Systems and methods for detecting REM behavior disorder

Abstract

A method for monitoring an individual's sleep period, comprising: receiving data associated with the individual's current sleep period; analyzing at least a portion of the received data to identify one or more sleep stages experienced by the individual during the current sleep period, the one or more sleep stages including a light sleep stage, a deep sleep stage, a typical rapid eye movement (REM) stage, an atypical REM stage, a wake stage, or any combination thereof; and generating a summary of the current sleep period, the summary including: (i) a plurality of atypical REM sleep stages experienced by the individual during the current sleep period, (ii) the time spent in the atypical REM sleep stage during the current sleep period, or (iii) both (i) and (ii).

Description

[0001] Cross - Reference to Related Applications

[0002] This application claims the benefit and priority of U.S. Provisional Patent Application No. 63 / 003,240, filed on Mar. 31, 2020, the entire contents of which are hereby incorporated by reference herein. Technical Field

[0003] The present invention generally relates to systems and methods for detecting rapid eye movement (REM) behavior disorder, and more particularly, to systems and methods for long - term monitoring of REM behavior disorder and real - time mitigation of dream enactment behavior. Background Art

[0004] Many individuals suffer from sleep - related disorders and / or breathing - related disorders, such as insomnia (e.g., difficulty initiating sleep, frequent or prolonged awakenings after initially falling asleep, and early awakenings that prevent return to sleep), periodic limb movement disorder (PLMD), obstructive sleep apnea (OSA), Cheyne - Stokes respiration (CSR), hypoventilation, obesity hypoventilation syndrome (OHS), chronic obstructive pulmonary disease (COPD), neuromuscular disease (NMD), etc. One such sleep - related disorder is rapid eye movement behavior disorder, also known as REM behavior disorder or RBD. RBD is characterized by a lack of muscle atonia during REM sleep, and in more severe cases, by an individual making movements and vocalizations during the REM sleep stage. RBD can sometimes be accompanied by dream enactment behavior (DEB), in which the individual acts out dreams they may be having, sometimes resulting in injury to themselves or their partner. RBD is often a precursor to a subset of neurodegenerative disorders, such as Parkinson's disease, dementia with Lewy bodies, and multiple system atrophy. Typically, RBD is diagnosed in a sleep laboratory by polysomnography. This process can be expensive and usually occurs late in the course of the disease, when remission therapies are difficult to administer and / or less effective. It can be difficult to monitor an individual during sleep and accurately determine whether the individual has RBD or DEB. Thus, it would be advantageous to be able to accurately detect and mitigate RBD and DEB. The present invention relates to systems and methods for non - invasive screening of RBD that can be used long - term with few barriers to the subject, facilitating early detection of RBD and related neurodegenerative disorders. Summary of the Invention

[0005] According to some embodiments of the present invention, a method for monitoring an individual's sleep period, the method comprising receiving data associated with the individual's current sleep period; analyzing at least a portion of the received data to identify one or more sleep stages experienced by the individual during the current sleep period, the one or more sleep stages including a light sleep stage, a deep sleep stage, a typical rapid eye movement (REM) stage, an atypical REM stage, a wake stage, or any combination thereof; and generating a summary of the current sleep period, the summary including (i) a plurality of atypical REM sleep stages experienced by the individual during the current sleep period, (ii) the time spent in the atypical REM sleep stages during the current sleep period, or (iii) both (i) and (ii).

[0006] According to some embodiments of the present invention, a method for monitoring an individual's sleep period, the method comprising: receiving data associated with the individual's sleep period; using one or more trained algorithms to identify one or more sleep stages experienced by the individual during the sleep period, the one or more sleep stages of the individual including a light sleep stage, a deep sleep stage, a typical rapid eye movement (REM) sleep stage, an atypical REM sleep stage, a wake state, or any combination thereof; determining a total number of atypical REM sleep stages experienced by the individual during the sleep period; determining a total amount of time the individual spends in atypical REM sleep stages during the sleep period; and in response to (i) the total number of the atypical REM sleep stages meeting a first threshold, causing an action to be performed, (ii) the total amount of time meeting a second threshold, causing the action to be performed, or (iii) both (i) and (ii).

[0007] According to some embodiments of the present invention, a method for monitoring an individual's sleep period, the method comprising determining a value of a historical sleep parameter associated with a severity of atypical rapid eye movement (REM) sleep stages experienced by the individual during a plurality of previous sleep periods based at least in part on historical physiological data associated with the plurality of previous sleep periods of the individual; receiving current physiological data associated with the individual's current sleep period; determining a value of a current sleep parameter associated with a severity of one or more atypical REM sleep stages experienced by the individual during the current sleep period during the current sleep period; comparing the value of the historical parameter with the value of the current parameter; and in response to the comparison indicating that a severity of at least one of the one or more atypical REM sleep stages experienced by the individual during the current sleep period is greater than a historical severity of the atypical REM sleep stages experienced by the individual, causing an action to be performed.

[0008] According to some embodiments of the present invention, a method for monitoring an individual's sleep period includes receiving data associated with the individual's current sleep period, the received data including (i) respiratory data, (ii) motion data indicating the individual's motion during the current sleep period, (iii) audio data indicating sounds detected during the current sleep period, (iv) optical data indicating light in the area where the individual is located during the sleep period, or (v) any combination thereof; inputting at least a portion of the received data into a trained dream enactment behavior (DEB) algorithm to determine whether the individual is experiencing DEB during the current sleep period; and in response to determining that the individual is experiencing DEB, causing an action to be performed to (i) help end the DEB, (ii) help mitigate the impact of the DEB on the individual, (iii) help mitigate the impact of the DEB on the individual's bed partner, or (iv) any combination thereof.

[0009] The foregoing summary is not intended to represent every implementation or every aspect of the present invention. Additional features and advantages of the present invention are apparent from the detailed description and the drawings set forth below. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 is a functional block diagram of a system for monitoring a sleep period according to some embodiments of the present invention;

[0011] Figure 2 is according to some embodiments of the present invention Figure 1 perspective view of the system, a user of the system, and the user's bed partner;

[0012] Figure 3 illustrates an exemplary timeline of a sleep period according to some embodiments of the present invention;

[0013] Figure 4 illustrates according to some embodiments of the present invention Figure 3 exemplary sleep chart associated with the sleep period;

[0014] Figure 5 is a functional block diagram of a first algorithm for monitoring a sleep period according to some embodiments of the present invention;

[0015] Figure 6 is a functional block diagram of a second algorithm for monitoring a sleep period according to some embodiments of the present invention;

[0016] Figure 7A is a sleep chart of a user who only experiences typical rapid eye movement sleep stages during a sleep period according to some embodiments of the present invention;

[0017] Figure 7Bis a hypnogram of a user who experiences typical REM sleep stages and atypical REM sleep stage behaviors during a sleep period according to some embodiments of the present invention;

[0018] Figure 7C is a hypnogram of a user who experiences typical REM sleep stages, atypical REM sleep stages, and dream enactment behaviors during a sleep period according to some embodiments of the present invention;

[0019] Figure 8 is a process flow diagram of a first method for monitoring a sleep period according to some embodiments of the present invention;

[0020] Figure 9 is a process flow diagram of a second method for monitoring a sleep period according to some embodiments of the present invention;

[0021] Figure 10 is a process flow diagram of a third method for monitoring a sleep period according to some embodiments of the present invention; and

[0022] Figure 11 is a process flow diagram of a fourth method for monitoring a sleep period according to some embodiments of the present invention.

[0023] While the present invention admits of various modifications and alternative forms, specific embodiments and examples thereof have been shown by way of illustration in the drawings and will be described in detail herein. It should be understood, however, that this is not intended to limit the present invention to the particular forms disclosed, but on the contrary, the present invention will cover all modifications, equivalents, and alternatives falling within the spirit and scope of the present invention as defined by the appended claims. Detailed Description

[0024] Many individuals suffer from sleep-related and / or breathing-related disorders. Examples of sleep-related and / or breathing-related disorders include periodic limb movement disorder (PLMD), restless legs syndrome (RLS), sleep-disordered breathing (SDB), obstructive sleep apnea (OSA), central sleep apnea (CSA), other types of apnea, Cheyne-Stokes respiration (CSR), respiratory insufficiency, obesity hypoventilation syndrome (OHS), chronic obstructive pulmonary disease (COPD), neuromuscular disease (NMD), chest wall disorders, and REM behavior disorder, also known as RBD.

[0025] Obstructive sleep apnea (OSA) is a form of sleep-disordered breathing (SDB) characterized by events including obstruction or blockage of the upper airway during sleep caused by a combination of an abnormally small upper airway and loss of normal muscle tone in the regions of the tongue, soft palate, and posterior oropharyngeal wall.

[0026] Central sleep apnea (CSA) is another form of SDB that occurs when the brain temporarily stops sending signals to the muscles that control breathing. More generally, apnea generally refers to a cessation of breathing caused by an airway obstruction or a halt in respiratory function. Typically, during an obstructive sleep apnea event, an individual will stop breathing for approximately 15 seconds to approximately 30 seconds. Mixed sleep apnea is another form of SDB that is a combination of OSA and CSA.

[0027] Other types of apnea include hypopnea, hyperventilation, and hypercapnia. Hypopnea is typically characterized by slow or shallow breathing caused by a narrowed airway rather than an obstructed airway. Hyperventilation is typically characterized by an increase in the depth and / or rate of breathing. Hypercapnia is typically characterized by an excess of carbon dioxide in the bloodstream, usually caused by hypopnea.

[0028] Cheyne-Stokes respiration (CSR) is another form of SDB. CSR is a disorder of the patient's respiratory controller in which there are regular alternating cycles of ventilation gain and loss called CSR cycles. CSR is characterized by the repeated deoxygenation and reoxygenation of arterial blood.

[0029] Obesity hypoventilation syndrome (OHS) is defined as the combination of severe obesity and chronic hypercapnia while awake, in the absence of other known causes of hypoventilation. Symptoms include dyspnea, morning headache, and excessive daytime sleepiness.

[0030] Chronic obstructive pulmonary disease (COPD) includes any of a group of lower airway diseases that share specific common characteristics, such as increased resistance to air movement, prolonged expiratory phase of breathing, and loss of normal elasticity of the lungs.

