Systems and methods for screening and managing insomnia

Abstract

A method includes receiving physiological data associated with a user during a sleep session. The method also includes determining a sleep-wake signal of the user during the sleep session based at least in part on the received physiological data. The method also includes determining one or more sleep-related parameters of the user during the sleep session based at least in part on the sleep-wake signal. The method also includes determining that the user experienced insomnia during the sleep session based at least in part on at least one of the one or more sleep-related parameters. The method also includes identifying a type of insomnia experienced by the user based at least in part on the one or more sleep-related parameters.

Description

[Technical Field]

[0001] (Cross-reference of related applications) This application claims the benefit and priority of U.S. Provisional Patent Application No. 62 / 928,508, filed on 31 October 2019, which is incorporated herein by reference in its entirety.

[0002] This disclosure relates, in general terms, to a system and method for screening and monitoring insomnia, and more specifically, to a system and method for determining whether a user experienced insomnia during a sleep session. [Background technology]

[0003] Many individuals suffer from insomnia (e.g., difficulty falling asleep, frequent or prolonged awakenings after initial sleep onset, and early morning awakenings inability to return to sleep) or other sleep-related disorders (e.g., periodic limb movement disorder (PLMD), obstructive sleep apnea (OSA), Cheyne-Stokes respiration (CSR), respiratory failure, obesity hyperventilation syndrome (OHS), chronic obstructive pulmonary disease (COPD), neuromuscular disorders (NMD), etc.). While many of these sleep-related disorders can be treated using respiratory systems (e.g., positive airway pressure (PAP) systems), insomnia requires different treatment approaches (e.g., improving sleep hygiene, using cognitive behavioral therapy, prescribing sleeping pills, etc.). Therefore, it would be beneficial to identify whether a user is experiencing insomnia or another sleep-related disorder so that they can be directed towards appropriate treatment. This disclosure aims to address these issues. [Overview of the Initiative]

[0004] According to some implementations of this disclosure, a method includes receiving user-associated physiological data during a sleep session. The method also includes determining the user's sleep-wake signal during the sleep session, at least in part, based on the received physiological data. The method also includes determining one or more of the user's sleep-wake signals during the sleep session, at least in part, based on the sleep-wake signal. The method also includes determining whether the user experienced insomnia during the sleep session, at least in part, based on at least one of the one or more sleep-wake parameters. The method also includes identifying the type of insomnia the user experienced, at least in part, based on the one or more sleep-wake parameters.

[0005] According to some implementations of this disclosure, a system includes an electronic interface, memory, and a control system. The electronic interface is configured to receive physiological data associated with a user. The memory stores machine-readable instructions. The control system includes one or more processors configured to execute those machine-readable instructions to determine the user's sleep-wake signals during a sleep session, at least in part on the physiological data. The control system is also configured to determine one or more sleep-related parameters of the user during a sleep session, at least in part on the sleep-wake signals. The control system is also configured to determine, at least in part on the one or more sleep-related parameters, that the user experienced insomnia during a sleep session. The control system is also configured, upon determining that the user experienced insomnia during a sleep session, to identify the type of insomnia the user experienced, at least in part on the one or more sleep-related parameters.

[0006] According to some implementations of this disclosure, a system includes a sensor configured to generate physiological data associated with a user during a sleep session; a memory for storing machine-readable instructions; and a control system including one or more processors configured to execute machine-readable instructions to determine that the user experienced insomnia during a sleep session, based at least in part on the physiological data, to determine one or more sleep-related parameters of the user during a sleep session, based at least in part on the sleep-wake signals, to determine that the user experienced insomnia during a sleep session, based at least in part on the one or more sleep-related parameters, and to determine that the user experienced insomnia during a sleep session, based at least in part on the one or more sleep-related parameters.

[0007] According to some implementations of this disclosure, a system includes: a first sensor configured to (i) generate first physiological data associated with a user during a first sleep session, and (ii) generate second physiological data associated with that user during a second sleep session; a second sensor configured to generate third physiological data associated with that user after the first sleep session and before the second sleep session; a memory for storing machine-readable instructions; a system for receiving first physiological data generated by the first sensor during the user's first sleep session; a system for determining a first sleep-wake signal of the user associated with the first sleep session, at least partially based on the first physiological data; a system for determining a first sleep-related parameter of the user associated with the first sleep session, at least partially based on the first sleep-wake signal, by comparing the first sleep-related parameter with a predetermined threshold to determine if the user was unwell during the first sleep session. The control system includes one or more processors configured to execute machine-readable instructions to determine whether a user experienced insomnia during the second sleep session by receiving second physiological data generated by a second sensor after the user's first sleep session and before the user's second sleep session, adjusting a predetermined threshold to a modified threshold different from the predetermined threshold, at least in part based on the second physiological data, receiving third physiological data generated by a first sensor during the user's second sleep session, determining the user's second sleep-wake signal associated with the second sleep session, at least in part based on the second physiological data, determining the user's second sleep-related parameter associated with the second sleep session, and comparing the second sleep-related parameter to its modified threshold to determine whether the user experienced insomnia during the second sleep session.

[0008] The above summary is not intended to illustrate any specific embodiment or aspect of the present invention. Further features and benefits of the present invention will become apparent from the detailed description and figures below. [Brief explanation of the drawing]

[0009] [Figure 1] This is a functional block diagram of a system relating to several implementations of the present disclosure that identifies whether a user experienced insomnia during a sleep session. [Figure 2] Figure 1 shows a perspective view of the system and user in several implementation forms of this disclosure. [Figure 3] This is a process flow diagram illustrating how a user may have experienced insomnia during a sleep session, relating to several implementations of this disclosure. [Figure 4] This shows an exemplary sleep progression diagram associated with a user during a sleep session, relating to several implementations of this disclosure. [Figure 5] Figure 4 shows an example time series of a sleep session relating to several implementations of the present disclosure. [Figure 6] This is a process flow diagram illustrating a method for determining whether a user has experienced insomnia across multiple sleep sessions, relating to several implementations of this disclosure. [Modes for carrying out the invention]

[0010] While various modifications and alternative forms are possible for this disclosure, specific implementations and embodiments of this disclosure are shown as examples in the drawings and are described in detail herein. However, it should be understood that this is not intended to limit this disclosure to any particular form, and that this disclosure encompasses all modifications, equivalents, and alternatives that fall within the spirit and scope of this disclosure as defined by the appended claims.

[0011] Many individuals suffer from insomnia, a condition generally characterized by dissatisfaction with the quality or duration of sleep (e.g., difficulty falling asleep, frequent or prolonged awakenings after initially falling asleep, and early morning awakenings that prevent them from returning to sleep). It is estimated that over 2.6 billion people worldwide experience some form of insomnia, and over 750 million have been diagnosed with an insomnia disorder. In the United States, insomnia accounts for an estimated $107.5 billion in annual economic costs, representing 13.6% of all days of absence from work and 4.6% of injuries requiring medical attention. Recent studies have also shown that insomnia is the second most common mental disorder and a primary risk factor for depression.

[0012] Common symptoms of nocturnal insomnia include, for example, poor sleep quality, shortened sleep duration, difficulty falling asleep, nocturnal awakenings, late-onset insomnia, mixed-type insomnia, and / or paradoxical insomnia. Difficulty falling asleep is characterized by difficulty falling asleep at bedtime. Nocturnal awakenings are characterized by frequent and / or prolonged awakenings during the night after initial sleep onset. Late-onset insomnia is characterized by early morning awakenings (e.g., before the target or desired wake-up time) that prevent the person from returning to sleep. Comorbid insomnia refers to a type of insomnia where the insomnia symptoms are, at least in part, caused by symptoms or complications of another physical or mental condition (e.g., anxiety, depression, medical condition, and / or medication use). Mixed-type insomnia refers to a combination of attributes of other types of insomnia (e.g., a combination of difficulty falling asleep, difficulty maintaining sleep, and late-onset insomnia symptoms). Paradoxical insomnia refers to a discrepancy or mismatch between the quality of sleep perceived by the user and the actual quality of sleep the user experiences.

[0013] Daytime (e.g., daytime) insomnia symptoms include, for example, fatigue, low energy, cognitive impairment (e.g., attention, concentration, and / or memory), difficulty functioning in academic or professional settings, and / or mood disturbances. These symptoms can lead to psychological complications such as, for example, decreased performance, delayed reaction time, increased risk of depression, and / or increased risk of anxiety disorders. Insomnia symptoms can also lead to physiological complications such as, for example, impaired immune system function, high blood pressure, increased risk of heart disease, increased risk of diabetes, weight gain, and / or obesity.

[0014] Comorbid insomnia and sleep apnea (COMISA) refers to a type of insomnia in which the subject experiences both insomnia and obstructive sleep apnea (OSA). OSA can be measured based on the apnea-hypopnea index (AHI) and / or oxygen desaturation level. The AHI is calculated by dividing the number of apnea and / or hypopnea events experienced by the user during a sleep session by the total sleep duration in that sleep session. This event may be, for example, a pause in breathing lasting at least 10 seconds. An AHI of less than 5 is considered normal. An AHI of 5 to less than 15 is considered to indicate mild OSA. An AHI of 15 to less than 30 is considered to indicate moderate OSA. An AHI of 30 or more is considered to indicate severe OSA. In children, an AHI greater than 1 is considered abnormal.

[0015] Insomnia can also be classified based on its duration. For example, if the onset of insomnia symptoms subsides within three months, it is considered acute or transient. Conversely, if insomnia symptoms persist for, for example, three months or longer, it is considered chronic or persistent. Persistent / chronic insomnia symptoms often require a different treatment approach than acute / transient insomnia symptoms.

[0016] Known risk factors for insomnia include gender (e.g., insomnia is more common in women than in men), family history, and stress exposure (e.g., severe and chronic life events). Age is a potential risk factor for insomnia. For example, sleep-onset insomnia is more common in young adults, while middle and late awakenings are more common in middle-aged and older adults. Other potential risk factors for insomnia include race, geography (e.g., living in a geographical area with long winters), altitude, and / or other sociodemographic factors (e.g., socioeconomic status, employment, academic performance, self-assessment of health, etc.).

[0017] The mechanisms of insomnia include predisposing factors, precipitating factors, and perpetuating factors. Predisposing factors include hyperarousal, characterized by increased physiological arousal during sleep and wakefulness. Measures of hyperarousal include, for example, increased cortisol levels, increased autonomic nervous system activity (e.g., indicated by increased resting heart rate and / or heart rate variability), increased brain activity (e.g., increased EEG frequency during sleep and / or increased number of awakenings during REM sleep), increased metabolic rate, increased body temperature, and / or increased activity in the pituitary-adrenal axis. Precipitating factors include stressful life events (e.g., those related to employment or education, interpersonal relationships, etc.). Perpetuating factors include sleep deprivation and excessive worry about its consequences, which can cause insomnia symptoms to persist even after the exacerbating factors have been removed.

[0018] Conventionally, diagnosing or screening for insomnia (including identifying the type or insomnia and / or specific symptoms) involves a series of steps. The screening process often begins with subjective complaints from the patient (e.g., the patient is unable to fall asleep or maintain sleep).

