒 It can be defined, for example, based on the duration of the arousal threshold (e.g., the user is awake for at least 15 minutes, at least 20 minutes, at least 30 minutes, at least 1 hour, etc.).

[0117] Similarly, wake-up time t 起床 This is associated with the time a user leaves the bed and remains outside the bed, intending to end their sleep (e.g., the opposite of a user getting up at night to go to the bathroom, care for a child or pet, or sleepwalking). In other words, wake-up time t 起床 This is the time when a user last leaves bed and does not return before the next sleep period (e.g., the following night). Therefore, wake-up time t 起床 The bedtime t for the second subsequent sleep period can be defined, for example, based on the duration of the wake-up threshold (e.g., at least 15 minutes, at least 20 minutes, at least 30 minutes, at least 1 hour, etc.). Alternatively, the bedtime t for the second subsequent sleep period can be defined based on the duration of the wake-up threshold (e.g., at least 4 hours, at least 6 hours, at least 8 hours, at least 12 hours, etc.). 入床 time.

[0118] As mentioned above, the user may initially t 入床 and finally t 起床 The person wakes up during the night and gets out of bed again. In some implementations, the final wake-up time t is identified or determined based on a predetermined threshold duration following the event (e.g., falling asleep or getting out of bed). 觉醒 and / or final wake-up time t 起床 Such a threshold duration can be customized for users. For a standard user who goes to bed at night and then wakes up and gets out of bed in the morning, any time period of approximately 12 to 18 hours can be used (during which time the user is awake). 觉醒 ) or get up (t 起床 ) and sleeping with users (t 入床 ), entering sleep (t GTS ) or fall asleep (t 睡眠For users who spend longer periods of time in bed, a shorter threshold time period can be used (e.g., approximately 8 hours to approximately 14 hours). The threshold time period can be initially selected and / or adjusted later based on the system monitoring the user's sleep behavior.

[0119] Total time in bed (TIB) is the time spent in bed (t). 入床 With wake-up time t 起床 The duration between sleep and wake times. Total sleep time (TST) is the duration between initial sleep and wake times, excluding any conscious or unconscious awakenings and / or micro-awakenings in between. Typically, total sleep time (TST) will be shorter than total bedtime (TIB) (e.g., one minute shorter, ten minutes shorter, one hour shorter, etc.). For example, refer to... Figure 3 Timeline 301, Total Sleep Time (TST) at initial sleep time t 睡眠 With awakening time t 觉醒 The duration spans between these periods, but does not include the durations of the first micro-wake (MA1), the second micro-wake (MA2), and wake-up A. As shown in the figure, in this example, the total sleep time (TST) is shorter than the total bed rest time (TIB).

[0120] In some implementations, total sleep time (TST) can be defined as total sustained sleep time (PTST). In such implementations, total sustained sleep time does not include a predetermined initial portion or a period of time in the first non-REM stage (e.g., a light sleep stage). For example, the predetermined initial portion could be between about 30 seconds and about 20 minutes, between about 1 minute and about 10 minutes, between about 3 minutes and about 5 minutes, etc. Total sustained sleep time is a measure of sustained sleep and smooths the sleep-wake sleep graph. For example, when a user initially falls asleep, the user may be in the first non-REM stage for a very short time (e.g., about 30 seconds), then return to the wake stage for a very short period of time (e.g., one minute), and then return to the first non-REM stage. In this example, total sustained sleep time does not include the first instance of the first non-REM stage (e.g., about 30 seconds).

[0121] In some implementations, sleep duration is defined as the time from bedtime (t... 入床 Start at wake-up time (t) 起床 The sleep period ends, meaning the sleep duration is defined as the total time spent in bed (TIB). In some implementations, the sleep period is defined as the time from the initial sleep time (t...). 睡眠 ) begins and at the awakening time (t) 觉醒 End. In some implementations, a sleep period is defined as total sleep time (TST). In some implementations, a sleep period is defined as the time from the onset of sleep (t). GTS ) begins and at the awakening time (t) 觉醒The sleep period ends at the time of sleep onset (t). In some implementations, the sleep period is defined as the time from the onset of sleep (t). GTS Start at wake-up time (t) 起床 The sleep period ends at bedtime. In some implementations, the sleep period is defined as the time from bedtime (t...). 入床 ) begins and at the awakening time (t) 觉醒 The sleep period ends at the initial sleep time (t). In some implementations, the sleep period is defined as the period from the initial sleep time (t) to the end. 睡眠 Start at wake-up time (t) 起床 )Finish.

[0122] refer to Figure 4 The illustration shows the timeline 400 according to some implementation methods. Figure 4 An exemplary sleep graph 400 is shown. As illustrated, the sleep graph 400 includes a sleep-wake signal 401, a wakefulness stage axis 410, a REM stage axis 420, a light sleep stage axis 430, and a deep sleep stage axis 440. The intersection of the sleep-wake signal 401 with one of the axes 410, 420, 430, and 440 indicates the sleep stage at any given time during a sleep period.

[0123] The sleep-wake signal 401 can be based on physiological data associated with the user (e.g., by the sensor 130 described herein). Figure 1 One or more of the following are generated: sleep-wake signals. Sleep-wake signals can indicate one or more sleep states or stages, including wakefulness, relaxed wakefulness, micro-awakeness, REM stage, first non-REM stage, second non-REM stage, third non-REM stage, or any combination thereof. In some embodiments, one or more of the first non-REM stage, second non-REM stage, and third non-REM stage can be grouped together and classified as light sleep stages or deep sleep stages. For example, light sleep stages may include the first non-REM stage, while deep sleep stages may include the second and third non-REM stages. Although in Figure 4 The sleep graph 400 shown includes a light sleep stage axis 430 and a deep sleep stage axis 440, but in some embodiments, the sleep graph 400 may include axes for each of the first non-REM stage, the second non-REM stage, and the third non-REM stage. In other implementations, sleep-wake signals may also indicate respiratory signals, respiratory rate, inspiratory amplitude, expiratory amplitude, inspiratory-expiratory ratio, number of events per hour, event pattern, or any combination thereof. Information describing sleep-wake signals may be stored in memory device 114.