[0031] Neuromuscular diseases (NMD) include many diseases and disorders that impair muscle function directly through intrinsic muscle pathology or indirectly through neuropathology. Chest wall diseases are a group of chest deformities that result in an inefficient coupling between the respiratory muscles and the thoracic cavity.

[0032] These and other conditions are characterized by specific events that occur when an individual sleeps (such as snoring, apnea, hypopnea, restless legs, sleep disturbances, choking, increased heart rate, dyspnea, asthma attacks, seizures, or any combination thereof).

[0033] The apnea-hypopnea index (AHI) is an index used to indicate the severity of sleep apnea during sleep. The AHI is calculated by dividing the number of apnea and / or hypopnea events experienced by a user during a sleep period by the total number of hours of sleep in the sleep period. The event can be, for example, an apnea lasting at least 10 seconds. An AHI less than 5 is considered normal. An AHI greater than or equal to 5 but less than 15 is considered an indication of mild sleep apnea. An AHI greater than or equal to 15 but less than 30 is considered an indication of moderate sleep apnea. An AHI greater than or equal to 30 is considered an indication of severe sleep apnea. In children, an AHI greater than 1 is considered abnormal. When the AHI is normal, or when the AHI is normal or mild, sleep apnea can be considered "controlled". The AHI can also be used in combination with the oxygen desaturation level to indicate the severity of obstructive sleep apnea.

[0034] RBD is a disorder characterized by movement patterns and vocalizations during REM sleep. When an individual sleeps, the individual experiences various different sleep stages, which can include light sleep stages (stages N1 and N2), deep sleep stages (stage N3), and REM sleep stages. Typically, the REM sleep stage is characterized by rapid eye movements, as well as a lack of muscle tone, such as muscle paralysis. However, individuals with RBD typically exhibit a lack of muscle tone and experience various movement patterns during the REM sleep stage. These movement patterns can include primitive movements, such as jerky movements of the head, neck, trunk, arms, legs, hands, feet, etc. The movement patterns can also be more complex and purposeful movements, such as gestures, pointing, punching, kicking, etc.

[0035] Individuals with RBD can also exhibit various types of vocalizations during atypical REM sleep stages, such as speech (e.g., sleep talking), muttering (intelligible or unintelligible), crying, or other types of non-speech. In addition, those with RBD can develop to exhibit dream enactment behavior (DEB) during atypical REM sleep stages. When an individual experiences DEB, the movements and vocalizations occur in the form of acting out whatever dream the individual may be having. As used herein, RBD is a sleep-related disorder characterized by atypical REM sleep stages. When a person with RBD experiences an atypical REM sleep stage, the person typically exhibits more movements and vocalizations than a person without RBD. In some instances, the atypical REM sleep stages of those patients with RBD further include DEB. However, not all atypical REM sleep stages are characterized by DEB, and not all patients with RBD experience DEB.

[0036] Reference Figure 1, shows a system 100 according to some embodiments of the present invention. The system 100 is used to monitor a user during a sleep period to determine whether the user is experiencing RBD. Generally, the system 100 is configured to determine when the user is experiencing a REM sleep stage that is different from a typical REM sleep stage, for example, characterized by movement and / or vocalization. These REM sleep stages that occur in users with RBD are referred to as atypical REM sleep stages. The system 100 includes a control system 110, a memory device 114, an electronic interface 119, one or more sensors 130, one or more external devices 170, and a DEB mitigation system 184. In some embodiments, the system 100 further includes a respiratory therapy system 120 (which includes a respiratory therapy device 122), a blood pressure device 180, an activity tracker 182, or any combination thereof.

[0037] The control system 110 includes one or more processors 112 (hereinafter referred to as the processor 112). The control system 110 is generally used to control various components of the system 100 and / or analyze data obtained and / or generated by the components of the system 100. The processor 112 can be a general-purpose or special-purpose processor or microprocessor. Although one processor 112 is shown in Figure 1 . The control system 110 can include any suitable number of processors (for example, one processor, two processors, five processors, ten processors, etc.), which can be in a single housing or located remotely from each other. The control system 110 (or any other control system) or a part of the control system 110, such as the processor 112 (or any other processor or any other part of the control system), can be used to perform one or more steps of any method described and / or claimed herein. The control system 110 can be coupled to and / or located within, for example, the housing of the external device 170 and / or the housing of one or more sensors 130. The control system 110 can be centralized (within one such housing) or decentralized (within two or more physically distinct such housings). In such embodiments that include two or more housings containing the control system 110, such housings can be located close to and / or remotely from each other.

[0038] The memory device 114 stores machine-readable instructions executable by the processor 112 of the control system 110. The memory device 114 can be any suitable computer-readable storage device or medium, such as a random or serial access memory device, a hard disk drive, a solid-state drive, a flash memory device, etc. While the memory device 114 is as Figure 1As shown, 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 devices 114 may be coupled to and / or located within the housing of the respiratory therapy device 122 of the respiratory therapy system 120, within the housing of the external device 170, within the housing of one or more sensors 130, or any combination thereof. Similar to the control system 110, the memory devices 114 may be centralized (within one such housing) or decentralized (within two or more physically distinct such housings).

[0039] In some embodiments, the memory device 114( Figure 1 ) stores a user profile associated with a 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 earlier sleep periods), or any combination thereof. Demographic information may include, for example, information indicating the user's age, user's gender, user's race, family medical history (e.g., family history of insomnia or sleep apnea), user's employment status, user's education status, user's 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 medication use, or both. The medical information data may also include multiple sleep latency test (MSLT) results or scores and / or Pittsburgh Sleep Quality Index (PSQI) scores or values. Self-reported user feedback may include information indicating self-reported subjective sleep ratings (e.g., poor, average, excellent), the user's self-reported subjective stress level, the user's self-reported subjective fatigue level, the user's self-reported subjective health status, life events recently experienced by the user, or any combination thereof.

[0040] The electronic interface 119 is configured to receive data (e.g., physiological data and / or acoustic data) from one or more sensors 130 such that the data can be stored in the memory device 114 and / or analyzed by the processor 112 of the control system 110. The electronic interface 119 can communicate with one or more sensors 130 using a wired connection or a wireless connection (e.g., using an RF communication protocol, a wifi communication protocol, a Bluetooth communication protocol, an IR communication protocol, via a cellular network, via any other optical communication protocol, etc.). The electronic interface 119 can 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 can also include more than one processor and / or more than one memory device that are the same as or similar to the processor 112 and the memory device 114 described herein. In some implementations, the electronic interface 119 is coupled to an external device 170 or integrated within the external device 170. In other implementations, the electronic interface 119 is coupled to and / or integrated with the control system 110 and / or the memory device 114 (e.g., within a housing).

[0041] As described above, in some implementations, the system 100 optionally includes a respiratory therapy system 120 (also referred to as a respiratory pressure therapy system). The respiratory therapy system 120 can include a respiratory therapy device 122 (also referred to as a respiratory pressure therapy device), a user interface 124, a conduit 126 (also referred to as a tube or air circuit), a display device 128, a humidification chamber 129, or any combination thereof. In some embodiments, one or more of the control system 110, the memory device 114, the display device 128, the sensor 130, and the humidification chamber 129 are part of the respiratory therapy device 122. Respiratory pressure therapy refers to the application of air to the inlet of a user's airway at a controlled target pressure that is nominally positive relative to the atmosphere throughout the user's respiratory cycle (e.g., as opposed to negative pressure therapy such as a tank ventilator or a cuirass). The respiratory therapy system 120 is typically used to treat individuals suffering from one or more sleep-related breathing disorders (e.g., obstructive sleep apnea, central sleep apnea, or mixed sleep apnea), other respiratory disorders (e.g., COPD), or other disorders that result in respiratory insufficiency, which can manifest during sleep or wakefulness.

[0042] Respiratory therapy device 122 is generally used to generate pressurized air (e.g., using one or more motors that drive one or more compressors) that is delivered to a user. In some embodiments, respiratory therapy device 122 generates a continuous and constant air pressure that is delivered to the user. In other embodiments, respiratory therapy device 122 generates two or more predetermined pressures (e.g., a first predetermined air pressure and a second predetermined air pressure). In other embodiments, respiratory therapy device 122 is configured to generate a variety of different air pressures within a predetermined range. For example, respiratory therapy device 122 can deliver at least about 6 cmH2O, at least about 10 cmH2O, at least about 20 cmH2O, between about 6 cmH2O and about 10 cmH2O, between about 7 cmH2O and about 12 cmH2O, etc. Respiratory therapy device 122 can also deliver pressurized air at a predetermined flow rate, such as between about -20 L / min and about 150 L / min, while maintaining a positive pressure (relative to ambient pressure). In some embodiments, control system 110, memory device 114, electronic interface 119, or any combination thereof can be coupled to and / or positioned within the housing of respiratory therapy device 122.

[0043] User interface 124 engages a portion of the user's face and delivers pressurized air from respiratory therapy device 122 to the user's airway to help prevent the airway from narrowing and / or collapsing during sleep. This can also increase the user's oxygen intake during sleep. Depending on the treatment to be applied, user interface 124 can, for example, form a seal with an area or portion of the user's face to facilitate delivery of gas at a pressure that varies sufficiently from ambient pressure, such as a positive pressure of about 10 cmH2O relative to ambient pressure, to effect the treatment. For other forms of treatment, such as the delivery of oxygen, the user interface may not include a seal sufficient to facilitate delivery of the gas supply to the airway at a positive pressure of about 10 cmH2O.

[0044] In some embodiments, user interface 124 is or includes a mask that covers the user's nose and mouth (e.g., as Figure 1as shown). 2). Alternatively, the user interface 124 is or includes a nasal mask that supplies 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 strap component having a plurality of straps (e.g., including hook and loop fasteners) on a portion of the user interface 124 for positioning and / or stabilizing the user interface 124 at a desired location (e.g., the face) of the user, and a conformable pad (e.g., silicone, plastic, foam, etc.) that helps provide an airtight seal between the user interface 124 and the user. The user interface 124 may also include one or more vents 125 for allowing carbon dioxide and other gases exhaled by the user to escape. In other embodiments, the user interface 124 includes a mouthpiece (e.g., a night guard mouthpiece molded to conform to the user's teeth, a mandibular repositioning device, etc.).