[0019] Next, the clinician assesses the subjective complaint using a checklist that includes insomnia symptoms, factors influencing insomnia symptoms, health factors, and social factors. Insomnia symptoms may include, for example, age of onset, exacerbating events (one or more), time of onset, current symptoms (e.g., difficulty falling asleep, maintaining sleep, late-onset insomnia), frequency of symptoms (e.g., nightly, occasional, specific nights, situation-specific, or seasonal variation), course since the onset of symptoms (e.g., changes in severity and / or relative symptom appearance), and / or perceived daytime outcomes. Factors influencing insomnia symptoms may include, for example, past and current treatments (including their effectiveness), factors that improve or reduce symptoms, factors that worsen insomnia (e.g., stress or schedule changes), factors that maintain insomnia, including behavioral factors (e.g., going to bed too early, sleeping in on weekends, alcohol consumption, etc.), and cognitive factors (e.g., unfounded beliefs about sleep, worry about the consequences of insomnia, fear of sleep deprivation, etc.). Health factors include medical disorders and symptoms, conditions that disrupt sleep (e.g., pain, discomfort, treatment), and pharmacological considerations (e.g., alert and sedative effects of medications). Social factors include work schedules that are incompatible with sleep, loss of sedation time due to late return home, nighttime domestic and social responsibilities (e.g., caring for children or the elderly), stressful life events (e.g., past high-stress events may be aggravating factors, and current high-stress events may be prolonging factors), and / or sleeping with pets.

[0020] After clinicians complete a checklist and assess insomnia symptoms, contributing factors, health factors, and / or social factors, patients are often instructed to keep a daily sleep diary and / or complete questionnaires (e.g., the Insomnia Severity Index or the Pittsburgh Sleep Quality Index). This traditional insomnia screening and diagnostic method is prone to errors because it relies on subjective complaints rather than objective sleep assessments. A discrepancy can occur between the patient's subjective complaints and actual sleep due to misperceptions of sleep state (paradoxical insomnia).

[0021] In addition, this conventional diagnostic method for insomnia does not exclude other sleep-related disorders, such as periodic limb movement disorder (PLMD), restless legs syndrome (RLS), sleep-disordered breathing (SDB), obstructive sleep apnea (OSA), Cheyne-Stokes respiration (CSR), respiratory failure, obesity hyperventilation syndrome (OHS), chronic obstructive pulmonary disease (COPD), neuromuscular disorders (NMD), and chest wall disorders. These and other disorders are characterized by specific events that occur during sleep (e.g., snoring, apnea, hypopnea, restless legs syndrome, sleep disturbance, suffocation, increased heart rate, dyspnea, asthma attacks, epileptic interstitial episodes, seizures, or any combination thereof). While these other sleep-related disorders may have symptoms similar to insomnia, differentiating them from insomnia is useful in customizing effective treatment plans that distinguish between features that may require various treatments. For example, while fatigue is generally a characteristic of insomnia, excessive daytime sleepiness is a characteristic specific to other disorders (e.g., PLMD) and reflects a physiological tendency to fall asleep unconsciously.

[0022] Once diagnosed, insomnia can be managed or treated using a variety of techniques and by providing patients with recommendations. Patients can generally be encouraged or recommended to practice healthy sleep habits (e.g., getting plenty of exercise, being active during the day, having a routine, avoiding naps, eating dinner early, relaxing before bedtime, avoiding afternoon caffeine, avoiding alcohol, making the bedroom comfortable, removing distractions from the bedroom, getting out of bed if not sleepy, and trying to wake up at the same time every day regardless of bedtime), or they can be encouraged to avoid specific habits (e.g., working in bed, going to bed extremely early, going to bed when not tired). In addition or alternatively, patients can also be treated with sleep aids and medical therapies, such as prescription sleep aids, over-the-counter sleep aids, and / or home herbal therapies.

[0023] Patients can also be treated with cognitive behavioral therapy (CBT) or cognitive behavioral therapy for insomnia (CBT-I), which generally includes sleep hygiene education, relaxation therapy, stimulus control, sleep restriction, and sleep management tools and devices. Sleep restriction is a method designed to limit time spent in bed (sleep window or duration of sleep) to actual sleep, thereby reinforcing homeostatic sleep motivators. The sleep window can be gradually increased over several days or weeks until the patient achieves an optimal duration of sleep. Stimulus control involves providing the patient with a set of instructions designed to reinforce the association of sleep between the bed and bedroom and to re-establish a consistent sleep-wake schedule (e.g., go to bed only when sleepy, get out of bed when unable to sleep, use the bed only for sleep purposes (e.g., not read a book or watch television), wake up at the same time every morning, and not take naps). Relaxation therapy includes clinical procedures aimed at reducing autonomic arousal, muscle tension, and intrusive thoughts that disrupt sleep (e.g., using progressive muscle relaxation). Cognitive therapy is a psychological approach designed to reduce excessive worry about sleep and reconstruct unfounded beliefs about insomnia and its daytime consequences (using techniques such as Socratic questions, behavioral experiences, and paradoxical orientation). Sleep hygiene education includes general guidelines on health habits (e.g., diet, exercise, substance use) and environmental factors (e.g., light, noise, excessive temperature) that can interfere with sleep. Mindfulness-based interventions include, for example, meditation.

[0024] Referring to Figure 1, several implementations of the System 100 of the Disclosure are shown. The System 100 includes a control system 110, a memory device 114, an electronic interface 119, a respiratory therapy system 120, one or more sensors 130, and one or more user devices 170.

[0025] The control system 110 includes one or more processors 112 (hereinafter, processor 112). The control system 110 is generally used to control (e.g., operate) various components of system 100 and / or to analyze data acquired and / or generated by the components of system 100. The processors 112 may be general-purpose or special-purpose processors or microprocessors. Although Figure 1 shows one processor 112, the control system 110 may include any appropriate number of processors (e.g., one processor, two processors, five processors, ten processors, etc.) which may reside in a single housing or located separately from one another. The control system 110 can be connected to, for example, the housing of the user device 170, a part of the breathing system 120 (e.g., a housing), and / or one or more housings of the sensor 130, and / or located inside them. The control system 110 can be centralized (in one such housing) or distributed (in two or more physically separate such housings). In such an implementation configuration, which includes two or more housings for housing the control system 110, the housings can be located close to and / or far apart from each other.

[0026] The memory device 114 stores machine-readable instructions that can be executed by the processor 112 of the control system 110. The memory device 114 can be any suitable computer-readable storage device or media, such as a random or serial access memory device, a hard drive, a solid-state drive, or a flash memory device. Although one memory device 114 is shown in Figure 1, the system 100 may include any suitable number of memory devices 114 (e.g., one memory device, two memory devices, five memory devices, ten memory devices, etc.). The memory device 114 can be coupled to and / or located inside the housing of the breathing device 122, the housing of the user device 170, one or more housings of the sensor 130, or any combination thereof. Similar to the control system 110, the memory device 114 can be centralized (within one such housing) or distributed (within two or more physically separate such housings).

[0027] In some implementations, the memory device 114 (Figure 1) stores a user profile associated with the user. The user profile may include, for example, demographic information associated with the user, biometric information associated with the user, medical information associated with the user, self-reported user feedback, sleep parameters associated with the user (e.g., sleep-related parameters recorded from one or more previous sleep sessions), or any combination thereof. Demographic information may include, for example, information indicating the user's age, gender, race, geographical location, relationship status, family history of insomnia, employment status, educational background, socioeconomic status, or any combination thereof. Medical information may include, for example, information indicating one or more medical conditions associated with the user, the user's use of medications, or both. Medical information data may further include the results or scores of the Multiple Sleep Latency Test (MSLT) and / or the scores or values ​​of the Pittsburgh Sleep Quality Index (PSQI). Self-reported user feedback may include information indicating the user's self-reported subjective sleep score (e.g., poor, normal, good), the user's self-reported subjective stress level, the user's self-reported subjective fatigue level, the user's self-reported subjective health status, recent life events experienced by the user, or any combination thereof.

[0028] The electronic interface 119 is configured to receive data (e.g., physiological data and / or audio data) from one or more sensors 130, which can be stored in a memory device 114 and / or analyzed by a processor 112 of the control system 110. The electronic interface 119 can communicate with one or more sensors 130 using wired or wireless connections (e.g., using RF communication protocols, WiFi communication protocols, Bluetooth® communication protocols, cellular networks, etc.). The electronic interface 119 may include an antenna, a receiver (e.g., an RF receiver), a transmitter (e.g., an RF transmitter), a transceiver, or any combination thereof. The electronic interface 119 may also include another processor and / or another memory device which are identical or similar to the processor 112 and memory device 114 described herein. In some implementations, the electronic interface 119 is coupled to or integrated with a user device 170. In other implementations, the electronic interface 119 is connected to or integrated with the control system 110 and / or the memory device 114 (for example, within the housing).

[0029] In some implementations, system 100 optionally includes a respiratory system 120 (also referred to as a respiratory therapy system). The respiratory system 120 may include a respiratory pressure therapy device 122 (hereinafter referred to as respiratory device 122), a user interface 124, a conduit 126 (also referred to as a tube or air circuit), a display device 128, a humidifier tank 129, or any combination thereof. In some implementations, one or more of the control system 110, memory device 114, display device 128, sensor 130, and humidifier tank 129 are part of the respiratory device 122. Respiratory pressure therapy refers to applying an air supply to the inlet of the user's airway at a controlled target pressure that is nominally positive to the atmosphere throughout the user's entire respiratory cycle (unlike negative pressure therapy, such as tank ventilators or positive / negative pressure external ventilators (cuirass)). The respiratory system 120 is generally 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).

[0030] The breathing device 122 is generally used to generate pressurized air delivered to a user (for example, using one or more motors that drive one or more compressors). In some implementations, the breathing device 122 generates a continuous, constant air pressure delivered to the user. In other implementations, the breathing device 122 generates two or more predetermined pressures (for example, a first predetermined air pressure and a second predetermined air pressure). In yet another implementation, the breathing device 122 is configured to generate a variety of different air pressures within a predetermined range. For example, the breathing device 122 can deliver at least about 6 cmH2O, at least about 10 cmH2O, at least about 20 cmH2O, from about 6 cmH2O to about 10 cmH2O, from about 7 cmH2O to about 12 cmH2O, and so on. The breathing device 122 can also deliver pressurized air at a predetermined flow rate between, for example, about -20 L / min and about 150 L / min while maintaining positive pressure (relative to ambient pressure).

[0031] The user interface 124 engages with a portion of the user's face and delivers pressurized air from the breathing device 122 to the user's airway to help prevent airway narrowing and / or obstruction during sleep. This may also increase the user's oxygen intake during sleep. Depending on the therapy applied, the user interface 124 may form a tight seal with, for example, a region or portion of the user's face, thereby facilitating gas delivery at a pressure sufficiently different from the ambient pressure to produce a therapeutic effect, such as a positive pressure of approximately 10 cmH2O relative to the ambient pressure. In other forms of therapy, such as oxygen delivery, the user interface may not include a seal sufficient to facilitate the delivery of gas to the airway at a positive pressure of approximately 10 cmH2O.