[0124] Sleep graph 400 can be used to determine one or more sleep-related parameters, such as sleep latency (SOL), wakefulness after sleep onset (WASO), sleep efficiency (SE), sleep fragmentation index, sleep blocks, or any combination thereof.

[0125] Sleep latency (SOL) is defined as the time it takes to enter sleep (t). GTS ) and initial sleep time (t 睡眠 The sleep latency period is the time between the initial attempt to fall asleep and the actual time it takes for a user to fall asleep. In some implementations, the sleep latency period is defined as the duration of sustained sleep latency (PSOL). The difference between PSOL and sleep latency is that PSOL is defined as the duration between the time it takes to fall asleep and a predetermined amount of sustained sleep. In some implementations, the predetermined amount of sustained sleep may include, for example, at least 10 minutes of sleep within the second non-REM stage, the third non-REM stage, and / or the REM stage, and awakenings, the first non-REM stage, and / or movement between them not exceeding 2 minutes. In other words, the sustained sleep latency requires sustained sleep for, for example, up to 8 minutes within the second non-REM stage, the third non-REM stage, and / or the REM stage. In other implementations, the predetermined amount of sustained sleep may include at least 10 minutes of sleep within the first non-REM stage, the second non-REM stage, the third non-REM stage, and / or the REM stage after the initial sleep time. In such implementations, the predetermined amount of continuous sleep may not include any micro-awakening (e.g., a ten-second micro-awakening does not restart the 10-minute time period).

[0126] Post-sleep wakefulness onset (WASO) is associated with the total duration of a user's wakefulness between the initial sleep time and the wake time. Therefore, WASO includes brief and micro-awakenings during the sleep period (e.g., Figure 4 Micro-awakenings (MA1 and MA2) are shown, whether conscious or unconscious. In some implementations, post-sleep awakening start (WASO) is defined as a sustained post-sleep awakening start (PWASO) that includes only the total duration of awakenings having a predetermined length (e.g., greater than 10 seconds, greater than 30 seconds, greater than 60 seconds, greater than about 5 minutes, greater than about 10 minutes, etc.).

[0127] Sleep efficiency (SE) is defined as the ratio of total time in bed (TIB) to total sleep time (TST). For example, if the total time in bed is 8 hours and the total sleep time is 7.5 hours, the sleep efficiency for that sleep period is 93.75%. Sleep efficiency indicates the user's sleep hygiene. For example, if a user goes to bed before sleep and spends time engaging in other activities (e.g., watching television), sleep efficiency is reduced (e.g., the user is punished). In some implementations, sleep efficiency (SE) can be calculated based on the total time in bed (TIB) and the total time the user attempts to sleep. In such implementations, the total time the user attempts to sleep is defined as the duration between the sleep time (GTS) and the wake-up time described herein. For example, if the total sleep time is 8 hours (e.g., 11 p.m. to 7 a.m.), the time to fall asleep is 10:45 p.m., and the wake-up time is 7:15 a.m., in such an implementation, the sleep efficiency parameter is calculated to be approximately 94%.

[0128] The segmentation index is determined at least in part based on the number of awakenings during sleep periods. For example, if a user has two micro-awakenings (e.g., Figure 4 For example, given micro-wake-up MA1 and micro-wake-up MA2, the segmentation index can be represented as 2. In some implementations, the segmentation index is scaled within a predetermined range of integers (e.g., 0 to 10).

[0129] Sleep blocks are associated with the transition between any sleep stage (e.g., first non-REM stage, second non-REM stage, third non-REM stage, and / or REM stage) and the waking stage. Sleep blocks can be calculated at a resolution of, for example, 30 seconds.

[0130] In some implementations, the systems and methods described herein may include generating or analyzing sleep maps that include sleep-wake signals to determine or identify bedtime (t) at least in part based on the sleep-wake signals of the sleep maps. 入床 ), time to fall asleep (t) GTS ), initial sleep time (t) 睡眠 ), one or more first micro-awakenings (e.g., MA1 and MA2), wake-up time (t) 觉醒 ), wake-up time (t) 起床 (or any combination thereof).

[0131] In other embodiments, one or more of the sensors 130 may be used to determine or identify bedtime (t). 入床 ), time to fall asleep (t) GTS ), initial sleep time (t) 睡眠 ), one or more first micro-awakenings (e.g., MA1 and MA2), wake-up time (t) 觉醒 ), wake-up time (t) 起床(or any combination thereof), which in turn define sleep periods. For example, bedtime t can be determined based on data generated, for example, by motion sensor 138, microphone 140, camera 150, or any combination thereof. 入床 The time to fall asleep can be determined based on, for example, data from motion sensor 138 (e.g., data indicating that the user is not moving), data from camera 150 (e.g., data indicating that the user is not moving and / or that the user has turned off the lights), data from microphone 140 (e.g., data indicating that the TV is turned off), data from user device 170 (e.g., data indicating that the user is no longer using user device 170), data from pressure sensor 132 and / or flow sensor 134 (e.g., data indicating that the user turns on breathing device 122, data indicating that the user puts on user interface 124, etc.), or any combination thereof.

[0132] Although sleep diagram 400 depicts REM stages that gradually shorten as the sleep period progresses, this is not always the case. In some cases, the duration of REM stages gradually increases as the sleep period progresses (e.g., the first REM stage is shorter than the last REM stage).

[0133] Figure 5 It is illustrated in relation to certain aspects of this disclosure. Figure 4 The sleep graph is associated with certain variables in Chart 500. Chart 500 can be used with... Figure 3 The usage is correlated with sleep periods. Figure 500 includes several usage variables, including usage time 514, event 516, and seal quality 518, as determined during the course of sleep period 502. Additionally, user interface compliance usage variables can be determined and / or displayed based on detected user interface transitions, which are represented as user interface transition time periods 506, 510 (e.g., gaps in other usage variables). In some cases, user interface compliance usage variables may be or may include one or more mask open-close events (e.g., events representing putting on or removing the mask or user interface). In some cases, user interface compliance usage variables may track when the user interface is put on and / or removed, and / or how many times the user interface is put on and / or removed.

