System and method for analyzing sleep-related parameters

Abstract

A method includes receiving first data associated with a first sleep session of a user (601). The method also includes determining a first set of sleep-related parameters associated with the first sleep session of the user based at least in part on the first data (602). The method also includes receiving second data associated with a second sleep session of the user (605). The method also includes determining a second set of sleep-related parameters associated with the second sleep session of the user based at least in part on the second data (606). The method also includes causing one or more indications associated with a variable condition and the first sleep session, the second sleep session, or both to be communicated to the user (604, 609).

Description

[0001] Cross-reference to related applications

[0002] This application claims the benefit and priority of U.S. Provisional Patent Application No. 63 / 012,869, filed April 20, 2020, and U.S. Provisional Patent Application No. 63 / 151,507, filed February 19, 2021, both of which are incorporated herein by reference. Technical Field

[0003] This disclosure generally relates to systems and methods for determining one or more sleep-related parameters for multiple sleep periods, and more specifically, to systems and methods for comparing one or more sleep-related parameters associated with a first sleep period and one or more sleep-related parameters associated with a second sleep period. 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), respiratory effort-related arousal (RERA), central sleep apnea (CSA), Cheyne-Stokes respiration (CSR), respiratory insufficiency, hyperventilation syndrome (OHS), chronic obstructive pulmonary disease (COPD), neuromuscular disease (NMD), and chest wall disorders. Breathing therapy systems are commonly used to treat these disorders. However, some users find such systems uncomfortable, difficult to use, expensive, aesthetically unappealing, and / or fail to perceive the benefits associated with using the system. As a result, some users discontinue the use of breathing therapy systems without encouraging or confirming that the system is improving their sleep quality and reducing the symptoms of these disorders. This disclosure aims to address these and other issues. Summary of the Invention

[0005] According to some implementations of this disclosure, a method includes receiving first data associated with a user's first sleep period. The method further includes determining a first set of sleep-related parameters associated with the user's first sleep period, at least in part, based on the first data. The method also includes receiving second data associated with a user's second sleep period. The method further includes determining a second set of sleep-related parameters associated with the user's second sleep period, at least in part, based on the second data. The method also includes receiving third data associated with variable conditions. The method further includes transmitting one or more indications associated with the variable conditions and the first sleep period, the second sleep period, or both, to the user.

[0006] According to some implementations of this disclosure, a method includes receiving physiological data associated with a user. The method further includes determining, at least in part, (i) a first implementation associated with the user, (ii) a level of drowsiness associated with the user, or both (i) and (ii) based on the physiological data. The method also includes transmitting prompts for interaction with a treatment system to the user, at least in part based on an emotion rating, a level of drowsiness, or both.

[0007] According to some implementations of this disclosure, a method includes receiving first data associated with a first sleep period of a user from one or more sensors, the first data including (i) first respiratory data associated with the user, (ii) first audio data reproducible as one or more sounds recorded during the first sleep period, or (iii) both of (i) and (ii), wherein the user does not use a respiratory therapy system during the first sleep period. The method also includes determining a first set of sleep-related parameters associated with the user's first sleep period based at least in part on the first data. The method further includes receiving second data associated with a second sleep period of the user from the one or more sensors, the second data including (i) second respiratory data associated with the user, (ii) second audio data reproducible as one or more sounds recorded during the second sleep period, or (iii) both of (i) and (ii), wherein the user uses a respiratory therapy system during at least a portion of the second sleep period. The method also includes determining a second set of sleep-related parameters associated with the user's second sleep period based at least in part on the second data. The method further includes transmitting one or more indications associated with the first sleep period, the second sleep period, or both, to the user via a user device after the second sleep period.

[0008] According to some implementations of this disclosure, a system includes a respiratory therapy system, a memory storing machine-readable instructions, and a control system. The respiratory therapy system includes a breathing apparatus configured to supply pressurized air and a user interface connected to the breathing apparatus via a conduit, the user interface being configured to engage a user and assist in directing the supplied pressurized air to the user's airway. The control system includes one or more processors configured to execute the machine-readable instructions to receive first data generated by one or more sensors and associated with a first sleep period of the user, wherein the user actually used the respiratory therapy system during the first sleep period. The control system is further configured to determine, at least partially, a first set of sleep-related parameters associated with the first sleep period for the user based on the first data. The control system is further configured to receive, from the one or more sensors, second data associated with a second sleep period of the user, wherein the user interface of the respiratory therapy system engages with the user during at least a portion of the second sleep period. The control system is further configured to determine, at least partially, a second set of sleep-related parameters associated with the second sleep period for the user based on the second data. The control system is further configured to cause one or more indications associated with the first sleep period, the second sleep period, or both to be transmitted to the user via the display of the user device after the second sleep period.

[0009] According to some implementations of this disclosure, a method includes receiving first data associated with a first sleep period of a user from one or more sensors, the first data including (i) first respiratory data associated with the user, (ii) first audio data reproducible as one or more sounds recorded during the first sleep period, or (iii) both of (i) and (ii), wherein the user did not use a respiratory therapy system during the first sleep period. The method further includes: determining, at least in part, a first set of sleep-related parameters associated with the first sleep period of the user, the first set of sleep-related parameters including a first apnea-hypopnea index (AHI) for the first sleep period; and determining, at least in part, a first sleep state for the first sleep period based on the first AHI. The method further includes receiving, from the one or more sensors, second data associated with a second sleep period of the user, the second data including (i) second respiratory data associated with the user, (ii) second audio data reproducible as one or more sounds recorded during the second sleep period, or (iii) both of (i) and (ii), wherein the user used a respiratory therapy system during at least a portion of the second sleep period. The method further includes: determining, at least in part, a second set of sleep-related parameters associated with the second sleep period of the user, the second set of sleep-related parameters including a second AHI for the second sleep period; determining, at least in part, a first sleep condition for the second sleep period based on the second AHI; and causing one or more indications of (i) the first sleep condition, (ii) the second sleep condition, or (iii) both of (i) and (ii) to be transmitted to the user via a user device after the first sleep period.

[0010] The above overview is not intended to represent every implementation or aspect of this disclosure. Additional features and benefits of this disclosure will become apparent from the detailed description and accompanying drawings set forth below. Attached Figure Description

[0011] Figure 1 This is a functional block diagram of a system based on some implementation methods of this disclosure;

[0012] Figure 2 This is based on some implementation methods of this disclosure. Figure 1 A perspective view of at least a portion of the system, users, and bed partners;

[0013] Figure 3 The illustration shows an exemplary timeline of sleep periods according to some implementations of this disclosure;

[0014] Figure 4 The diagram illustrates some implementations of this disclosure. Figure 3 An exemplary sleep graph associated with sleep periods;

[0015] Figure 5A The illustration shows an exemplary settings view for manually initiating a sleep period according to some implementations of this disclosure;

[0016] Figure 5B An exemplary sleep view for manually terminating a sleep period is illustrated according to some implementations of this disclosure;

[0017] Figure 5C The illustration shows an exemplary alarm view for terminating a sleep period according to some implementations of this disclosure;

[0018] Figure 6 This is a flowchart illustrating a method for comparing a first sleep period and a second sleep period according to some implementations of this disclosure.

[0019] Figure 7A The illustration shows a first cue for providing subjective feedback related to sleep periods, according to some implementations of this disclosure;

[0020] Figure 7B The illustration shows a second cue for providing subjective feedback associated with a first sleep period, according to some implementations of this disclosure;

[0021] Figure 7C The illustration shows a third cue for providing subjective feedback associated with a first sleep period, according to some implementations of this disclosure;

[0022] Figure 7D The illustration shows a fourth cue for providing subjective feedback associated with a first sleep period, according to some implementations of this disclosure;

[0023] Figure 8A The illustration shows a first plurality of instructions associated with a first sleep period according to some implementations of this disclosure;

[0024] Figure 8B The illustration shows a plurality of second instructions associated with a first sleep period according to some implementations of this disclosure;

[0025] Figure 8C The illustration shows a third plurality of instructions associated with a first sleep period according to some implementations of this disclosure;

[0026] Figure 9A The illustration shows prompts for providing subjective feedback associated with the use of a respiratory therapy system during a second sleep period, according to some implementations of this disclosure;

[0027] Figure 9B The illustration shows a plurality of first indications associated with a second sleep period according to some implementations of this disclosure;

[0028] Figure 9C The illustration shows a second plurality of instructions associated with a second sleep period according to some implementations of this disclosure;

[0029] Figure 10 The illustration shows a comparison between one or more sleep-related parameters of a second sleep period according to some implementations of this disclosure and one or more sleep-related parameters of the second sleep period;

[0030] Figure 11 This is a flowchart illustrating the process of implementing some of the methods disclosed herein;

[0031] Figure 12 This is a flowchart illustrating the process of implementing some of the methods disclosed herein;

[0032] Figure 13A This is a first sleep marker view according to some implementations of this disclosure;

[0033] Figure 13B This is a second sleep marker view according to some implementations of this disclosure;

[0034] Figure 13C This is a third sleep marker view based on some implementations of this disclosure;

[0035] Figure 14 This is an activity report view based on some implementations of this disclosure;

[0036] Figure 15 This is a sleep dashboard based on some implementations of this disclosure;

[0037] Figure 16A This is a first trend view based on some implementations of this disclosure; and

[0038] Figure 16B This is a second trend view based on some implementations of this disclosure.

[0039] While this disclosure allows for various modifications and alternatives, specific implementations and embodiments thereof have been illustrated by way of example in the accompanying drawings and will be described in detail herein. However, it should be understood that this is not intended to limit this disclosure to the specific forms disclosed, but rather, this disclosure is intended to cover all modifications, equivalents, and substitutions falling within the spirit and scope of this disclosure as defined by the appended claims. Detailed Implementation

[0040] Many individuals suffer from sleep-related and / or breathing disorders. Examples of sleep-related and / or breathing disorders include periodic limb movement disorder (PLMD), restless legs syndrome (RLS), sleep-disordered breathing (SDB), obstructive sleep apnea (OSA), central sleep apnea (CSA) and other types of apnea, such as mixed apnea and hypoventilation, effort-related arousal (RERA), Cheyne-Stokes respiration (CSR), respiratory insufficiency, hyperventilation syndrome (OHS), chronic obstructive pulmonary disease (COPD), neuromuscular disease (NMD), and chest wall disorders.

[0041] Obstructive sleep apnea (OSA) is a form of sleep-disordered breathing (SDB) characterized by obstruction or blockage of the upper airway during sleep, resulting from a combination of abnormally small upper airway and loss of normal muscle tone in the areas of the tongue, soft palate, and posterior oropharyngeal walls. More generally, apnea generally refers to the cessation of breathing caused by air obstruction (obstructive sleep apnea) or cessation of respiratory function (often called central sleep apnea). Other types of apnea include hypoventilation, hyperventilation, and hypercapnia. Hypoventilation is typically characterized by slow or shallow breathing caused by a narrowed airway, rather than airway obstruction. Hyperventilation is typically characterized by an increased depth and / or rate of breathing. Hypercapnia is typically characterized by an excess of carbon dioxide in the bloodstream and is usually caused by hypoventilation.

[0042] Cheyne-Stokes respiration (CSR) is another form of sleep-disordered breathing. CSR is a dysregulation of the patient's respiratory controller, in which there is a rhythmic alternation of waxing and waning ventilation called the CSR cycle. CSR is characterized by repetitive hypoxia and reoxygenation of arterial blood.

[0043] Obesity hyperventilation syndrome (OHS) is defined as a combination of severe obesity and waking chronic hypercapnia in the absence of other known causes of hypoventilation. Symptoms include dyspnea, morning headache, and excessive daytime sleepiness.

[0044] Chronic obstructive pulmonary disease (COPD) includes any of the lower airway diseases that share certain common characteristics, such as increased resistance to air movement, prolonged expiratory phase of breathing, and loss of normal lung elasticity.

[0045] Neuromuscular diseases (NMDs) encompass a wide range of conditions and ailments that impair muscle function directly through intrinsic muscle pathology or indirectly through neuropathology. Chest wall disorders are a group of chest wall deformities that result in inefficient connection between the respiratory muscles and the thorax.

[0046] A respiratory effort-related awakening (RERA) event is typically characterized by an increased respiratory effort lasting ten seconds or longer, resulting in an awakening from sleep, and does not meet the criteria for apnea or hypopnea events. RERA is defined as a respiratory sequence characterized by increased respiratory effort leading to a sleep awakening, but not meeting the criteria for apnea or hypopnea. These events must meet two criteria: (1) a gradually increasing pattern of negative esophageal pressure, culminating in a sudden change in pressure to a lower negative level and termination of the awakening, and (2) the event lasting ten seconds or longer. In some implementations, a nasal cannula / pressure transducer system is sufficient and reliable for detecting RERA. The RERA detector can be based on an actual flow signal derived from a respiratory therapy device. For example, a flow restriction measurement can be determined based on the flow signal. The awakening measurement can then be derived from the flow restriction measurement and the measurement of a sudden increase in ventilation. One such method is described in International Patent Publication No. 2008 / 138040, assigned to ResMed, and U.S. Patent Publication No. 2011 / 0203588, the disclosures of which are incorporated herein by reference in their entirety.

[0047] These and other sleep-related disorders are characterized by specific events that occur during an individual's sleep (e.g., snoring, sleep apnea, insomnia, restless legs, sleep disturbances, apnea, increased heart rate, difficulty breathing, asthma attacks, seizures, epileptic seizures, or any combination thereof). While these other sleep-related disorders can have symptoms similar to insomnia, distinguishing them from insomnia can be used to tailor effective treatment plans that may require different treatments. For example, fatigue is often characteristic of insomnia, while excessive daytime sleepiness is characteristic of other disorders (e.g., OSA) and reflects a physiological tendency to fall asleep involuntarily.

[0048] The Apnea-Hypopnea Index (AHI) is an index used to indicate the severity of sleep apnea during sleep. An AHI is calculated by dividing the number of apnea and / or hypopnea events experienced by the user during a sleep period by the total number of hours of sleep in that period. An event can be, for example, an apnea lasting at least 10 seconds. An AHI less than 5 is considered normal. An AHI greater than or equal to 5 but less than 15 is considered an indicator of light sleep apnea. An AHI greater than or equal to 15 but less than 30 is considered an indicator of moderate sleep apnea. An AHI greater than or equal to 30 is considered an indicator of severe sleep apnea. In children, an AHI greater than 1 is considered abnormal. When the AHI is normal, or when the AHI is normal or mild, sleep apnea can be considered “controlled.” The AHI can also be used in conjunction with oxygen desaturation levels to indicate the severity of obstructive sleep apnea.

[0049] Many individuals also suffer from insomnia, which is typically characterized by dissatisfaction with the quality or duration of sleep (e.g., difficulty falling asleep, frequent or prolonged awakenings after initial sleep onset, and early awakenings that fail to restore sleep). It is estimated that over 2.6 billion people worldwide experience some form of insomnia, and over 750 million have been diagnosed with insomnia disorder. In the United States, insomnia is estimated to impose a total economic burden of $107.5 billion annually, representing 13.6% of all those not employed and 4.6% of those requiring medical care due to injury or illness. Recent research also indicates that insomnia is the second most common mental disorder and a major risk factor for depression.

[0050] Comorbid insomnia refers to a type of insomnia where insomnia symptoms are at least partially caused by symptoms or complications of another physical or mental disorder (e.g., anxiety, depression, medical disorder, and / or medication use). Mixed insomnia refers to a combination of attributes of other types of insomnia (e.g., a combination of sleep attacks, sleep maintenance, and late-stage insomnia symptoms). Paradoxical insomnia refers to a discrepancy or inconsistency between a user's perceived sleep quality and their actual sleep quality.

[0051] Nighttime insomnia symptoms typically include, for example, reduced sleep quality, reduced sleep duration, sleep apnea, sleep maintenance insomnia, late-night insomnia, mixed insomnia, and / or paradoxical insomnia. Sleep onset insomnia is characterized by difficulty initiating sleep at bedtime. Sleep maintenance insomnia is characterized by frequent and / or prolonged awakenings throughout the night after the initial sleep onset. Late-night insomnia is characterized by early morning awakenings (e.g., before the target or desired wake-up time) and an inability to return to sleep.