[0032] As shown in Figure 2, in some implementations, the user interface 124 is a face mask that covers the user's nose and mouth. Alternatively, the user interface 124 may be a nasal mask that provides 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 a number of straps (e.g., including hook-and-loop fasteners) for positioning and / or stabilizing the interface on a portion of the user (e.g., the face), and a shape-conforming cushion (e.g., silicone, plastic, foam, etc.) that helps provide an airtight seal between the user interface 124 and the user. In some examples, the user interface 124 may be a tube-up mask, in which the straps of the mask are configured to function as conduits (one or more) for delivering pressurized air to the face or nasal mask. The user interface 124 may also include one or more vents to allow carbon dioxide and other gases exhaled by the user 210 to escape. In other implementations, the user interface 124 may include a mouthpiece (for example, a night guard mouthpiece molded to fit the user's teeth, a mandibular repositioning device, etc.).

[0033] The conduit 126 (also referred to as the air circuit or tube) allows air to flow between two components of the breathing system 120, such as the breathing device 122 and the user interface 124. In some implementations, this conduit may have separate branches for inhalation and exhalation. In other implementations, a single branch conduit is used for both inhalation and exhalation.

[0034] One or more of the breathing device 122, user interface 124, conduit 126, display device 128, and humidification tank 129 may include one or more sensors (e.g., pressure sensors, flow sensors, or more generally, any of the other sensors 130 described herein). These one or more sensors can be used, for example, to measure the air pressure and / or flow rate of the pressurized air supplied by the breathing device 122.

[0035] The display device 128 is generally used to display images (one or more) including still images, moving images, or both, and / or information about the breathing device 122. For example, the display device 128 can provide information about the status of the breathing device 122 (e.g., whether the breathing device 122 is on or off, the pressure of the air delivered by the breathing device 122, the temperature of the air delivered by the breathing device 122, etc.) and / or other information (e.g., a sleep score or therapy score (such as a myAir® score), the current date / time, personal information of user 210, etc.). In some implementations, the display device 128 functions as a human-machine interface (HMI), including a graphical user interface (GUI) configured to display images (one or more) as an input interface. The display device 128 may be an LED display, an organic EL display, a liquid crystal display, etc. The input interface may be, for example, a touchscreen or contact-sensing substrate, a mouse, a keyboard, or any sensor system configured to sense input made by a human user interacting with the breathing device 122.

[0036] The humidifying tank 129 is connected to or integrated with the breathing device 122 and includes a water reservoir that can be used to humidify the pressurized air delivered from the breathing device 122. The breathing device 122 may include a heater that heats the water in the humidifying tank 129 to humidify the pressurized air provided to the user. In addition, in some implementations, the conduit 126 may also include a heating element (e.g., connected to and / or embedded in the conduit 126) that heats the pressurized air delivered to the user.

[0037] The respiratory system 120 can be used as a ventilator or positive airway pressure (PAP) system, such as a continuous positive airway pressure (CPAP) system, an automated positive airway pressure (APAP) system, a biphasic or variable positive airway pressure (BPAP or VPAP) system, or any combination thereof. A CPAP system delivers a predetermined air pressure to the user (determined, for example, by a sleep physician). An APAP system automatically changes the air pressure delivered to the user based, for example, on respiratory data associated with the user. A BPAP or VPAP system is configured to deliver a first predetermined pressure (e.g., 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).

[0038] Referring to Figure 2, parts of system 100 (Figure 1) relating to several implementation configurations are shown. The user 210 and bedmate 220 of the respiratory system 120 are in bed 230 and lying on mattress 232. A user interface 124 (e.g., a full-face mask) may be worn by the user 210 during a sleep session. The user interface 124 is fluidically connected to and / or connected to a respiratory device 122 via a conduit 126. The respiratory device 122 delivers pressurized air to the user 210 via the conduit 126 and user interface 124 to increase air pressure in the user 210's throat, helping to prevent airway obstruction and / or narrowing during sleep. The respiratory device 122 can be positioned on a nightstand 240 directly adjacent to the bed 230, or more generally, on any surface or structure substantially adjacent to the bed 230 and / or the user 210, as shown in Figure 2.

[0039] Generally, users prescribed to use the respiratory system tend to experience improved sleep quality and reduced fatigue the day after using the respiratory system 120 during sleep, compared to not using it (especially when the user suffers from sleep apnea or other sleep-related disorders). However, many users do not follow the prescribed usage due to the user interface 124 being unpleasant or cumbersome, or other side effects (e.g., dry eyes, dry throat, noise, etc.). Users are more likely to not use (or completely stop using) the respiratory system 120 as prescribed if they do not perceive that they are experiencing some benefit (e.g., reduced daytime fatigue). However, the reason for the lack of improvement in sleep quality or daytime fatigue may not be a lack of therapeutic effectiveness, but rather insomnia. Therefore, it is beneficial to identify whether users of the respiratory therapy system are experiencing insomnia and to treat the insomnia symptoms appropriately, thereby preventing users from stopping or reducing their use of the respiratory therapy system due to a perceived lack of benefits (one or more) (e.g., the onset of insomnia).

[0040] The respiratory therapy system 120 described herein is an example of a therapy system, but other types of therapy systems that assist in the treatment of sleep-related disorders are also envisioned. Other therapy systems include, for example, dental instruments or oral instruments such as mandibular repositioning devices (MRDs). Oral instrument therapy can help prevent collapse of the tongue and soft tissues at the back of the throat by supporting the jaw (mandible) in an anterior position and keeping the user's airway open during sleep.

[0041] Referring again to Figure 1, one or more sensors 130 of system 100 include a pressure sensor 132, a flow sensor 134, a temperature sensor 136, a motion sensor 138, a microphone 140, a speaker 142, a radio frequency (RF) receiver 146, an RF transmitter 148, a camera 150, an infrared sensor 152, a photoelectric (PPG) sensor 154, an electrocardiogram (ECG) sensor 156, an electroencephalogram (EEG) sensor 158, a capacitance sensor 160, a force sensor 162, a strain gauge sensor 164, an electromyogram (EMG) sensor 166, an oxygen sensor 168, an analyte sensor 174, a moisture sensor 176, a LiDAR sensor 178, or any combination thereof. Generally, one or each of the sensors 130 is configured to output sensor data received and stored in the memory device 114 or one or more other memory devices.

[0042] One or more sensors 130 are illustrated and described as including each of the following: pressure sensor 132, flow sensor 134, temperature sensor 136, motion sensor 138, microphone 140, speaker 142, RF receiver 146, RF transmitter 148, camera 150, infrared sensor 152, photoelectric (PPG) sensor 154, electrocardiogram (ECG) sensor 156, electroencephalogram (EEG) sensor 158, capacitance sensor 160, force sensor 162, strain gauge sensor 164, electromyogram (EMG) sensor 166, oxygen sensor 168, analyte sensor 174, moisture sensor 176, and LiDAR sensor 178, but it is more common for one or more sensors 130 to include any combination and any number of each of the sensors described and / illustrated herein.

[0043] One or more sensors 130 can be used to generate, for example, physiological data, audio data, or both. Physiological data generated by one or more of the sensors 130 can be used by the control system 110 to determine sleep-wake signals and one or more sleep-related parameters associated with the user during a sleep session. Sleep-wake signals can indicate one or more sleep states, including wakefulness, relaxed wakefulness, micro-wakefulness, rapid eye movement (REM) stage, first non-REM stage (often referred to as "N1"), second non-REM stage (often referred to as "N2"), third non-REM stage (often referred to as "N3"), or any combination thereof. Sleep-wake signals can also be timestamped to indicate the user's bedtime, wake-up time, user's attempt to fall asleep, etc. Sleep-wake signals can be measured during a sleep session by the sensor(s) 130 at a predetermined sampling rate, for example, one sample per second, one sample per 30 seconds, or one sample per minute. Examples of one or more sleep-related parameters that can be used to determine a user during a sleep session based on sleep-wake signals include total time in bed, total sleep duration, sleep latency, post-sleep wakefulness parameters, sleep efficiency, fragmentation index, or any combination thereof.

[0044] The sleep-wake signal can also be accompanied by timestamps that determine the user's bedtime, wake-up time, and sleep-fall attempt time. The sleep-wake signal can be measured during a sleep session by one or more sensors 130 at a predetermined sampling rate, such as one sample per second, one sample per 30 seconds, or one sample per minute. In some implementations, the sleep-wake signal may also indicate respiratory signals during the sleep session, such as respiratory rate, inspiratory amplitude, expiratory amplitude, inspiratory-to-expiratory ratio, number of events per hour, event patterns, pressure settings of the respiratory device 122, or any combination thereof. These events may include snoring, apnea, central apnea, obstructive apnea, mixed apnea, hypopnea, mask leakage (e.g., from the user interface 124), lower limb immobility, sleep disturbance, suffocation, increased heart rate, dyspnea, asthma attack, epileptic interstitial, seizure, or any combination thereof. One or more sleep-related parameters that can be used to determine a user during a sleep session based on sleep-wake signals include, for example, total time in bed, total sleep duration, sleep latency, post-sleep wakefulness parameters, sleep efficiency, fragmentation index, or any combination thereof. As further detailed herein, these sleep-related parameters can be analyzed to determine whether a user experienced insomnia during sleep, to identify the type of insomnia, and / or to identify insomnia symptoms.

[0045] 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 a sleep session. Respiratory signals generally indicate the user's breathing or breath during a sleep session. Respiratory signals may indicate, for example, respiratory rate, respiratory rate variability, inspiratory amplitude, expiratory amplitude, inspiratory-to-expiratory ratio, number of events per hour, event patterns, pressure settings of the breathing device 122, or any combination thereof. These events (one or more) may include snoring, apnea, central apnea, obstructive apnea, mixed apnea, hypopnea, mask leakage (e.g., from the user interface 124), lower limb restlessness, sleep disturbance, suffocation, increased heart rate, dyspnea, asthma attack, epileptic interstitial, seizure, or any combination thereof.

[0046] The pressure sensor 132 outputs pressure data that can be stored in the memory device 114 and / or analyzed by the processor 112 of the control system 110. In some implementations, the pressure sensor 132 is an air pressure sensor (e.g., atmospheric pressure sensor) that generates sensor data indicating the user's breathing (e.g., inhalation and / or exhalation) and / or ambient pressure of the breathing system 120. In such implementations, the pressure sensor 132 can be connected to or integrated with the breathing device 122. The pressure sensor 132 may be, for example, a capacitive sensor, an electromagnetic sensor, a piezoelectric sensor, a strain gauge sensor, an optical sensor, a potentiometric sensor, or any combination thereof. In one example, the pressure sensor 132 can be used to determine the user's blood pressure.

[0047] The flow sensor 134 outputs flow data that can be stored in the memory device 114 and / or analyzed by the processor 112 of the control system 110. In some implementations, the flow sensor 134 is used to determine the airflow rate from the breathing device 122, the airflow rate through the conduit 126, the airflow rate through the user interface 124, or any combination thereof. In such implementations, the flow sensor 134 can be connected to or integrated with the breathing device 122, the user interface 124, or the conduit 126. The flow sensor 134 may be a mass flow sensor such as a rotary flow meter (e.g., a Hall effect flow meter), a turbine flow meter, an orifice flow meter, an ultrasonic flow meter, a hot-wire sensor, an eddy current sensor, a membrane sensor, or any combination thereof.