[0134] The usage time of 514 can indicate a respiratory therapy system (e.g., Figure 1The respiratory therapy system 120 is used to provide the user with a certain amount of time for respiratory therapy. As depicted in Table 500, a set of boxes 520 are depicted throughout the sleep period of usage time 514, representing boxes where the user is using the respiratory therapy system. For example, the respiratory therapy system is used during a first time period 504, a second time period 508, and a third time period 512. Between the first time period 504 and the second time period 508, the user may have temporarily stopped using the respiratory therapy system (e.g., by removing and replacing the user interface), as identified by the user interface transition time period 506. The start of the user interface transition time period 506 may indicate a first user interface transition (e.g., removing the user interface), while the end of the user interface transition time period 506 may indicate a second user interface transition (e.g., putting on the user interface). Similarly, a similar user interface transition time period 510 is located between the second time period 508 and the third time period 512.

[0135] Event 516 can be represented using variables as a set of timestamped values ​​(or timestamped only), as indicated by events 522 and 524 depicted in Figure 500. Events 522 and 524 can be apnea events, hypoventilation events, or other events.

[0136] The sealing quality 518 variable can be represented by line 526, which represents a value associated with the sealing quality during the sleep period. In instances of sealing quality 518, there are two examples 528 and 530 of low sealing quality, during which time line 526 drops below threshold line 532. In some cases, user interface transition time periods 506 and 510 may be ignored for the purpose of using the sealing quality 518 variable, or may indicate examples of low sealing quality.

[0137] When comparing the variables used in Figure 500 with the sleep stages described in Sleep Figure 400, it can be seen that the first time period 504 includes sleep stages from t 入床 This continues until time MA1. It is presumed that the user used the breathing therapy device during this time, only temporarily removing it during MA1. During this first time period 504, the user went through four light sleep stages, two deep sleep stages, and one REM sleep stage. During this first time period 504, a low seal quality example 528 was detected, consistent with detected event 522. It is presumed that the low seal quality under example 528 may have led to insufficient breathing therapy, thus allowing event 522 to occur. Event 522 may also coincide with the user temporarily transitioning from a deep sleep stage to a light sleep stage.

[0138] The second time period 508 shows the usage time extending from the end of MA1 to the beginning of MA2, which includes four light sleep stages, two deep sleep stages, and one REM sleep stage. During the second time period 508, the seal quality 518 is shown as very strong (line 526 is above the threshold line 542), and an event 524 is detected. Comparing graph 500 and sleep graph 400, event 524 occurs approximately at the same time the user is in the REM sleep stage.

[0139] The third time period 512 shows the time from the end of MA2 to t. 觉醒 The usage time included one REM sleep stage, two light sleep stages, and one deep sleep stage. No events were detected during the third time period 512, but the third time period 512 began with an example 530 of low seal quality occurring during the REM sleep stage.

[0140] Because event 524 occurs during REM sleep and event 522 occurs during deep sleep, the occurrence of event 524 can be more weighted than the occurrence of event 522. For example, event 524 can reduce the overall sleep performance score more than event 522.

[0141] Because low seal quality example 530 occurs during REM sleep, while low seal quality example 528 occurs during both light and deep sleep, the occurrence of low seal quality example 530 can be more weighted than the occurrence of low seal quality example 528. For example, low seal quality example 530 can reduce the overall sleep performance score more than low seal quality example 528.

[0142] Figure 500 illustrates that data can be generated based on one or more sensors (e.g., Figure 1 A visual indication of a set of example usage variables determined by sensor data collected by one or more sensors 130. Other sets of usage variables may include one or more usage variables disclosed herein or any combination of other similar usage variables associated with the use of the respiratory therapy system. Additionally, any set of usage variables may be presented, stored and / or otherwise represented in any suitable form, such as graphs, numbers, spreadsheets, databases, data strings or other formats.

[0143] Figure 6 This is a flowchart depicting a process 600 for scoring sleep performance according to certain aspects of this disclosure. Process 600 can be performed by, for example... Figure 1 Any suitable system execution of System 100, including by Figure 1The process 600 is executed by the processor 112 of the control system 110. One, some, or all of the frames of process 600 may occur during a sleep period (e.g., calculating a sleep performance score for a given sleep period or subsequent sleep periods), immediately after a sleep period, or at another time. In some cases, process 600 is executed by a processor such as... Figure 1 The user equipment 170 is used to perform this action.

[0144] At box 602, sensor data is received. The received sensor data can be collected from one or more sensors, such as one or more sensors associated with the user's sleep periods, during which the user is receiving data from a respiratory therapy system (e.g., [insert sensor name here]). Figure 1 The respiratory therapy system 120) provides respiratory therapy. Although other sensors can be used, one or more such sensors (e.g., Figure 1 One or more sensors (130) may include a set of sensors from a respiratory therapy system (e.g., pressure sensors and flow sensors) and / or a set of sensors from a user device (e.g., acoustic sensors or RF sensors from a smartphone). In some cases, sensor data may be preprocessed prior to reception at box 602. In some cases, receiving sensor data at box 602 may include preprocessing the sensor data to improve the ability to later determine any usage variables and / or sleep stage information that may be desired. In some cases, no preprocessing is performed on the sensor data.

[0145] At box 604, one or more usage variables may be determined from sensor data. Determining one or more usage variables may include processing the sensor data (e.g., via equations, functions, or machine learning algorithms) to identify one or more values ​​of one or more usage variables. The one or more usage variables may be any number or combination of suitable usage variables, such as those disclosed herein. In some cases, the usage variable determined at box 604 may be a single-valued usage variable, such as the average leakage flow rate, which may be represented as a single number, or the count of detected events, which may be indicated as a single number. However, in some cases, the usage variable determined at box 604 may be a set of values ​​that occur throughout the sleep period, such as timestamped values ​​or the timestamp itself. For example, a seal quality usage variable may be represented as a set of seal quality values ​​(e.g., 0 to 100%, 0 to 20 on a 20-point scale) collected periodically (e.g., based on a sampling rate).