[0045] The conduit 126 allows air to flow between two components of the respiratory therapy system 120, such as between the respiratory therapy device 122 and the user interface 124. In some embodiments, there may be separate branches for the inhalation and exhalation conduits. In other embodiments, a single branched conduit is used for both inhalation and exhalation. Generally, the respiratory therapy system 120 forms an air passage that extends between the motor of the respiratory therapy device 122 and the user and / or the user's airway. Thus, the air passage generally includes at least the motor of the respiratory therapy device 122, the user interface 124, and the conduit 126.

[0046] One or more of the respiratory therapy device 122, the user interface 124, the conduit 126, the display device 128, and the humidification chamber 129 may include one or more sensors (e.g., a pressure sensor, a flow sensor, or any other sensor 130 generally 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 respiratory therapy device 122.

[0047] The display device 128 is generally used to display images including still images, video images, or both and / or information about the respiratory therapy device 122. For example, the display device 128 may provide information about the status of the respiratory therapy device 122 (e.g., whether the respiratory therapy device 122 is on / off, the pressure of the air delivered by the respiratory therapy device 122, the temperature of the air delivered by the respiratory therapy device 122, etc.) and / or other information (e.g., a sleep score or a therapy score (also known as myAir TMScores, such as those described in WO 2016 / 061629, which are incorporated herein by reference in their entirety), the current date / time, the individual information of the user, etc.). In some embodiments, the display device 128 serves as a human-machine interface (HMI) including a graphical user interface (GUI) configured to display images as an input interface. The display device 128 can be an LED display, an OLED display, an LCD display, etc. The input interface can be, for example, a touch screen or a touch-sensitive substrate, a mouse, a keyboard, or any sensor system configured to sense inputs made by a human user interacting with the respiratory therapy device 122.

[0048] The humidification chamber 129 is coupled to or integrated in the respiratory therapy device 122 and includes a water reservoir that can be used to humidify the pressurized air delivered from the respiratory therapy device 122. The respiratory therapy device 122 can include a heater to heat the water in the humidification chamber 129 to humidify the pressurized air provided to the user. Additionally, in some embodiments, the conduit 126 can also include a heating element (e.g., coupled to and / or embedded in the conduit 126) that heats the pressurized air delivered to the user. In other embodiments, the respiratory therapy device 122 or the conduit 126 can include a waterless humidifier. The waterless humidifier can include sensors interfaced with other sensors located elsewhere in the system 100.

[0049] The respiratory therapy system 120 can be used as, for example, a ventilator or a positive airway pressure (PAP) system, such as a continuous positive airway pressure (CPAP) system, an auto positive airway pressure system (APAP), a bilevel or variable positive airway pressure system (BPAP or VPAP), or any combination thereof. The CPAP system delivers a predetermined air pressure (e.g., determined by a sleep physician) to the user. The APAP system automatically changes the air pressure delivered to the user at least in part based on, for example, respiratory data associated with the user. The 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).

[0050] The DEB mitigation system 184 is configured to perform actions that help mitigate any DEB experienced by the user during a sleep period. The DEB mitigation system 184 can include an alarm 186, a light 188, a bed adjustment mechanism 190, a physical contact mechanism 192, a barrier 194, or any combination thereof. The onset of DEB often includes sudden and potentially violent movements that can harm the user or the user's bed partner. Moreover, even certain vocalizations can be harmful to the user, such as vocalizations at high volume. The DEB mitigation system 184 can wake the user to stop the DEB episode, or can take other actions to mitigate the impact of the DEB on the user or their bed partner. AsFigure 1 As shown, the DEB mitigation system 184 includes various different devices or components that can mitigate or end a DEB episode.

[0051] The alarm 186 is any audible noise configured to wake up the user or interrupt the user's sleep to reduce the severity of the DEB. The alarm 186 can be implemented on a standalone alarm clock, a speaker (such as an internet-connected smart speaker), on an external device 170 (which can be a mobile device such as a mobile phone or a tablet), or any other suitable device. The alarm 186 can produce an alarm-like noise (e.g., a ringing noise, a siren noise, etc.), but can also be configured to play music, natural sounds, spoken words, or other soothing sounds. Generally, the alarm 186 has a volume level sufficient to wake up the user from a sleep period, but in some embodiments, the volume level of the alarm 186 can be selected such that when the alarm 186 is activated, the user exits atypical REM sleep and enters one of the other sleep stages.

[0052] The light 188 is configured to activate and wake up the user, or cause the user to exit the atypical REM sleep stage. The light 188 can be a light on the external device 170 (e.g., a flashlight on the user's phone or the display device 172 itself), a standalone light in the user's bedroom (e.g., a ceiling light or a table lamp), or can be a light on a standalone alarm clock that includes the alarm 186.

[0053] The bed adjustment mechanism 190 is configured to adjust the user's bed or a portion of the user's bed in order to wake up the user and stop the user's DEB. In some embodiments, the bed adjustment mechanism 190 is configured to vibrate the user's bed or a portion of the user's bed when activated. This vibration can be achieved using the vibration or massage function of the bed on which the user sleeps. In other embodiments, the vibration can be achieved through a vibration pad or other device on which the user lies during the sleep period. In other embodiments, the bed adjustment mechanism 190 includes a motor or other similar device capable of tilting or lowering the user's bed. When the bed adjustment mechanism 190 is activated, the physical movement of the bed wakes up the user, or interrupts the sleep period sufficiently such that the user exits the atypical REM sleep stage, thereby ending the DEB.

[0054] The physical contact mechanism 192 can be any mechanism that physically contacts the user. The physical contact mechanism 192 can be used to end or mitigate the user's DEB by waking up the user or causing the user to exit the atypical REM sleep stage. For example, the physical contact mechanism 192 can be a type of actuator that contacts the user when activated. The physical contact mechanism 192 can also be a fan configured to blow air onto the user to help wake up the user, or interrupt the sleep period to a sufficient extent such that the user exits the atypical REM sleep stage, thereby ending the DEB.

[0055] In some embodiments, the physical contact mechanism 192 is additionally or alternatively used to physically prevent harm from the user or the user's bed partner when the user is experiencing a DEB. In one example, the physical contact mechanism 192 is a strap that extends over at least a portion of the user and / or around at least a portion of the user. When the system 100 detects that a DEB is occurring, the strap can be tightened. A strap located on the user's arm can prevent the user's arm from moving or breaking through and potentially hitting the user's bed partner or other entity, such as a bed frame or bedside table. Straps can also be placed on the user's legs to prevent the user from kicking the user's bed partner or other objects. Tightening of the straps can also be achieved with the user's sheets or blankets. Typically, the user will sleep under the sheets and / or blankets. The sheets and / or blankets can be connected to a device (such as a motor) that can tighten the sheets and / or blankets around the user, which can prevent the user from breaking through or kicking and hurting themselves or their bed partner.

[0056] Finally, the barrier 194 can be activated to provide protection for one or both of the user and the user's bed partner. In some embodiments, the barrier 194 is positioned between the user and the bed partner, and when a DEB is detected, the barrier 194 is moved to a position designed to prevent the user from accidentally hitting the bed partner. In some embodiments, this position is an elevated position such that the barrier 194 separates the user from the user's bed partner. The barrier 194 (or an additional barrier 194) can also be placed between the user and the side of the bed, which can be used to prevent the user from hitting the bedside table or even falling out of the bed.

[0057] Generally, the system 100 can include any one or more of the illustrated components of the DEB mitigation system 184. The DEB mitigation system 184 can also additionally or alternatively include other components. In any embodiment, the DEB mitigation system 184 can help end a DEB, for example, by waking the user or causing the user to exit an atypical REM sleep stage. The DEB mitigation system 184 can also be used to mitigate the effects of a DEB on the user and / or the user's bed partner, such as by reducing the user's movement or completely preventing the user from moving, or by providing a degree of physical protection to the user and / or the user's bed partner.

[0058] Reference Figure 2 , according to some embodiments, the system 100 is shown ( Figure 1) as part of. The user 210 and the bed partner 220 of the respiratory therapy system 120 are located in the bed 230 and lying on the mattress 232. The user interface 124 (e.g., a full face mask) can be worn by the user 210 during sleep periods. The user interface 124 is fluidly coupled and / or connected to the respiratory therapy device 122 via a conduit 126. The respiratory therapy device 122 in turn delivers pressurized air to the user 210 via the conduit 126 and the user interface 124 to increase the air pressure in the user 210's throat, thereby helping to prevent the airway from closing and / or narrowing during sleep. The respiratory therapy device 122 can include a display device 128, which can allow the user to interact with the respiratory therapy device 122. The respiratory therapy device 122 can also include a humidification chamber 129, which stores water for humidifying the pressurized air. The respiratory therapy device 122 can be positioned on the bedside table 240 located directly adjacent to the bed 230, as Figure 2 shown, or more generally, on any surface or structure that is typically adjacent to the bed 230 and / or the user 210. When lying on the mattress 232 in the bed 230, the user can also wear a blood pressure device 180 and an activity tracker 182.

[0059] The alarm clock 234 and the table lamp 238 are also located on the bedside table 240. The alarm clock 234 can be used to implement one or both of the alarm 186 and the light 188 of the DEB mitigation system 184 ( Figure 1 ). The light 188 of the DEB mitigation system 184 can additionally or alternatively use the table lamp 238 to be implemented. Figure 2 Also shown is a strap 236 placed above the user. The strap 236 can be used as the physical contact mechanism 192 of the DEB mitigation system 184. The shown strap 236 is placed on the torso of the user 210, but can also be placed on the arm or leg of the user 210.

[0060] Figure 2 A system 100 having components of the respiratory therapy system 120 and the DEB mitigation system 184 is shown. However, in some embodiments, the user 210 can use the DEB mitigation system 184 without using the respiratory therapy system 120. For example, the user 210 may not suffer from any sleep-related and / or respiratory-related diseases (e.g., OSA) that require the use of the respiratory therapy system 120. However, the user 210 may still suffer from RBD, in which case, the components of the DEB mitigation system 184 can be utilized.

[0061] 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 (IR) sensor 152, a photoplethysmogram (PPG) sensor 154, an electrocardiogram (ECG) sensor 156, an electroencephalogram (EEG) sensor 158, a capacitance sensor 160, a force sensor 162, a strain gauge sensor 164, an electromyogram (EMG) sensor 166, an oxygen sensor 168, an analyte sensor 174, a humidity sensor 176, a light detection and ranging (lidar) sensor 178, or any combination thereof. Generally, each of the one or more sensors 130 is configured to output sensor data that is received and stored in the memory device 114 or one or more other memory devices. The sensors 130 may also include an electrooculogram (EOG) sensor, a peripheral oxygen saturation (SpO2) sensor, a galvanic skin response (GSR) sensor, a carbon dioxide (CO2) sensor, or any combination thereof.