[0048] The temperature sensor 136 outputs temperature data that can be stored in the memory device 114 and / or analyzed by the processor 112 of the control system 110. In some implementations, the temperature sensor 136 generates temperature data indicating the core body temperature of the user 210 (Figure 2), the skin temperature of the user 210, the temperature of the air flowing from the respiratory device 122 and / or through the conduit 126, the temperature inside the user interface 124, the ambient temperature, or any combination thereof. The temperature sensor 136 may be, for example, a thermocouple sensor, a thermistor sensor, a silicon bandgap temperature sensor or semiconductor-based sensor, a resistance temperature detector, or any combination thereof.

[0049] The microphone 140 outputs audio data that can be stored in the memory device 114 and / or analyzed by the processor 112 of the control system 110. The audio data generated by the microphone 140 can be reproduced as one or more sounds (one or more) during a sleep session (e.g., sounds from user 210). The audio data from the microphone 140 can also be used to identify events experienced by the user during a sleep session (e.g., using the control system 110), as further detailed herein. The microphone 140 can be connected to or integrated with the breathing device 122, the user interface 124, the conduit 126, or the user device 170.

[0050] Speaker 142 outputs sound waves that are audible to the user of system 100 (for example, user 210 in Figure 2). Speaker 142 can be used, for example, as an alarm clock, or to play an alert or message to user 210 (for example, in response to an event). In some implementations, speaker 142 can be used to transmit audio data generated by microphone 140 to the user. Speaker 142 can be connected to or integrated with the breathing device 122, user interface 124, conduit 126, or user device 170.

[0051] The microphone 140 and speaker 142 can be used as separate devices. In some implementations, the microphone 140 and speaker 142 can be incorporated into an acoustic sensor 141, for example, as described in WO2018 / 050913, which is entirely incorporated herein by reference. In such an implementation, the speaker 142 generates or emits sound waves at predetermined intervals, and the microphone 140 detects reflections of the sound waves emitted from the speaker 142. The sound waves generated or emitted by the speaker 142 have frequencies inaudible to the human ear (e.g., less than 20 Hz or greater than about 18 kHz) so as not to disturb the sleep of the user 210 or the person sharing a bed 220 (Figure 2). Based at least in part on the data from the microphone 140 and / or speaker 142, the control system 110 can determine the location of the user 210 (Figure 2) and / or one or more of the sleep-related parameters described herein.

[0052] In some implementations, the sensor 130 includes (i) a first microphone which is identical or similar to microphone 140 and integrated into acoustic sensor 141, and (ii) a second microphone which is identical or similar to microphone 140 but is separate and distinct from the first microphone integrated into acoustic sensor 141.

[0053] The RF transmitter 148 generates and / or emits radio waves having a predetermined frequency and / or amplitude (e.g., within the high frequency band, within the low frequency band, long wave signal, short wave signal, etc.). The RF receiver 146 detects the reflection of the radio waves emitted from the RF transmitter 148, and this data can be analyzed by the control system 110 to determine the location of the user 210 (Figure 2) and / or one or more of the sleep-related parameters described herein. The RF receiver (either the RF receiver 146 and the RF transmitter 148, or another RF pair) can also be used for wireless communication between the control system 110, the breathing device 122, one or more sensors 130, the user device 170, or any combination thereof. Although the RF receiver 146 and the RF transmitter 148 are shown as separate and distinct elements in Figure 1, in some implementations the RF receiver 146 and the RF transmitter 148 are combined as part of an RF sensor 147. In some such implementations, the RF sensor 147 includes a control circuit. The specific form of RF communication could be Wi-Fi or Bluetooth (registered trademark), among others.

[0054] In some implementations, the RF sensor 147 is part of a mesh system. An example of a mesh system is a WiFi mesh system, which may include mesh nodes, mesh routers (one or more), and mesh gateways (one or more), each of which may be mobile / movable or fixed. In such an implementation, the WiFi mesh system includes WiFi routers and / or WiFi controllers, as well as one or more satellites (e.g., access points), each of which includes an RF sensor identical or similar to the RF sensor 147. The WiFi routers and satellites communicate with each other in constant communication using WiFi signals. The WiFi mesh system can be used to generate motion data based on changes in the WiFi signal (e.g., differences in received signal strength) between the routers and satellites (one or more) caused by the movement of objects or people partially interfering with the signal. This motion data may represent motion, breathing, heart rate, walking, falls, behavior, or any combination thereof.

[0055] Camera 150 outputs image data that can be reproduced as one or more images (e.g., still images, videos, thermal images, or a combination thereof) that can be stored in memory 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. For example, the image data from camera 150 can be used to determine the user's location, the time when user 210 enters bed 230 (Figure 2), and the time when user 210 leaves bed 230.

[0056] The infrared (IR) sensor 152 outputs infrared image data that can be reproduced as one or more infrared images (e.g., still images, videos, or both) that can be stored in the memory device 114. The infrared data from the IR sensor 152 can be used to determine one or more sleep-related parameters during a sleep session, including the user 210's temperature and / or movement. The IR sensor 152 can also be used in combination with the camera 150 to measure the presence, location, and / or movement of the user 210. The IR sensor 152 can detect infrared light with wavelengths between approximately 700 nm and 1 mm, for example, while the camera 150 can detect visible light with wavelengths between approximately 380 nm and 740 nm.

[0057] The PPG sensor 154 outputs physiological data associated with user 210 (Figure 2) that 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-to-expiratory ratio, estimated blood pressure parameters (single or multiple), or any combination thereof. The PPG sensor 154 can be worn by user 210, embedded in clothing and / or fabrics worn by user 210, embedded in and / or connected to the user interface 124 and / or associated headgear (e.g., a strap).

[0058] The ECG sensor 156 outputs physiological data associated with the electrical activity of the user 210's heart. In some implementations, the ECG sensor 156 includes one or more electrodes positioned on or around a portion of the user 210 during a sleep session. The physiological data from the ECG sensor 156 can be used, for example, to determine one or more of the sleep-related parameters described herein.

[0059] The EEG sensor 158 outputs physiological data associated with the electrical activity of the user 210's brain. In some implementations, the EEG sensor 158 includes one or more electrodes positioned on or around the user 210's scalp during a sleep session. The physiological data from the EEG sensor 158 can be used, for example, to determine the user 210's sleep state at any given time during a sleep session. In some implementations, the EEG sensor 158 can be integrated into the user interface 124 and / or its associated headgear (e.g., a strap).

[0060] The capacitance sensor 160, force sensor 162, and strain gauge sensor 164 output data that can be stored in the memory device 114 and used by the control system 110 to determine one or more of the sleep-related parameters described herein. The EMG sensor 166 outputs physiological data associated with electrical activity produced by one or more muscles. The oxygen sensor 168 outputs oxygen data indicating the oxygen concentration of a gas (e.g., in the conduit 126 or in the user interface 124). The oxygen sensor 168 may be, for example, an ultrasonic oxygen sensor, an electro-oxygen sensor, a chemical oxygen sensor, an optical oxygen sensor, or any combination thereof. In some implementations, one or more sensors 130 also include a galvanic skin response (GSR) sensor, a blood flow sensor, a respiratory sensor, a pulse sensor, a blood pressure sensor, an oximetry sensor, or any combination thereof.

[0061] The analyte sensor 174 can be used to detect the presence of analytes in the exhaled breath of the user 210. The data output by the analyte sensor 174 can be stored in the memory device 114 and used by the control system 110 to determine the identity and concentration of any analyte contained in the user 210's breath. In some implementations, the analyte sensor 174 is positioned near the user 210's mouth to detect analytes contained in the breath exhaled from the user 210's mouth. For example, if the user interface 124 is a face mask that covers the user 210's nose and mouth, the analyte sensor 174 may be positioned inside the face mask to monitor the user 210's mouth breathing. In other implementations, such as if the user interface 124 is a nasal mask or nasal pillow mask, the analyte sensor 174 may be positioned near the user 210's nose to detect analytes contained in the breath exhaled through the user's nose. In other implementations, if the user interface 124 is a nasal mask or nasal pillow mask, the analyte sensor 174 may be positioned near the user 210's mouth. In this implementation, the analyte sensor 174 can be used to detect whether air is inadvertently leaking from the user 210's mouth. In some implementations, the analyte sensor 174 is a volatile organic compound (VOC) sensor that can be used to detect carbonaceous chemicals or compounds. In some implementations, the analyte sensor 174 can also be used to detect whether the user 210 is breathing through their nose or mouth. For example, if the presence of an analyte is detected by data output from the analyte sensor 174 positioned near the user 210's mouth or (in implementations where the user interface 124 is a face mask) within the face mask, the control system 110 can use this data as an indicator that the user 210 is breathing through their mouth.

[0062] The moisture sensor 176 outputs data that can be stored in the memory device 114 and used by the control system 110. The moisture sensor 176 can be used to detect moisture in various areas surrounding the user (e.g., inside the conduit 126 or user interface 124, near the user 210's face, near the connection between the conduit 126 and the user interface 124, near the connection between the conduit 126 and the breathing device 122, etc.). Therefore, in some implementations, the moisture sensor 176 can be connected to or integrated with the user interface 124 or integrated with the conduit 126 to monitor the humidity of the pressurized air from the breathing device 122. In other implementations, the moisture sensor 176 is placed near any area where the moisture level needs to be monitored. The moisture sensor 176 can also be used to monitor the humidity of the surrounding environment surrounding the user 210, such as the air in a bedroom.

[0063] The LiDAR (Light Detection and Ranging) 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 create a three-dimensional (3D) map of the surrounding environment, such as a living space. LiDAR generally uses pulsed lasers to measure time of flight. LiDAR is also called 3D laser scanning. In one use case of such a sensor, a stationary or mobile device (such as a smartphone) having a LiDAR sensor 166 can measure and map an area more than 5 meters away from the sensor. LiDAR data can be fused with point cloud data estimated by, for example, an electromagnetic RADAR sensor. The LiDAR sensor(s) 178 can also use artificial intelligence (AI) to automatically create a geofence for a RADAR system by detecting and classifying features in space that may pose problems for the RADAR system, such as glass windows (which may be highly reflective to RADAR). LiDAR can also be used to estimate a person's height, as well as changes in height that occur when a person sits down, falls down, etc. LiDAR can be used to form a 3D mesh representation of the environment. In further applications, LiDAR can reflect off solid surfaces (e.g., radio-transparent materials) through which radio waves pass, enabling the classification of different types of obstacles.

[0064] In some implementations, system 100 also includes an activity tracker 180. The activity tracker 180 is typically used to help generate physiological data to determine activity metrics associated with the user. These activity metrics may include, for example, steps taken, distance traveled, steps uphill, duration of physical activity, type of physical activity, intensity of physical activity, time spent standing, respiratory rate, mean respiratory rate, resting respiratory rate, maximum respiratory rate, respiratory rate variability, heart rate, mean heart rate, resting heart rate, maximum heart rate, heart rate variability, calories burned, blood oxygen saturation, skin electrical activity (also referred to as skin conductance or galvanic skin response) or any combination thereof. The activity tracker 180 includes, for example, one or more of the sensors 130 described herein, such as a motion sensor 138 (e.g., one or more accelerometers and / or gyroscopes), a PPG sensor 154, and / or an ECG sensor 156.