[0146] At box 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 throughout the sleep period, such as identifying transitions between different sleep stages and the duration spent in each sleep stage. The time spent in a sleep stage may refer to the total time spent in all examples of a particular sleep stage (e.g., a total of 90 minutes of REM sleep throughout the sleep period) or the time spent in individual examples of each sleep stage (e.g., 40 minutes of REM sleep followed by 10 minutes of light sleep, followed by 5 minutes of wakefulness (e.g., micro-awakening), followed by 30 minutes of light sleep, followed by 10 minutes of deep sleep, followed by 15 minutes of light sleep, followed by another 20 minutes of REM sleep). In some cases, sleep stage information may include the duration of the entire sleep period. In some cases, sleep stage information may include one or more ratios between the durations of sleep stages and / or between the duration of each sleep stage and the duration of the total sleep period.

[0147] At box 612, a sleep performance score can be calculated. The sleep performance score can be calculated using the usage variables determined from box 604 and the sleep stage information determined from box 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 may be determined from one, some, or all of the usage variables from box 604 and / or from the sleep stage information determined at box 606.

[0148] In some cases, determining the sleep performance score at box 612 may include determining one or more weighting values ​​at box 614 and applying one or more weighting values ​​at box 616. Weighting values ​​can be determined for any combination of usage variables, sleep stage information, segmented usage variables, or segmented sleep stage information. In some cases, determining weighting values ​​may include segmenting the usage variable into multiple usage variable segments. These segments may be based on sleep stages and / or other usage variables. For example, usage time usage variables may be segmented based on sleep stages, or event information usage variables may be segmented based on sealing quality usage variables.

[0149] Determining weights can include accessing predefined weights, calculating weights, or receiving weights (e.g., receiving weights from the output of a machine learning algorithm). In some cases, the determined weights can be neutral weights, such as 1.0x or a 100% weight. In some cases, the determined weights can be incremental weights, such as 1.5x or a 150% weight. In some cases, the determined weights can be decremental weights, such as 0.5x or a 50% weight.

[0150] In some cases, the weights of the use variables may be determined based on the sleep stage information from box 606 and / or other use variables from box 604. In some cases, determining the weights at box 614 may include determining a set of weights for a given use variable, such as the weights for each combination of the given use variable with the sleep stage from the sleep stage information and / or other use variables. In one instance, determining the weights of the event information use variable (e.g., a detected apnea or hypopnea event) may include: determining 1) the weights for the awake sleep stage combined with the event information use variable; 2) the weights for the event information use variable combined with the light sleep stage; 3) the weights for the event information use variable combined with the deep sleep stage; and 4) the weights for the event information use variable combined with the REM sleep stage.

[0151] In some cases, determining the weighting of a given usage variable at box 604 may include applying another usage function (e.g., a time-related usage variable) to the function. For example, the weighting of a given usage variable may be a direct or inverse proportional function of another usage variable.

[0152] In some cases, determining weighted values ​​may include accessing a database of weighted values. Accessing such a database may involve using user-associated information (e.g., physiological and / or demographic information) to select one or more weighted values ​​from the database. For example, user-associated information may be used to determine the user's ethnic group (e.g., based on age range, gender, geographic location, etc.), and then one or more weighted values ​​associated with that determined ethnic group may be selected. In some cases, health information (e.g., professional diagnoses, self-reported diagnoses, and / or health-related measurements) may be used to determine one or more weighted values.

[0153] Applying weighted values ​​at box 616 may include applying one or more weighted values ​​to one or more usage variables and / or sleep stage information. Applying weighted values ​​may include using the weighted values ​​to calculate component scores for usage variables and / or calculate sub-component scores for segmented usage variables. In some cases, applying weighted values ​​may include multiplying the weighted values ​​by the usage variable (or segmented usage variable or other such value). In some cases, applying weighted values ​​at box 616 may include applying multiple weighted values ​​to a given usage variable or usage variable segment. For example, a usage variable segment that is a usage time segment during REM sleep may be applied with a first weighted value, which is a specifically calculated and / or selected weighted value for the usage time segment during REM sleep, and a second weighted value, which is a globally calculated and / or selected weighted value for the usage variable and / or sleep stage. For example, the first weighted value may be based on a preset weighted value, and the second weighted value may be based on user information.

[0154] In some cases, the calculation of the sleep performance score at box 612 can be performed in other ways while using the determined usage variables from box 604 and the sleep stage information from box 606.

[0155] At box 618, the sleep performance score can be presented to a user, caregiver, or other entity, such as a respiratory therapy system. Presenting the sleep performance score can include presenting it in an easily understandable manner, such as numbers (e.g., numbers from 0 to 100), percentages (e.g., percentages from 0% to 100%), color-coded indicators, graphical indicators (e.g., bars or circles filled according to the sleep performance score), or other such methods.

[0156] In some cases, presenting the sleep performance score at box 618 may also include presenting additional information, such as by default and / or upon receiving a trigger action (e.g., pressing a button). 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 graph 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 indication of how much a component score or sub-component score contributes to the sleep performance score. In some cases, the additional information may include recommendations for adjustments to the respiratory therapy system to improve the sleep performance score. For example, the recommendation may include instructions to replace the user interface or adjust settings on the respiratory therapy device. In some cases, the additional information may include trend data indicating a trend in the sleep performance score for a given sleep period and the number of previous sleep periods.

[0157] In some optional cases, out-of-range usage variables can be identified at box 608. Identifying out-of-range usage variables can be based on sensor data received from box 602. At box 604, identifying out-of-range usage variables can be separate from and / or part of identifying usage variables, and can include identifying values ​​of a given usage variable that exceed a threshold range (e.g., below a threshold level, above a threshold level, and / or between a lower threshold level and an upper threshold level).

[0158] At optional box 610, an out-of-range use variable can be identified as a tolerable use variable based on the sleep performance score calculated from box 612 and the out-of-range use variable identified from box 608. An out-of-range use variable can be identified as a tolerable use variable while the sleep performance score remains above a threshold. Therefore, even though a given use variable is outside the expected range, the sleep performance score still indicates a good sleep period with respiratory therapy (e.g., a sleep period with high-quality sleep and / or a sleep period with effective and / or efficient use of respiratory therapy). In some cases, identifying an out-of-range use variable as a tolerable use variable at box 610 may also include presenting the out-of-range use variable as a tolerable use variable (e.g., presenting an indication that the given use variable is well-tolerated).