[0052] Diurnal (e.g., daytime) insomnia symptoms include, for example, fatigue, decreased energy, impaired cognition (e.g., attention, concentration, and / or memory), difficulty functioning in academic or professional settings, and / or mood disorders. These symptoms can lead to psychological complications such as poor performance, reduced reaction time, increased risk of depression, and / or increased risk of anxiety disorders. Insomnia symptoms can also lead to physical complications such as poor immune system function, high blood pressure, increased risk of heart disease, increased risk of diabetes, weight gain, and / or obesity. Insomnia can also be classified based on its duration. For example, if insomnia symptoms have occurred for less than 3 months, they are generally considered acute or transient. Conversely, if, for example, insomnia symptoms have occurred for 3 months or longer, they are generally considered chronic or persistent. Persistent / chronic insomnia symptoms typically require a different treatment approach than acute / transient insomnia symptoms.

[0053] The mechanisms of insomnia include precipitating factors, triggering factors, and persistent factors. Precipitating factors include hyperarousal, characterized by increased physiological arousal during sleep and wakefulness. Measures of hyperarousal include, for example, increased cortisol levels, increased activity of the autonomic nervous system (e.g., as indicated by increased resting heart rate and / or changes in heart rate), increased brain activity (e.g., increased EEG frequency during sleep and / or increased number of awakenings during REM sleep), increased metabolic rate, elevated body temperature, and / or increased activity of the pituitary-adrenal axis. Influencing factors include stressful life events (e.g., those related to employment or education, relationships, etc.). Influencing factors also include excessive worry about sleep loss and its consequences, which can persist insomnia symptoms even after the removal of the influencing factors.

[0054] refer to Figure 1 The illustration shows a system 100 according to some implementations of the present disclosure. The system 100 includes a control system 110, a storage device 114, an electronic interface 119, one or more sensors 130, and one or more user devices 170. In some implementations, the system 100 may also optionally include a respiratory therapy system 120 and / or an activity tracker 180.

[0055] The control system 110 includes one or more processors 112 (hereinafter referred to as processors 112). The control system 110 is typically used to control 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 geographically dispersed from each other. The control system 110 may be coupled to and / or located within, for example, the housing of the user device 170, and / or the housing of one or more sensors 130. The control system 110 may be centralized (within one such housing) or distributed (within two or more physically different such housings). In such implementations including two or more housings containing the control system 110, such housings may be located close to and / or far from each other.

[0056] Storage device 114 stores machine-readable instructions executable by processor 112 of control system 110. Storage device 114 can be any suitable computer-readable storage device or medium, such as, for example, random or serial access storage devices, hard disk drives, solid-state drives, flash memory devices, etc. Although Figure 1A storage device 114 is shown, but system 100 may include any suitable number of storage devices 114 (e.g., one storage device, two storage devices, five storage devices, ten storage devices, etc.). Storage devices 114 may be coupled to and / or located within the housing of the respiratory therapy device 122, the housing of the user device 170, the housing of one or more sensors 130, or any combination thereof. Similar to control system 110, storage devices 114 may be centralized (within one such housing) or distributed (within two or more physically different such housings).

[0057] In some implementations, storage device 114 ( Figure 1 This system stores user profiles associated with each user. User profiles may include, for example, user-associated demographic information, user-associated biostatistics, user-associated medical information, self-reported user feedback, user-associated sleep parameters (e.g., sleep-related parameters recorded from one or more earlier sleep periods), or any combination thereof. Demographic information may include, for example, information indicating the user's age, gender, ethnicity, family history of insomnia or sleep apnea, employment status, education level, socioeconomic status, or any combination thereof. Medical information may include, for example, information indicating one or more medical conditions associated with the user, medication use, or both. Medical information data may also include Multisleep Waiting 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.

[0058] 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 storage 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, Wi-Fi 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 storage devices that are the same as or similar to processor 112 and storage device 114 described herein. In some implementations, electronic interface 119 is coupled to or integrated into user device 170. In other implementations, electronic interface 119 is coupled to or integrated with control system 110 and / or storage device 114 (e.g., in a housing).

[0059] As described above, in some implementations, system 100 may optionally include a respiratory therapy system 120 (also referred to as a respiratory therapy system). The respiratory therapy system 120 may include a respiratory pressure therapy device 122 (referred to herein as respiratory therapy 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 implementations, one or more of the control system 110, storage device 114, display device 128, sensor 130, and humidifier 129 are part of the respiratory therapy device 122. Respiratory pressure therapy refers to applying an air supply to the inlet of a user's airway at a controlled target pressure that is nominally positive relative to the atmosphere throughout the user's respiratory cycle (e.g., in contrast to negative pressure therapy such as a cannula ventilator or thoracic ventilator). The respiratory therapy 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).

[0060] The respiratory therapy device 122 is typically used to generate pressurized air to be delivered to a user (e.g., using one or more motors driving one or more compressors). In some implementations, the respiratory therapy device 122 generates a continuous, constant air pressure that is delivered to the user. In other implementations, the respiratory therapy device 122 generates two or more predetermined pressures (e.g., a first predetermined air pressure and a second predetermined air pressure). In still other implementations, the respiratory therapy device 122 is configured to generate a variety of different air pressures within a predetermined range. For example, the respiratory therapy device 122 may deliver at least about 6 cmH2O, at least about 10 cmH2O, at least about 20 cmH2O, between about 6 cmH2O and about 10 cmH2O, between about 7 cmH2O and about 12 cmH2O, etc. The respiratory therapy device 122 may also deliver pressurized air at predetermined flow rates, for example, between about -20 L / min and about 150 L / min, while maintaining positive pressure (relative to ambient pressure).

[0061] User interface 124 engages with a portion of the user's face and delivers pressurized air from respiratory therapy device 122 to the user's airway to help prevent airway narrowing and / or collapse during sleep. This can also increase the user's oxygen intake during sleep. Depending on the treatment to be applied, user interface 124 may, for example, form a seal with an area or portion of the user's face to facilitate the delivery of gas to achieve the treatment at a pressure sufficiently varied relative to ambient pressure, for example, at a positive pressure of about 10 cmH2O relative to ambient pressure. For other forms of treatment, such as oxygen delivery, the patient interface may not include a seal sufficient to facilitate the delivery of a gas supply at a positive pressure of about 10 cmH2O to the airway.

[0062] like Figure 2 As shown, in some implementations, the user interface 124 is a mask covering 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 part of the user (e.g., the face) and conformal cushioning pads (e.g., silicone, plastic, foam, etc.) to help provide an airtight seal between the user interface 124 and the user. The user interface 124 may also include one or more vents for allowing carbon dioxide and other gases exhaled by the user 210 to escape. In other implementations, the user interface 124 includes a mouthpiece (e.g., a night-time protective mouthpiece molded to conform to the user's teeth, a mandibular repositioning device (MRD), etc.).

[0063] The conduit 126 (also referred to as an air circuit or tube) allows air to flow between two components of the respiratory therapy system 120, such as the respiratory therapy device 122 and the user interface 124. In some implementations, there may be separate branches of the conduit for inhalation and exhalation. In other implementations, a single branch conduit is used for both inhalation and exhalation.

[0064] One or more of the respiratory therapy device 122, user interface 124, catheter 126, display device 128, and humidifier 129 may include one or more sensors (e.g., pressure sensors, flow sensors, or any other sensors 130 described more generally herein). These one or more sensors may be used, for example, to measure the air pressure and / or flow rate of pressurized air supplied by the respiratory therapy device 122.

[0065] Display device 128 is typically used to display images, including still images, video images, or both, and / or information about respiratory therapy device 122. For example, display device 128 may provide information about the status of respiratory therapy device 122 (e.g., whether respiratory therapy device 122 is on / off, the pressure of the air delivered by respiratory therapy device 122, the temperature of the air delivered by respiratory therapy device 122, etc.) and / or other information (e.g., sleep score or therapy score (also known as myAir™ score, such as described in WO 2016 / 061629 and U.S. Patent Publication No. 2017 / 0311879, the entire contents of which are incorporated herein by reference), current date / time, personal information of user 210). In some implementations, display device 128 acts as a human-machine interface (HMI) including a graphical user interface (GUI) configured to display images as an input interface. Display device 128 may be an LED display, an OLED display, an LCD display, etc. The input interface may be, for example, a touch screen or touch-sensitive substrate, a mouse, a keyboard, or any sensor system configured to sense input made by a human user interacting with the respiratory therapy device 122.

[0066] The humidification tank 129 is coupled to or integrated into the respiratory therapy device 122 and includes a water reservoir for humidifying pressurized air delivered from the respiratory therapy device 122. The respiratory therapy device 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 implementations, the conduit 126 may include a heating element (e.g., coupled to and / or embedded in the conduit 126) that heats the pressurized air delivered to the user.

[0067] The respiratory therapy system 120 can be used as, for example, a positive airway pressure (PAP) system, 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, a ventilator, or any combination thereof. A CPAP system delivers a predetermined pressure (e.g., determined by a sleep physician) to the user. An APAP system automatically changes the pressure delivered to the user based on, for example, breathing data associated with the user. A BPAP or VPAP system is configured to deliver a first predetermined pressure (e.g., inspiratory positive airway pressure or IPAP) and a second predetermined pressure lower than the first predetermined pressure (e.g., expiratory positive airway pressure or EPAP).

[0068] refer to Figure 2 The diagram illustrates system 100 according to some implementation methods. Figure 1 As part of the respiratory therapy system 120, the user 210 and bed partner 220 are located in bed 230 and lying on 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 respiratory therapy device 122 via conduit 126. The respiratory therapy device 122, in turn, delivers pressurized air to the user 210 via conduit 126 and user interface 124 to increase air pressure in the user 210's throat, thereby helping to prevent airway closure and / or narrowing during sleep. The respiratory therapy device 122 can be positioned such as... Figure 2 The bedside table 240 directly adjacent to the bed 230, or more generally, positioned on any surface or structure generally adjacent to the bed 230 and / or the user 210.

[0069] Refer again 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 storage device 114 or one or more other storage devices.

[0070] 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.

[0071] As described herein, system 100 can typically be used to generate data with users (e.g., Figure 1 Physiological data associated with the user of the respiratory therapy system 120 shown. 2) During sleep periods. Physiological data can be analyzed to generate one or more sleep-related parameters, which may include any parameters, measurements, etc., related to the user during sleep periods. One or more sleep-related parameters that can be determined for the user 210 during sleep periods include, for example, apnea-hypopnea index (AHI) score, sleep score, flow signal, respiratory rate, inspiratory amplitude, expiratory amplitude, inspiratory-expiratory ratio, number of events per hour, event pattern, stage, pressure setting of respiratory therapy device 122, heart rate variability, user 210's movement, temperature, EEG activity, EMG activity, arousal, snoring, choking, coughing, whistling, wheezing, or any combination thereof.

[0072] One or more sensors 130 may be used to generate, for example, physiological data, audio data, or both. The control system 110 may 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 may indicate one or more sleep states and / or sleep stages, including wakefulness, relaxed wakefulness, micro-wakefulness, rapid eye movement (REM) stage, first non-REM stage (commonly referred to as "N1"), second non-REM stage (commonly referred to as "N2"), third non-REM stage (commonly referred to as "N3"), or any combination thereof. Methods for determining sleep state and / or sleep stage based on physiological data generated by one or more sensors (such as one or more sensors 130) are described, for example, in International Patent Publication No. WO2014 / 047310, U.S. Patent Publication No. 2015 / 0230750, U.S. Patent Publication No. 2014 / 0088373, WO 2017 / 132726, WO 2019 / 122413 and WO 2019 / 122414, and U.S. Patent Publication No. 2020 / 383580, and the entire contents of each of these patents are incorporated herein by reference.

[0073] In some implementations, the sleep-wake signals described herein can be timestamped to indicate the time a user goes to bed, the time a user wakes up, the time a user attempts to fall asleep, etc. The sleep-wake signals can be measured by one or more sensors 130 at a predetermined sampling rate during the sleep period, such as one sample per second, one sample every 30 seconds, one sample per minute, etc. In some implementations, the sleep-wake signals can also indicate respiratory signals, respiratory rate, inspiratory amplitude, expiratory amplitude, inspiratory-expiratory ratio, number of events per hour, event pattern, pressure setting of the respiratory therapy device 122, or any combination thereof, during the sleep period. Events can include snoring, sleep apnea, central sleep apnea, obstructive sleep apnea, mixed sleep apnea, hypopnea, mask leakage (e.g., from user interface 124), restless legs, sleep disturbance, apnea, increased heart rate, dyspnea, asthma attack, seizure, or any combination thereof. One or more sleep-related parameters can be determined for a user during sleep periods based on sleep-wake signals, including, for example, total time in bed, total sleep time, sleep onset wait time, sleep onset wake-up parameters, sleep efficiency, segmentation index, or any combination thereof. As described further in detail herein, physiological data and / or sleep-related parameters can be analyzed to determine one or more sleep-related scores.

[0074] 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 breathing or exhalation during sleep periods. Respiratory signals may 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 the respiratory therapy device 122, or any combination thereof. Events may include snoring, sleep apnea, central sleep apnea, obstructive sleep apnea, mixed sleep apnea, hypopnea, mask leakage (e.g., from user interface 124), restless legs, sleep disturbance, apnea, increased heart rate, dyspnea, asthma attack, seizure, or any combination thereof.

[0075] Pressure sensor 132 outputs pressure data that can be stored in storage device 114 and / or analyzed by processor 112 of control system 110. In some implementations, pressure sensor 132 is an air pressure sensor (e.g., an atmospheric pressure sensor) that generates sensor data indicating the breathing (e.g., inhalation and / or exhalation) and / or ambient pressure of the user of respiratory therapy system 120. In such implementations, pressure sensor 132 can be coupled to or integrated into respiratory therapy 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 potential sensor, or any combination thereof.

[0076] The flow sensor 134 outputs flow data that can be stored in storage device 114 and / or analyzed by processor 112 of control system 110. In some implementations, the flow sensor 134 is used to determine the airflow rate from respiratory therapy device 122, the airflow rate through conduit 126, the airflow rate through user interface 124, or any combination thereof. In such implementations, the flow sensor 134 may be coupled to or integrated into respiratory therapy device 122, user interface 124, or conduit 126. The flow sensor 134 may be a mass flow sensor, such as a rotary flow meter (e.g., a Hall effect flow meter), turbine flow meter, orifice flow meter, ultrasonic flow meter, hot wire sensor, eddy current sensor, membrane sensor, or any combination thereof.

[0077] Temperature sensor 136 outputs temperature data that can be stored in storage device 114 and / or analyzed by processor 112 of control system 110. In some implementations, temperature sensor 136 generates instructions for user 210 ( Figure 21) Core body temperature data. 2) User 210 skin temperature, temperature of air flowing from the respiratory therapy device 122 and / or through the conduit 126, temperature in the 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.

[0078] Motion sensor 138 outputs motion data that can be stored in storage device 114 and / or analyzed by processor 112 of control system 110. Motion sensor 138 can be used to detect the movement of user 210 during sleep periods, and / or the movement of any component of respiratory therapy system 120 (such as respiratory therapy device 122, user interface 124, or catheter 126). Motion sensor 138 may include one or more inertial sensors, such as accelerometers, gyroscopes, and magnetometers. In some implementations, motion sensor 138 alternatively or additionally generates one or more signals representing the user's body movements, from which signals representing the user's sleep state can be obtained; for example, via the user's breathing movements. In some implementations, motion data from motion sensor 138 may be combined with additional data from another sensor 130 to determine the user's sleep state.

[0079] The output of microphone 140 can be stored in storage device 114 and / or analyzed by processor 112 of control system 110 as sound and / or audio data. The audio data generated by microphone 140 can be reproduced as one or more sounds (e.g., sounds 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 respiratory therapy device 122, user interface 124, catheter 126, or user device 170. In some implementations, system 100 includes multiple microphones (e.g., two or more microphones and / or a microphone array with beamforming) such that sound data generated by each of the multiple microphones can be used to distinguish sound data generated by another of the multiple microphones.

[0080] The speaker 142 outputs to the user of the system 100 (e.g., Figure 2 The speaker 142 can be used as, for example, an alarm clock or to play alarms or messages to the user 210 (e.g., in response to an event). In some implementations, the speaker 142 can be used to transmit audio data generated by the microphone 140 to the user. The speaker 142 can be coupled to or integrated into the respiratory therapy device 122, user interface 124, catheter 126, or user device 170.