[0062] Although one or more sensors 130 are shown and described as including each of a pressure sensor 132, a flow rate sensor 134, a temperature sensor 136, a motion sensor 138, a microphone 140, a speaker 142, an RF receiver 146, an RF transmitter 148, a camera 150, an IR sensor 152, a PPG sensor 154, an ECG sensor 156, an EEG sensor 158, a capacitance sensor 160, a force sensor 162, a strain gauge sensor 164, an EMG sensor 166, an oxygen sensor 168, an analyte sensor 174, a humidity sensor 176, and a lidar sensor 178, more generally, one or more sensors 130 may include any combination and any number of each of the sensors described and / or shown herein.

[0063] One or more sensors 130 can be used to generate, for example, with respect to a user of the respiratory therapy system 120 (e.g., Figure 2User 210), a respiratory therapy system 120, physiological data, acoustic data, or both associated with the user and the respiratory therapy system 120 or other entities, objects, activities, etc. The control system 110 can use the physiological data generated by one or more sensors 130 to determine a sleep-wake signal and one or more sleep-related parameters associated with the user during a sleep period. The sleep-wake signal can indicate one or more sleep stages and / or sleep states (which can be used interchangeably herein), including sleep, wakefulness, relaxed wakefulness, micro-awakenings, or different sleep stages, such as the rapid eye movement (REM) stage (which can include typical REM stages and atypical REM stages), the first non-REM stage (commonly referred to as "N1"), the second non-REM stage (commonly referred to as "N2"), the third non-REM stage (commonly referred to as "N3"), or any combination thereof. Methods for determining sleep stages and / or sleep states based on physiological data generated by one or more sensors (e.g., sensor 130), as well as methods for training algorithms for determining sleep stages and / or sleep states, are described in, for example, WO2014 / 047310, US2014 / 0088373, WO2017 / 132726, WO2019 / 122413, and WO2019 / 122414, each of which is incorporated herein by reference in its entirety.

[0064] The sleep-wake signal can also be timestamped to indicate the time the user enters the bed, the time the user leaves the bed, the time the user attempts to fall asleep, etc. The sleep-wake signal can be measured in one or more of the sensors 130 at a predetermined sampling rate (e.g., one sample per second, one sample per 30 seconds, one sample per minute) during the sleep period. Examples of one or more sleep-related parameters that can be determined for the user at least partially based on the sleep-wake signal during the sleep period include total time in bed, total sleep time, total wake time, sleep onset latency, post-sleep onset wakefulness parameter, sleep efficiency, fragmentation index, amount of time to fall asleep, consistency of respiratory rate, time to fall asleep, wake time, sleep disruption rate, number of movements, or any combination thereof.

[0065] Physiological data and / or acoustic data generated by one or more sensors 130 can also be used to determine a respiratory signal associated with a user during a sleep period. The respiratory signal generally represents the respiration of the user during the sleep period. The respiratory signal can indicate, for example, respiratory rate, respiratory rate variability, inspiratory amplitude, expiratory amplitude, inspiratory-expiratory amplitude ratio, inspiratory-expiratory duration ratio, number of events per hour, event pattern, pressure settings of the respiratory therapy device 122, or any combination thereof. Events can include snoring, apnea, central apnea, obstructive apnea, mixed apnea, hypopnea, mask leak (e.g., from the user interface 124), restless legs, sleep disorder, choking, increased heart rate, heart rate variation, dyspnea, asthma attack, seizure, epilepsy attack, fever, cough, sneeze, snoring, wheezing, presence of a disease such as a common cold or flu, elevated stress level, etc.

[0066] 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 embodiments, the pressure sensor 132 is an air pressure sensor (e.g., an atmospheric pressure sensor) that generates sensor data indicative of the respiration (e.g., inhalation and / or exhalation) of a user of the respiratory therapy system 120 and / or the ambient pressure. In such an embodiment, the pressure sensor 132 can be coupled to or integrated within the respiratory therapy device 122. The pressure sensor 132 can be, for example, a capacitance sensor, an electromagnetic sensor, an inductive sensor, a resistive 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 blood pressure of the user.

[0067] The flow sensor 134 outputs flow data that can be stored in the memory device 114 and / or analyzed by the processor 112 of the control system 110. In some embodiments, the flow sensor 134 is used to determine the air flow from the respiratory therapy device 122, the air flow through the conduit 126, the air flow through the user interface 124, or any combination thereof. In such an embodiment, the flow sensor 134 can be coupled to or integrated within the respiratory therapy device 122, the user interface 124, or the conduit 126. The flow sensor 134 can 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, a vortex sensor, a membrane sensor, or any combination thereof.

[0068] 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 embodiments, the temperature sensor 136 generates temperature data indicative of the user's core body temperature, the user's skin temperature, the temperature of the air flowing from and / or through the breathing therapy device 122 and / or the conduit 126, the temperature in the user interface 124, the ambient temperature, or any combination thereof. The temperature sensor 136 can 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.

[0069] The motion sensor 138 outputs motion data that can be stored in the memory device 114 and / or analyzed by the processor 112 of the control system 110. The motion sensor 138 can be used to detect the user's motion during a sleep period, and / or to detect the motion of any component of the breathing therapy system 120, such as the breathing therapy device 122, the user interface 124, or the conduit 126. The motion sensor 138 can include one or more inertial sensors, such as an accelerometer, a gyroscope, and a magnetometer. The motion sensor 138 can be used to detect motion or acceleration associated with an arterial pulse, such as a pulse in or around the user's face and proximate to the user interface 124, and is configured to detect characteristics of the pulse shape, velocity, amplitude, or volume.

[0070] The microphone 140 outputs acoustic data that can be stored in the memory device 114 and / or analyzed by the processor 112 of the control system 110. The acoustic data generated by the microphone 140 can be reproduced as one or more sounds (e.g., the sound from the user) during the sleep period to determine (e.g., using the control system 110) one or more sleep-related parameters, as described in further detail herein. The acoustic data from the microphone 140 can also be used to identify (e.g., using the control system 110) events experienced by the user during the sleep period, as described in further detail herein. In other embodiments, the acoustic data from the microphone 140 represents noise associated with the respiratory therapy system 120. The microphone 140 can generally be coupled to or integrated into the respiratory therapy system 120 (or system 100) in any configuration. For example, the microphone 140 can be disposed inside the respiratory therapy device 122, the user interface 124, the conduit 126, or other components. The microphone 140 can also be positioned adjacent to or coupled to the outside of the respiratory therapy device 122, the outside of the user interface 124, the outside of the conduit 126, or the outside of any other component. The microphone 140 can also be a component of the external device 170 (e.g., the microphone 140 is the microphone of a smart phone). The microphone 140 can be integrated into the user interface 124, the conduit 126, the respiratory therapy device 122, or any combination thereof. Generally, the microphone 140 can be located at any position within or near the air passage of the respiratory therapy system 120, which at least includes the motor of the respiratory therapy device 122, the user interface 124, and the conduit 126. Thus, the air passage can also be referred to as the acoustic passage.

[0071] The speaker 142 outputs sound waves audible to the user. The speaker 142 can be used, for example, as an alarm clock or to play an alert or message to the user (e.g., in response to an event). In some embodiments, the speaker 142 can be used to transmit the acoustic data generated by the microphone 140 to the user. The speaker 142 can be coupled to or integrated into the respiratory therapy device 122, the user interface 124, the conduit 126, or the external device 170.

[0072] The microphone 140 and the speaker 142 can be used as separate devices. In some embodiments, the microphone 140 and the speaker 142 can be combined into an acoustic sensor 141 (e.g., a sonar sensor), as described in, for example, WO2018 / 050913 and WO2020 / 104465, each of which is hereby incorporated by reference in its entirety. In such embodiments, the speaker 142 generates or emits sound waves at a predetermined interval and / or frequency, and the microphone 140 detects the reflection of the emitted sound waves from the speaker 142. The sound waves generated or emitted by the speaker 142 have a frequency inaudible to the human ear (e.g., below 20 Hz or above about 18 kHz) so as not to disturb the user or the user's bed partner (e.g.,Figure 2 The sleep of the bed partner 220). Based at least in part on data from the microphone 140 and / or the speaker 142, the control system 110 can determine the position of the user and / or one or more of the sleep-related parameters described herein, such as a respiratory signal, respiratory rate, inspiratory amplitude, expiratory amplitude, inspiratory-expiratory ratio, number of events per hour, event pattern, sleep stage, pressure setting of the respiratory therapy device 122, or any combination thereof. In this context, a sonar sensor can be understood to involve active acoustic sensing, such as by generating / transmitting an ultrasonic or low-frequency ultrasonic sensing signal through air (e.g., in a frequency range such as about 17 - 23 kHz, 18 - 22 kHz, or 17 - 18 kHz). Such a system can be considered in relation to the above WO2018 / 050913 and WO2020 / 104465. In some embodiments, the speaker 142 is a bone conduction speaker. In some embodiments, one or more of the sensors 130 include (i) a first microphone that is the same as or similar to the microphone 140 and is integrated into the acoustic sensor 141; and (ii) a second microphone that is the same as or similar to the microphone 140 but is different from the first microphone integrated into the acoustic sensor 141.

[0073] The RF transmitter 148 generates and / or transmits radio waves having a predetermined frequency and / or a predetermined amplitude (e.g., within a high frequency band, within a low frequency band, long wave signal, short wave signal, etc.). The RF receiver 146 detects the reflection of the radio waves transmitted from the RF transmitter 148, and this data can be analyzed by the control system 110 to determine the position of the user and / or one or more of the sleep-related parameters described herein. The RF receiver (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 respiratory therapy device 122, one or more of the sensors 130, the external device 170, or any combination thereof. Although the RF receiver 146 and the RF transmitter 148 are Figure 1 shown as separate and distinct elements in, in some embodiments, the RF receiver 146 and the RF transmitter 148 are combined as part of an RF sensor 147 (e.g., a radar sensor). In some such embodiments, the RF sensor 147 includes control circuitry. The specific format of the RF communication can be wifi, Bluetooth, etc.