[0159] In some cases, once a use variable is identified as a tolerable use variable, further examples of determining weights at box 614 may include determining adjusted weights for any use variable identified as a tolerable use variable. The adjusted weights may not emphasize the impact of the tolerable use variable on the sleep performance score. For example, if the user tolerates a significant decrease in seal quality well, further calculation of the sleep performance score may apply a lower weight to the seal quality variable.

[0160] The boxes in process 600 can be executed in any suitable order, including performing some boxes simultaneously. For example, calculating the sleep performance score at box 612 can occur simultaneously with determining the out-of-range usage variables. In another instance, determining sleep stage information can occur after determining the usage variables. Additionally, while process 600 is described using certain boxes, one, some, or all of the boxes in process 600 can be removed and / or replaced with other boxes. Additionally, in some cases, process 600 may include Figure 6 Additional boxes not depicted herein. For example, in some cases, calculating the sleep performance score at box 612 may also include determining the sleep quality score, as disclosed in further detail herein.

[0161] Figure 7 This is a flowchart depicting a process 700 for scoring sleep performance using adaptation stages, according to certain aspects of this disclosure. Process 700 can be performed by, for example... Figure 1 Any suitable system execution of System 100, including by Figure 1 The process 700 is executed by the processor 112 of the control system 110. One, some, or all of the frames of process 700 may occur during a sleep period (e.g., calculating a sleep performance score for a given sleep period or a subsequent sleep period), immediately after the sleep period, or at another time. In some cases, process 700 is executed by a processor such as... Figure 1The user equipment 170 is used to execute the process. In some cases, some or all of the process 700 may be used as referenced. Figure 6 The calculation of the sleep performance score is part of the process described in box 612.

[0162] Process 700 involves identifying an adaptation phase at box 708, using the adaptation phase to calculate a sleep performance score at box 710, and / or presenting the adaptation phase at box 708. Each adaptation phase may be modified to otherwise calculate the sleep performance score and / or otherwise encourage the user to achieve a certain goal. Each adaptation phase may be a phase with a different purpose. For example, an early adaptation phase may be designed to encourage the user to fall asleep while using the therapy; an intermediate adaptation phase may be designed to encourage the user to sleep longer while using the therapy; a late adaptation phase may be designed to encourage the user to achieve good sleep overall while using the therapy; and a maintenance adaptation phase may be designed to encourage the user to maintain good sleep overall while using the therapy.

[0163] Possible adaptation phases can be established sequentially, such as starting with an early adaptation phase, moving to an intermediate adaptation phase, then to a late adaptation phase, and finally to a maintenance adaptation phase. For descriptive purposes, adaptation phases can be described vertically, starting with the early adaptation phase at the bottom and moving upwards until reaching the maintenance adaptation phase at the top. Any number of adaptation phases can be used, such as two, three, four, or more. In some cases, non-sequential adaptation phases can be used. For example, a set of possible adaptation phases may include starting with an early adaptation phase and ending with a maintenance adaptation phase, but with many different, potentially intermediate adaptation phases that can be used depending on the user's situation. In such an instance, the user could begin in the early adaptation phase, move to the intermediate adaptation phase of treatment time, then to the intermediate adaptation phase of total sleep time, then to the late adaptation phase, and finally to the maintenance adaptation phase. While it is expected that users move sequentially through the adaptation phases, in some cases, it may be possible for the user to move backwards to a previous adaptation phase, such as if certain usage variables and / or sleep stage information indicate that the user's sleep is deteriorating or not improving sufficiently, or that the user is not engaging in treatment.

[0164] In some cases, determining the adaptation phase at box 702 may include using information received at box 702. At box 702, one or more usage variables and / or sleep phase information are received. Receiving usage variables may include determining usage variables, such as reference variables. Figure 6 As described in box 604. Receiving sleep stage information may include determining sleep stage information, such as references Figure 6As described in box 606. Using the information received at box 702, the adaptation phase can be determined at box 708 based at least in part on usage variables and / or sleep stage information. For example, in some cases, the adaptation phase can be based on whether the user achieves a sleep latency equal to or less than a threshold time. Users achieving longer sleep latencies can be placed in an earlier adaptation phase until they are able to achieve shorter sleep latencies. Any usage variable information and / or sleep stage information can be used to determine the adaptation phase. In some cases, the determination of the adaptation phase can be based on achieving one or more expected thresholds for one or more usage variables within a threshold duration (e.g., achieving an average leakage flow below a threshold for at least 120 minutes or for at least 50% of sleep time).

[0165] In some cases, determining the adaptation stage at box 708 is based at least in part on historical usage variables and / or historical sleep stage information accessed at box 704. This historical data received at box 704 may be usage variables and / or sleep stage information associated with one or more sleep stages prior to the current sleep stage, such as historical data associated with a past set number of days (e.g., the past 7 days or the past 30 days), the number of past sleep stages, etc., during which treatment was used. By analyzing the historical usage variables and / or historical sleep stage information (e.g., identifying one or more usage variables or sleep stages that meet or exceed a threshold duration), a determination can be made as to which adaptation stage to use. For example, if the number of sleep stages with available data is less than a threshold number (e.g., data from only two nights is available), a default adaptation stage (e.g., an early adaptation stage) may be used. If historical data shows that the user has achieved certain qualifying sleep achievements, such as a sleep latency of 30 minutes or less for at least three consecutive days, the adaptation stage may be determined as a different adaptation stage (e.g., an intermediate adaptation stage). Similarly, if a user’s sleep latency is shown to be equal to or greater than 30 minutes for at least three consecutive days, the adaptation phase can be identified as a different adaptation phase that emphasizes sleep latency (e.g., the early adaptation phase).