[0065] In some implementations, the activity tracker 180 is a wearable device that the user can wear, such as a smartwatch, wristband, ring, or patch. For example, referring to Figure 2, the activity tracker 190 is worn on the wrist of user 210. The activity tracker 190 can also be attached to or integrated with clothing or garments worn by the user. As a further alternative, the activity tracker 190 can also be attached to or integrated with user device 170 (e.g., within the same housing). It is more common for the activity tracker 190 to be communicatively attached to, or physically integrated with (e.g., within, the housing of), the control system 110, memory 114, breathing system 120, and / or user device 170.

[0066] Although shown separately in Figure 1, any combination of one or more sensors 130 can be integrated and / or connected to any one or more components of system 100, including the breathing device 122, user interface 124, conduit 126, humidification tank 129, control system 110, user device 170, or any combination thereof. For example, the microphone 140 and speaker 142 are integrated and / or connected to the user device 170, and the pressure sensor 130 and / or flow sensor 132 are integrated and / or connected to the breathing device 122. In some implementations, at least one of the one or more sensors 130 is not connected to the breathing device 122, control system 110, or user device 170, but is positioned generally adjacent to the user 210 during a sleep session (e.g., positioned on or in contact with a part of the user 210, worn by the user 210, connected to or positioned on a nightstand, connected to a mattress, connected to the ceiling, etc.).

[0067] The user device 170 (Figure 1) includes a display device 172. The user device 170 may be a mobile device such as a mobile device, smartphone, tablet, or laptop. Alternatively, the user device 170 may be an external sensing system, a television (e.g., a smart TV), or another smart home device (e.g., a smart speaker such as Google Home, Amazon Echo, or Alexa). In some implementations, this user device is a wearable device (e.g., a smartwatch). The display device 172 is generally used to display images (one or more) including still images, videos, or both. In some implementations, the display device 172 functions as a human-machine interface (HMI) including a graphic user interface (GUI) configured to display images (one or more) and an input interface. The display device 172 may be an LED display, an OLED display, a liquid crystal display, etc. The input interface may be, for example, a touchscreen or contact sensing board, a mouse, a keyboard, or any sensor system configured to sense input made by a human user interacting with the user device 170. In some implementations, one or more user devices may be used by and / or included in the system 100.

[0068] Although the control system 110 and the memory device 114 are shown and illustrated in Figure 1 as separate and distinct components of system 100, in some implementations the control system 110 and / or the memory device 114 are integrated into the user device 170 and / or the breathing device 122. Alternatively, in some implementations the control system 110 or a part thereof (e.g., the processor 112) may reside in the cloud (e.g., integrated into a server, integrated into an Internet of Things (IoT) device (e.g., smart TV, smart thermostat, smart home appliance, smart lighting, etc.), connected to the cloud, and capable of edge cloud processing), or may reside on one or more servers (e.g., remote servers, local servers, etc., or any combination thereof).

[0069] Although System 100 is shown as including all of the above components, depending on the various implementations of this disclosure, the components that can be included in the system for generating physiological data and determining notifications or actions recommended to the user may be more or less. For example, a first alternative system includes a control system 110, a memory device 114, and at least one of one or more sensors 130. Alternatively, a second alternative system includes a control system 110, a memory device 114, at least one of one or more sensors 130, and a user device 170. Alternatively, a third alternative system includes a control system 110, a memory device 114, a respiratory system 120, at least one of one or more sensors 130, and a user device 170. Thus, any(s) of the components shown and described herein can be used and / or combined with one or more other components to form a variety of systems.

[0070] As used herein, a sleep session can be defined in several ways, for example, based on the initial start and end times. In some implementations, a sleep session is the time a user is asleep. In such implementations, a sleep session has a start time and an end time, and the user remains awake until the end time during the sleep session. That is, time the user is awake is not included in the sleep session. From the first definition of a sleep session, if a user wakes up and falls asleep multiple times during the night, each sleep period separated by those periods of wakefulness constitutes a sleep session.

[0071] Alternatively, in some implementations, a sleep session has a start time and an end time, and during that sleep session, the user can remain awake without the sleep session ending, as long as the continuous time the user is awake is less than the wakefulness duration threshold. The wakefulness duration threshold can be defined as a percentage of the sleep session. The wakefulness duration threshold could be, for example, about 20 percent of the sleep session, about 15 percent of the sleep session duration, about 10 percent of the sleep session duration, about 5 percent of the sleep session duration, about 2 percent of the sleep session duration, etc., or a percentage of any other arbitrary threshold. In some implementations, the wakefulness duration threshold is defined as, for example, about 1 hour, about 30 minutes, about 15 minutes, about 10 minutes, about 5 minutes, about 2 minutes, etc., or any other arbitrary amount of time.

[0072] In some implementations, a sleep session is defined as the total time from the time the user first goes to bed at night until the time the user last wakes up the following morning. In other words, a sleep session can be defined as the time that begins at a first time (e.g., 10:00 p.m.) on a first date that can be called the current night (e.g., Monday, January 6, 2020) when the user first goes to bed with the intention of sleeping (not if the user first intends to watch TV or use their smartphone before going to sleep), and ends at a second time (e.g., 7:00 a.m.) on a second date that can be called the following morning (e.g., Tuesday, January 7, 2020) when the user first wakes up with the intention of not going to sleep again the following morning.

[0073] In some implementations, users can manually define the start of a sleep session and / or manually end it. For example, a user can manually start or end a sleep session by selecting a user-selectable element displayed on the display device 172 of the user device 170 (Figure 1) (for example, by clicking or tapping).

[0074] Referring to Figure 3, a method 300 for determining whether a user experienced insomnia during a sleep session is shown. One or more steps of method 300 described herein can be performed using system 100 (Figure 1).

[0075] Step 301 of Method 300 includes receiving personal data associated with the user. The personal data may be received and stored, for example, in memory device 114 and / or memory device 174 of user device 170. The personal data may be provided by the user through the interface device of display device 172 of user device 170 (Figure 1) and / or by a third party (e.g., a healthcare provider). The personal data may include, for example, medical data such as information indicating one or more medical conditions, medication use, or both diagnosed for that user (e.g., medical records). The personal data may include demographic data such as the user's age, gender, race, employment status, educational background, socioeconomic status, whether the user has a family history of insomnia, or any combination thereof. Personal data may also include subjective user data, such as self-reported subjective sleep scores (e.g., poor, average, good), self-reported subjective fatigue levels, self-reported subjective stress levels, self-reported subjective health status (e.g., healthy or unhealthy), recent life events (e.g., changes in relationship status, birth of a child, death of a family member), or any combination thereof. Personal data may also include information provided by third parties (e.g., medical records from healthcare providers, questionnaires or feedback from family or friends associated with the user). Personal data may further include the results or scores of the Multiple Sleep Latency Test (MSLT) and / or the scores or values ​​of the Pittsburgh Sleep Quality Index (PSQI). In some implementations, personal data may also include target or desired sleep onset times and / or target or desired wake-up times specified by or recommended to the user.

[0076] Step 302 of Method 300 includes receiving user-associated physiological data during at least a portion of the sleep session. Physiological data may be generated or acquired using at least one of one or more sensors 130 (Figure 1). For example, in some implementations, physiological data is generated or acquired using a pressure sensor 132 and / or a flow sensor 134 (Figure 1) connected to or integrated with the respiratory device 122. In other implementations, physiological data is generated using the microphone 140 described above, connected to or integrated with the user device 170. Physiological data may, for example, be received by the electronic interface 119 and / or the user device 170 from at least one of the one or more sensors 130 and stored in memory 114 (Figure 1). Physiological data may be received by the electronic interface 119 and / or the user device 170 directly or indirectly (e.g., using one or more intermediaries) from at least one of the one or more sensors 130. Information describing physiological data can be stored in memory device 114 and / or memory device 174 or user device 170.

[0077] Step 303 of Method 300 includes analyzing physiological data received during the execution of Step 302 to determine the user's sleep-wake signal during the sleep session. As described herein, the sleep-wake signal may indicate one or more sleep states, including wakefulness, relaxed wakefulness, micro-wakefulness, REM phase, first non-REM phase, second non-REM phase, third non-REM phase, or any combination thereof. In some implementations, one or more of the first non-REM phase, second non-REM phase, and third non-REM phase may be grouped together and classified as a light sleep phase or a deep sleep phase. For example, a light sleep phase may include the first non-REM phase, and a deep sleep phase may include the second and third non-REM sleep phases. In other implementations, the sleep-wake signal may also indicate respiratory signals, respiratory rate, inspiratory amplitude, expiratory amplitude, inspiratory-to-expiratory ratio, number of events per hour, event pattern, pressure setting of the respiratory device 122, or any combination thereof. Information describing sleep-wake signals can be stored in memory device 114.

[0078] Referring to Figure 4, in some implementations, step 303 may include generating a sleep timeline 400 showing sleep-wake signals. The sleep timeline 400 can be displayed using the display device 172 of the user device 170. As shown, the sleep timeline 400 includes sleep-wake signals 401 (step 303), an awakening stage axis 410, a REM stage axis 420, a light sleep stage axis 430, and a deep sleep stage axis 440. The sleep timeline 400 shows the time of going to bed during the sleep session. bed And, the time of falling asleep t GTS And, the first sleep time t sleep And, awakening time t wake and wake up time t rise This includes a first micro-awakening MA1 and a second micro-awakening MA2. The intersection of the sleep-wake signal 401 and one of axes 410-440 indicates the sleep stage at any given time during the sleep session.

[0079] The sleep progression diagram 400 is shown in FIG. 4 as including a light sleep stage axis 430 and a deep sleep stage axis 440. However, in some implementations, the sleep progression diagram 400 may include axes representing each of the first non-REM stage, the second non-REM stage, and the third non-REM stage.

[0080] Step 304 of method 300 (FIG. 3) includes analyzing the sleep-wake signal (step 303) to determine one or more sleep-related parameters associated with the user during a sleep session. As described herein, the one or more sleep-related parameters may include bedtime, initial sleep time, wake time, wake-up time, total in-bed time (TIB), total sleep time (TST), time to sleep (GTS), sleep onset latency (SOL), wake after sleep onset (WASO) parameter, sleep efficiency (SE), fragmentation index, sleep blocks, excessive wakefulness, or any combination thereof.

[0081] Bedtime is associated with the time when the user first gets into bed (e.g., bed 240) to start a sleep session (e.g., the user lies down or sits on the bed). For example, referring to FIG. 4, the bedtime is represented as time t bed in the sleep progression diagram 400. The bedtime t bed can be determined based on data generated by the motion sensor 138, the microphone 140, and the speaker 142, the camera 150, or any combination thereof.

[0082] Wake time is the time associated with the period when the user wakes up without returning to sleep (e.g., not when the user wakes up in the middle of the night and then goes back to sleep). Similarly, wake-up time is associated with the time when the user leaves and exits the bed with the intention of ending the sleep session (e.g., not when the user goes to the toilet in the middle of the night, takes the dog out, takes care of a child, sleepwalks, etc.). Referring to FIG. 4, the wake time is represented as time t wake on the sleep progression diagram 400, and the wake-up time is represented as time t rise in the sleep progression diagram 400.