[0074] In some embodiments, the RF sensor 147 is part of a mesh network system. An example of a mesh network system is a wifi mesh network system, which may include mesh nodes, mesh routers, and mesh gateways, each of which may be mobile / removable or fixed. In such an embodiment, the wifi mesh network system includes a wifi router and / or a wifi controller and one or more satellites (e.g., access points), each satellite including an RF sensor that is the same as or similar to the RF sensor 147. The wifi router and the satellites continuously communicate with each other using wifi signals. The wifi mesh network system can be used to generate motion data based at least in part on changes in the wifi signals between the router and the satellites (e.g., differences in received signal strength), which are caused by a moving object or person partially blocking the signal. The motion data can indicate motion, breathing, heart rate, gait, falls, behavior, etc., or any combination thereof.

[0075] The camera 150 outputs image data that can be reproduced as one or more images (e.g., still images, video images, thermal images, or a combination thereof) that can be stored in the memory device 114. The image data from the camera 150 can be used by the control system 110 to determine one or more of the sleep-related parameters described herein. For example, the image data from the camera 150 can be used to identify the user's location to determine the time when the user enters the user's bed (e.g., Figure 2 bed 230), and to determine the time when the user exits the bed 230. The camera 150 can also be used to track eye movement, pupil dilation (if one or both of the user's eyes are open), blink rate, or any changes during REM sleep. The camera 150 can also be used to track the user's location, which can affect the duration and / or severity of apnea events in a user suffering from obstructive sleep apnea.

[0076] The IR sensor 152 outputs infrared image data that can be reproduced as one or more infrared images (e.g., still images, video images, or both) that can be stored in 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 period, including the user's temperature and / or the user's movement. The IR sensor 152 can also be used in combination with the camera 150 when measuring the user's presence, location, and / or movement. For example, the IR sensor 152 can detect infrared light having a wavelength between approximately 700 nm and approximately 1 mm, while the camera 150 can detect visible light having a wavelength between approximately 380 nm and approximately 740 nm.

[0077] The IR sensor 152 outputs IR image data that can be reproduced as one or more infrared images (e.g., still images, video images, or both) that can be stored in 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 period, including the user's temperature and / or the user's movement, including movement associated with RBD or DEB. The IR sensor 152 can also be used in combination with the camera 150 when measuring the user's presence, location, and / or movement. For example, the IR sensor 152 can detect infrared light having a wavelength between about 700 nm and about 1 mm, while the camera 150 can detect visible light having a wavelength between about 380 nm and about 740 nm.

[0078] The PPG sensor 154 outputs physiological data associated with the user, which can be used to determine one or more sleep-related parameters, such as heart rate, heart rate pattern, heart rate variability, cardiac cycle, respiratory rate, inspiratory amplitude, expiratory amplitude, inspiratory-expiratory ratio, estimated blood pressure parameters, or any combination thereof. The PPG sensor 154 can be worn by the user, embedded in clothing and / or fabric worn by the user, embedded in and / or coupled to the user interface 124 and / or its associated helmet (e.g., strap, etc.).

[0079] The ECG sensor 156 outputs physiological data associated with the electrical activity of the user's heart. In some embodiments, the ECG sensor 156 includes one or more electrodes that are positioned over or around a portion of the user during a sleep period. The physiological data from the ECG sensor 156 can be used to determine, for example, one or more of the sleep-related parameters described herein.

[0080] The EEG sensor 158 outputs physiological data related to the electrical activity of the user's brain. In some embodiments, the EEG sensor 158 includes one or more electrodes that are positioned on or around the user's scalp during a sleep period. The physiological data from the EEG sensor 158 can be used to determine, for example, the user's sleep stage and / or sleep state at any given time during a sleep period stage. In some embodiments, the EEG sensor 158 can be integrated in the user interface 124 and / or its associated helmet (e.g., strap, etc.).

[0081] The capacitive sensor 160, the force sensor 162, and the strain gauge sensor 164 output data that can be stored in the memory device 114 and used by the control system 110 to determine one or more of the sleep-related parameters described herein. The EMG sensor 166 outputs physiological data related to the electrical activity generated by one or more muscles. The oxygen sensor 168 outputs oxygen data indicating the oxygen concentration of a gas (e.g., in the conduit 126 or at the user interface 124). The oxygen sensor 168 can be, for example, an ultrasonic oxygen sensor, an electro-oxygen sensor, a chemical oxygen sensor, an optical oxygen sensor, or any combination thereof. In some embodiments, the one or more sensors 130 further include a galvanic skin response (GSR) sensor, a blood flow sensor, a respiration sensor, a pulse sensor, a sphygmomanometer sensor, a blood oxygen sensor, or any combination thereof.

[0082] The analyte sensor 174 can be used to detect the presence of analytes in the user's exhaled breath. 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 analytes in the user's breath. In some embodiments, the analyte sensor 174 is positioned near the user's mouth to detect analytes in the breath exhaled from the user's mouth. For example, when the user interface 124 is a mask that covers the user's nose and mouth, the analyte sensor 174 can be located inside the mask to monitor the user's mouth breathing. In other embodiments, for example, when the user interface 124 is a nasal mask or a nasal pillow mask, the analyte sensor 174 can be positioned near the user's nose to detect analytes in the breath exhaled through the user's nose. In other embodiments, when the user interface 124 is a nasal mask or a nasal pillow mask, the analyte sensor 174 can be positioned near the user's mouth. In this embodiment, the analyte sensor 174 can be used to detect whether any air is inadvertently leaking from the user's mouth. In some embodiments, the analyte sensor 174 is a volatile organic compound (VOC) sensor, which can be used to detect carbon-based chemicals or compounds, such as carbon dioxide. In some embodiments, the analyte sensor 174 can also be used to detect whether the user is breathing through their nose or mouth. For example, if the presence of an analyte is detected by the data output by the analyte sensor 174 located near the user's mouth or inside the mask (in embodiments where the user interface 124 is a mask), the control system 110 can use this data as an indication that the user is breathing through their mouth.

[0083] The output of the humidity sensor 176 can store and the data used by the control system 110 in the memory device 114. The humidity sensor 176 can be used to detect the humidity in various areas around the user (e.g., within the conduit 126 or the user interface 124, near the user's face, near the connection between the conduit 126 and the user interface 124, near the connection between the conduit 126 and the respiratory therapy device 122, etc.). Thus, in some embodiments, the humidity sensor 176 can be coupled to or integrated into the user interface 124 or the conduit 126 to monitor the humidity of the pressurized air from the respiratory therapy device 122. In other embodiments, the humidity sensor 176 is placed near any area where the humidity level needs to be monitored. The humidity sensor 176 can also be used to monitor the humidity of the surrounding environment around the user, such as the air in the user's bedroom. The humidity sensor 176 can also be used to track the user's biometric response to environmental changes.

[0084] One or more lidar sensors 178 can be used for depth sensing. This type of optical sensor (e.g., a laser sensor) can be used to detect objects and construct a three-dimensional (3D) map of the surrounding environment (e.g., a living space). Lidar typically can utilize pulsed lasers for time-of-flight measurements. Lidar is also known as 3D laser scanning. In an example of using such a sensor, a stationary or mobile device (such as a smartphone) having a lidar sensor 178 can measure and map an area extending 5 meters or more from the sensor. For example, lidar data can be fused with point cloud data estimated by an electromagnetic radar sensor. The lidar sensor 178 can also use artificial intelligence (AI) to automatically geofence a radar system by detecting and classifying features in the space that may cause problems for the radar system, such as a glass window (which can be highly reflective to radar). For example, lidar can also be used to provide an estimate of a person's height and the change in height when a person sits down or falls. Lidar can be used to form a 3D mesh representation of the environment. In a further use, for a solid surface through which radio waves pass (e.g., a radiolucent material), lidar can reflect off such a surface, thereby allowing classification of different types of obstacles.

[0085] Although in Figure 1Shown separately in the figure, any combination of one or more sensors 130 can be integrated in and / or coupled to any one or more components of system 100, including respiratory therapy device 122, user interface 124, conduit 126, humidification chamber 129, control system 110, external device 170, DEB mitigation system 184, or any combination thereof. For example, acoustic sensor 141 and / or RF sensor 147 can be integrated in and / or coupled to external device 170. In such an embodiment, external device 170 can be considered an auxiliary device that generates additional or auxiliary data for use by system 100 (e.g., control system 110) in accordance with some aspects of the present invention. In some embodiments, pressure sensor 132 and / or flow sensor 134 are integrated in and / or coupled to respiratory therapy device 122. In some embodiments, at least one of the one or more sensors 130 is not coupled to respiratory therapy device 122, control system 110, or external device 170, and is generally positioned adjacent to the user during a sleep period (e.g., positioned on or in contact with a portion of the user, worn by the user, coupled to or positioned on a bedside table, coupled to a mattress, coupled to a ceiling, etc.). More generally, the one or more sensors 130 can be located in any suitable position relative to the user such that the one or more sensors 130 can generate physiological data associated with the user and / or bed partner 220 during one or more sleep periods.

[0086] Data from the one or more sensors 130 can be analyzed to determine one or more sleep-related parameters, which can include respiratory signals, respiratory rate, respiratory pattern, inspiratory amplitude, expiratory amplitude, body movement (e.g., gross body movement such as movement of one or more limbs, fine body movement such as movement of the chest, movement in the bed, etc.), inspiratory-expiratory ratio, occurrence of one or more events, number of events per hour, pattern of events, average duration of events, range of event durations, ratio between the number of different events, sleep stage, apnea-hypopnea index (AHI), or any combination thereof. The one or more events can include snoring, apnea, central apnea, obstructive apnea, mixed apnea, hypopnea, intentional user interface leak, unintentional user interface leak, mouth leak, cough, restless legs, sleep disorder, choking, increased heart rate, dyspnea, asthma attack, seizure, seizure, increased blood pressure, or any combination thereof. Many of these sleep-related parameters are physiological parameters, although some sleep-related parameters can be considered non-physiological parameters. Other types of physiological and non-physiological parameters can also be determined based on data from the one or more sensors 130 or based on other types of data.