[0081] Microphone 140 and speaker 142 can be used as separate devices. In some implementations, microphone 140 and speaker 142 can be combined into acoustic sensor 141, as described, for example, in International Patent Publications WO 2018 / 050913 and WO 2020 / 104465, each of which is incorporated herein by reference in its entirety. In such implementations, speaker 142 generates or emits sound waves at predetermined intervals and / or frequencies, and microphone 140 detects reflections of the 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 sonar sensor and / or one or more of the sleep-related parameters described herein, such as respiratory signal, respiratory rate, inspiratory amplitude, expiratory amplitude, inspiratory-expiratory ratio, number of events per hour, event pattern, sleep state, pressure setting of the respiratory therapy device 122, or any combination thereof. In this document, the sonar sensor may be understood to involve active acoustic sensing, such as by generating / emitting ultrasonic or low-frequency ultrasonic sensing signals (e.g., in the frequency range of approximately 17-23 kHz, 18-22 kHz, or 17-18 kHz) through the air. Such systems may be considered in relation to WO 2018 / 050913 and WO 2020 / 104465 above.

[0082] In some implementations, 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.

[0083] 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 2The 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 respiratory therapy device 122, one or more sensors 130, the user device 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 implementations, the RF receiver 146 and the RF transmitter 148 are combined as part of the RF sensor 147. In some such implementations, the RF sensor 147 includes control circuitry. The specific format of the RF communication may be Wi-Fi, Bluetooth, etc.

[0084] In some implementations, RF sensor 147 is part of a mesh system. An example of a mesh system is a Wi-Fi mesh system, which may include mesh nodes, one or more mesh routers, and one or more mesh gateways, each of which may be mobile / movable or fixed. In such implementations, the Wi-Fi mesh system includes Wi-Fi routers and / or Wi-Fi controllers, and one or more satellites (e.g., access points), each of which includes the same or similar RF sensor as RF sensor 147. The Wi-Fi routers and satellites communicate continuously with each other using Wi-Fi signals. The Wi-Fi mesh system can be used to generate motion data based on changes in the Wi-Fi signal between the router and the satellite (e.g., differences in received signal strength) caused by signal obstruction due to partial movement of objects or people. Motion data may indicate movement, breathing, heart rate, gait, falls, behavior, etc., or any combination thereof.

[0085] Camera 150 outputs image data that can be reproduced as one or more images (e.g., still images, video images, thermal images, or any combination thereof) that can be stored in storage device 114. The image data from camera 150 can be used by control system 110 to determine one or more of the sleep-related parameters described herein, such as, for example, one or more events (e.g., periodic limb movements or restless legs syndrome), respiratory signals, respiratory rate, inspiratory amplitude, expiratory amplitude, inspiratory-expiratory ratio, number of events per hour, pattern of events, sleep state, sleep stage, or any combination thereof. Furthermore, the image data from camera 150 can be used, for example, to identify the user's position to determine chest movement of user 210 (…). Figure 2 ), to determine the airflow from user 210's mouth and / or nose, to determine the time user 210 went to bed 230 ( Figure 2 ( ), and determine the time when user 210 wakes up 230. In some implementations, camera 150 includes a wide-angle lens or a fisheye lens.

[0086] Infrared (IR) sensor 152 outputs infrared image data that can be reproduced as one or more infrared images (e.g., still images, video images, or both) that can be stored in storage device 114. The infrared data from IR sensor 152 can be used to determine one or more sleep-related parameters during a sleep period, including the user 210's temperature and / or the user 210's movement. IR sensor 152 can also be used in conjunction with camera 150 when measuring the presence, location, and / or movement of user 210. For example, IR sensor 152 can detect infrared light with wavelengths between about 700 nm and about 1 mm, while camera 150 can detect visible light with wavelengths between about 380 nm and about 740 nm.

[0087] 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.).

[0088] ECG sensor 156 outputs physiological data associated with the electrical activity of the heart of user 210. In some implementations, ECG sensor 156 includes one or more electrodes located above or around a portion of user 210 during sleep periods. The physiological data from ECG sensor 156 can be used, for example, to determine one or more of the sleep-related parameters described herein.

[0089] EEG sensor 158 outputs physiological data associated with the electrical activity of the user 210's brain. In some implementations, 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 or sleep stage at any given time during a sleep period. In some implementations, EEG sensor 158 may be integrated into user interface 124 and / or an associated helmet (e.g., a strap, etc.).

[0090] The capacitive sensor 160, force sensor 162, and strain gauge sensor 164 output data that can be stored in storage device 114 and used by control system 110 to determine one or more of the sleep-related parameters described herein. The EMG sensor 166 outputs physiological data associated with electrical activity generated by one or more muscles. The oxygen sensor 168 outputs oxygen data indicating the oxygen concentration of a gas (e.g., in conduit 126 or at user interface 124). The oxygen sensor 168 can be, for example, an ultrasonic oxygen sensor, an electro-oxygen sensor, a chemical oxygen sensor, a photo-oxygen sensor, a pulse oximeter (e.g., an SpO2 sensor), or any combination thereof. In some implementations, one or more sensors 130 also include a skin conductance 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.

[0091] 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 storage 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 implementations, analyte sensor 174 is located near the mouth of user 210 to detect analytes in the breath exhaled from the mouth of user 210. For example, when user interface 124 is a mask covering the nose and mouth of user 210, analyte sensor 174 can be located inside the mask to monitor mouth breathing of user 210. In other implementations, such as when user interface 124 is a nasal mask or nasal pillow mask, analyte sensor 174 can be positioned near the nose of user 210 to detect analytes in the breath exhaled through the nose of user 210. In other implementations, when user interface 124 is a nasal mask or nasal pillow mask, analyte sensor 174 can be located near the mouth of user 210. In this implementation, the analyte sensor 174 can be used to detect whether any air is unintentionally leaking from the mouth of user 210. In some implementations, the analyte sensor 174 is a volatile organic compound (VOC) sensor that can be used to detect carbon-based chemicals or compounds. In some implementations, 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.

[0092] The humidity sensor 176 outputs data that can be stored in the storage device 114 and used by the control system 110. The humidity sensor 176 can be used to detect humidity in various areas surrounding 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 the respiratory therapy device 122, etc.). Therefore, in some implementations, the humidity sensor 176 can be coupled to or integrated into the user interface 124 or the conduit 126 to monitor the humidity of pressurized air from the respiratory therapy device 122. In other implementations, 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.

[0093] A 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 using such sensors, 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 off solid surfaces (e.g., transmissive materials) through which they pass, thus allowing for the classification of different types of obstacles.

[0094] In some implementations, one or more sensors 130 may also include a skin conductance response (GSR) sensor, a blood flow sensor, a respiration sensor, a pulse sensor, a blood pressure sensor, a blood oxygen measurement sensor, a sonar sensor, a RADAR sensor, a blood glucose sensor, a color sensor, a pH sensor, an air quality sensor, a tilt sensor, a rain sensor, a soil moisture sensor, a water flow sensor, an alcohol sensor, or any combination thereof.

[0095] Although Figure 1While shown separately, any combination of one or more sensors 130 may be integrated into and / or coupled to any one or more components of system 100, including respiratory therapy device 122, user interface 124, catheter 126, humidifier 129, control system 110, user device 170, activity tracker 180, 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 respiratory therapy device 122. In some implementations, at least one of the one or more sensors 130 is not coupled to respiratory therapy 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.).

[0096] User device 170 ( Figure 1 This includes a display device 172. The user device 170 can be, for example, a mobile device such as a smartphone, tablet, game console, smartwatch, laptop, etc. Alternatively, the user device 170 can be an external sensing system, a television (e.g., a smart TV), or another smart home device (e.g., such as Google...). Amazon (Smart speakers such as Amazon Alexa). Display device 172 is typically used to display images including still images, video images, or both. In some implementations, display device 172 acts as a human-machine interface (HMI) including a graphical user interface (GUI) configured to display images and an input interface. Display device 172 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 from a human user interacting with user device 170. In some implementations, one or more user devices may be used by system 100 and / or included in system 100.

[0097] In some implementations, user device 170 is a smartphone and includes a display device 172, one or more processors (e.g., the same as or similar to processor 112), one or more storage devices (e.g., the same as or similar to storage device 114), and one or more of sensors 130 (e.g., microphone 140, speaker 142, RF receiver 146, RF transmitter 148, and camera 150). In other implementations, user device 170 is a smart speaker or hub (e.g., with Google...). Google Google Nest Amazon The device (e.g., Amazon Alexa, or similar) includes a display device 172, one or more processors (e.g., the same as or similar to processor 112), one or more storage devices (e.g., the same as or similar to storage device 114), and one or more of sensors 130 (e.g., microphone 140, speaker 142, RF receiver 146, RF transmitter 148). In such implementations, the sensors included in the user device 170 can be used to generate or acquire data associated with the sleep periods described herein (e.g., physiological data, audio data, etc.). In some implementations, the user device is a wearable device (e.g., a smartwatch).

[0098] In some implementations, user device 170 includes mobile application 174 for performing and / or providing a user interface for any of the methods described herein. Mobile application 174 may be downloaded to user device 170 from an app store or pre-installed on user device 170 (e.g., as part of the native operating system).

[0099] In some implementations, system 100 also includes an activity tracker 180. The activity tracker 180 is typically used to help generate physiological data associated with the user. The activity tracker 180 may include one or more of the sensors 130 described herein, such as, for example, motion sensors 138 (e.g., one or more accelerometers and / or gyroscopes), PPG sensors 154, and / or ECG sensors 156. Physiological data from the activity tracker 180 can be used to determine, 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 HE respiratory rate, respiratory rate variability, heart rate, mean heart rate, resting heart rate, maximum heart rate, heart rate variability, calories burned, blood oxygen saturation, electrodermal activity (also known as skin conductance or skin response), or any combination thereof. In some implementations, the activity tracker 180 is coupled (e.g., electronically or physically) to user device 170.

[0100] In some implementations, the activity tracker 180 is a wearable device that can be worn by the user, such as a smartwatch, wristband, ring, or patch. For example, see reference... Figure 2 The activity tracker 180 is worn on the wrist of the user 210. The activity tracker 180 can also be connected to or integrated into the clothing or garments worn by the user.

[0101] Alternatively, the activity tracker 180 may also be coupled to or integrated into the user device 170 (e.g., within the same housing). More generally, the activity tracker 180 may be communicatively coupled to or physically integrated therein (e.g., within the housing) with the control system 110, memory 114, respiratory therapy system 120, and / or user device 170. In other words, the activity tracker 180 is capable of synchronizing with the control system 110, memory 114, respiratory therapy system 120, and / or user device 170.

[0102] Return to reference Figure 1 Although the control system 110 and the storage device 114 are in Figure 1 While described and shown as separate and distinct components of system 100, in some implementations, control system 110 and / or storage device 114 are integrated into user device 170 and / or respiratory therapy device 122. Alternatively, in some implementations, control system 110 or a portion thereof (e.g., processor 112) may reside in the cloud (e.g., integrated in a server, integrated in an Internet of Things (IoT) device, connected to the cloud, subjected to edge cloud processing, etc.), or on one or more servers (e.g., remote servers, local servers, etc., or any combination thereof). In some implementations, some aspects of the processing described herein are performed in the cloud, which advantageously allows for the updating of physiological data and / or sleep-related parameters.

[0103] While system 100 is shown to include all of the aforementioned components, according to implementations of this disclosure, more or fewer components may be included in the system for generating physiological data and determining suggested notifications or actions for the user. For example, a first alternative system includes a control system 110, a storage 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 storage 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 storage device 114, a respiratory therapy 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.

[0104] As used herein, sleep periods can be defined in various ways based on, for example, an initial start time and an end time. In some implementations, a sleep period is the duration of a user's sleep; that is, a sleep period has a start time and an end time, and during a sleep period, the user does not wake up until the end time. In other words, any period during which the user is awake is not included in a sleep period. According to this first definition of a sleep period, if a user wakes up and falls asleep multiple times in the same night, each sleep interval separated by the wake-up intervals is a sleep period.

[0105] Alternatively, in some implementations, the sleep period has a start time and an end time, and during the sleep period, the user can remain awake 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, approximately 20% of the sleep period, approximately 15% of the sleep period duration, approximately 10% of the sleep period duration, approximately 5% of the sleep period duration, approximately 2% of the sleep period duration, etc., or any other threshold percentage. In some implementations, the wakefulness duration threshold is defined as a fixed amount of time, such as approximately one hour, approximately thirty minutes, approximately fifteen minutes, approximately ten minutes, approximately five minutes, approximately two minutes, etc., or any other amount of time.

[0106] 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 at the first time of the current night (e.g., 10:00 PM) 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 falling asleep), and ends at the second time of the following morning (e.g., 7:00 AM) when the user first gets out of bed and does not want to sleep the following morning, and ends at the second time of the following morning (e.g., 7:00 AM) (e.g., Tuesday, July 7, 2020).

[0107] 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 One or more user-selectable elements are displayed on the display device 172. 1) Manually initiate or terminate a sleep period.

[0108] Typically, a sleep period includes any point in time after the user 210 has lay down or sat on bed 230 (or another area or object on which they intend to sleep) and has turned on the breathing therapy device 122 and worn the user interface 124. A sleep period can therefore include time periods (i) when the user 210 is using the CPAP system but before attempting to fall asleep (e.g., when the user 210 is lying on bed 230 while reading); (ii) when the user 210 begins to attempt to fall asleep but remains awake; (iii) when the user 210 is in light sleep (also known as stages 1 and 2 of non-rapid eye movement (NREM) sleep); (iv) when the user 210 is in deep sleep (also known as slow-wave sleep (SWS), or stage 3 of NREM sleep); (v) when the user 210 is in rapid eye movement (REM) sleep; (vi) when the user 210 periodically wakes between light sleep, deep sleep, or REM sleep; or (vii) when the user 210 wakes up and does not return to sleep.

[0109] In some examples, a sleep period can typically be defined as ending once user 210 removes user interface 124, shuts down respiratory therapy device 122, and wakes up 230. In some implementations, a sleep period may include additional time cycles, or may be limited to only some of the time cycles disclosed above. For example, a sleep period may be defined as including when respiratory therapy device 122 begins supplying pressurized air to the airway or user 210, ending when respiratory therapy device 122 stops supplying pressurized air to the airway of user 210, and including some or all of the time points in between, the period when user 210 is asleep or awake.

[0110] refer to Figure 3 The illustration shows an exemplary timeline 300 for sleep periods. Timeline 300 includes bedtime (t... 入床 ), time to fall asleep (t) GTS ), initial sleep time (t) 睡眠 First micro-awakening MA1, second micro-awakening MA2, awakening A1, awakening time (t) 觉醒 ) and wake-up time (t 起床 ).

[0111] Bedtime t 入床 The user initially goes to bed before falling asleep (e.g., when the user lies down or sits on the bed). Figure 2 The time of going to bed (230) is associated with the bed time. Bed time 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 and the time a user goes to bed for other reasons (e.g., watching TV). For example, the bed threshold duration could be at least about 10 minutes, at least about 20 minutes, at least about 30 minutes, at least about 45 minutes, at least about 1 hour, at least about 2 hours, etc. Although the term "bed" is used to describe bed entry time in this document... 入床 However, more generally, bedtime t 入床 This can refer to the time when a user initially enters any location to sleep (e.g., a sleeping chair, a chair, a sleeping bag, etc.).

[0112] Sleep onset time (t) GTS ) and the time it takes for the user to first try to fall asleep after going to bed (t 入床 This is related to the initial sleep time (t). For example, after going to bed, a user can engage in one or more activities to relax before attempting sleep (e.g., reading, watching TV, listening to music, using user device 170, etc.). 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.

[0113] Awakening Time t 觉醒 This is the time associated with when a user wakes up without returning to sleep (e.g., the opposite of when a user wakes up at night and returns to sleep). A user may experience one of several unconscious micro-awakenings (e.g., micro-awakenings MA1 and MA2) with short durations (e.g., 5 seconds, 10 seconds, 30 seconds, 1 minute, etc.) after initially falling asleep. This is related to the wakefulness time t. 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 A1) after initially falling asleep (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 A1. Therefore, the wakefulness time t can be defined, for example, based on the duration of an 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.). 觉醒 .

[0114] Similarly, wake-up time t 起床 This is associated with the time a user wakes up and gets out of bed to end a sleep period (e.g., the opposite of a user waking up at night to go to the bathroom, care for a child or pet, or sleepwalk). In other words, wake-up time t 起床This is the time from when a user last gets up and doesn't return to bed until the next sleep period (e.g., the next night). Therefore, the wake-up time t can be defined, for example, based on a rise threshold duration (e.g., the user has been awake for at least 15 minutes, at least 20 minutes, at least 30 minutes, at least 1 hour, etc.). 起床 The bedtime t for the second subsequent sleep period can also be defined based on the duration of the rise threshold (e.g., the user has been awake for at least 4 hours, 6 hours, 8 hours, 12 hours, etc.). 入床 time.