[0083] Total time in bed (TIB) is the duration from the time of going to bed to the time of waking up. Therefore, TIB almost always includes one or more periods in which the user is asleep and one or more periods in which the user is awake (for example, the time from when the user goes to bed until they fall asleep, the time between the time of waking up and the time of waking up). For example, Figure 5A shows the time of going to bed (t bed ), first sleep time (t sleep ), wake-up time (t wake ), wake-up time (t rise This shows an exemplary time series 501 including the time of going to bed t, and total time in bed (TIB). As shown in the figure, total time in bed (TIB) is the time of going to bed t bed From wake time rise This includes the total duration up to that point.

[0084] Total sleep time (TST) is defined as the duration from the time of first sleep to the time of wakefulness, excluding conscious and unconscious wakefulness and / or minute wakefulness during that time. The total sleep time parameter is typically shorter than the total time in bed parameter (e.g., 1 minute shorter, 10 minutes shorter, 1 hour shorter, etc.). For example, referring to time series 501 in Figure 5A, total sleep time (TST) is calculated from the time of first sleep to t sleep From awakening time t wake However, the durations of the first micro-awakening MA1 and the second micro-awakening MA2 have been excluded. As shown in the figure, in this example, total sleep time (TST) is shorter than total time in bed (TIB).

[0085] In some implementations, total sleep time can be defined as total continuous sleep time (PTST). In such implementations, total continuous sleep time excludes a predetermined initial portion or duration of the first non-REM stage (e.g., light sleep stage). For example, this predetermined initial portion may be approximately 30 seconds to 20 minutes, approximately 1 minute to 10 minutes, or approximately 3 minutes to 5 minutes. Total continuous sleep time is a measure of continuous sleep and smooths the sleep-wake sleep progression diagram. For example, upon a user's initial sleep onset, they may enter a first non-REM stage for a very short period (e.g., 30 seconds), return to a short period (e.g., 1 minute) of wakefulness, and then return to the first non-REM stage. In this example, total continuous sleep time excludes the first instance (30 seconds) of the first non-REM stage. Since users may be accustomed to long blocks in their sleep stages (e.g., they may not remember how they fell asleep), it may be useful to present a smoothed sleep progression diagram (e.g., sleep progression diagram 400) based on total continuous sleep time (PTST).

[0086] The time to fall asleep (GTS) is associated with the time when the user first attempts to fall asleep. For example, after going to bed, a user may engage in one or more activities to relax before attempting to sleep (e.g., reading, watching television, listening to music, using user device 170 (Figure 1)). In other words, the time of going to bed is often different from the time to fall asleep. The time to fall asleep can be determined based on, for example, data from motion sensor 138 (e.g., data indicating no user movement), data from camera 150 (e.g., data indicating no user movement and / or that the user has turned off the lights), data from microphone 140 (e.g., data indicating that the user has turned off the television), 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 has powered on breathing device 122, data indicating that the user has put on user interface 124), or any combination thereof.

[0087] Sleep latency (SOL) is associated with the duration between the time of falling asleep and the time of first sleep. As will be further detailed herein, sleep latency can be used to determine whether a user experienced insomnia (e.g., difficulty falling asleep) during a sleep session. Referring to time series 501 in Figure 5A, sleep latency (SOL) is the time between falling asleep and the time of first sleep. GTS ) and first sleep time (t sleep This includes the time between ) and ).

[0088] In some implementations, sleep latency is defined as sustained sleep latency (PSOL). Sustained sleep latency differs from sleep latency in that it is defined as the duration from the time of falling asleep to a predetermined amount of sustained sleep. In some implementations, a predetermined amount of sustained 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 wake of 2 minutes or less, a first non-REM phase, and / or a transition between these phases within a REM phase. In other implementations, sustained sleep latency requires, for example, at least 8 minutes of sustained sleep within a second non-REM phase, a third non-REM phase, and / or a REM phase. In other implementations, a predetermined amount of sustained 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 implementation, a predetermined amount of continuous sleep can eliminate all minute awakenings (for example, after a 10-second minute awakening, sleep will not resume for the entire 10 minutes).

[0089] Post-sleep-onset wakefulness (WASO) is associated with the total duration of wakefulness a user experiences from the time of initial sleep to the time of wakefulness. Therefore, WASO includes short-term and minute wakefulnesses during a sleep session, whether conscious or unconscious (e.g., minute wakefulnesses MA1 and MA2 shown in Figure 4). In some implementations, WASO is defined as persistent post-sleep-onset wakefulness (PWASO), which includes only the total wakefulness duration having a predetermined length (e.g., more than 10 seconds, more than 30 seconds, more than 60 seconds, more than approximately 5 minutes, more than approximately 10 minutes, etc.).

[0090] Sleep efficiency (SE) is determined by the ratio of total time in bed (TIB) to total sleep time (TST). For example, if total time in bed is 8 hours and total sleep time is 7.5 hours, the sleep efficiency for that sleep session is 93.75%. Sleep efficiency represents the user's sleep hygiene. For example, if a user goes to bed and spends time on other activities (e.g., watching television) before falling asleep, their sleep efficiency decreases (for example, the user may be penalized).

[0091] In some implementations, sleep efficiency (SE) can be calculated based on total time in bed (TIB) and the total time the user attempts to fall asleep. In such implementations, the total duration the user attempts to fall asleep is defined as the time from the time of sleep onset (GTS) to the time of wake-up as described herein. For example, if the total sleep time is 8 hours (e.g., from 11 p.m. to 7 a.m.), the time of sleep onset is 10:45 p.m., and the time of wake-up is 7:15 a.m., then in such an implementation, the sleep efficiency parameter would be calculated as approximately 94%.

[0092] The fragmentation index is determined at least partially based on the number of awakenings during a sleep session. For example, if a user had two minor awakenings (e.g., minor awakenings MA1 and MA2 shown in Figure 4), the fragmentation index could be represented as 2. In some implementations, this fragmentation index is scaled within a predetermined range of integers (e.g., between 0 and 10).

[0093] A sleep block is associated with a transition between any sleep stage (e.g., the first non-REM stage, the second non-REM stage, the third non-REM stage, and / or REM) and the wakefulness stage. Sleep blocks can be calculated, for example, with a resolution of 30 seconds.

[0094] As described herein, hyperarousal is characterized by increased physiological activity and can indicate a user's stress level. Therefore, in some implementations, step 304 includes determining the user's hyperarousal level based on sleep-wake signals (step 303), physiological data (step 302), and / or personal data (step 301). For example, the hyperarousal level can be determined by comparing the user's self-reported subjective stress level, included in the personal data (step 301), with previously recorded subjective stress levels and / or population criteria for that user. In another example, the hyperarousal level can be determined based on the user's respiration during a sleep session (e.g., respiratory rate, respiratory variability, respiratory duration, respiratory interval, mean respiratory rate, respiration during each sleep stage). In yet another example, the hyperarousal level can be determined based on the user's movement during a sleep session (e.g., based on data from motion sensor 138). In yet another example, the hyperarousal level can be determined based on the user's heart rate data during a sleep session or during the day.

[0095] Step 305 of Method 300 includes determining whether the user experienced insomnia during a sleep session. In some implementations, Step 305 of Method 300 includes identifying the type of insomnia the user experienced and / or identifying one or more insomnia symptoms the user experienced. Examples of insomnia types include persistent or chronic insomnia, acute insomnia, difficulty falling asleep, nocturnal awakenings, late-onset insomnia, mixed-type insomnia, comorbid insomnia, and paradoxical insomnia. For example, Step 305 may include comparing one or more sleep-related parameters (Step 305) to a predetermined threshold to determine whether the user experienced insomnia, including a specific type of insomnia.

[0096] In some implementations, step 305 includes determining whether the user experienced difficulty falling asleep during a sleep session by comparing the sleep latency (SOL) or sustained sleep latency (PSOL) with a predetermined threshold associated with difficulty falling asleep. In such implementations, the predetermined threshold may be, for example, between approximately 15 minutes and approximately 60 minutes, between approximately 20 minutes and approximately 30 minutes, at least 20 minutes, at least 30 minutes, or at least 45 minutes. Preferably, the sleep latency (SOL) is compared with a predetermined threshold of approximately 30 minutes, and the sustained sleep latency (PSOL) is compared with a predetermined threshold of approximately 20 minutes. If the sleep latency is greater than or equal to the predetermined threshold, the control system 110 determines that the user experienced difficulty falling asleep during a sleep session. If the sleep latency does not exceed the predetermined threshold, the control system 110 determines that the user did not experience difficulty falling asleep.

[0097] In some implementations, step 305 includes determining that the user experienced a nocturnal awakening during a sleep session, at least partially based on sleep-onset wakefulness (WASO), total sleep time (TST), sleep efficiency (SE), sleep blocks, or any combination thereof. Specifically, each of these sleep-related parameters can be compared to a predetermined threshold associated with nocturnal awakening. The predetermined threshold(s) can be stored in memory device 114 and / or memory device 172 of user device 170 (Figure 1).

[0098] For example, step 305 may include comparing the wake-up time (WASO) or persistent wake-up time (PWASO) to predetermined thresholds such as approximately 5 minutes to approximately 60 minutes, approximately 15 minutes to approximately 45 minutes, more than approximately 20 minutes, more than approximately 30 minutes, or more than approximately 45 minutes. Preferably, the wake-up time (WASO) is compared to a predetermined threshold of approximately 30 minutes, and the persistent wake-up time (PWASO) is compared to a predetermined threshold of approximately 45 minutes.

[0099] Alternatively, step 305 may include comparing total sleep time (TST) to predetermined thresholds such as approximately 3 to 7 hours, approximately 4 to 6.5 hours, at least 4 hours, at least 5 hours, at least 6 hours, or at least 6.5 hours. If total sleep time (TST) is not equal to or greater than the predetermined threshold, the control system 110 determines that the user experienced an awakening during the sleep session.

[0100] In another example, step 305 may include comparing the sleep efficiency (SE) to a predetermined threshold, such as approximately 50% to approximately 100%, approximately 75% to approximately 90%, at least approximately 75%, at least approximately 85%, or at least approximately 90%. If the sleep efficiency (SE) is not above the predetermined threshold, the control system 110 determines that the user has experienced an awakening during a sleep session.

[0101] As a further example, step 305 may include comparing the sleep fragmentation index to one or more predetermined thresholds, including a predetermined number of awakenings (e.g., more than 3 awakenings, more than 5 awakenings, more than 10 awakenings, etc.) and / or a predetermined duration of each awakening (e.g., at least 15 seconds, at least 30 seconds, at least 1 minute, at least 3 minutes, etc.). If the sleep fragmentation index exceeds the predetermined threshold, the control system 110 determines that the user experienced an awakening during a sleep session.