[0166] In some cases, determining the adaptation stage at box 708 may be based at least in part on one or more historical adaptation stages received at box 706. Receiving historical adaptation stages at box 706 may include receiving the current adaptation stage (e.g., the last determined adaptation stage for a user). Based on the current and / or historical data received at boxes 702 and / or 704, a determination may be made at box 708 to maintain the current adaptation stage or move the user to a new adaptation stage (e.g., moving up from an intermediate adaptation stage to a later adaptation stage, or moving down from an intermediate adaptation stage to an earlier adaptation stage). Thus, in some cases, determining adaptation stage 708 may include: i) using a default (e.g., initial) 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 the previous adaptation stage in sequence.

[0167] In some cases, defining an adaptation phase at box 708 may include determining an adaptation score. The adaptation score may be based on one or more expected values ​​of one or more usage variables and / or specific sleep stage information. The adaptation score may increase as the user approaches the expected value. Once the user achieves or exceeds the expected value, the adaptation score may meet or exceed a threshold score, which indicates that the next adaptation phase should be performed sequentially. In some cases, each adaptation phase includes its own set of expected values ​​of one or more usage variables and / or certain sleep stage information. For example, in an early adaptation phase, the adaptation score may be based on the user's sleep latency and average leak flow. The adaptation score may increase as sleep latency and average leak flow decrease. Once the user achieves a sufficiently low sleep latency (e.g., less than 30 minutes) and a sufficiently low average leak flow (e.g., no leak or at most an acceptable leak level), the adaptation score may meet or exceed a threshold score necessary to move to a new adaptation phase (e.g., to an intermediate adaptation phase). In an intermediate adaptation phase, the adaptation score may be based on the user's sleep latency and total sleep time, as well as the treatment time. In the later adaptation phase, the adaptation score can be based on the user's sleep latency, total sleep time, time spent in different sleep stages, heart rate / respiratory rate during sleep, and other treatment-related usage variables. In the maintenance adaptation phase, the adaptation score can be based on the same or similar usage variables and sleep stage information as in the later adaptation phase, but with different weightings for the usage variables and sleep stage information.

[0168] In some cases, at box 710, the determined adaptation stage is used in the calculation of the sleep performance score. Besides using the adaptation stage, the calculation of the sleep performance score at box 710 can be combined with... Figure 6The sleep performance score calculated at box 612 is the same or similar. In some cases, calculating the sleep performance score may include modifying the sleep performance score based on the determined adaptation stage from box 708, such as by directly modifying the score based on the adaptation score or by modifying the weighting value used for the sleep performance score based on the determined adaptation stage.

[0169] For example, at box 712, a set of weighted values ​​can be determined at least in part based on the identified adaptation stage. Because each identified adaptation stage can emphasize different aspects of sleep and / or sleep therapy, different adaptation stages can have different associated sets of weighted values. For example, an early adaptation stage can be associated with a first set of weighted values ​​emphasizing sleep latency and / or average leak flow; an intermediate adaptation stage can be associated with a second set of weighted values ​​emphasizing sleep latency, total sleep time, and treatment time; a late adaptation stage can be associated with a third set of weighted values ​​emphasizing sleep latency, total sleep time, time in one or more selected sleep stages (e.g., time in REM sleep and time in deep sleep), heart rate, respiratory rate, and / or other used variables; and a maintenance adaptation stage can be associated with a fourth set of weighted values ​​designed to encourage maintaining sleep quality scores at or above a threshold sleep quality score. Determining weighted values ​​at box 712 can also be considered in relation to determining… Figure 6 The weighted aspect associated with box 614.

[0170] After determining the weighting value at box 712, the weighting value can be applied at box 714 to calculate the sleep performance score. Applying the weighting value at box 714 can be compared with... Figure 6 The same or similar weighted values ​​are applied at box 616.

[0171] Therefore, in some cases, each adaptation phase can affect how sleep performance scores are calculated when a user is in that adaptation phase.

[0172] Additionally, or instead of calculating a sleep performance score at box 710, adaptation stage information may be presented at box 716, such as by presenting it to the user or a third party monitoring the user (e.g., a caregiver or healthcare provider). Presenting adaptation stage information may include i) presenting which adaptation stage the user is in (e.g., “You are in the middle adaptation stage!”); ii) presenting an adaptation score (e.g., “78%” or “78 out of 100” or “78”); iii) presenting recommendations associated with the adaptation stage (e.g., “Try using your treatment for as long as possible tonight” for an early adaptation stage and “You are doing well; don’t forget to clean the tubing weekly” for a later adaptation stage or maintaining the adaptation stage); iv) presenting a requirement to move to the next adaptation stage (e.g., “You have fallen asleep within 30 minutes for the past five nights; after two more nights, you will move to the next stage”); or v) any combination of i through iv.

[0173] In some cases, the adaptation stage information presented in box 716 only occurs when the adaptation stage identified in box 708 is different from the immediately preceding adaptation stage (e.g., "Congratulations on falling asleep while wearing your therapy device, you did a great job" when moving from an early adaptation stage to an intermediate adaptation stage, or "Your therapy device seems to have been leaking for the past few nights; please try to improve the device" when moving from an intermediate adaptation stage to an early adaptation stage).

[0174] The boxes of process 700 can be executed in any suitable order, including executing some boxes simultaneously. Additionally, while process 700 is described using certain boxes, one, some, or all of the boxes in process 700 may be removed and / or replaced with other boxes. Additionally, in some cases, process 700 may include... Figure 7 Additional boxes not depicted in the text.

[0175] Figure 8 This is a diagram 800 illustrating the progress of a user through adaptation phases according to certain aspects of this disclosure. Diagram 800 depicts four adaptation phases, including an early phase 804, an intermediate phase 806, a late phase 808, and a maintenance phase 810. The adaptation phases depicted in diagram 800 can be related to… Figure 7 The process of determining and utilizing the adaptation phase of 700.

[0176] Line 802 represents the user's current adaptation stage over time. The timeline shows the user's participation in multiple sleep periods over a multi-day process (e.g., over a 30-day or 60-day process).

[0177] On day 812, the user can begin treatment for the first time. When treatment begins for the first time, the user is automatically placed in the early stage 804.