[0115] As mentioned above, in the initial t 入床 And the last t 起床 During the night, a user can wake up and get up more than once. In some implementations, the final wake-up time t 觉醒 and / or final wake-up time t 起床 It is identified or determined based on a predetermined threshold duration following an event (e.g., falling asleep or waking up). This threshold duration can be customized for the user. For a standard user who falls asleep at night and then wakes up and gets out of bed in the morning, any time period between approximately 12 and approximately 18 hours can be used (the time between the user waking up and waking up). 觉醒 ) or get up (t 起床 Between ) and users going to bed (t 入床 ), entering sleep (t GTS ) or fall asleep (t 睡眠 For users who spend longer periods in bed, shorter threshold periods can be used (e.g., between approximately 8 and 14 hours). The threshold period can be initially selected and / or adjusted later based on the system monitoring the user's sleep behavior.

[0116] Total time in bed (TIB) is the time spent in bed (t). 入床 and wake-up time t 起床 The duration between the initial sleep time and the wake time. Total sleep time (TST) is associated with the duration between the initial sleep time and the wake time, excluding any conscious or unconscious wake-ups and / or micro-awakenings in between. Typically, total sleep time (TST) will be shorter than total time in bed (TIB) (e.g., one minute shorter, ten minutes shorter, one hour shorter, etc.). For example, refer to... Figure 3 Timeline 300, Total Sleep Time (TST) between initial sleep time t 睡眠 and awakening time t 觉醒 The duration of sleep time (TST) is between the durations of the first micro-wake (MA1), the second micro-wake (MA2), and wake-up A, but does not include these durations. As shown in the figure, in this example, the total sleep time (TST) is shorter than the total bed rest time (TIB).

[0117] In some implementations, Total Sleep Time (TST) can be defined as Persistent Total Sleep Time (PTST). In these implementations, PTST excludes a predetermined initial portion or period of the first non-REM stage (e.g., a light sleep stage). For example, the predetermined initial portion could be between approximately 30 seconds and approximately 20 minutes, between approximately 1 minute and approximately 10 minutes, between approximately 3 minutes and approximately 5 minutes, etc. PTST is a measure of persistent sleep and smooths the sleep-wake sleep graph. For example, when a user initially falls asleep, the user might be in the first non-REM stage for a very short time (e.g., approximately 30 seconds), then return to the wake stage for a very short time (e.g., one minute), and then return to the first non-REM stage. In this example, PTST excludes the first instance of the first non-REM stage (e.g., approximately 30 seconds).

[0118] In some implementations, the sleep period 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 awakening time (t) 觉醒 End. In some implementations, the sleep period is defined as the total sleep time (TST). In some implementations, the sleep period is defined as the time from the start of sleep (t). GTS ) begins and at the awakening time (t) 觉醒 End. In some implementations, a 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 time from the start of sleep (t). 睡眠 Start at wake-up time (t) 起床 )Finish.

[0119] refer to Figure 4 The diagram illustrates the corresponding timeline 300 based on some implementations. Figure 3 An exemplary sleep graph 400 is shown. As illustrated, the sleep graph 400 includes a sleep-wake signal 401, a wake-up 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 410-440 indicates the sleep stage at a given time during a sleep period.

[0120] The sleep-wake signal 401 may be generated based on physiological data associated with the user (e.g., generated by one or more of the sensors 130 described herein). The sleep-wake signal may indicate one or more sleep states, including wakefulness, relaxed wakefulness, micro-wakefulness, REM sleep, a first non-REM sleep stage, a second non-REM sleep stage, a third non-REM sleep stage, or any combination thereof. In some implementations, one or more of the first non-REM sleep stage, the second non-REM sleep stage, and the third non-REM sleep stage may be grouped together and categorized as light sleep stages or deep sleep stages. For example, light sleep stages may include the first non-REM sleep stage, while deep sleep stages may include the second and third non-REM sleep stages. Although sleep diagram 400 is in Figure 4 The sleep graph 400 is shown as including a light sleep stage axis 430 and a deep sleep stage axis 440, but in some implementations, 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, the sleep-wake signal 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 the sleep-wake signal may be stored in storage device 114.

[0121] Sleep graph 400 can be used to determine one or more sleep-related parameters, such as sleep onset wait time (SOL), wake-up time after sleep (WASO), sleep efficiency (SE), sleep segmentation index, sleep block, or any combination thereof.

[0122] Sleep onset wait time (SOL) is defined as the time to enter sleep (t). GTS ) and initial sleep time (t 睡眠The sleep start wait time (SOL) represents the time it takes for a user to actually fall asleep after their initial attempt to fall asleep. In some implementations, the sleep start wait time is defined as the continuous sleep start wait time (PSOL). The difference between PSOL and the initial sleep start wait time is that PSOL is defined as the duration between the time the user enters sleep and a predetermined amount of continuous sleep. In some implementations, the predetermined amount of continuous sleep may include, for example, at least 10 minutes of sleep within a second non-REM phase, a third non-REM phase, and / or a REM phase, with awakenings not exceeding 2 minutes, a first non-REM phase, and / or movement between them. In other words, continuous sleep for up to, for example, 8 minutes within a second non-REM phase, a third non-REM phase, and / or a REM phase. In other implementations, the predetermined amount of continuous sleep may include at least 10 minutes of sleep within a first non-REM phase, a second non-REM phase, a third non-REM phase, and / or a REM phase after the initial sleep time. In this type of implementation, a predetermined amount of continuous sleep can exclude arbitrary micro-awakening (e.g., a ten-second micro-awakening does not restart the 10-minute period).

[0123] Sleep-onset wakefulness (WASO) is associated with the total duration of a user's wakefulness between the initial sleep time and wake time. Therefore, WASO includes brief and micro-awake periods during sleep (e.g., Figure 4 Micro-awakenings (MA1 and MA2) are shown, whether conscious or unconscious. In some implementations, sleep-onset wakefulness time (WASO) is defined as 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.) that includes only the total duration of awakenings.

[0124] Sleep efficiency (SE) is defined as the ratio of total time spent in bed (TIB) to total sleep time (TST). For example, if the total time spent in bed is 8 hours and the total sleep time is 7.5 hours, then the sleep efficiency for that sleep period is 93.75%. Sleep efficiency indicates a user's sleep hygiene. For example, if a user goes to bed before sleep and spends time engaging in other activities (e.g., watching television), sleep efficiency will decrease (e.g., the user is punished). In some implementations, sleep efficiency (SE) can be calculated based on total time spent in bed (TIB) and the total time the user attempts to sleep. In such implementations, the total time the user attempts to sleep is defined as the duration between the time to fall asleep (GTS) and the wake-up time described herein. For example, if the total sleep time is 8 hours (e.g., between 11 PM and 7 AM), the time to fall asleep is 10:45 PM, and the wake-up time is 7:15 AM, then in such implementations, the sleep efficiency parameter is calculated to be approximately 94%.

[0125] 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 The micro-wake-up MA1 and micro-wake-up MA2 shown in the diagram have a segmentation exponent of 2. In some implementations, the segmentation exponent is scaled within a predetermined range of integers (e.g., between 0 and 10).

[0126] Sleep blocks are associated with the transition between any sleep stage (e.g., the first non-REM stage, the second non-REM stage, the third non-REM stage, and / or REM) and the wake-up stage. Sleep blocks can be calculated at a resolution of, for example, 30 seconds.

[0127] In some implementations, the systems and methods described herein may include generating or analyzing a sleep map including sleep-wake signals to determine or identify bedtime based at least in part on the sleep-wake signals of the sleep map. 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).

[0128] In other implementations, one or more of the sensors 130 can be used to determine or identify the bed entry time (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 For example, the time to fall asleep can be determined based on 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 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 therapy device 122, data indicating that the user puts on user interface 124, etc.), or any combination thereof.

[0129] General reference Figures 5A-5C In some implementations, users can manually define the start and / or end of sleep periods. (See reference) Figure 5A via user device 170 ( Figure 1 The display device 172 displays a settings view 500. The settings view 500 includes a first user-selectable element 501, a second user-selectable element 502, and a third user-selectable element 503. The first user-selectable element 501 allows the user to define the start of a sleep period (e.g., by clicking or tapping the first user-selectable element 501). The second user-selectable element 502 allows the user to specify a wake-up time (e.g., 6:30 AM). The second user-selectable element 502 may include, for example, a scroll wheel menu or a drop-down menu to allow the user to specify the wake-up time. The third user-selectable element 503 allows the user to select (e.g., using a toggle button) a smart alarm feature that generates an alarm based on the selected wake-up time.

[0130] Typically, a smart alarm can generate an alert within a predetermined time range (e.g., between 6:30 AM and 7:00 AM) based on a selected wake-up time, and at the optimal time within that range based on physiological data and / or sleep-related parameters. For example, a smart alarm can generate an alert within a predetermined time window relative to a desired wake-up time (e.g., within 15 minutes, 20 minutes, 30 minutes, 45 minutes, 1 hour, etc.), during which the user is closest to light sleep (as described herein, determined based on one or more sleep-related parameters). To illustrate, if the predetermined time window is 30 minutes and the desired wake-up time is 7:00 AM, then the smart alarm could generate an alert between 6:30 AM and 7:00 AM when the user is closest to light sleep.

[0131] refer to Figure 5B In user device 170 ( Figure 1 The sleep view 510 is displayed on the display device 172. The user selects the settings view 500. Figure 5A After the first user-selectable element 501 is selected (e.g., in response to the user selection), sleep view 510 is displayed. Sleep view 510 ( Figure 5B It includes a first user-selectable element 511 that allows the user to manually terminate the sleep period (e.g., by clicking or tapping the first user-selectable element 511).

[0132] refer to Figure 5C Alarm view 520 is displayed on user device 170 ( Figure 1 The alarm view 520 is displayed on the display device 172 via the setting view 500. Figure 5A The alarm view 520 is displayed after (e.g., in response to) an alarm generated by the second user-selectable element 502. The alarm view 520 includes a first user-selectable element 521 that allows the user to turn off the alarm (e.g., by clicking or tapping the first user-selectable element 521). As shown in the example, the alarm is generated at 6:43 AM, which falls within a predetermined range (between 6:30 AM and 7:00 AM) set by the aforementioned smart alarm features.

[0133] refer to Figure 6 The illustration depicts a method 600 for comparing a user's first sleep state during a first sleep period and a user's second sleep state during a second sleep period. As described herein, the second sleep period differs from the first sleep period in that the user uses a respiratory therapy system (e.g., the same as or similar to the respiratory therapy system 120 described herein) during at least a portion of the second sleep period, but does not use a respiratory therapy system during the first sleep period. One or more steps of method 600 can be performed using system 100 described herein (…). Figure 1-2 It can be implemented by any element or aspect of ).

[0134] Step 601 of method 600 includes generating and / or receiving first data associated with a first sleep period of the user. When the first data is generated or received, the user is not using a respiratory therapy system (e.g., the same as or similar to the respiratory therapy system 120 described herein) during the first sleep period. The first data (step 601) may be generated by, for example, one or more sensors 130 described herein (…). Figure 1 ) is generated, including acoustic sensor 141.

[0135] The first data may include, for example, first respiratory data associated with the user, first audio data associated with the user, or both. The first respiratory data indicates the user's first breathing signal during at least a portion of the first sleep period (e.g., at least 10%, at least 50%, 75%, at least 90%, etc. of the first sleep period). The breathing signal indicates the user's respiratory rate, respiratory rate variation, tidal volume, inspiratory amplitude, expiratory amplitude, inspiratory-expiratory ratio, etc., or any combination thereof, during at least a portion of the first sleep period. The first audio data may be reproduced as one or more sounds (e.g., snoring, choking, difficulty breathing, etc.) recorded during the first sleep period.

[0136] In some implementations, both the first respiratory data and the first audio data are generated by the acoustic sensor 141. Figure 1 The first breathing data is generated by the acoustic sensor 141, while the first audio data is generated by a separate and different microphone (e.g., the same as or similar to microphone 140) from the acoustic sensor. The first data may be generated by, for example, the electronic interface 119 described herein and / or the user device 170. Figure 1 )take over.

[0137] In some implementations, the first data received during step 601 may include sleep marker data. Typically, sleep marker data indicates the user's use of the product (e.g., a respiratory therapy system or device) during a first sleep period. In such implementations, the first data received during step 601 may be associated with a selected sleep marker.

[0138] For example, refer to Figure 13A A first sleep marker view 1300 can be displayed on the display device 172 before the first sleep period. The first sleep marker view 1300 may include a first user-selectable element 1310 and a second user-selectable element 1320. Figure 13A In the exemplary implementation shown, the user can select a first user-selectable element 1310 to apply a first sleep marker (e.g., indicating that a respiratory therapy system or device is not used during a first sleep period). Alternatively, the user can select a second user-selectable element 1320 to apply a second sleep marker (e.g., indicating that a respiratory therapy system or device is used during a first sleep period). The first sleep marker or second sleep marker is associated with at least a portion of other first data received during step 601 via the first sleep marker view 1300.

[0139] In some implementations, the first sleep marker view 1300 also includes a third user-selectable element 1314, which can be selected by a user to create another user-selectable element associated with the third sleep marker (e.g., a third sleep marker that is different from the first sleep marker associated with the first user-selectable element 1312 and different from the second sleep marker associated with the second user-selectable element 1314) is displayed within the first sleep marker view 1300. For example, refer to... Figure 13B The selection of the third user-selectable element 1316 in the first sleep marker view 1300 causes the second sleep marker view 1302 to be displayed on the display device 172. Typically, the second sleep marker view 1302 allows the user to select from options added to the first sleep marker view 1300 (…). Figure 13A You can choose from multiple pre-defined sleep tags or create custom sleep tags.

[0140] exist Figure 13B In the example shown, the second sleep marker view 1302 includes a plurality of user-selectable elements 1340A to 1340E, each of which is associated with one of a plurality of predetermined sleep markers. For example, a first user-selectable element 1340A may be associated with a first user interface type (e.g., a full-face mask) for a respiratory therapy system, a second user-selectable element 1340B may be associated with a second user interface type (e.g., a nasal cradle mask) for a respiratory therapy system, a third user-selectable element 1340C may be associated with a third user interface type (e.g., a pillow mask) for a respiratory therapy system, and a fourth user-selectable element 1340D may be associated with a different sleep marker than the first sleep marker view 1300. Figure 13A The first user-selectable element 1310 is associated with the type of respiratory therapy system (e.g., travel version). The fifth user-selectable element 1340E can be associated with a treatment system (e.g., for insomnia) that is different from the one associated with the first sleep marker view 1300. Figure 13AThe first user-selectable element 1310 is associated with a respiratory therapy system. Each user-selectable element 1340A-1340E may include alphanumeric text and / or images associated with the corresponding device or system. The second sleep marker view 1302 may also include a sixth user-selectable element 1350 for creating customized sleep markers (e.g., a user can input customized sleep markers using alphanumeric text). In other implementations, additional sleep markers may include markers for the use of supplementary devices (e.g., pillows, bedding, sleep blankets, etc.) or medication (e.g., medications used by the user before sleep). In some implementations, the supplementary device is a smart pillow, such as those described in International Application PCT / US2020 / 048633 (International Publication No. WO 2021 / 041987) and / or International Application PCT / IB2020 / 061244, the entire contents of which are incorporated herein by reference.

[0141] In some implementations, this can be in response to a second sleep marker view 1302 ( Figure 13B Selecting one of the multiple user-selectable elements 1340A to 1340E in the display shows a third sleep marker view. For example, refer to Figure 13C In response to the selection of the fifth user-selectable element 1340E, a third sleep marker view 1303 is displayed on the display device 172. The third sleep marker view 1303 includes alphanumeric text and / or images that are the same as or similar to the fifth user-selectable element 1340E (e.g., an enlarged version of the fifth user-selectable element 1340E) and another user-selectable element 1342E (e.g., alphanumeric text, links to web pages, user manuals, etc.) that can be selected to provide more information associated with the insomnia treatment device. The user can add another user-selectable element associated with the insomnia treatment device to the first sleep marker view 1300 by selecting user-selectable element 1344E. Figure 13A ).

[0142] In some implementations, sleep markers can be determined automatically and associated with first data. For example, step 601 may include automatically detecting that the user is using a respiratory therapy system (e.g., using one or more sensors 130 described herein), rather than the user manually instructing the use of the respiratory therapy system. Furthermore, in some examples, the type of therapy device (e.g., the type of user interface) can be automatically detected and applied as a sleep marker. For instance, data from camera 150 can be analyzed (e.g., using an object recognition algorithm) to identify the device of the therapy device (e.g., the type of user interface).