[0087] The external device 170 includes a display device 172. The external device 170 can be, for example, a mobile device such as a smart phone, a tablet computer, a laptop computer, etc. Alternatively, the external device 170 can be an external sensing system, a television (e.g., a smart TV), or another smart home device (e.g., a smart speaker such as Google Home, Amazon Echo, Alexa, etc.). In some embodiments, the external device 170 is a wearable device (e.g., a smart watch). The display device 172 is generally used to display images including still images, video images, or both. In some embodiments, the display device 172 serves as a human-machine interface (HMI) including a graphical user interface (GUI) configured to display images and an input interface. The display device 172 can be an LED display, an OLED display, an LCD display, etc. The input interface can be, for example, a touch screen or a touch-sensitive substrate, a mouse, a keyboard, or any sensor system configured to sense inputs made by a human user interacting with the external device 170. In some embodiments, one or more external devices 170 can be used by and / or included in the system 100.

[0088] The blood pressure device 180 is generally used to help generate physiological data for determining one or more blood pressure measurements associated with a user. The blood pressure device 180 can include at least one of the one or more sensors 130 to measure, for example, a systolic blood pressure component and / or a diastolic blood pressure component.

[0089] In some embodiments, the blood pressure device 180 is a sphygmomanometer that includes an inflatable cuff that can be worn by a user and a pressure sensor (e.g., the pressure sensor 132 described herein). For example, as shown in the example of Figure 2 , the blood pressure device 180 can be worn on the upper arm of the user. In such an embodiment where the blood pressure device 180 is a sphygmomanometer, the blood pressure device 180 further includes a pump (e.g., a manually operated bulb) for inflating the cuff. In some embodiments, the blood pressure device 180 is coupled to the respiratory therapy device 122 of the respiratory therapy system 120, which in turn delivers pressurized air to inflate the cuff. More generally, the blood pressure device 180 can be communicatively coupled and / or physically integrated (e.g., within a housing) with the control system 110, the memory device 114, the respiratory therapy system 120, the external device 170, and / or the activity tracker 182.

[0090] Activity tracker 182 is generally used to help generate physiological data for determining activity measurements associated with a user. Activity measurements can include, for example, steps, distance traveled, steps climbed, duration of physical activity, type of physical activity, intensity of physical activity, time spent standing, respiratory rate, average respiratory rate, resting respiratory rate, maximum respiratory rate, respiratory rate variability, heart rate, average heart rate, resting heart rate, maximum heart rate, heart rate variability, calories burned, blood oxygen saturation, skin electrical activity (also known as skin conductance or galvanic skin response), or any combination thereof. Activity tracker 182 includes one or more of the sensors 130 described herein, such as motion sensor 138 (e.g., one or more accelerometers and / or gyroscopes), PPG sensor 154, and / or ECG sensor 156.

[0091] In some embodiments, activity tracker 182 is a wearable device that can be worn by a user, such as a smartwatch, wristband, ring, or patch. For example, referring to Figure 2 , activity tracker 182 is worn on the user's wrist. Activity tracker 182 can also be coupled to or integrated into clothing or apparel worn by the user. Alternatively, activity tracker 182 can also be coupled to or integrated within an external device 170 (e.g., within the same housing). More generally, activity tracker 182 can be communicatively coupled to or physically integrated within (e.g., within a housing) control system 110, memory device 114, respiratory therapy system 120, external device 170, and / or blood pressure device 180.

[0092] Although control system 110 and memory device 114 are described and shown in Figure 1 as separate and distinct components of system 100, in some embodiments, control system 110 and / or memory device 114 are integrated within external device 170 and / or respiratory therapy device 122. Alternatively, in some embodiments, control system 110 or a portion thereof (e.g., processor 112) can be located in the cloud (e.g., integrated in a server, integrated in an Internet of Things (IoT) device, connected to the cloud, subject to edge cloud processing, etc.), located in one or more servers (e.g., remote server, local server, etc., or any combination thereof).

[0093] Although system 100 is shown as including all of the above components, in accordance with embodiments of the present invention, more or fewer components may be included in a system for monitoring a user during one or more sleep periods to determine whether the user is suffering from RBD and / or DEB. For example, a first alternative system includes at least one of control system 110, memory device 114, and one or more sensors 130. As another example, a second alternative system includes at least one of control system 110, memory device 114, one or more sensors 130, and external device 170. As yet another example, a third alternative system includes control system 110, memory device 114, respiratory therapy system 120, at least one of one or more sensors 130, and external device 170. As another example, a fourth alternative system includes control system 110, memory device 114, respiratory therapy system 120, at least one of one or more sensors 130, external device 170, and blood pressure device 180 and / or activity tracker 182. Thus, various systems for monitoring a user during one or more sleep periods to determine whether the user is suffering from RBD and / or DEB may be formed using any portion or portions of the components shown and described herein and / or in combination with one or more other components.

[0094] As used herein, a sleep period may be defined in a variety of ways, based at least in part on, for example, an initial start time and an end time. In some embodiments, a sleep period is the duration of time that a user sleeps, i.e., the sleep period has a start time and an end time, and during the sleep period, the user does not wake up until the end time. That is, any period of time during which the user is awake is not included in the sleep period. According to this first definition of a sleep period, if a user wakes up and falls asleep multiple times during the same night, each sleep interval separated by a wake interval is a sleep period.

[0095] Alternatively, in some embodiments, a sleep period has a start time and an end time, and during the sleep period, the user may be awake as long as the consecutive duration of the user's wakefulness is below a wake duration threshold, and the sleep period does not end. The wake duration threshold may be defined as a percentage of the sleep period. The wake duration threshold may be, for example, approximately 20% of the sleep period, approximately 15% of the sleep period duration, approximately 10% of the sleep period duration, approximately 5% of the sleep period duration, approximately 2% of the sleep period duration, etc., or any other threshold percentage. In some embodiments, the wake duration threshold is defined as a fixed amount of time, such as approximately one hour, approximately thirty minutes, approximately fifteen minutes, approximately ten minutes, approximately five minutes, approximately two minutes, etc., or any other amount of time.

[0096] In some embodiments, the sleep period is defined as the entire time between the time when the user first enters the bed at night and the time when the user finally leaves the bed the next morning. In other words, the sleep period can be defined as a time period that starts at a first time (e.g., 10:00 p.m.) on a first date (e.g., Monday, January 6, 2020), which can be referred to as the current night, when the user first enters the bed in order to go to sleep (e.g., if the user does not intend to watch TV or play on a smart phone before going to sleep), and ends at a second time (e.g., 7:00 a.m.) on a second date (e.g., Tuesday, January 7, 2020), which can be referred to as the next morning, when the user first leaves the bed with the intention of not returning to sleep the next morning.

[0097] In some embodiments, the user can manually define the start of the sleep period and / or manually terminate the sleep period. For example, the user can select (e.g., by clicking or tapping) one or more user-selectable elements displayed on the display device 172 of the external device 170 ( Figure 1 ) to manually initiate or terminate the sleep period.

[0098] Reference Figure 3 Illustrates an exemplary timeline 300 of the sleep period. The timeline 300 includes the time of entering the bed (t 入床 ), the time of falling asleep (t GTS ), the initial sleep time (t 睡眠 ), the first micro-arousal MA1, the second micro-arousal MA2, the arousal A, the time of waking up (t 觉醒 ) and the time of getting up (t 起床 ).

[0099] The time of entering the bed t 入床 is associated with the time when the user first enters the bed (e.g., Figure 2 the bed 230 in 入床 ) before falling asleep (e.g., when the user lies down or sits on the bed). The time of entering the bed t 入床 can be identified at least in part based on a bed threshold duration to distinguish the time when the user enters the bed for sleep from the time when the user enters the bed for other reasons (e.g., watching TV). For example, the bed threshold duration can be at least about 10 minutes, at least about 20 minutes, at least about 30 minutes, at least about 45 minutes, at least about 1 hour, at least about 2 hours, etc. Although the time of entering the bed t 入床 is described herein with respect to the bed, more generally, the time of entering the bed t 入床 can refer to the time when the user first enters any position (e.g., a chaise longue, a chair, a sleeping bag, etc.) for sleep.

[0100] The time of falling asleep (GTS) is related to the time when the user enters the bed (t 入床) is associated with the time of the first attempt to fall asleep after that. For example, after getting into bed, the user can engage in one or more activities to relax before attempting to sleep (e.g., reading, watching TV, listening to music, using external device 170, etc.). The initial sleep time (t 睡眠 ) is the time when the user initially falls asleep. For example, the initial sleep time (t 睡眠 ) can be the time when the user initially enters the first non-REM sleep stage.

[0101] The wake-up time t 觉醒 is the time associated with the time when the user wakes up without returning to sleep (e.g., as opposed to the user waking up at night and returning to sleep). The user can experience one of multiple unconscious micro-awakenings (e.g., micro-awakenings MA1 and MA2) with short durations (e.g., 5 seconds, 10 seconds, 30 seconds, 1 minute, etc.) after initially falling asleep. Contrary to the wake-up time t 觉醒 , the user returns to sleep after each of the micro-awakenings MA1 and MA2. Similarly, the user can have one or more conscious awakenings (e.g., awakening A) after initially falling asleep (e.g., getting up to go to the bathroom, taking care of a child or pet, sleepwalking, etc.). However, the user returns to sleep after awakening A. Thus, the wake-up time t 觉醒 can be defined, for example, at least in part based on a wake-up threshold duration (e.g., the user is awakened for at least 15 minutes, at least 20 minutes, at least 30 minutes, at least 1 hour, etc.).

[0102] Similarly, the getting-up time t 起床 is associated with the time when the user leaves the bed to end the sleep period (e.g., as opposed to the user getting up at night to go to the bathroom, taking care of a child or pet, sleepwalking, etc.). In other words, the getting-up time t 起床 is the time when the user finally leaves the bed and does not return to the bed until the next sleep period (e.g., the next night). Thus, the getting-up time t 起床 can be defined, for example, at least in part based on a getting-up threshold duration (e.g., the user has been out of bed for at least 15 minutes, at least 20 minutes, at least 30 minutes, at least 1 hour, etc.). The time to get into bed for the second subsequent sleep period, t 入床 , can also be defined at least in part based on a getting-up threshold duration (e.g., the user has been out of bed for at least 4 hours, at least 6 hours, at least 8 hours, at least 12 hours, etc.).