[0102] In some implementations, step 305 includes determining that the user has experienced delayed-onset insomnia, at least in part, based on the wake time, wake time, target or desired wake time, target or desired wake time, or any combination thereof. As described above, delayed-onset insomnia occurs when the user is awake and unable to return to sleep. For example, step 305 may include comparing the duration between the wake time and wake time to a predetermined threshold associated with delayed-onset insomnia (e.g., at least 20 minutes, at least 30 minutes, at least 60 minutes, etc.). If the duration between the wake time and wake time exceeds the predetermined threshold, the control system 110 determines that the user has experienced delayed-onset insomnia. In another example, step 305 may include comparing the wake time to a target or desired wake time (which may be received as part of the personal data in step 301). If the difference between the wake-up time and the target or desired wake-up time exceeds a predetermined threshold (e.g., at least 20 minutes, at least 30 minutes, at least 45 minutes, at least 60 minutes, etc.), the control system 110 determines that the user has experienced delayed-onset insomnia. In a further example, step 305 may include comparing the wake-up time with a target or desired wake-up time (which may be received as part of personal data during the execution of step 301). If the difference between the wake-up time and the target or desired wake-up time exceeds a predetermined threshold (e.g., at least 20 minutes, at least 30 minutes, at least 45 minutes, at least 60 minutes, etc.), the control system 110 determines that the user has experienced delayed-onset insomnia.

[0103] In some implementations, step 305 includes determining, at least in part, that the user has experienced comorbid insomnia and / or pharmacological / treatment-related insomnia, based on received personal data (step 301) stored in memory device 114 (Figure 1). In such implementations, step 305 includes determining whether the personal data contains information indicating the use of one or more medical conditions and / or prescription drugs associated with comorbid insomnia. For example, certain prescription drugs for treating depression, pain, and / or flammability are known to be associated with insomnia-related symptoms. If the personal data indicates that a medical condition the user has or a prescription drug they are using is associated with comorbid insomnia, and the control system 110 determines that the user has experienced difficulty falling asleep, waking up in the middle of the night, and / or late-onset insomnia, the control system 110 determines that the user has experienced comorbid insomnia during a sleep session.

[0104] In some implementations, step 305 includes determining that the user has experienced paradoxical insomnia, at least in part, based on personal data received during the execution of step 301. As described above, personal data may include self-reported subjective sleep scores (e.g., poor, normal, good), self-reported subjective fatigue levels, self-reported subjective stress levels, and / or self-reported subjective health status. Paradoxical insomnia occurs when the quality of sleep perceived by the user differs from the quality of actual sleep-wake data. The control system 110 may determine that the user has experienced paradoxical insomnia, for example, if the total sleep time satisfies or exceeds a predetermined threshold (e.g., 8 hours of sleep), but the self-reported subjective sleep score is poor (step 301).

[0105] In some implementations, step 305 includes determining that the user has experienced mixed insomnia. Mixed insomnia occurs when the user has a combination of the following attributes: difficulty falling asleep, waking up in the middle of the night, and / or late-onset insomnia. For example, if the control system 110 determines that the user has experienced two or more of the following: difficulty falling asleep, waking up in the middle of the night, and late-onset insomnia, then the control system 110 may determine that the user has experienced mixed insomnia during the sleep session.

[0106] In some implementations, step 305 may also include determining, at least in part, that the user experienced another sleep-related disorder (other than insomnia) during the sleep session, based on sleep-wake signals (step 303) and sleep-related parameters (step 304). For example, step 305 may include determining that the user experienced one or more of the following: periodic limb movement disorder (PLMD), obstructive sleep apnea (OSA), Cheyne-Stokes respiration (CSR), respiratory failure, obesity hyperventilation syndrome (OHS), chronic obstructive pulmonary disease (COPD), neuromuscular disease (NMD), chest wall disorder, snoring, apnea, hypopnea, inability to keep the lower limbs still, sleep disturbance, suffocation, increased heart rate, dyspnea, asthma attack, epileptic interstitial, seizure, or any combination thereof. Thus, method 300 can be used to distinguish insomnia from other sleep-related disorders and avoid false-positive identification of insomnia.

[0107] In some implementations, step 305 may include using a machine learning algorithm to determine if the user experienced insomnia during the sleep session. For example, step 305 may include using a neural network (e.g., a shallow or deep approach) to determine if the user experienced insomnia and to identify the type of insomnia.

[0108] In some implementations, Method 300 also includes generating a report upon determining that the user experienced insomnia during a sleep session. This report may provide information indicating, for example, the type of insomnia identified during the execution of step 305 (e.g., difficulty falling asleep, waking up in the middle of the night, or late-onset insomnia), and / or information indicating one or more sleep-related parameters determined during the execution of step 305. This report may be stored in the memory device 114 of the control system 110 and / or the memory device 174 of the user device 170 (Figure 1), displayed on the display device 172 of the user device 170, and / or transmitted to a third party (e.g., a healthcare provider).

[0109] Method 300 may also include generating individualized treatments or actions recommended to a user based on the determination that the user experienced insomnia during a sleep session (Step 305), or that the user experienced a different sleep-related disorder during a sleep session. For example, Method 300 may include recommending changes to sleep habits or hygiene, recommending sleeping pills and / or medical therapy, recommending cognitive behavioral therapy, or any combination thereof. As described above, personal data received during the execution of Step 301 may include information indicating the user's stress or anxiety level and self-reported subjective feedback. Therefore, recommended treatments can be individualized to suggest one or more treatments that are likely to be effective without increasing the user's anxiety or stress level. Recommended treatments may also be determined based on the personal data received and individualized based on the user's tendency to continue with the treatment. Individualized treatments can help connect the user to appropriate therapy to address specific conditions (therefore reducing reliance on sleep specialists and doctors), and can generally improve the user's quality of life.

[0110] In some implementations, Method 300 also includes adjusting one or more settings of the respiratory system 120 (Figure 2) at least in part based on the determined type of insomnia to help alleviate insomnia symptoms (Step 305). Such implementations may include, for example, adjusting the pressure settings of the respiratory device 122 (e.g., lowering the pressure setting, adjusting the pressure increase, extending the delivery of pressurized air, etc.).

[0111] Referring to Figure 6, a method 600 for determining whether multiple users experienced insomnia during a sleep session is shown. One or more steps of method 600 described herein can be performed using system 100 (Figure 1).

[0112] Step 601 of Method 600 is identical or similar to Step 301 of Method 300 (Figure 3) and includes receiving personal data associated with the user. This personal data may include, for example, medical data such as information indicating one or more medical conditions, medication use, or both. The personal data may include demographic data such as the user's age, gender, race, employment status, education level, socioeconomic status, whether the user has a family history of insomnia, or any combination thereof. The personal data may also include subjective user data such as a self-reported subjective sleep score (e.g., poor, normal, good), a self-reported subjective fatigue level, a self-reported subjective stress level, a self-reported subjective health status (e.g., healthy or unhealthy), recent life events (e.g., changes in relationship status, birth of a child, death of a family member), or any combination thereof. In some implementations, the personal data may also include target or desired sleep onset times and / or target or desired wake-up times specified by or recommended to the user.

[0113] Step 602 of Method 600 is identical or similar to step 302 of Method 300 (Figure 3) and includes receiving first physiological data associated with the user during a first sleep session. For example, step 602 may include receiving first physiological data from at least a first sensor of one or more sensors 130 (Figure 1) described herein. In some implementations, the first sensor used during the execution of step 602 is coupled to or integrated with the respiratory device 122 (for example, the first sensor includes a pressure sensor 132 and / or a flow sensor 134).

[0114] Step 603 of Method 600 is identical or similar to step 303 of Method 300 (Figure 3) and includes analyzing the first physiological data (step 602) to determine sleep-wake signals associated with the user during the first sleep session. As described herein, step 603 may include generating a sleep-wake chart (e.g., a sleep-wake chart identical or similar to sleep-wake chart 400 shown in Figure 4) that displays the sleep-wake signals.

[0115] Step 604 of Method 600 is identical or similar to step 304 of Method 300 (Figure 3) and includes determining at least a first sleep-related parameter associated with the user during a first sleep session, at least in part on the sleep-wake signal (step 603). As described herein, the first sleep-related parameter may include, for example, bedtime, first sleep time, wake time, wake time, total bedtime (TIB), total sleep time (TST), time to fall asleep (GTS), sleep latency (SOL), sleep-onset wakefulness (WASO) parameter, sleep efficiency (SE), fragmentation index, sleep block, or any combination thereof.

[0116] Step 605 of Method 600 is identical or similar to Step 305 of Method 300 (Figure 3) and includes determining whether the user experienced insomnia during a first sleep session, at least in part, based on personal data (Step 601) and / or first sleep-related parameters (Step 604). Step 605 may include determining, for example, that the user experienced difficulty falling asleep, middle-of-the-night awakenings, late-onset insomnia, comorbid insomnia, etc., as described herein.

[0117] Step 606 of Method 600 includes receiving second physiological data associated with the user after a first sleep session but before a second subsequent sleep session, using at least a second sensor. In some implementations, the second physiological data can be generated or acquired using a second sensor different from the first. In such implementations, for example, the first sensor (step 602) can be connected to or integrated with the respiratory device 122 (Figure 1), while the second sensor is connected to or integrated with the user device 170. The second physiological data is identical or similar to the first physiological data (step 602), but is generated or acquired during the day rather than during the first sleep session.

[0118] Step 607 of Method 600 includes analyzing second physiological data (Step 606) to identify one or more daytime symptoms experienced by the user. As described herein, certain insomnia symptoms may be characterized as daytime symptoms such as fatigue, low energy, cognitive impairment (e.g., attention, concentration, and / or memory), difficulty functioning in an academic or occupational setting, and / or mood disturbance. Step 607 may include, for example, determining the user's daytime activity level using the second physiological data and comparing the determined activity level to a predetermined threshold to determine whether the user experienced symptoms of fatigue during the day. Step 607 may also include, for example, determining the user's reaction time (e.g., to a stimulus) and comparing the determined reaction time to a predetermined threshold to determine whether the user experienced symptoms of cognitive impairment during the day.

[0119] In some implementations, step 607 includes receiving subjective user-reported feedback associated with the first sleep session. For example, after the first sleep session, the user may provide information (e.g., using user device 170 (Figure 1)) describing a self-reported subjective sleep score (e.g., poor, normal, good), a self-reported subjective fatigue level, a self-reported subjective stress level, etc. The user may also provide, for example, a target or desired sleep onset time and / or target or desired wake-up time for a second subsequent sleep session.

[0120] Step 608 of Method 600 includes adjusting a predetermined threshold used during the execution of Step 605 to determine whether the user experienced insomnia during the first sleep session. This adjustment may be based at least in part on the identification of insomnia during the first sleep session (Step 605), the insomnia symptoms identified during the execution of Step 607, or both. This adjustment may include increasing or decreasing the predetermined threshold.

[0121] For example, as described above, the determined sleep latency (SOL) can be compared to a predetermined threshold to determine whether the user experienced difficulty falling asleep (e.g., whether the sleep latency was above the threshold). In some implementations, this adjustment involves lowering this predetermined threshold, which is compared to the sleep latency of the second sleep session (e.g., lowering this threshold from approximately 30 minutes to approximately 20 minutes). For example, this predetermined threshold can be lowered at least in part based on the identified daytime symptoms and / or self-reported subjective feedback described above. If a user is severely fatigued during the day, they should fall asleep faster than if they were not fatigued. Therefore, in this case, the predetermined threshold can be lowered to take this fact into account when determining whether the user experienced difficulty falling asleep.