[0143] Step 602 of method 600 includes determining a first set of sleep-related parameters associated with a first sleep period, at least in part based on first data generated and / or received during step 601. For example, control system 110 may analyze the first data (e.g., data stored in storage device 114) to determine the first set of sleep-related parameters for the first sleep period. For example, information describing the determined first set of sleep-related parameters may be stored in storage device 114. Figure 1 )middle.

[0144] The first set of sleep-related parameters may include, for example, the apnea-hypopnea index (AHI), identifiers of one or more events experienced by the user, the number of events per hour, event patterns, total sleep time, total bedtime, wake-up time, wake-up time, sleep graph, total light sleep time, total deep sleep time, total REM sleep time, number of awakenings, sleep onset wait time, or any combination thereof. In some implementations, the first set of sleep-related parameters may include a sleep score, such as the sleep score described in International Publication No. WO2015 / 006364 and U.S. Patent Publication No. 2016 / 0151603, the entire contents of which are incorporated herein by reference. The first set of sleep-related parameters may include any number of sleep-related parameters (e.g., 1 sleep-related parameter, 2 sleep-related parameters, 5 sleep-related parameters, 50 sleep-related parameters, etc.).

[0145] Select a sleep marker before the first sleep period ( Figures 13A-13C In the implementation that associates it with the first data, the first set of sleep-related parameters can be associated with sleep markers.

[0146] Step 603 of method 600 includes prompting the user to provide first subjective feedback associated with the first sleep period after the first sleep period. The first subjective feedback may be received by user device 170 (e.g., via display device 172) and stored in storage device 114. Figure 1 In, for example, the user can be prompted to click or tap the sleep view 510. Figure 5B The first user-selectable element 511 or alarm view 520 () Figure 5CThe first follow-up feedback is provided immediately after the first user-selectable element 521. More generally, the user may be prompted at any time after the first sleep period but before the next follow-up sleep period (e.g., 30 seconds after the first sleep period, 1 minute after the first sleep period, 5 minutes after the first sleep period, 15 minutes after the first sleep period, 30 minutes after the first sleep period, 1 hour after the first sleep period, 8 hours after the first sleep period, 12 hours after the first sleep period, etc.). The first subjective feedback may include, for example, a subjective level of drowsiness after the first sleep period, a subjective level of drowsiness before the first sleep period, a subjective sleep satisfaction rating during the first sleep period, or any combination thereof.

[0147] Information associated with or indicating the user's first subjective feedback may be received, for example, via user device 170 (e.g., via alphanumeric text, speech-to-text, etc.). (General reference) Figures 7A-7D In some implementations, it can be done on user device 170 ( Figure 1 A series of continuous prompts are displayed on the display device 172 to the user to provide first subjective feedback.

[0148] refer to Figure 7A The first prompt 710, containing alphanumeric text, prompts the user to use multiple user-selectable elements 712 (e.g., by clicking or tapping one or more elements 712) to indicate how they feel after the first sleep period. For example, the user can provide an indication of fatigue or drowsiness after the first sleep period. Figure 7A In the exemplary implementation shown, the plurality of user-selectable elements 712 include individual elements for indicating to the user fatigue, excitement, drowsiness, groubaine, happiness, exhaustion, or any combination thereof. The user can finalize their selection of element 712 by selecting (e.g., clicking or tapping) navigation element 714 and proceeding to the next (or more) prompts (e.g., ...). Figure 7B ).

[0149] refer to Figure 7B The second prompt, containing alphanumeric text, prompts the user to indicate a rating for the first sleep period. The rating typically indicates the user's perceived sleep quality for the first sleep period (e.g., poor, good, average, excellent, etc.). Figure 7B In the exemplary implementation shown, the second prompt 720 includes a user-selectable star input 722 for rating the first sleep period in 1-star, 2-star, 3-star, 4-star, or 5-star ratings (5 stars being the best and 1 star being the worst). The user can complete their star selection by selecting (e.g., clicking or tapping) a navigation element 724 and proceed to the next prompt.

[0150] refer to Figure 7C A third prompt 730, including alphanumeric text, prompts the user to indicate a level of drowsiness or fatigue before initiating the first sleep period. The third prompt 730 includes multiple user-selectable elements 732 to allow the user to indicate a level of drowsiness or fatigue (e.g., extremely tired, quite tired, not tired, etc.) before the first sleep period. In some implementations, this can be done before the first sleep period (e.g., in settings view 500...). Figure 5A After selecting the first user-selectable element 501, the user is provided with a third prompt 730.

[0151] refer to Figure 7D A fourth prompt 740, including alphanumeric text, prompts the user to indicate whether they can remain awake without napping or drowsing during the day following a sleep period (e.g., 8 hours after the first sleep period, 12 hours after the first sleep period, 16 hours after the first sleep period, the entire time between the end of the first sleep period and the next sleep period, etc.). The fourth prompt 740 includes a first plurality of user-selectable elements 742 for indicating "yes" or "no". The user makes their selection by selecting a navigation element 744 and viewing information associated with the first sleep period. Typically, the fourth prompt 740 may be displayed after the first sleep period but before a second subsequent sleep period (e.g., the next immediate sleep period after the first sleep period).

[0152] In some implementations, subjective feedback may include activity information. Activity information may be associated with, for example, activities performed by the user before, or after, and before a second sleep period. Activity information (e.g., a daily log) may be received before or after the first sleep period. Activity information may include information associated with exercise, NAPS, caffeine intake, alcohol intake, or any combination thereof. In some examples, sleep is taken without the use of a therapeutic system (e.g., the breathing therapy system described herein). In other examples, naps are taken using a therapeutic system. Napping using a therapeutic system can help the user adapt to future use of the therapeutic system (e.g., at night).

[0153] refer to Figure 14 In some implementations, the activity report view 1400 may be displayed on the display device 172. The activity report view 1400 includes a first plurality of user-selectable elements 1410A-1410D, a second plurality of user-selectable elements 1420A-1420D, a third plurality of user-selectable elements 1430A-1430C, and a fourth plurality of user-selectable elements 1440A-1440D.

[0154] The first plurality of user-selectable elements 1410A-1410D are associated with the amount of time the user exercises (e.g., before the first sleep period). By selecting one of the first plurality of user-selectable elements 1410A-1410D, the user can indicate, for example, no exercise, exercise for more than 30 minutes, exercise for more than 1 hour, or exercise for more than 2 hours.

[0155] The second plurality of user-selectable elements 1420A-1420D are associated with multiple naps taken by the user (e.g., before the first sleep period). By selecting a corresponding one of the second plurality of user-selectable elements 1420A-1420D, the user can indicate, for example, no nap, 1 nap, between 2 and 3 naps, or more than 4 naps.

[0156] The third set of multiple user-selectable elements 1430A-1430C are associated with caffeine intake (e.g., before the first sleep period). By selecting one of the corresponding third set of multiple user-selectable elements 1410C-1410C, the user can indicate, for example, whether to consume caffeine in the morning (AM), afternoon, or evening (PM).

[0157] The fourth plurality of user-selectable elements 1440A-1440D are associated with the user's alcohol intake (e.g., before the first sleep period). By selecting one of the fourth plurality of user-selectable elements 1440A-1440D, the user can indicate, for example, no alcohol intake, one alcoholic beverage, two to three alcoholic beverages, or more than four alcoholic beverages (e.g., consumed before the first sleep period).

[0158] Method 600 ( Figure 6 Step 604 includes causing an indication of at least a portion of the determined first set of sleep-related parameters (step 602) to be transmitted to the user after the first sleep period. This can be achieved using, for example, user device 170. Figure 1 The determined first set of sleep-related parameters is transmitted to the user via alphanumeric text, images, audio, or any combination thereof.

[0159] refer to Figure 8A Multiple indicators 810-820 associated with the first sleep period are displayed on the user device 170 ( Figure 8A The navigation element 744 is displayed on the display device 172. In some implementations, the navigation element 744 can be activated in response to user selection (e.g., clicking or tapping). Figure 7DThe indicators 810-820 are displayed. Multiple indicators include AHI indicator 810, which includes information (e.g., alphanumeric text) indicating a determined AHI for the first sleep period. As described herein, AHI is one of sleep-related parameters that can be determined based on first data associated with the first sleep period. AHI indicator 810 may include, for example, a determined AHI value (e.g., numerical value) and / or relative qualifiers (e.g., low, medium, high, etc.). Figure 8A In the example, the AHI value is 21 / hour, and the relative pass rate is high. The AHI indicator 810 may also include alphanumeric text explaining the AHI value and severity to the user (e.g., "On the last night, you stopped breathing 20 times per hour. This indicates you have severe sleep apnea").

[0160] Multiple indicators also include sleep analysis indicator 812, which includes information indicating total sleep time, sleep onset time, wake-up time, or any combination thereof for the first sleep period. Figure 8A In the example, sleep analysis indication 812 includes alphanumeric text indicating a total sleep time of 8 hours and 33 minutes, a bedtime of 10:30 p.m., and an awakening time of 6:30 a.m.

[0161] like Figure 8A As shown, the sleep summary element 814 can also be displayed on the display device 172 to allow the user to view a sleep summary associated with the first sleep period. (Reference) Figure 8B In selecting sleep summary element 814 ( Figure 8A After that (for example, in response to selecting the sleep summary element), sleep summary 820 is displayed on the display device 172.

[0162] The sleep summary 820 includes sleep analysis indicators 822, sleep status indicators 824, AHI indicators 826, and sleep score indicators 828.

[0163] Sleep analysis indicator 822 and the above-mentioned sleep analysis indicator 812 ( Figure 8A The sleep status indicator 824 provides information indicating whether the user experienced, for example, sleep apnea during the first sleep period and the severity of the sleep apnea (e.g., mild, moderate, severe, etc.). Figure 8B In the example, sleep condition indicator 824 indicates that the user experienced sleep apnea during the first sleep period, and that the sleep apnea is considered severe. AHI indicator 826 is similar to the aforementioned AHI indicator 810. Figure 8A(Similar or identical.) The sleep score indicator 828 provides information indicating whether the sleep score of the first sleep period and / or the determined sleep score meets or exceeds a target sleep score. The sleep score can be, for example, the myAir™ score, as described in International Patent Publication No. WO2016 / 061629 and U.S. Patent Publication No. 2017 / 0311879, the entire contents of which are incorporated herein by reference. Figure 8B In the example, sleep score indicator 828 indicates that the sleep score for the first sleep period is 50, and that the score is lower than the user's target score.

[0164] The navigation element 829 can also be displayed on the display device 172 together with the sleep summary 820, allowing the user to view additional instructions on whether to view the determined set of first sleep-related parameters for the first sleep period. (See reference...) Figure 8C In selecting navigation element 829 ( Figure 8B Following this (e.g., in response to the selection), a plurality of indications 830-840 associated with the first sleep period are displayed on the display device 172. The plurality of indications 830-840 include a sleep graph 830, a light sleep indication 832, a deep sleep indication 834, a REM sleep indication 836, a wake-up indication 838, and a sleep start waiting time indication 840.

[0165] Sleep graph 830 is a bar graph indicating interruptions during sleep periods (e.g., when the user is awake), absences during sleep periods (e.g., when the user gets out of bed at night), wakefulness, REM sleep, light sleep, deep sleep, or any combination thereof. Light sleep indicator 832 provides information indicating the duration of light sleep in the first sleep period (in...). Figure 8C In one example, 1 hour and 20 minutes). The deep sleep indicator 834 provides information indicating the duration of deep sleep in the first sleep period (in... Figure 8C In the example, 2 hours and 30 minutes). REM sleep indicator 836 provides the REM sleep duration indicating the first sleep period (in Figure 8C (In the example, it is 40 minutes). Wake-up indicator 838 provides information indicating the number of wake-ups during the first sleep period (in...). Figure 8C In the example, there are 3 wake-ups. The sleep-on wait time indicator 840 provides an indication of the first sleep period (in... Figure 8C In the example, the sleep activation wait time is 54 minutes.

[0166] In some implementations, step 604 includes transmitting an audio indication to the user after the first sleep period. As described above, the first data (step 601) may include audio data reproducible as one or more sounds recorded during the first sleep period. During step 602, one or more events such as snoring, choking, or difficulty breathing may be identified, at least in part, based on the first data. These events may be identified in the audio data based, for example, a comparison between the audio data and previously recorded audio data (e.g., using a machine learning algorithm) or the audio data exceeding a predetermined decibel level. The audio data including these events received during step 601 may be stored in storage device 114. Figure 1 The audio data is stored in the storage device 114 for playback, while other audio data that does not identify the event can be deleted to preserve the storage capacity in the storage device 114.

[0167] Although noise associated with certain events (such as snoring, choking, or labored breathing) can be quite loud (e.g., from...) Figure 2 (From the perspective of the bed partner 220), but the user cannot hear these noises because they are asleep. If the user could hear these noises, they would be more likely or encouraged to seek treatment and use a respiratory therapy system in the future to reduce or eliminate these events. Therefore, step 604 may include transmitting portions of the audio data corresponding to the event to the user (e.g., using speaker 142) so that the user can hear these events after the first sleep period.

[0168] In some implementations, step 604 includes transmitting an indication of a breathing signal during the first sleep period to the user (e.g., via display device 172 of user device 170). The indication of the breathing signal may be, for example, a chart or graph. In such implementations, one or more indications of events determined to be associated with the first sleep period (e.g., apnea, snoring, choking, etc.) may be superimposed on the displayed breathing signal.

[0169] Return to reference Figure 6 Step 605 of method 600 includes receiving second data associated with a second sleep period of the user, which follows the first sleep period. The second sleep period differs from the first sleep period in that the user uses the breathing therapy system 120 described herein during the second sleep period. Figure 1 Instead of the first sleep period, the second data may be generated by, for example, the electronic interface 119 and / or user device 170 described herein. Figure 1 )take over.

[0170] The second data (step 605) can be generated using the same sensor as the first data (step 601) or one or more different sensors. In some implementations, both the first data (step 601) and the second data (step 605) are generated by the acoustic sensor 141 ( Figure 1 In other implementations, the first data is generated by an acoustic sensor 141 coupled to or integrated into the user device 170, while the second data is generated by one or more of the sensors 130 described herein coupled to or integrated into the respiratory therapy device 122 (e.g., pressure sensor 132, flow sensor 134, or both). In other implementations, the second data is generated by one or more of the acoustic sensor 141 coupled to or integrated into the user device 170 and the sensors 130 described herein coupled to or integrated into the respiratory therapy device 122.

[0171] The second data is the same as or similar to the first data (step 601) and may include, for example, second breathing data associated with the user, second audio data associated with the user, or both. The second breathing data indicates the user's second breathing signal during at least a portion of the second sleep period (e.g., at least 10%, at least 50%, 75%, at least 90%, etc. of the second sleep period). The breathing signal indicates the user's respiratory rate, inspiratory amplitude, expiratory amplitude, inspiratory-expiratory ratio, etc., or any combination thereof, during at least a portion of the second sleep period. The second audio data may be reproduced as one or more sounds recorded during the second sleep period (e.g., snoring, choking, labored breathing, etc.). The second data (step 602) may be associated with one or more sleep markers in the same or similar manner as the first data (step 601). Figures 13A-13C The method may include receiving a first sleep marker associated with a first sleep period during step 601, and receiving a second sleep marker different from the first sleep marker for a second sleep period during step 605.

[0172] In some implementations, the second sleep period (step 605) is the next immediate sleep period following the first sleep period (step 601) (e.g., the first sleep period is Monday night, and the second sleep period is Tuesday night). In other implementations, one or more other sleep periods exist between the first and second sleep periods (e.g., the first sleep period is Monday night, and the second sleep period is the following Thursday night). The second sleep period can be manually initiated and / or terminated by the user in the same or similar manner as the first sleep period. Figures 5A-5C ).

[0173] Step 606 of method 600 includes determining a second set of sleep-related parameters associated with a second sleep period, at least in part based on second data generated and / or received during step 605. For example, control system 110 may analyze the second data (e.g., data stored in storage device 114) to determine the second set of sleep-related parameters for the second sleep period. For example, information describing the determined second set of sleep-related parameters may be stored in storage device 114. Figure 1 )middle.