[0178] On day 814, the user may have achieved several days of qualified sleep accomplishments associated with the early stage 804 (e.g., sleep latency of less than 30 minutes and below the threshold level or leakage), thus causing the user to move to the intermediate stage 806.

[0179] However, on day 816, the user may have achieved one or more days of poor sleep accomplishment (e.g., unacceptably high levels of leakage), causing the user to move back to the earlier stage 804. By day 818, the user has again achieved a sufficient number of days of satisfactory sleep accomplishment, moving the user to the intermediate stage 806.

[0180] On day 820, the user may have achieved several new qualifying sleep achievements associated with intermediate stage 806 (e.g., sleep latency at or below the threshold level, total sleep time above the threshold duration, and therapy time above the threshold duration), thus allowing the user to move to later stage 808.

[0181] On day 822, the user may have achieved several new qualifying sleep accomplishments associated with later stage 808, allowing the user to move to maintenance stage 810. After this, the user can remain in maintenance stage 810. In some cases, the use of the adaptation stage may cease entirely after the user has remained in maintenance stage 810 for a threshold duration.

[0182] In some cases, a user may regress to a previous adaptation phase, such as as depicted on day 816 with the movement from intermediate phase 806 to early phase 804; however, this is not always the case. In some cases, adaptation phases may be established to proceed only sequentially. In such cases, even with poor sleep performance, a user may remain in the same phase until eligible to proceed to the next phase (e.g., a user may remain in intermediate phase 806 from day 814 to day 816 and day 818 until eligible to enter late phase 808 on day 820).

[0183] The foregoing description of the embodiments, including the illustrated embodiments, is for illustrative and descriptive purposes only and is not intended to be exhaustive or limited to the precise forms disclosed. Many modifications, adaptations, and uses will be apparent to those skilled in the art. Various changes can be made to the disclosed embodiments without departing from the spirit or scope of this disclosure. Therefore, the breadth and scope of this disclosure should not be limited to any of the foregoing embodiments.

[0184] While certain aspects of this disclosure have been illustrated and described with respect to one or more embodiments, equivalent substitutions and modifications will be conceived or known by those skilled in the art upon reading and understanding this specification and the accompanying drawings. Furthermore, while a particular feature of one aspect of this disclosure may be disclosed with respect to only one of several embodiments, such feature may be combined with one or more other features of other embodiments, which may be desirable and advantageous for any given or particular application.

[0185] One or more elements, aspects or steps, or any part thereof, from any one or more of claims 1 to 35 may be combined with one or more elements, aspects or steps, or any part thereof, or combinations thereof, from any one or more of other claims 1 to 35 to form one or more additional embodiments and / or claims of this disclosure.

Claims

1. A method for scoring sleep performance, comprising: Sensor data is received from one or more sensors, and the sensor data is associated with the sleep periods of users using the respiratory therapy system; Determine one or more usage variables associated with the use of the respiratory therapy system from the received sensor data; From the received sensor data, sleep stage information associated with the sleep period is determined, wherein the sleep stage information indicates the duration spent in multiple sleep stages; and A sleep performance score for the sleep period is calculated at least in part based on one or more determined usage variables and the sleep stage information, wherein calculating the sleep performance score for each of the one or more usage variables includes: The determined usage variables are segmented into multiple segments based at least in part on the sleep stage information, wherein each of the multiple segments is associated with one of the multiple sleep stages; Determine the weighted values ​​of the usage variables for each of the plurality of sleep stages; and The weighted values ​​of the usage variables associated with the corresponding sleep stages are applied to each of the plurality of segments, the corresponding sleep stages being associated with the corresponding segments.

2. The method according to claim 1, wherein the one or more variables used include: i) Indicates the duration of use of the respiratory therapy system during the sleep period; ii) A seal quality variable indicating the seal quality between the user and the user interface of the respiratory therapy system during use of the respiratory therapy system; iii) Event information indicating the number of events detected that occurred during the sleep period; iv) User interface compliance information associated with the number of detected user interface transition events in which the user interface was put on or removed during the sleep period; or any combination of v)i to iv.

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

4. The method according to any one of claims 1 to 3, wherein calculating the sleep performance score comprises: The weighting value for each of the more than one used variables is determined at least in part based on the sleep stage information; as well as The weighted value associated with the usage variable is applied to each of the more than one usage variables.

5. The method of claim 1, wherein the one or more usage variables include usage time indicating the duration of use of the respiratory therapy system during the sleep period.

6. The method of claim 1, wherein the one or more usage variables include a seal quality variable indicating the seal quality between the user and the user interface of the respiratory therapy system during use of the respiratory therapy system.

7. The method of claim 1, wherein the one or more variables used include event information indicating the number of events detected occurring during the sleep period.

8. The method of claim 1, wherein the one or more usage variables include user interface compliance information associated with the number of detected user interface transition events in which the user interface is put on or removed during the sleep period.

9. The method according to any one of claims 1 to 3, further comprising determining a sleep quality score associated with the sleep period, wherein determining the sleep quality score is based at least in part on the sleep stage information.

10. The method of claim 9, wherein the sleep stage information indicates the duration spent in a plurality of physiological sleep stages, and wherein determining the sleep quality score comprises: The sleep stage information is segmented into multiple sleep stage segments based at least in part on one or more of the aforementioned variables. Determine the weighted values ​​for each of the plurality of sleep stage segments; Within each of the plurality of sleep stage segments, a corresponding usage weighting value for the corresponding sleep stage segment is applied to each physiological sleep stage within the corresponding sleep stage segment.

11. The method of claim 9, wherein receiving the sensor data includes receiving physiological data associated with the user, and wherein determining the sleep quality score is further based at least in part on the received physiological data.

12. The method of claim 11, wherein the physiological data includes: i) Respiratory rate; ii) Heart rate; iii) Heart rate variability; iv) Motion data; v) Electroencephalogram (EEG) data; vi) Blood oxygen saturation data; vii) Respiratory rate variability; viii) Respiratory depth; ix) Tidal volume data; x) Inspiratory amplitude data; xi) Expiratory amplitude data; xii) Inspiratory volume data; xiii) Expiratory volume data; xiv) Inspiratory-expiratory ratio data; xv) Sweating data; xvi) Temperature data; xvii) Pulse wave transit time data; xviii) Blood pressure data; xix) Location data; xx) Postural data; xxi) Blood glucose level data; or xxii) Any combination of i to xxi.