[0103] As described above, during the night between the initial t 入床 and the final t 起床 , the user can wake up and leave the bed more than once. In some embodiments, the final wake-up time t is identified or determined at least in part based on a predetermined threshold duration after an event (e.g., falling asleep or leaving the bed)觉醒 and / or the final get-up time t 起床 This threshold duration can be customized for the user. For any period of time when getting out of bed at night and then waking up and getting out of bed in the morning (during the user's awakening (t 觉醒 ) or getting up (t 起床 ) and the user getting into bed (t 入床 ), falling asleep (t GTS ) or being asleep (t 睡眠 )) for a standard user, it can be used for about 12 to about 18 hours. For users who spend a longer period in bed, a shorter threshold period can be used (e.g., between about 8 hours and about 14 hours). The threshold period can be initially selected and / or later adjusted at least in part based on a system that monitors the user's sleep behavior.

[0104] The total in-bed time (TIB) is the duration between the time t 入床 of getting into bed and the time t 起床 of getting up. The total sleep time (TST) is associated with the duration between the initial sleep time and the awakening time, excluding any conscious or unconscious awakenings and / or micro-awakenings during that period. Generally, the total sleep time (TST) will be shorter than the total in-bed time (TIB) (e.g., one minute shorter, ten minutes shorter, one hour shorter, etc.). For example, referring to Figure 3 the timeline 300, the total sleep time (TST) spans between the initial sleep time t 睡眠 and the awakening time t 觉醒 but does not include the durations of the first micro-awakening MA1, the second micro-awakening MA2, and the awakening A. As shown, in this example, the total sleep time (TST) is shorter than the total in-bed time (TIB).

[0105] In some embodiments, the total sleep time (TST) can be defined as the persistent total sleep time (PTST). In such an embodiment, the persistent total sleep time does not include a predetermined initial portion or period of the first non-REM stage (e.g., the light sleep stage). For example, the predetermined initial portion can be between about 30 seconds and about 20 minutes, between about 1 minute and about 10 minutes, between about 3 minutes and about 5 minutes, etc. The persistent total sleep time is a measure of continuous sleep and smooths the sleep-wake sleep graph. For example, when the user initially falls asleep, the user can be in the first non-REM stage for a very short time (e.g., about 30 seconds), then return to the awakening stage for a very short time (e.g., one minute), and then return to the first non-REM stage. In this example, the persistent total sleep time excludes the first instance of the first non-REM stage (e.g., about 30 seconds).

[0106] In some embodiments, the sleep period is defined as between the time of getting into bed (t 入床) begins and ends at the time of getting up (t 起床 ) ends, i.e., the sleep period is defined as the total in-bed time (TIB). In some embodiments, the sleep period is defined as starting at the initial sleep time (t 睡眠 ) and ending at the wake-up time (t 觉醒 ). In some embodiments, the sleep period is defined as the total sleep time (TST). In some embodiments, the sleep period is defined as starting at the sleep onset time (t GTS ) and ending at the wake-up time (t 觉醒 ). In some embodiments, the sleep period is defined as starting at the sleep onset time (t GTS ) and ending at the time of getting up (t 起床 ). In some embodiments, the sleep period is defined as starting at the time of getting into bed (t 入床 ) and ending at the wake-up time (t 觉醒 ). In some embodiments, the sleep period is defined as starting at the initial sleep time (t 睡眠 ) and ending at the time of getting up (t 起床 ).

[0107] Reference Figure 4 , according to some embodiments, shows an exemplary sleep graph 400 corresponding to the timeline 300( Figure 3 ). As shown, the sleep graph 400 includes a sleep-wake signal 401, a wakefulness phase axis 410, a REM phase axis 420, a light sleep phase axis 430, and a deep sleep phase axis 440. The intersection between the sleep-wake signal 401 and one of the axes 410 - 440 indicates the sleep phase at a given time during the sleep period.

[0108] The sleep-wake signal 401 can be generated at least in part based on physiological data associated with the user (e.g., generated by one or more of the sensors 130 described herein). The sleep-wake signal can indicate one or more sleep phases, including wakefulness, relaxed wakefulness, micro-awakening, REM phase, first non-REM phase, second non-REM phase, third non-REM phase, or any combination thereof. In some embodiments, one or more of the first non-REM phase, second non-REM phase, and third non-REM phase can be grouped together and classified as a light sleep phase or a deep sleep phase. For example, the light sleep phase can include the first non-REM phase, while the deep sleep phase can include the second non-REM phase and the third non-REM phase. Although the sleep graph 400 is in Figure 4is shown as including a light sleep stage axis 430 and a deep sleep stage axis 440, but in some embodiments, the sleep chart 400 may include axes for each of a first non-REM stage, a second non-REM stage, and a third non-REM stage. In other embodiments, the sleep-wake signal may also indicate a respiratory signal, a respiratory rate, an inspiratory amplitude, an expiratory amplitude, an inspiratory-expiratory amplitude ratio, an inspiratory-expiratory duration ratio, a number of events per hour, a pattern of events, or any combination thereof. Information describing the sleep-wake signal may be stored in the memory device 114.

[0109] The sleep chart 400 can be used to determine one or more sleep-related parameters, such as sleep onset latency (SOL), wake after sleep onset (WASO), sleep efficiency (SE), sleep fragmentation index, sleep disruption, or any combination thereof.

[0110] Sleep onset latency (SOL) is defined as the time between the time of going to bed (t GTS ) and the initial sleep time (t 睡眠 ). In other words, sleep onset latency indicates the time it takes for the user to actually fall asleep after initially attempting to fall asleep. In some embodiments, sleep onset latency is defined as persistent sleep onset latency (PSOL). Persistent sleep onset latency differs from sleep onset latency in that persistent sleep onset latency is defined as the duration between the time of going to bed and a predetermined amount of persistent sleep. In some embodiments, the predetermined amount of persistent sleep may include, for example, at least 10 minutes of sleep within the second non-REM stage, the third non-REM stage, and / or the REM stage, with no more than 2 minutes of wakefulness, the first non-REM stage, and / or movement therebetween. In other words, the persistent sleep onset latency is up to, for example, 8 minutes within the second non-REM stage, the third non-REM stage, and / or the REM stage. In other embodiments, the predetermined amount of persistent sleep may include at least 10 minutes of sleep within the first non-REM stage, the second non-REM stage, the third non-REM stage, and / or the REM stage after the initial sleep time. In such embodiments, the predetermined amount of persistent sleep may exclude any micro-arousals (e.g., a ten-second micro-arousal does not restart the 10-minute period).

[0111] Wake after sleep onset (WASO) is associated with the total duration that the user is awake between the initial sleep time and the wake time. Thus, wake after sleep onset includes brief and micro-arousals during the sleep period (e.g., Figure 4The micro-awakenings MA1 and MA2 shown in , whether conscious or unconscious. In some embodiments, the wake after sleep onset (WASO) is defined as the persistent wake after sleep onset (PWASO) that includes only the total duration of awakenings having a predetermined length (e.g., greater than 10 seconds, greater than 30 seconds, greater than 60 seconds, greater than about 5 minutes, greater than about 10 minutes, etc.).

[0112] The sleep efficiency (SE) is determined as the ratio of the total in-bed time (TIB) to the total sleep time (TST). For example, if the total in-bed time is 8 hours and the total sleep time is 7.5 hours, the sleep efficiency for that sleep period is 93.75%. The sleep efficiency indicates the user's sleep hygiene. For example, if the user enters the bed before sleeping and spends time engaged in other activities (e.g., watching TV), the sleep efficiency will decrease (e.g., the user is penalized). In some embodiments, the sleep efficiency (SE) can be calculated at least in part based on the total in-bed time (TIB) and the total time the user attempts to sleep. In such embodiments, the total time the user attempts to sleep is defined as the duration between the lights out to sleep (GTS) time described herein and the time of getting up. For example, if the total sleep time is 8 hours (e.g., between 11 PM and 7 AM), the lights out to sleep time is 10:45 PM, and the time of getting up is 7:15 AM, then in such embodiments, the sleep efficiency parameter is calculated to be approximately 94%.

[0113] The fragmentation index is determined at least in part based on the number of awakenings during the sleep period. For example, if the user has two micro-awakenings (e.g., Figure 4 the micro-awakenings MA1 and micro-awakening MA2 shown in ), the fragmentation index can be expressed as 2. In some embodiments, the fragmentation index is scaled between a predetermined range of integers (e.g., between 0 and 10).

[0114] Sleep disruptions are associated with transitions between any sleep stage (e.g., the first non-REM stage, the second non-REM stage, the third non-REM stage, and / or REM) and the wake stage. Sleep disruptions can be calculated at a resolution of, for example, 30 seconds.

[0115] In some embodiments, the systems and methods described herein can include generating or analyzing a hypnogram that includes sleep-wake signals to determine or identify the time of entering the bed (t 入床 ), the time of lights out to sleep (t GTS ), the initial sleep time (t 睡眠 ), one or more first micro-awakenings (e.g., MA1 and MA2), the wake time (t 觉醒 ), the time of getting up (t 起床) or any combination thereof. The sleep chart can be generated in real time during the sleep period or can be generated after the sleep period is completed.

[0116] In other embodiments, one or more of the sensors 130 can be used to determine or identify the time of getting into bed (t 入床 ), the time of falling asleep (t GTS ), the initial sleep time (t 睡眠 ), one or more first micro-awakenings (e.g., MA1 and MA2), the awakening time (t 觉醒 ), the waking time (t 起床 ), or any combination thereof, which in turn defines the sleep period. For example, the time of getting into bed t 入床 can be determined at least in part based on data generated, for example, by the motion sensor 138, the microphone 140, the camera 150, or any combination thereof. The time of falling asleep can be determined at least in part based on data from, for example, the motion sensor 138 (e.g., data indicating that the user is not moving), the camera 150 (e.g., data indicating that the user is not moving and / or the user has turned off the light), the microphone 140 (e.g., data indicating that the TV is being turned off), the external device 170 (e.g., data indicating that the user is no longer using the external device 170), data from the pressure sensor 132 and / or the flow sensor 134 (e.g., data indicating that the user has turned on the respiratory therapy device 122, data indicating that the user is wearing the user interface 124, etc.), or any combination thereof.