[0174] The second set of sleep-related parameters may include, for example, apnea-hypopnea index (AHI), identifiers of one or more events experienced by the user, number of events per hour, event patterns, sleep score, total sleep time, total time in bed, wake-up time, wake-up time, sleep graph, total light sleep time, total deep sleep time, total REM sleep time, number of awakenings, sleep onset wait time, respiratory rate, or any combination thereof. The second set of sleep-related parameters (step 606) may include the same parameters as or different from the first set of sleep-related parameters (step 602). More generally, the second set of sleep-related parameters may include any number of sleep-related parameters (e.g., 1 sleep-related parameter, 2 sleep-related parameters, 5 sleep-related parameters, 50 sleep-related parameters, etc.).

[0175] Step 607 of method 600 includes prompting the user to provide second subjective feedback associated with the second sleep period. For example, information associated with or indicating the second subjective feedback from the user can be received via user device 170 (e.g., via alphanumeric text, speech-to-text, etc.). The second subjective feedback can be received by user device 170 (e.g., via display device 172) and stored in storage device 114. Figure 1 More generally, the user can be prompted at any time after the second sleep period but before the next subsequent sleep period (e.g., 30 seconds, 1 minute, 5 minutes, 15 minutes, 30 minutes, 1 hour, 8 hours, 12 hours, etc. after the second sleep period). The second subjective feedback may include, for example, subjective drowsiness level after the second sleep period, subjective drowsiness level before the second sleep period, subjective sleep satisfaction rating during the second sleep period, or any combination thereof.

[0176] The prompts for the second sleep period (step 607) may be the same as or similar to the prompts for the first sleep period (step 603) described above, including the first prompt 710 ( Figure 7A ), Second prompt 720 ( Figure 7B ), Third Tip 730 ( Figure 7C), Fourth Tip 740 ( Figure 7D (or any combination thereof.) (See reference) Figure 9A In some implementations, a fifth prompt 910 containing alphanumeric text prompts the user to indicate feedback associated with the use of the breathing therapy system during the second sleep period. As shown, the fifth prompt 910 includes multiple user-selectable elements 912 for prompting the user to indicate whether the use of the breathing therapy system is easy, difficult, or uncertain. The user can finalize the selection of one of the user-selectable elements 912 by selecting a navigation element 914. The second subjective feedback may also include activity information and may be in accordance with the above for step 603 ( Figure 14 The same or similar manner described is received.

[0177] Step 608 of method 600 includes causing one or more indications associated with at least a portion of the second sleep-related parameter set to be transmitted to the user after the second sleep period (step 606). This can be achieved using, for example, user device 170. Figure 1 The determined second set of sleep-related parameters is transmitted to the user via alphanumeric text, images, audio, or any combination thereof. One or more indications of the second set of sleep-related parameters (step 606) may be the same as one or more indications of the first set of sleep-related parameters (step 604).

[0178] refer to Figure 9B Sleep apnea indicator 920 and treatment indicator 922 are displayed on user device 170. Figure 1 The sleep apnea indicator 920 provides information indicating whether the user experienced sleep apnea (e.g., based on AHI) during a second sleep period, displayed on the device 172. Figure 9B In the example, the user did not experience sleep apnea during the sleep period (e.g., AHI less than 5), and sleep apnea indicator 920 indicates that the apnea is controlled. Treatment indicator 922 provides information indicating the use of the respiratory therapy system during the second sleep period, including, for example, total sleep time, bedtime, wake-up time, or any combination thereof. The user can navigate to a summary of the second sleep period by selecting (e.g., clicking or tapping) navigation element 924.

[0179] refer to Figure 9C In user device 170 ( Figure 1 The display device 172 displays a summary view 930 of the second sleep period. The summary view 930 includes multiple indicators 932-938. The first indicator 932 and the treatment indicator 922 ( Figure 9BThe first instruction 934 is identical or similar to the second instruction 920 and provides information indicating whether the user experienced sleep apnea (e.g., based on AHI) during the second sleep period. The second instruction 936 provides information indicating the AHI during the second sleep period. The fourth instruction 938 provides information indicating whether the sleep score during the second sleep period and / or the determined sleep score meets or exceeds the target sleep score.

[0180] In some implementations, when the user is using the respiratory therapy system, steps 605-608 can be repeated for one or more additional sleep periods following the second sleep period (e.g., the third, fourth, tenth, one hundredth sleep periods, etc.). When the user is not using the respiratory therapy system and / or any of the other additional sleep periods, the sleep-related parameters determined for these additional sleep periods can be compared with the sleep-related parameters determined for the first sleep period. Similarly, when the user is not using the respiratory therapy system, steps 601-604 can be repeated for one or more additional sleep periods following the first sleep period. For example, when the user is using the respiratory therapy system, steps 605-608 can be repeated for the third sleep period, while when the user is not using the respiratory therapy system, steps 601-605 can be repeated for the fourth sleep period.

[0181] Step 609 of method 600 includes transmitting one or more comparisons between at least a portion of a first set of sleep-related parameters and at least a portion of a second set of sleep-related parameters to a user. For example, one of the first sleep-related parameters associated with a first sleep period may be compared with a corresponding one in the second set of sleep-related parameters associated with a second sleep period. The comparison with the determined second set of sleep-related parameters can be performed, for example, using user device 170 (…). Figure 1 It can be delivered to the user via alphanumeric text, images, graphics or charts (e.g., line graphs or plots, bar graphs or charts, etc.), audio, or any combination thereof.

[0182] refer to Figure 10 The first comparison 1010, the second comparison 1020, and the third comparison 1030 are displayed on the user device 170. Figure 1The first comparison 1010 is a line graph comparing sleep satisfaction associated with a first sleep period (e.g., provided as part of a first subjective feedback in step 603) and sleep period satisfaction associated with a second sleep period (e.g., provided as part of a second subjective feedback in step 607). As shown, sleep satisfaction during the second sleep period is generally improved when the user uses the respiratory therapy system.

[0183] The second comparison 1020 is a bar graph comparing the AHI of the first sleep period and the AHI of the second sleep period. As shown in the figure, comparison 1020 shows that the user experienced severe sleep apnea during the first sleep period (without a respiratory therapy system), but the user only experienced mild sleep apnea during the second sleep period, demonstrating the effectiveness of using a respiratory therapy system during the second sleep period. The third comparison 1030 includes alphanumeric text providing information about a first set of sleep-related parameters associated with the first sleep period and other parameters in the second set of sleep-related parameters associated with the second sleep period.

[0184] Date element 1040 is also displayed along with the first comparison 1010, the second comparison 1020, and the third comparison 1030. By selecting date element 1040, the user can specify a range of dates for sleep periods to be included in the comparison (e.g., all sleep periods between March 4th and March 11th). In this way, the user can view comparisons of more than two sleep periods (e.g., three sleep periods, five sleep periods, seven sleep periods, thirty sleep periods, etc.).

[0185] In some implementations, step 609 includes transmitting an indication to the user (e.g., via display device 172) that is associated with the amount of time the user stops breathing during a first sleep period, a second sleep period, or both. For example, one or more indications may instruct the user to stop breathing for 10 minutes during the first sleep period (e.g., when the breathing therapy system 120 is not in use) and not to stop breathing during the second sleep period (e.g., when the breathing therapy system 120 is in use).

[0186] In some implementations, step 609 includes transmitting one or more instructions to the user associated with one or more events experienced during a first sleep period, a second sleep period, or both. In such implementations, one or more instructions may overlay on at least a portion of a sleep graph associated with the corresponding sleep period (e.g., the same as or similar to sleep graph 500 in Figure 5). For example, the instructions can help the user understand when an event (e.g., apnea) is most likely to occur during sleep (e.g., at what time, in what sleep stage, etc.). As described herein, some users of a respiratory therapy system may begin using the system but remove it during sleep (e.g., because it is uncomfortable). In some cases, the user may be informed that only a certain period of time (e.g., four hours) is required for clinically compliant use of the respiratory therapy system. However, transmitting these instructions can help the user understand that, for example, the event is more likely to occur after the user has been asleep for four hours, thereby encouraging the user to continue using the respiratory therapy system during sleep periods. Therefore, these instructions can help improve user compliance.

[0187] Some users of the respiratory therapy systems described herein (e.g., CPAP systems) find such systems uncomfortable, difficult to use, expensive, and / or aesthetically unappealing. Some users of these systems may also not immediately notice any benefits of use after the first treatment session. As a result, these users may choose not to use their respiratory therapy system as prescribed (e.g., every night) or even discontinue its use entirely. Presenting a comparison between the first sleep period (without the respiratory therapy system) and the second sleep period (with the respiratory therapy system) can help encourage users to continue using the respiratory therapy system in subsequent sleep periods. For example, the comparison can inform users that they are benefiting from using the respiratory therapy system, as demonstrated by lower or improved AHI in the second sleep period, fewer awakenings during the second sleep period, longer sleep blocks (e.g., REM sleep) in the second sleep period, improved subjective feedback on the second sleep period, etc. The comparison can also remind users of negative symptoms and influence negative symptoms felt after the first sleep period when the user is not using the respiratory therapy system.

[0188] Alternatively, in some implementations, method 600 includes providing the user with a recommendation to discontinue the use of the respiratory therapy system. For example, a comparison between the determined set of first sleep-related parameters associated with the first sleep period and the set of second sleep-related parameters associated with the second sleep period (and / or additional sleep periods following the second sleep period) reveals that the user has not benefited from the respiratory therapy system (e.g., AHI remains the same or worse when the respiratory therapy system is used), and the user may have experienced comorbid sleep apnea requiring different treatment or intervention.

[0189] In some implementations, one or more indications displayed during steps 604 and / or 609 include information associated with a selected sleep marker for a first sleep period, a second sleep period, or both. Furthermore, in some implementations, one or more comparisons transmitted to the user during step 609 may also include information associated with a selected sleep marker for a first sleep period, a second sleep period, or both. Comparisons between the first and second sleep markers can help the user select a product (e.g., a respiratory therapy system or device) that helps improve sleep quality. For example, if the first sleep marker indicates no treatment use and the second sleep marker indicates the use of a respiratory therapy system, these comparisons can help the user see differences in sleep (e.g., improvement) when using treatment. As another example, if the first sleep marker indicates the use of a first treatment device (e.g., a first user interface) and the second sleep marker indicates the use of a second treatment device (e.g., a second user interface different from the first user interface), these comparisons can help the user select which treatment device is most effective for improving sleep. Additionally, sleep markers can be used to recommend alternative treatment systems or devices (e.g., MRD instead of a CPAP system) or surgical procedures to the user. In addition, sleep markers can be used to recommend supplementary devices (e.g., bedding, sleep blankets, pillows, etc.) to users to improve sleep quality.

[0190] refer to Figure 15 In some implementations, method 600 includes enabling sleep dashboard 1500 to communicate with a user and / or a third party (e.g., a sleep tutor). For example, sleep dashboard 1500 may be displayed on display device 172. Sleep dashboard 1500 may be displayed after a first sleep period (e.g., during step 604), after a second sleep period (e.g., during step 608 or 609), or after both. Typically, sleep dashboard 1500 includes... Figures 8A-8C Information of the same or similar type as shown in 9A-9C and 10. Specifically, the sleep dashboard 1500 includes guidance information 1510. Typically, guidance information 1510 includes interactions between the user and a third party (e.g., a medical professional such as a physician, a treatment system manufacturer, or a distributor) regarding the use (or lack thereof) of the sleep-related parameters and / or treatment systems (e.g., respiratory therapy systems) identified herein. Guidance information 1510 may include, for example, a first guidance indicator 1512A, a second guidance indicator 1512B, and a third guidance indicator 1512C. These guidance indicators 1512A-1512C provide a summary or overview of the user's progress and can help encourage the user to use the recommended treatment system.

[0191] Additionally, in some implementations, the guidance indicators 1512A-1512C may include a training program (e.g., training or instructing a user on how to use a respiratory therapy system, typically to help the user sleep, etc.). For example, the training program may instruct the device (e.g., user device 170, respiratory therapy device 122, etc.) when and for how long to automatically generate one or more lights and / or sounds. In some examples, the training program causes light of a predetermined color to signal at a predetermined time to notify the user when to wake up and get out of bed. The training program can be scheduled based on multiple setting parameters, including program start time, program end time or duration, program frequency or start date, or any combination thereof. The training program may be stored in memory 114 ( Figure 1 )middle.

[0192] In some implementations, the systems and methods described herein include interactive chat assistant software modules and interfaces. Typically, a chat assistant (also referred to as a chatbot) provides users with automated guidance information (e.g., the same as or similar to the guidance information described above). Specifically, the chat assistant can provide information (e.g., guidance information) in response to input from a user (e.g., a question). The interactive chat assistant interface can be displayed via the user device 170 described herein to prompt the user, for example, to provide input (e.g., a question) using one or more chat windows. Input from the user (e.g., receiving input from the user (e.g., a question)) (e.g., the user can ask a question via alphanumeric text entered via a keyboard or mouse or verbally using a voice-to-text method).

[0193] In this type of implementation, memory 114 ( Figure 1This includes a chat assistant database storing various images, videos, animations, or other media. Such information in the chat assistant database can, for example, help patients set up and configure part of system 100. For example, the chat assistant database may include images or videos, or other helpful content, showing how a patient sets up, turns on, and uses the respiratory therapy system 120, how to adjust, adjust, and position the user interface 124, to reduce the learning curve for patients using the respiratory therapy system 120. Information in the chat assistant database may be associated with or linked to certain inputs from the user (e.g., questions) to deliver appropriate information to the user in response to the input. For example, a user may provide input with a question (e.g., How do I put on my interface / mask?) and the chat assistant may provide information in response to that question (e.g., images / videos with text / audio instructions for placement on the interface / mask). While the chat assistant interface is described as automatic, in some implementations, the chat assistant interface may allow the user to speak to a live human representative. For example, the chat assistant interface includes options (e.g., optional elements) that allow the user to choose to speak to a live representative. As another example, if the chat assistant cannot answer a user's question, the chat assistant may automatically direct the user to a live representative.

[0194] As described herein, in some implementations, steps 601-604 are performed without using the first sleep period of the respiratory therapy system, and steps 605-607 are performed for the second sleep period in which the user uses the respiratory therapy system. In other implementations, steps 601-604 may be repeated once or more for sleep periods in which the user does not use the respiratory therapy system, and then steps 605-609 may be performed for one or more sleep periods in which the user uses the respiratory therapy system. For example, steps 601-604 may be repeated for the first, second, and third sleep periods in which the user does not use the respiratory therapy system. As described herein, the determined sleep-related parameters can be used to diagnose a user with a sleep-related disorder, allowing the user to be prescribed a respiratory therapy system.

[0195] Repeating steps 601-604 for multiple sleep periods in the absence of a respiratory therapy system can be advantageous because sleep-related parameters determined for multiple sleep periods can be evaluated to diagnose a user with a sleep-related disorder. For example, instead of diagnosing the user based on the AHI of the first sleep period, the AHI of the three sleep periods can be averaged for diagnosis. More generally, any suitable statistical analysis can be applied to sleep-related parameters for multiple sleep periods without using a respiratory therapy system to help accurately diagnose or identify sleep-related disorders (e.g., outlier removal, averaging, weighted averaging, etc.).

[0196] Although method 600 is described herein as including each of steps 601-609, more generally, method 600 may include any suitable combination of steps 601-609. For example, a first alternative method may include steps 601, 602, 603, 605, 606, 607, and 609. As another example, a second alternative method may include steps 601, 602, 605, 606, and 609. Furthermore, although method 600 has been shown and described herein as occurring in a particular order, more generally, the steps of method 600 may be performed in any suitable order.

[0197] refer to Figure 11 The illustration shows a method 1100 according to some implementations of this disclosure. One or more steps of method 1100 can be implemented using the system 100 described herein. Figure 1 It can be implemented by any element or aspect of ).

[0198] Step 1101 of method 1100 includes generating and / or receiving first data associated with a user's first sleep period. The first data may include, for example, first respiratory data associated with the user, first audio data associated with the user, or both. The first respiratory data indicates a user's first respiratory signal during at least a portion of the first sleep period (e.g., at least 10%, at least 50%, 75%, at least 90%, etc. of the first sleep period). The respiratory signal indicates the user's respiratory rate, respiratory rate variation, tidal volume, inspiratory amplitude, expiratory amplitude, inspiratory-expiratory ratio, etc., or any combination thereof, during at least a portion of the first sleep period. The first audio data may be reproduced as one or more sounds (e.g., snoring, choking, difficulty breathing, etc.) recorded during the first sleep period. More generally, the first data may include any physiological data associated with the user's first sleep period.

[0199] The first data can be obtained, for example, from the sensor 130 described herein ( Figure 1 One or more of the data are generated. In some implementations, both the first breathing data and the first audio data are generated by the acoustic sensor 141. Figure 1 The first breathing data is generated by the acoustic sensor 141, while the first audio data is generated by a separate and different microphone (e.g., the same as or similar to microphone 140) from the acoustic sensor. The first data may be generated by, for example, the electronic interface 119 described herein and / or the user device 170. Figure 1 )take over.