[0122] As an alternative, as described above, the determined post-sleep awakening (WASO) can be compared to a predetermined threshold to determine whether the user experienced nocturnal awakenings (e.g., if the WASO is above the threshold). In some implementations, this adjustment involves lowering the predetermined threshold compared to the WASO (e.g., lowering the threshold from approximately 30 minutes to approximately 15 minutes) after determining that the user did not experience nocturnal awakenings during the first sleep session. Alternatively, as described above, the determined total sleep time (TST) can be compared to a predetermined threshold to determine whether the user experienced nocturnal awakenings (e.g., if the TST is below the threshold). In some implementations, this adjustment involves raising or lowering this threshold in part based on identified insomnia symptoms (step 607). For example, if the user is determined to be fatigued the day after the first sleep session, the threshold for comparison with the TST can be lowered to determine whether the user experienced insomnia during the second sleep session (step 612).

[0123] Step 609 of Method 600 is identical or similar to Step 602 and includes receiving third physiological data associated with the user during a second sleep session using the first sensor described above. Alternatively, the third physiological data may be generated or acquired using one or more sensors different from the sensor(s) used to generate or acquire the first physiological data (Step 602).

[0124] Step 610 of Method 600 is identical or similar to Step 603 above and includes analyzing third physiological data to determine a second sleep-wake signal associated with the user during the second sleep session. The second sleep-wake signal of the second sleep session, like the first sleep-wake signal (Step 602), can be diagrammed as a sleep-wake chart identical or similar to the sleep-wake chart 400 (Figure 4). In some implementations, Method 600 includes, for example, using the display device 172 of the user device 170 (Figure 1) to simultaneously display the first sleep-wake chart (Step 603) and the second sleep-wake chart (Step 610) of the second sleep-wake signal.

[0125] Step 611 of Method 600 is identical or similar to Step 604 above and includes determining at least a second sleep-related parameter associated with the user during a second sleep session. The second sleep-related parameter (Step 611) may be identical or different from the first sleep-related parameter (Step 604).

[0126] Step 612 of Method 600 is identical or similar to Step 605 above and includes determining whether the user experienced insomnia during the second sleep session, at least in part on a predetermined adjusted threshold (Step 605). Since this predetermined threshold is adjusted between the first and second sleep sessions (Step 605), the determination in Step 612 may differ from the determination in Step 605, even if the sleep-related parameter values ​​are the same.

[0127] Although Method 600 is described herein for two sleep sessions, the steps of Method 600 can be repeated one or more times for any number of sleep sessions (e.g., three sleep sessions, ten sleep sessions, fifty sleep sessions, one hundred sleep sessions, etc.).

[0128] In short, the systems and methods described herein can help individuals suffering from adverse physiological conditions (e.g., poor academic performance, increased reaction time, increased risk of depression, anxiety disorders, etc.) and adverse physiological conditions (e.g., hypertension, increased risk of heart disease, impaired immune system function, obesity, etc.) by automatically determining whether a user experienced insomnia during a sleep session, rather than another sleep-related disorder or problem. These systems and methods classify the type of insomnia experienced by a user based on physiological data and the user's subjective feelings, thereby enabling individualized treatment pathways. These and other benefits can reduce reliance on pharmacological therapy and its associated downsides (e.g., side effects and / or dependence), thereby alleviating the burden on busy clinicians and sleep specialists.

[0129] One or more further implementations and / or claims of the present disclosure can be formed by combining one or more elements, aspects, steps, or parts thereof from any one or more of the following claims 1 to 54 with one or more elements, aspects, steps, or parts thereof from any one or more of the other claims 1 to 54 or any combination thereof.

[0130] While this disclosure has been described with reference to one or more specific embodiments or implementations, those skilled in the art will recognize that numerous modifications are possible without departing from the intent and scope of this disclosure. Each of these implementations and its clearest modifications is intended to fall within the intent and scope of this disclosure. Furthermore, it is intended that further implementations in various aspects of this disclosure may combine any number of features from any of the implementations described herein.

Claims

1. An electronic interface configured to receive first physiological data associated with the user during a first sleep session, second physiological data associated with the user after the first sleep session and before a second sleep session which is a subsequent sleep session, and third physiological data associated with the user during the second sleep session, A therapy system comprising: a breathing device configured to supply pressurized air; a user interface connected to the breathing device via a conduit, configured to engage with a portion of the user during the sleep session to assist in directing the supplied pressurized air into the user's airway; and one or more sensors configured to generate the first physiological data; Memory for storing machine-readable instructions, Executing the aforementioned machine-readable instruction, Based at least in part on the first physiological data, the sleep-wake signals of the user during the first sleep session are determined. Based at least partially on the sleep-wake signal, the values ​​of one or more sleep-related parameters of the user during the first sleep session are determined. With respect to the first sleep session, the system determines whether the user experienced insomnia during the first sleep session by comparing the value of at least one of the one or more sleep-related parameters in the first sleep session with a predetermined threshold, based at least in part on the values ​​of the one or more sleep-related parameters, and the value of at least one of the one or more sleep-related parameters in the first sleep session that satisfies the predetermined threshold indicates that the user experienced insomnia during the first sleep session. Based on the determination that the user experienced insomnia during the first sleep session, the type of insomnia experienced by the user is identified, at least in part on one or more sleep-related parameters. Based at least in part on the first physiological data, it is determined whether the user experienced any other sleep-related disorders during the first sleep session. Based on the determination that the user experienced insomnia and other sleep-related disorders during the first sleep session, a recommended treatment plan is generated based on the type of insomnia and other sleep-related disorders. The predetermined thresholds of one or more sleep-related parameters are adjusted to adjusted thresholds based at least in part on the second physiological data, and at least one value among the one or more sleep-related parameters in the second sleep session that satisfies the adjusted threshold indicates that the user experienced insomnia during the second sleep session. A control system including one or more processors configured as follows: A system equipped with these features.

2. The system according to claim 1, further comprising a sensor configured to generate the first physiological data associated with the user during the sleep session.

3. The system according to claim 1 or 2, wherein the sleep-wake signal indicates one or more sleep stages during the first sleep session, and the one or more sleep stages include a wakefulness stage, a first non-REM stage, a second non-REM stage, a third non-REM stage, a REM stage, or any combination thereof.

4. The system according to any one of claims 1 to 3, wherein the one or more sleep-related parameters include total sleep time, total time in bed, time of falling asleep, time of first sleep, sleep latency, sustained sleep latency, post-sleep wakefulness parameter, sustained post-sleep wakefulness parameter, sleep efficiency, fragmentation index, or any combination thereof.

5. The system according to claim 4, wherein the sleep latency is determined to be a function of the time of falling asleep and the time of the first sleep.

6. The system according to claim 5, wherein the identified type of insomnia is sleep-onset disorder, and identifying the type of insomnia includes determining that the value of the sleep latency parameter is greater than or equal to a predetermined threshold for the sleep latency parameter.

7. The system according to claim 6, wherein the predetermined threshold for the sleep latency parameter is between approximately 15 minutes and approximately 30 minutes.

8. The system according to claim 4, wherein the type of insomnia is middle-of-the-night awakening.

9. The system according to claim 8, wherein identifying the type of insomnia includes determining that the value of the post-sleep wakefulness parameter is equal to or greater than a predetermined threshold for the post-sleep wakefulness parameter.

10. The system according to claim 9, wherein the predetermined threshold for the post-sleep wakefulness parameter is between approximately 20 minutes and approximately 45 minutes.

11. The system according to claim 8, wherein identifying the type of insomnia includes determining that the sleep efficiency parameter is less than or equal to a predetermined threshold for the sleep efficiency parameter.

12. The system according to any one of claims 1 to 11, wherein the control system is further configured to determine that the user has experienced a different sleep-related disorder during the first sleep session, the different sleep-related disorder being central apnea, obstructive apnea, mixed apnea, hypopnea, snoring, periodic limb movements, restless limb syndrome, asphyxiation, increased heart rate, dyspnea, asthma attack, epileptic interstitial, seizure, or any combination thereof.

13. The control system is further configured to receive personal data associated with the user, and to identify the type of insomnia based at least in part on the personal data. The system according to any one of claims 1 to 12, wherein the personal data includes (i) medical data including information indicating one or more medical conditions associated with the user, the user's use of medications, or both; (ii) demographic data including information indicating the user's age, gender, race, family history of insomnia, employment status, educational background, socioeconomic status, or any combination thereof; (iii) subjective user data including information indicating the user's self-reported subjective stress level, self-reported subjective fatigue level, self-reported subjective health status, recent life events experienced by the user, or any combination thereof; or (iv) any combination of (i) to (iii).

14. The control system is further configured to (i) determine, based on the identified type of insomnia, a user recommendation action to help reduce or prevent insomnia symptoms in the second sleep session, and (ii) display an indication of the recommendation action on the display device. The system according to any one of claims 1 to 13, wherein the recommended behavior includes a suggested bedtime, a suggested waketime, a suggested meal, a suggested daily exercise, a suggested sleep medication, a suggested relaxation program, a suggested masking sound, a suggested breathing program, a suggested change in bedroom activity, or any combination thereof.

15. The system according to claim 1, wherein the therapeutic system includes a mandibular repositioning device.

16. The system according to claim 1, wherein the one or more sleep-related parameters include a duration between the wake time and the wake time, the predetermined threshold is a predetermined threshold duration, and the adjustment includes lowering the predetermined threshold duration.

17. The system according to claim 1, wherein the control system is further configured to determine a second sleep-wake signal associated with the second sleep session based at least in part on the third physiological data, to determine a second sleep-related parameter associated with the second sleep session based at least in part on the second sleep-wake signal, and to determine whether the user experienced insomnia during the second sleep session based at least in part on the second sleep-related parameter.

18. The system according to claim 17, wherein the control system is further configured to determine whether the user experienced insomnia during the second sleep session by comparing the second sleep-related parameter with the adjusted threshold for the second sleep-related parameter.

19. The system according to any one of claims 1 to 18, further comprising: a first sensor configured to generate the first physiological data and the third physiological data; and a second sensor configured to generate the second physiological data.

20. The system according to claim 1, wherein the control system is further configured to analyze the second physiological data to identify daytime symptoms experienced by the user after the first sleep session and before the second sleep session, and the determination of whether the user has experienced insomnia is based on both the values ​​of the one or more sleep-related parameters in the first sleep session and the daytime symptoms experienced by the user.

21. The system according to claim 20, wherein the control system is further configured to analyze the daytime symptoms experienced by the user and distinguish between daytime symptoms caused by insomnia and daytime symptoms caused by other sleep-related disorders, and the recommended treatment plan is at least in part based on the identification of daytime symptoms caused by insomnia and the identification of daytime symptoms caused by other sleep-related disorders.

22. The system according to claim 21, wherein the recommended treatment plan includes notifying the user of the identification of daytime symptoms caused by insomnia.

23. The system according to claim 21, wherein the recommended treatment plan comprises adjusting one or more settings of the respiratory device configured to help alleviate daytime symptoms resulting from insomnia.

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