13. The method of claim 9, wherein the sleep performance score for the sleep period is calculated at least in part based on the sleep quality score.

14. The method of claim 13, wherein calculating the sleep performance score based at least in part on the sleep quality score comprises applying one or more weights to the determined one or more usage variables based at least in part on the sleep quality score.

15. The method of claim 9, further comprising receiving user feedback associated with the sleep period, wherein calculating the sleep performance score based at least in part on the sleep quality score includes applying more than one weighting to the sleep quality score based at least in part on the user feedback.

16. The method of any one of claims 1 to 3, further comprising receiving user feedback associated with the sleep period, wherein calculating the sleep performance score based at least in part on the determined one or more usage variables includes applying one or more weights to the determined one or more usage variables based at least in part on the user feedback.

17. The method according to any one of claims 1 to 3, further comprising: Receive user feedback associated with the sleep period; The modified values ​​are determined at least in part based on the user feedback received; as well as The sleep performance score is updated by incorporating the modified value into the sleep performance score.

18. The method of any one of claims 1 to 3, wherein the one or more usage variables comprises at least a first usage variable and a second usage variable, wherein calculating the sleep performance score based at least in part on the determined one or more usage variables comprises at least in part applying a weighted average to the first usage variable based on the second usage variable.

19. The method of claim 18, wherein applying the weighting to the first usage variable based at least in part on the second usage variable comprises: Identify multiple ranges associated with the second used variable; The first variable is divided into a plurality of first use variable segments at least in part based on the second use variable, wherein each of the plurality of first use variable segments is associated with one of the plurality of ranges associated with the second use variable; Determine the weighting value for each of the plurality of ranges; as well as The weighted value associated with a corresponding range of the plurality of ranges and the corresponding first used variable segment is applied to each of the plurality of first used variable segments.

20. The method according to any one of claims 1 to 3, wherein the one or more variables used include: i) The average leakage flow rate during the sleep period; ii) The number of treatment sub-segments within the sleep period; iii) Average user interface stress during the sleep period; iv) A statistical summary of the different one of the more than one used variables; or v) Any combination of i to iv.

21. The method according to any one of claims 1 to 3, wherein the one or more used variables include event information indicating the number of events detected occurring during the sleep period, wherein calculating the sleep performance score based at least in part on the determined one or more used variables and the sleep stage information comprises: The user is not asleep during the sleep period, at least in part, based on the sleep stage information. as well as Remove any detected events that occurred while the user was not asleep from the event information.

22. The method according to any one of claims 1 to 3, further comprising: Identify out-of-range usage variables from the one or more usage variables, wherein the out-of-range usage variables are outside the expected threshold range; 4 The sleep performance score is identified as being higher than the sleep performance threshold; and Presenting identified out-of-range usage variables is an indication of tolerable usage variables.

23. The method according to any one of claims 1 to 3, further comprising presenting the sleep performance score after the sleep period has ended.

24. The method of claim 23, wherein the sleep stage information indicates the duration spent in a plurality of sleep stages, and wherein presenting the sleep performance score includes presenting the total contribution of each of the more than one use variable to the sleep performance, wherein presenting the total contribution of a given use variable among the more than one use variables includes presenting a plurality of sub-contributions of the given use variable binned by sleep stages.

25. The method according to any one of claims 1 to 3, wherein calculating the sleep performance score for the sleep period comprises calculating only the portion of the sleep period consistent with the use of the respiratory therapy system.

26. The method according to any one of claims 1 to 3, further comprising: Determine the adaptation phase associated with the sleep period; The weighting value for each of the more than one used variables is determined at least in part based on the adaptation phase; as well as For each of the more than one used variables, the weighted value associated with the used variable is applied.

27. The method of claim 26, wherein determining the adaptation phase comprises: Access i) one or more historical usage variables associated with one or more historical sleep periods of the user; ii) historical sleep stage information associated with the one or more historical sleep periods of the user; or iii) both i) and ii); and The adaptation stage is identified at least in part based on i) one or more historical usage variables, ii) historical sleep stage information, or iii) both i) and ii).

28. The method of claim 27, further comprising accessing a historical adaptation phase associated with the user, wherein the adaptation phase is determined at least in part based on the historical adaptation phase.

29. The method of claim 26, wherein determining the adaptation phase comprises: The adaptation score is calculated at least in part based on i) one or more of the variables described, ii) the sleep stage information described, or iii) both i) and ii); as well as The adaptation score is determined to exceed a threshold score associated with the adaptation phase.

30. The method of claim 26, wherein determining the adaptation phase comprises selecting the adaptation phase from a set of possible adaptation phases, wherein the set of possible adaptation phases includes: i) Early adaptation phase, where the weighted values ​​are the first set of weighted values ​​that emphasize sleep latency; ii) intermediate adaptation phase, wherein the weighted values ​​are a second set of weighted values ​​emphasizing total sleep time; iii) late adaptation phase, wherein the weighted values ​​are a third set of weighted values ​​emphasizing the duration of more than one sleep phase; or iv) any combination of i to iii.

31. The method of claim 30, wherein the set of possible adaptation phases further comprises a maintenance adaptation phase, wherein the weighting values ​​are a fourth set of weighting values ​​associated with maintaining the sleep quality score at or above a threshold sleep quality score.

32. A system comprising: A control system, wherein the control system includes one or more processors; as well as It contains a memory that stores machine-readable instructions; The control system is coupled to the memory, and the method according to any one of claims 1 to 31 is implemented when machine-executable instructions in the memory are executed by at least one of the more than one processors of the control system.

33. A system for scoring sleep performance, the system comprising a control system configured to implement the method according to any one of claims 1 to 31.

34. A computer program product comprising instructions that, when executed by a computer, cause the computer to perform the method according to any one of claims 1 to 31.

35. The computer program product of claim 34, wherein the computer program product is a non-transitory computer-readable medium.

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