[0200] Step 1102 of method 1100 includes determining a first set of sleep-related parameters associated with a first sleep period of the user, at least in part, based on the first data. For example, system 100 ( Figure 1 The control system 110 can analyze first data (e.g., data stored in storage device 114) to determine a first set of sleep-related parameters for a first sleep period. For example, information describing the determined first set of sleep-related parameters can be stored in storage device 114. Figure 1 )middle.

[0201] The first set of sleep-related parameters may include, for example, the apnea-hypopnea index (AHI), identifiers of one or more events experienced by the user, the number of events per hour, event patterns, total sleep time, total bedtime, wake-up time, wake-up time, sleep graph, total light sleep time, total deep sleep time, total REM sleep time, number of awakenings, sleep onset wait time, or any combination thereof. In some implementations, the first set of sleep-related parameters may include a sleep score, such as the sleep score described in International Publication No. WO2015 / 006364 and U.S. Patent Publication No. 2016 / 0151603, the entire contents of which are incorporated herein by reference. The first set of sleep-related parameters may include any number of sleep-related parameters (e.g., 1 sleep-related parameter, 2 sleep-related parameters, 5 sleep-related parameters, 50 sleep-related parameters, etc.).

[0202] In some implementations, method 1100 also includes methods similar to method 600 described herein. Figure 6 Step 603 receives the first subjective feedback associated with the first sleep period following the first sleep period in the same or similar manner.

[0203] Step 1103 of method 1100 includes receiving second data associated with a second sleep period of the user. The second data may be generated by, for example, the electronic interface 119 and / or user device 170 described herein. Figure 1 The data is received. The second data can be generated using the same sensor as the first data or one or more different sensors (step 1101). In some implementations, both the first data (step 1101) and the second data (step 1105) are received by the acoustic sensor 141. Figure 1The first data is generated by an acoustic sensor 141 coupled to or integrated into the user device 170, while the second data is generated by one or more of the sensors 130 described herein coupled to or integrated into the respiratory therapy device 122 (e.g., pressure sensor 132, flow sensor 134, or both). In other implementations, the second data is generated by one or more of the acoustic sensor 141 coupled to or integrated into the user device 170 and the sensors 130 described herein coupled to or integrated into the respiratory therapy device 122.

[0204] The second data is the same as or similar to the first data (step 1101) and may include, for example, second breathing data associated with the user, second audio data associated with the user, or both. The second breathing data indicates the user's second breathing signal during at least a portion of the second sleep period (e.g., at least 10%, at least 50%, 75%, at least 90%, etc. of the second sleep period). The breathing signal indicates the user's respiratory rate, inspiratory amplitude, expiratory amplitude, inspiratory-expiratory ratio, etc., or any combination thereof, during at least a portion of the second sleep period. The second audio data may be reproduced as one or more sounds recorded during the second sleep period (e.g., snoring, choking, labored breathing, etc.).

[0205] The second sleep period follows the first sleep period. In some implementations, the second sleep period (step 1103) is the next immediate sleep period after the first sleep period (step 1101) (e.g., the first sleep period is Monday night, and the second sleep period is Tuesday night). In other implementations, one or more other sleep periods exist between the first and second sleep periods (e.g., the first sleep period is Monday night, and the second sleep period is the following Thursday night). The second sleep period can be manually initiated and / or terminated by the user in the same or similar manner as the first sleep period. Figures 5A-5C ).

[0206] Step 1104 of method 1100 includes determining a second set of sleep-related parameters associated with a second sleep period of the user, at least in part, based on second data. For example, control system 110 may analyze the second data (e.g., data stored in storage device 114) to determine the second set of sleep-related parameters for the second sleep period. For example, information describing the determined second set of sleep-related parameters may be stored in storage device 114. Figure 1The second set of sleep-related parameters may include, for example, apnea-hypopnea index (AHI), identifiers of one or more events experienced by the user, number of events per hour, event patterns, sleep score, total sleep time, total time in bed, wake-up time, wake-up time, sleep graph, total light sleep time, total deep sleep time, total REM sleep time, number of awakenings, sleep onset waiting time, respiratory rate, or any combination thereof. The second set of sleep-related parameters (step 1106) may include the same parameters as or different from the first set of sleep-related parameters (step 1102). More generally, the second set of sleep-related parameters may include any number of sleep-related parameters (e.g., 1 sleep-related parameter, 2 sleep-related parameters, 5 sleep-related parameters, 50 sleep-related parameters, etc.).

[0207] In some implementations, method 1100 further includes methods similar to method 600 described herein. Figure 6 Step 607 receives second subjective feedback associated with the second sleep period following the second sleep period in the same or similar manner.

[0208] Step 1105 of method 1100 includes receiving third data associated with a variable condition. The third data may be provided, for example, by the electronic interface 119 and / or user device 170 described herein. Figure 1 The third data may be received using the same sensor as the first data (step 1101) and / or the second data (step 1103) or one or more different sensors. In other implementations, the third data may be received via or in response to one or more user inputs. In some implementations, at least a portion of the third data may be received (i) before the first sleep period, (ii) during at least a portion of the first sleep period, (iii) after the first sleep period and before the second sleep period, (iv) during at least a portion of the second sleep period, (v) after the second sleep period, or (vi) any combination thereof.

[0209] Typically, variable conditions are conditions that change (e.g., altered by the user) between a first sleep period and a second sleep period. Information about variable conditions can provide the user with insights into how these conditions affect their sleep. Variable conditions can be associated with the first sleep period, the second sleep period, or both. In some implementations, variable conditions are associated with the user's use of a treatment system during the first sleep period, the second sleep period, or both. For example, a user may not use the treatment system during the first sleep period (e.g., the respiratory therapy system 120 described herein), but may use it during at least a portion of the second sleep period. In this example, third data reflects the fact that the user did not use the treatment system during at least a portion of the second sleep period (e.g., as indicated by one or more sensors coupled to or integrated into the respiratory therapy system). As another example, a user may use the treatment system for a first duration during the first sleep period and for a second duration during the second sleep period. As yet another example, a user may use a first treatment system (e.g., an alternative therapy system such as MRD) during at least a portion of the first sleep period and a second treatment system (e.g., a respiratory therapy system) during at least a portion of the second sleep period. In this example, the user can manually provide instructions on the use of the first treatment system and / or the second treatment system, or the use of the first treatment system and / or the second treatment system can be detected automatically.

[0210] In some implementations, variable conditions are associated with sleep environment conditions. Sleep environment conditions can be associated with supplementary devices or supplementary therapies utilizing supplementary devices. For example, a monitoring device can be bedding used by the user during a first sleep period and / or a second sleep period, such as, for example, a pillow, pillowcase, mattress, mattress cover, mattress upholstery, sheet, blanket, or any combination thereof. For example, the user may use a first pillow during at least a portion of the first sleep period and a second pillow (or no pillow) during at least a portion of the second sleep period. In this example, third data may be received via or in response to one or more user inputs (e.g., instructions associated with the bed) or automatically (e.g., by analyzing data from camera 150 using, for example, an object recognition algorithm). For example, the presence of supplementary devices can be automatically detected by communicating with and / or identifying any "smart" device, including, for example, one or more supplementary devices, such as bed, blanket, or mattress sensors with communication capabilities (e.g., Wi-Fi or Bluetooth). In this example, one or more sensors 130 (e.g., camera 150) can be used to scan complementary devices (e.g., a bed) to estimate, for example, the type of bedding used. Such scanning may include, for example, scanning of QR codes and / or RFID tags. The system can also be connected to other devices, such as thermostats (e.g., Google Nest). TM Thermostats, air purifiers, humidifiers, electrically actuated curtains or blinds, audio equipment programs, etc., or any combination thereof.

[0211] In other implementations, sleep environment conditions are associated with ambient temperature, ambient humidity, ambient lighting conditions, location, or any combination thereof. For example, a first sleep period may occur at a first location (e.g., at home), while a second sleep period may occur at a second location (e.g., a hotel). In this example, third data includes location-related information, such as information provided by one or more inputs from the user or sensors (e.g., GPS or other location-based sensors). As another example, a first ambient temperature may exist during the first sleep period, and a second ambient temperature may exist during the second sleep period (e.g., as determined by a temperature sensor as described herein). As yet another example, ambient lighting conditions can be modified (e.g., ambient lighting can be turned on or off, the intensity or brightness of ambient lighting can be increased or decreased, the color of ambient lighting can be modified, etc.). As yet another example, ambient sounds (e.g., audio such as music from one or more speakers or a TV) can be modified (e.g., turned on or off, the volume can be increased or decreased, etc.).

[0212] In some implementations, the variable status is associated with the user's activity level. For example, the variable status may be associated with the user's first activity level before the first sleep period, the user's second activity level after the first sleep period and before the second sleep period, or both. In such implementations, third data can be obtained from the activity tracker 180 described herein. Figure 1 The variable is generated or obtained. In other implementations, the variable is associated with the user's diet. For example, the variable may be associated with the user's caffeine or alcohol intake (e.g., before the first sleep period, before the second sleep period, or both).

[0213] Step 1106 of method 1100 includes transmitting one or more instructions to a user. One or more instructions may be used, for example, by user device 170 (…). Figure 1 The indications may be delivered to the user via alphanumeric text, images, graphics or charts (e.g., line graphs or plots, bar charts or graphs, etc.), audio, or any combination thereof. These indications may be associated with variable conditions, a first sleep period, a second sleep period, or any combination thereof. In some implementations, the indications may also be associated with one or both of a first set of sleep-related parameters and a second set of sleep-related parameters (e.g., in the same or similar manner as step 609 of method 600). In such implementations, the indications may include a comparison between a first sleep-related parameter in the first set of sleep-related parameters for the first sleep period and a second sleep-related parameter in the second set of sleep-related parameters for the second sleep period.

[0214] Typically, one or more instructions are delivered to the user to illustrate or demonstrate the impact of variable conditions on sleep. For example, one or more instructions conveying the impact of a variable condition on sleep, along with a comparison between one or more sleep-related parameters from a first set of sleep-related parameters and one or more sleep-related parameters from a second set of sleep-related parameters, can help encourage the user to change the variable conditions for future sleep periods to help achieve better sleep. For example, if the variable condition is bedding, such as a pillow used during a second sleep period instead of the pillow used during the first sleep period, a comparison between sleep-related parameters (such as AHI) for the first and second sleep periods can show the user that the pillow improves sleep quality (e.g., by helping to prevent OSA). In this way, instructions can provide insight into the impact of one or more variable conditions on a user's sleep.

[0215] In some implementations, steps 1103-1106 can be repeated for one or more additional sleep periods following the second sleep period (e.g., the third sleep period, the fourth sleep period, the tenth sleep period, the hundredth sleep period, etc.). The sleep-related parameters determined for these additional sleep periods can be compared with those determined for the first sleep period to further illustrate the impact of time-varying states on sleep.

[0216] refer to Figure 12 The illustration shows method 1200 according to some implementations of this disclosure. One or more steps of method 1100 can be used with system 100 described herein. Figure 1 It can be implemented by any element or aspect of ).

[0217] Step 1201 of method 1200 includes receiving data associated with a user. The data may include, for example, physiological data associated with the user during sleep periods. The data associated with the user can be compared with data received in step 601 of method 600. Figure 6 The first data received during the process or in step 1101 of method 1100 Figure 11 The first data received during this period is the same or similar. The data may be from one or more sensors 130 described herein. Figure 1 The data is generated and received by, for example, the electronic interface 119 and / or the user device 170 described herein.

[0218] Step 1202 of method 1200 includes determining a first emotion score associated with the user, at least in part, based on data associated with the user. Typically, this is implemented in a way that indicates the level of anxiety or stress the user is currently experiencing. For example, the user may experience anxiety, stress, worry, discomfort, etc., before using the breathing therapy system 120. Higher levels of stress, anxiety, discomfort, etc., may make it more difficult for the user to fall asleep and / or may prompt the user to discontinue using the breathing therapy system 120. The quantification of the user's stress or anxiety via the emotion score can be used to suggest or recommend when the user should begin using the breathing therapy system 120.

[0219] Emotion ratings can be, for example, numerical values ​​on a predetermined scale (e.g., between 1 and 10, between 1 and 100, etc.), letter grades (e.g., A, B, C, D, or F), or descriptors (e.g., high, low, medium, poor, normal, abnormal, fair, good, excellent, average, below average, above average, needs improvement, satisfied, etc.). In some implementations, emotion ratings are determined relative to previous emotion ratings (e.g., emotion rating is better than previous emotion ratings (e.g., emotion rating from the previous day), emotion rating is worse than previous emotion ratings, emotion core is the same as previous emotion ratings, etc.) or benchmark emotion ratings (e.g., emotion rating is 50% greater than benchmark emotion ratings, emotion rating is equal to benchmark emotion ratings, etc.).

[0220] In some implementations, step 1202 includes determining one or more physiological parameters associated with the user, such as respiratory rate, heart rate variability, cardiac waveform, respiratory rate variability, respiratory depth, tidal volume, inspiratory amplitude, expiratory amplitude, inspiratory-expiratory ratio, perspiration, temperature (e.g., ambient temperature, body temperature, core body temperature, surface temperature, etc.), blood oxygen, photoplethysmography (e.g., which can be used to measure SpO2, peripheral perfusion, pulse rate, other cardiac-related parameters, etc.), pulse transit time, blood pressure, or any combination thereof.

[0221] These physiological parameters can indicate a user's mood or anxiety. For example, hyperventilation, increased respiratory rate (e.g., relative to a user-associated baseline, a group of users' baseline, normative values, etc.), decreased heart rate variability (e.g., relative to a user-associated baseline, a group of users' baseline, normative values, etc.), arrhythmias, heart rate, and blood pressure (e.g., adjusted for whether the user is hypertensive or not, nocturnal decrease in blood pressure, etc.) can indicate an increased level of anxiety. Conversely, increased heart rate variability (e.g., relative to a user-associated baseline, a group of users' baseline, normative values, etc.) can indicate a more relaxed state. Mood scores can be determined at least in part by scaling or standardizing one or more physiological parameters based on previously recorded physiological parameters of the user stored in the aforementioned user profile, physiological parameters of several other previously recorded users, or both. Alternatively, mood scores can be determined by scaling (one or more) associated physiological parameters using expected or target values ​​of (one or more) parameters.

[0222] In some implementations, the data received in step 1201 also includes subjective feedback from the user, and step 1202 includes determining an emotion rating based at least in part on the received subjective feedback. Subjective feedback may include, for example, self-reported user feedback representing the user's current level of stress or anxiety, in the form of descriptive indicators (e.g., high, low, moderate, uncertain), numerical values ​​(e.g., on a scale of 1 to 10, where 10 is very stressed and 0 is not stressed at all), etc. Subjective feedback may be received, for example, via user device 170. In such implementations, method 1200 may include transmitting one or more prompts to the user to request subjective feedback (e.g., via display device 172 of user device 170).

[0223] In some implementations, step 1202 includes determining an emotion score based at least in part on demographic information associated with the user. For this information, demographic information may include indications of the user's age, gender, body mass index (BMI), height, ethnicity, relationship or marital status, family history of insomnia, employment status, education level, socioeconomic status, or any combination thereof. Demographic information may also include medical information associated with the user, such as indications of one or more medical conditions associated with the user, medication use, or both. The demographic information may be stored in memory 114 ( Figure 1 The information is received and stored therein. Demographic information may be provided manually by the user, for example, via user device 170 (e.g., via a questionnaire or survey presented via display device 172). Alternatively, demographic information may be automatically collected from one or more data sources associated with the user (e.g., medical records).

[0224] Step 1203 of method 1200 includes determining a first level of drowsiness associated with the user, at least in part, based on data associated with the user. The drowsiness level typically indicates the user's fatigue, drowsiness, alertness, and / or consciousness, and more generally indicates the degree to which the user has fallen asleep. The drowsiness level can be determined and / or expressed in various ways. For example, the drowsiness level can be a scaled value within a predetermined range (e.g., between 1 and 10), where the highest value indicates extreme drowsiness and the lowest value indicates no drowsiness (or vice versa). Alternatively, the drowsiness level can be represented using subjective descriptors (e.g., extremely drowsy, very drowsy, neutral, alert, very alert, extremely alert, etc.). Other examples of expressing drowsiness levels include using the Epworth drowsiness scale, the Stanford drowsiness scale, the Karolinska drowsiness scale, etc.

[0225] Drowsiness levels can be determined based on various types of data or combinations of data. In one example, it can be based on data from the EEG sensor 158 described herein. Figure 1 Physiological data from camera 150 can be used to determine drowsiness levels. In another example, data from camera 150 can be used to determine drowsiness levels. Figure 1 Data can be used to determine drowsiness levels by identifying one or more attributes of the user's eyes that indicate drowsiness (e.g., measuring vertical eye opening or eye height, eye opening, eye closing, blinking, eye movements, pupil dilation, etc.). In another example, drowsiness data can be determined based on the user's heart rate, heart rate variability, respiratory rate, respiratory rate variability, body temperature, or any combination thereof.

[0226] In some implementations, step 1201 includes receiving subjective feedback from the user, and step 1202 includes determining an initial level of drowsiness based at least in part on the subjective feedback. Subjective feedback may include, for example, self-reported subjective levels of drowsiness (e.g., tired, sleepy, average, moderate, alert, resting, etc.). For example, information associated with or indicating feedback from the user may be received via an external device 170 (e.g., via alphanumeric text, speech-to-text, etc.). In some implementations, method 1200 includes prompting the user to provide feedback. For example, control system 110 may cause one or more prompts to be displayed on external device 170 (…). Figure 1 On the display device 172, the external device provides an interface for the user to provide feedback (e.g., the user clicks or taps to input feedback, the user inputs feedback using an alphanumeric keypad, etc.). The received user feedback can be stored, for example, in the storage device 114 described herein. Figure 1 )middle.

[0227] Step 1204 of method 1200 includes prompting the user to interact with the treatment system based at least in part on the implementation method, drowsiness level, or both. As described herein, many users of a breathing therapy system may not be motivated to use the system as prescribed for a variety of reasons. Typically, users need several steps to set up the breathing therapy system before use, which can take several minutes. This setup process can be another reason why a given user may decide not to use the breathing therapy system. For example, if a user is too tired or in a bad mood before bedtime, they may decide to go to sleep rather than set up and use the breathing therapy system. Conversely, if a user is alert and in a good mood or motivated, they are more likely to spend time setting up and using the therapy system. For these and other reasons, it would be advantageous to prompt the user to interact with (e.g., set up) the therapy system at a predetermined time based at least in part on mood ratings and / or drowsiness levels.

[0228] For example, in some implementations, a prompt is sent to the user in response to a first emotion score meeting a predetermined condition. The predetermined condition could indicate that the user's anxiety or stress is at an acceptable level (e.g., allowing the user to fall asleep and begin using the breathing therapy system 120). In other words, when the implementation meets the predetermined condition, the user is sufficiently relaxed, making it more likely that the user will set up and use the breathing therapy system 120. For example, if the implementation is a value that indicates higher anxiety or stress, the predetermined condition could be a value that indicates the user's anxiety or stress is at an acceptable level. In this example, the implementation meets the predetermined condition if it is equal to or less than the predetermined condition.

[0229] In some implementations, predetermined conditions are determined at least in part based on previously recorded physiological data associated with the user. In such implementations, machine learning algorithms can be used to determine the predetermined conditions. The machine learning algorithm can be trained (e.g., using supervised or unsupervised training techniques) using previously recorded physiological data associated with the user, such that the machine learning algorithm is configured to determine the predetermined conditions. In such implementations, the previously recorded physiological data may include corresponding data related to the user's ability to fall asleep, such as sleep onset wait time, wake-up time after falling asleep, sleep efficiency, segmentation index, bedtime, total sleep time, or any combination thereof. As described herein, the previously recorded data used to train the machine learning algorithm may also include subjective feedback from the user.

[0230] For example, in some implementations, a prompt is sent to the user in response to a first drowsiness level meeting a predetermined condition or threshold. The predetermined condition or threshold may indicate an acceptable level of drowsiness or alertness in the user (e.g., enabling effective setup of the breathing therapy system 120). In other words, when the first drowsiness level meets the predetermined condition, the user is adequately alerted, making it more likely that the user will set up and use the breathing therapy system 120. For example, if a higher drowsiness level represents more fatigue and less alertness, the predetermined condition may be a value indicating that the user's anxiety or stress is at an acceptable level. In this example, if the drowsiness level is equal to or less than the predetermined condition, the mood score meets the predetermined condition.

[0231] In some implementations, the predicted sleep onset time can be determined at least in part based on a determined level of drowsiness. For example, the predicted sleep onset time may indicate when a user is likely to fall asleep within a predetermined time frame (e.g., within 1 minute, within 5 minutes, within 15 minutes, within 1 hour, within 3 hours, etc.) or within a time range (e.g., between approximately 5 and 15 minutes, between approximately 15 and 30 minutes, etc.). The predicted sleep onset time can be determined, for example, using a trained machine learning algorithm (e.g., trained using prior data from one or more users to receive drowsiness levels as input and determine the predicted sleep onset time as output). In some implementations, the user may be consuming media (e.g., watching, listening, etc.) before initiating a sleep session. In such implementations, the methods and systems described herein can be configured to stop media playback or modify one or more parameters of the media (e.g., volume, brightness, etc.) based on the predicted sleep onset time.

[0232] refer to Figure 16A and 16B One or more trend views can be displayed according to some implementations of this disclosure (e.g., as part of any of the methods disclosed herein). For example, display device 172 can be used to display such trend views.

[0233] refer to Figure 16A A trend view 1600 is displayed on a display device 172. The trend view 1600 includes a week filter 1602 and a month filter 1604. The user can select (e.g., click or tap) the week filter 1602 to view the trend over a week. The user can select (e.g., click or tap) the month filter 1604 to view the trend over a month. Figure 16A In the example shown, month filter 1604 is selected. The trend view also includes a month / week selector 1606, which allows users to filter specific weeks or months (e.g., by clicking or tapping an arrow or other optional element). For example, if month filter 1604 is selected, a user can use month / week selector 1606 to select a month (e.g., September 2020).

[0234] The trend view 1600 includes a sleep apnea curve 1610 and a breathing curve 1620. The sleep apnea curve 1610 typically indicates the average severity of sleep apnea (e.g., severe, moderate, mild, normal (no sleep apnea)) when the user is not using a breathing therapy system (e.g., a marked sleep test) and when the user is using a breathing therapy system (e.g., marked CPAP therapy). The sleep apnea curve 1610 can also help visualize the benefits of using a breathing therapy system and can therefore further help encourage or promote its use. The sleep apnea curve 1620 typically indicates sleep apnea over a selected time period (e.g., a month or a week) (e.g., using AHI representation along the y-axis).

[0235] refer to Figure 16B The trend view 1600 includes a sleep quality curve 1630 and a sleep consistency curve 1640. The sleep quality curve 1630 generally indicates sleep quality over a selected time period (e.g., month, week, etc.). For example, sleep quality can be represented using one of the sleep ratings described herein (e.g., using a line graph or other suitable diagram, chart, or plot). Additionally, the user can scroll (e.g., by waving left or right) to see additional days and / or weeks within a specified time range. The sleep consistency curve 1640 typically indicates sleep consistency over a selected time period (e.g., month, week, etc.). For example, sleep quality can be shown on a curve graph where the y-axis is time (e.g., between 8 am and 10 pm), with lines or bands for each day representing sleep stages or states (e.g., using different colors for different sleep stages or states, such as a first color for light sleep, a second color for deep sleep, and a third color for REM sleep). In some implementations, each bar or line in the sleep consistency graph can be selected by the user (e.g., by clicking or tapping) to display additional information (e.g., total sleep time in hours and minutes, date, etc.).

[0236] One or more elements, aspects or steps or any part thereof from any of claims 1-114 may be combined with one or more elements, aspects or steps or any part thereof from any of the other claims 1-114 to form one or more additional embodiments and / or claims of this disclosure.

[0237] While this disclosure has been described with reference to one or more specific embodiments or implementations, those skilled in the art will recognize that many changes can be made thereto without departing from the spirit and scope of this disclosure. Each of these implementations and its obvious variations are considered to fall within the spirit and scope of this disclosure. It is also contemplated that further implementations of aspects of this disclosure may combine any number of features from any of the implementations described herein.

Claims

1. A system for analyzing sleep-related parameters, comprising: Memory on which machine-readable instructions are stored; as well as A control system, coupled to the memory, includes one or more processors configured to execute the machine-readable instructions to: Receive first data associated with a user’s first sleep period, the first data including (i) first breathing data associated with the user, (ii) first audio data reproducible as one or more sounds recorded during the first sleep period, or (iii) both (i) and (ii), wherein the user does not use the breathing therapy system during the first sleep period; A first set of sleep-related parameters associated with the user's first sleep period is determined, at least in part, based on the first data; Receive second data associated with a second sleep period of the user, the second data including (i) second breathing data associated with the user, (ii) second audio data reproducible as one or more sounds recorded during the second sleep period, or (iii), (i) and (ii) both, wherein the user uses the breathing therapy system during at least a portion of the second sleep period, the use being automatically detected by the system based on a treatment use signal received from the breathing therapy system; A second set of sleep-related parameters associated with the user's second sleep period is determined, at least in part, based on the second data; One or more instructions associated with the first sleep period, the second sleep period, or both, are transmitted to the user via a user device after the second sleep period to help encourage the user to use the respiratory therapy system; and After the completion of the second sleep period and before the start of the subsequent sleep period, counseling information from a third party is transmitted to the user. This counseling information is provided by a third-party counseling entity that is different from the user and the respiratory therapy system and is not the user's bed partner, and is provided via a remote counseling interface.

2. The system according to claim 1, wherein, The one or more instructions also include a recommendation to discontinue the use of the respiratory therapy system.

3. The system according to claim 1, wherein, The one or more indications include (i) an indication associated with at least a portion of the first sleep-related parameter set, (ii) an indication associated with at least a portion of the second sleep-related parameter set, or both (iii), (i), and (ii).

4. The system according to any one of claims 1 to 3, wherein, The first set of sleep-related parameters and the second set of sleep-related parameters include the apnea-hypopnea index (AHI), identification of one or more events experienced by the user, the number of events per hour, the pattern of events, sleep score, total sleep time, total time in bed, wake-up time, wake-up time, sleep graph, total light sleep time, total deep sleep time, total REM sleep time, number of wake-ups, sleep onset waiting time, or any combination thereof.

5. The system according to claim 4, wherein, The one or more indications include an indication of the first AHI during the first sleep period and an indication of the second AHI during the second sleep period.

6. The system according to claim 4, wherein, The one or more events include snoring, sleep apnea, central sleep apnea, obstructive sleep apnea, mixed sleep apnea, insufficiency, mask leakage, restless legs, sleep disturbance, choking, increased heart rate, difficulty breathing, asthma attack, seizure, or any combination thereof.

7. The system according to any one of claims 1 to 3, wherein, The one or more processors are also configured to execute the instructions in order to: Receive first subjective feedback from the user associated with the first sleep period; and The user receives second subjective feedback associated with the second sleep period.

8. The system according to claim 7, wherein, The one or more processors are also configured to execute the instructions in order to: The user is prompted to provide the first subjective feedback after the first sleep period and before the second sleep period; as well as The user is prompted to provide a second subjective feedback after the second sleep period.

9. The system according to claim 7, wherein, The first subjective feedback includes the subjective level of drowsiness after the first sleep period, the subjective level of drowsiness before the first sleep period, the subjective sleep satisfaction rating of the first sleep period, or any combination thereof.

10. The system according to claim 7, wherein, The one or more indications include (i) an indication of at least a portion of the first subjective feedback during the first sleep period, (ii) an indication of at least a portion of the second subjective feedback during the second sleep period, or (iii) both (i) and (ii).

11. The system according to any one of claims 1 to 3, wherein, The first set of sleep-related parameters includes the identification of one or more events experienced by the user during the first sleep period.

12. The system according to claim 11, wherein, The one or more processors are also configured to execute the instructions in order to: A portion of the first audio data associated with one or more events experienced by the user during the first sleep period is transmitted to the user via a speaker after the first sleep period.

13. The system according to any one of claims 1 to 3, wherein, The first sleep period, the second sleep period, or both (i) begin in response to receiving a selection of a first user-selectable element displayed on the display device, and (ii) terminate in response to receiving a selection of a second user-selectable element displayed on the display device.

14. The system according to any one of claims 1 to 3, wherein, The first data, the second data, or both are generated by one or more sensors.

15. The system according to claim 14, wherein, The one or more sensors include acoustic sensors, microphones, speakers, or any combination thereof.

16. The system according to claim 14, wherein, The one or more sensors include (i) an acoustic sensor having a first microphone and a speaker, and (ii) a second microphone.

17. The system according to claim 16, wherein, The first breathing data and the second breathing data are generated by the acoustic sensor, and the first audio data and the second audio data are generated by the second microphone.

18. The system according to claim 14, wherein, The one or more sensors are connected to or integrated into the user device.

19. The system according to claim 18, wherein, The user device is a smartphone.

20. The system according to claim 18, wherein, The user device is a smart speaker device that includes a speaker and a microphone.

21. The system according to any one of claims 1 to 3, wherein, The one or more indications include a comparison between a first sleep-related parameter in the first sleep-related parameter set for the first sleep period and a second sleep-related parameter in the second sleep-related parameter set for the second sleep period.

22. The system according to claim 21, wherein, The first sleep-related parameter in the first sleep-related parameter set is the first AHI, and the second sleep-related parameter in the second sleep-related parameter set is the second AHI.

23. The system according to claim 22, wherein, The comparison is a bar chart.

24. The system according to claim 7, wherein, The one or more indications include a comparison between a portion of the first subjective feedback during the first sleep period and a portion of the second subjective feedback during the second sleep period.

25. The system according to claim 24, wherein, The first subjective feedback includes a first subjective sleep satisfaction during the first sleep period, and the second subjective feedback includes a second subjective sleep satisfaction during the second sleep period.

26. The system according to claim 25, wherein, The comparison is a line graph.

27. The system according to any one of claims 1 to 3, wherein, The one or more processors are also configured to execute the instructions in order to: (i) determining whether the user experiences comorbid insomnia and sleep apnea based at least in part on the first sleep period, (ii) at least in part on the second sleep period, or (iii) both (i) and (ii).

28. The system according to any one of claims 1 to 3, wherein, The first respiratory data indicates a first respiratory signal of the user during at least a portion of the first sleep period, and the second respiratory data indicates a second respiratory signal of the user during at least a portion of the second sleep period.

29. The system according to claim 28, wherein, The one or more processors are also configured to execute the instructions so as to: (i) At least a portion of the first respiratory signal is displayed on the display device after the first sleep period. (ii) At least a portion of the second respiratory signal is displayed on the display device after the second sleep period, or Both (iii), (i) and (ii).

30. The system according to claim 29, wherein, The one or more processors are also configured to execute the instructions so as to: (i) Indications of one or more events experienced by the user during the first sleep period are superimposed on the display representation of the first respiratory signal. (ii) Indications of one or more events experienced by the user during the second sleep period are superimposed on the display of the second respiratory signal, or Both (iii), (i) and (ii).

31. The system according to any one of claims 1 to 3, wherein, The one or more processors are also configured to execute the instructions to determine a first sleep state during the first sleep period based at least in part on the first set of sleep-related parameters.

32. The system according to claim 31, wherein, The first sleep state is light sleep apnea, moderate sleep apnea, or severe sleep apnea.

33. The system according to claim 32, wherein, The one or more processors are also configured to execute the instructions to determine a second sleep state during the second sleep period, at least in part, based on the second set of sleep-related parameters.

34. The system according to claim 33, wherein, The second sleep state is controlled.

35. The system according to claim 34, wherein, The one or more processors are also configured to execute the instructions to transmit an indication of the first sleep state to the user after the first sleep period and an indication of the second sleep state to the user after the second sleep period.

36. The system according to any one of claims 1 to 3, wherein, The first respiratory data indicates the user's respiratory rate during at least a portion of the first sleep period, and the second respiratory data indicates the user's respiratory rate during at least a portion of the second sleep period.

37. The system according to any one of claims 1 to 3, wherein, The first respiratory data indicates the user's respiratory rate during at least a portion of the first sleep period, and the second respiratory data indicates (i) the user's respiratory rate during at least a portion of the second sleep period, (ii) the user's tidal volume during at least a portion of the second sleep period, or (iii) both of (i) and (ii).

38. A computer program product comprising instructions that, when executed by a computer, cause the computer to function as a system according to any one of claims 1 to 37.

39. The computer program product according to claim 38, wherein, The computer program product is a non-transitory computer-readable medium.

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