[0117] Figure 5 FIG. shows a block diagram of an example algorithm 500 for monitoring a user (such as user 210) and determining whether the user 210 has RBD and / or DEB. The algorithm 500 generally includes two separate components 502 and 552. The component 502 is used for daily and nightly monitoring of the user, while the component 552 can be used for long-term evaluation and diagnosis. In one embodiment, the component 502 is implemented as an individual device of the user (such as the external device 170). Thus, in some embodiments, the component 502 is implemented as a program or application on the user's mobile device (e.g., a phone, a tablet, a laptop, etc.). The component 552 can also be implemented on the user's individual device, but in many embodiments is implemented as part of a cloud-based platform. This allows third parties (e.g., the user's healthcare provider, caregiver, friend, family member, etc.) to access more long-term evaluation and diagnostic information. In other embodiments, the components 502 and 552 can be implemented on any combination of devices, platforms, etc. Generally, the components 502 and 552 can be implemented as part of the system 100 ( Figure 1 ).

[0118] Component 502 includes a night monitoring portion (shown in the upper and middle blocks of component 502) and a day monitoring portion (shown in the lower block of component 502). Blocks 504A and 504B are inputs to the night monitoring portion of component 502. At block 504A, the user is monitored during the sleep period to detect any atypical REM sleep stages and / or DEB. The sleep period monitoring at block 504A generally includes, for example, using any one of the sensors 130( Figure 1 ) to track the movement and vocalizations (e.g., speech and non-speech) of the user during the sleep period. Various different motion sensors that generate motion data can be used to track movement. Audio sensors that generate audio data can be used to track the vocalizations (e.g., speech, silence, crying, snoring, coughing, choking, etc.) made by the user during the sleep period. Other types of data can also be used. For example, respiratory data related to the user's breathing (along with motion data referred to as biomotion data) can be generated. Optical data related to the light level in the user's surrounding environment (e.g., in the user's bedroom) can be generated. And time data related to the time elapsed during the sleep period can be generated. Generally, the data is obtained from non-invasive devices, such as by using motion sensors, audio sensors, etc. There is no need to use more invasive sensors such as EEG sensors, ECG sensors, or EOG sensors.

[0119] At block 504B, audio samples from the user are generated. The audio samples are known to belong to the user who makes various vocalizations (including speech and non-speech, such as coughing, grunting, snoring, crying, etc.), and can help determine whether the vocalizations detected during the sleep period are actually made by the user (rather than the user's bed partner), and if so, determine whether the vocalization is speech or non-speech. In some embodiments, the user is recorded during the day to generate known audio samples. In other embodiments, the user is prompted to say a specific recorded phrase. A user's mobile device (e.g., a phone, a tablet) or any other suitable device can be used to complete the recording. For example, in some embodiments, a smart speaker in the user's bedroom can record audio samples from the user before the user starts the sleep period (e.g., before the user goes to bed at night) or after the user ends the sleep period (e.g., after the user gets out of bed in the morning).

[0120] Data from block 504A and audio samples from block 504B are used to analyze the sleep period at block 506A. Generally, the analysis performed at block 506A can be implemented using one or more algorithms. These algorithms can be machine learning algorithms that have been trained to identify different sleep stages experienced by the user during the sleep period based at least on data from the nocturnal monitoring block 504A and data from the audio sampling block 504B. Modules of algorithm 500 can be pre-trained with data from clinical references (data recorded in a sleep laboratory, data identifying sleep stages, RBD and DEB episodes scored by experts, etc.). When applied to a new user, the pre-training can provide good baseline performance (data that algorithm 500 has not seen before). Once algorithm 500 is deployed, it can be automatically re-trained with user-specific data. This training can optimize the performance of algorithm 500 for a particular user. In some embodiments, separate training algorithms are used to determine the individual sleep stages and to detect whether the user is experiencing DEB. In other embodiments, a single trained algorithm can determine the various sleep stages and detect whether the user is experiencing DEB. Thus, when the user is experiencing an atypical REM sleep stage indicative of RBD, the sleep period analysis at block 506A can indicate whether the user is experiencing DEB during any atypical REM sleep stage. Based on the data from the sleep period monitoring at block 504A, the sleep period analysis at block 506A can occur in real time during the sleep period.

[0121] The sleep period analysis at block 506A typically has two different outputs at blocks 508A and 508B. At block 508A, sleep period summary data is generated. The sleep period summary includes details about the number of different sleep stages experienced by the user during the sleep period and the amount of time spent in each different sleep stage during the sleep period. Thus, the sleep period analysis indicates to the user and / or a third party the total number of atypical REM sleep stages experienced by the user during the sleep period and the total amount of time spent in atypical REM sleep stages during the sleep period. In some embodiments, the sleep period summary is generated as a sleep graph that shows the user's sleep period and the timeline of the various sleep stages experienced by the user throughout the sleep period. In some embodiments, the sleep period summary is generated at the end of the sleep period. In other embodiments, the sleep period summary can be generated in real time during the sleep period, which may be useful when monitoring the third part of the user's sleep period.

[0122] Another output of the sleep session analysis block 506A is block 508B, where DEB mitigation occurs. If the real-time sleep session analysis of block 506A determines that the user is currently experiencing DEB, the system 100 may activate the DEB mitigation system 184 to reduce or end the user's DEB, or to mitigate the impact of the DEB on the user or the user's bed partner. As discussed herein, DEB mitigation can be achieved in a variety of ways, such as using an audible alarm, a light, a bed adjustment mechanism that moves or vibrates the user's bed, a physical contact mechanism that contacts the user, or a protective device or barrier that mitigates any harm that the DEB may cause to the user or the user's bed partner.

[0123] The component 502 further includes daytime monitoring at block 504C. Generally, daytime monitoring includes monitoring the user's movement during the day. A variety of different movement abnormalities can be associated with neurodegenerative diseases (including RBD), such as stiffness, abnormal gait, bradykinesia of limb movement, tremors, etc. A variety of different devices can be used to track the user's movement and generate movement data, such as a pedometer, a smart watch, a wearable fitness device, a mobile device (such as the external device 170). At block 506B, the movement data is used to perform a movement skills analysis. The movement data can be analyzed to determine various characteristics related to the user's postural sway, gait, and limb movement. Aspects of the user's gait can include jerkiness, harmonic stability, and range of vibration. Aspects of the user's limb movement can include arm swing, or asymmetry between the left and right arms or the left and right legs.

[0124] The output of the movement skills analysis at block 506B is the movement skills summary at block 508C. In some embodiments, a summary table can be generated for the user to view. In other embodiments, the movement skills summary data is used as part of a long-term assessment.

[0125] The data from the sleep session summary of block 508A and the movement skills summary of block 508C can be used as part of the long-term assessment and diagnosis performed by the component 552. Generally, the sleep session summary data for each sleep session is an input to the long-term assessment performed at block 556 of the component 552. For example, every morning after the user wakes up and the sleep session ends, a new set of sleep session summary data is provided to block 556. The movement skills summary data from block 508C can also be periodically input into block 556.

[0126] Block 556 may also include demographic information provided at block 554. The demographic information may be demographic information related to the user, such as age, sex, gender, race, medical history, etc. However, the demographic information may also include demographic information from other users. In these embodiments, the demographic information typically further includes information on whether those users have RBD, and may include sleep period data, movement data, and voice data on those other users. This enables RBD and / or DEB to be correlated with specific demographic factors and also allows the user's data to be compared with the data of other users from the same population.

[0127] At block 558, the output of the long-term assessment is a diagnosis and recommendations. Block 558 typically provides an assessment of the user's long-term prognosis. Based on data from multiple sleep periods and based on movement data over a long period of time, the user's long-term prognosis may show the potential development of RBD over time. Typically, RBD is a slowly developing condition and thus the diagnosis at block 558 may include risk factors or severity scores, such as if the user is in the early stages of RBD. The risk factors may also indicate the risk of the user developing other types of neurodegenerative conditions. The recommendations may include recommendations for treatment options (e.g., medications), recommendations for alleviating or ending the DEB that the user experiences during sleep periods, recommendations for following up with the user's healthcare provider, or related advice or suggestions.

[0128] In some embodiments, the audio sampling of block 504B is also used directly for the long-term assessment at block 556. There are various vocalization abnormalities that can be analyzed together with or separately from the vocalizations that occur during sleep periods, which can also be early indicators of neurodegenerative conditions such as RBD. These abnormalities may be related to sustained vocalizations, such as pitch fluctuations, noise, or aperiodicity during sustained vocalizations. These abnormalities may be related to pronunciation, such as imprecise vowels and consonants, slow and irregular syllables, and decay of pronunciation. In another example, the abnormalities may be related to prosody and may include reduced loudness, reduced loudness variation, reduced pitch variation, and inappropriate silences. Long-term tracking of the user's vocalizations can identify these abnormalities, which aids in the early diagnosis of RBD.

[0129] Now refer to Figure 6, an example algorithm 600 for monitoring a sleep period is shown. Generally, algorithm 600 is a more detailed description of the nighttime monitoring portion of component 502 of algorithm 500. Algorithm 600 includes a model component 602 and a retraining component 652. The inputs to the model component 602 of algorithm 600 can include motion data at block 604A, respiratory data at block 604B, audio data at block 604C, and optical data at block 604D. In some embodiments, all of this data can be generated by any one or more of the sensors 130 of system 100. The motion data at block 604A can be generated by the motion sensor 138, which can include an accelerometer, a gyroscope, a magnetometer, etc. The respiratory data at block 604B can be generated by the pressure sensor 132, the flow sensor 134, the motion sensor 138, the microphone 140, or any combination thereof. The audio data at block 604C can be produced by the microphone 140. The optical data at block 604D can be generated by the camera 150 or the IR sensor 152. Any of the data at blocks 604A - 604D can also be generated by other sensors.

[0130] The motion data 604A is analyzed at block 606A to detect motions made by the user during the sleep period, including limb motions. The motion detection at block 606A can also identify other motions, such as the motions of the user's bed partner. The respiratory data of block 604B is analyzed at block 606B to detect the user's respiration. The audio data at block 604C is analyzed at block 606C to detect sounds produced during the sleep period, which can include the user's speech and non-speech, the user's bed partner's speech and non-speech, and any other detected audio. In some embodiments, machine learning algorithms such as recurrent neural networks are used to analyze the audio data at block 604C to extract specific events. Finally, the optical data at block 604D can be analyzed at block 606D to determine the light level in the area where the user is located during the sleep period (e.g., the user's bedroom).

[0131] At block 608A, the motions detected at block 606A and the respiration detected at block 606B are used to label identifiable motion events during the sleep period. This can include specific motion patterns of the user's limbs, such as punching, kicking, etc.; and various attributes of the motion, such as speed, acceleration, etc. Identifiable motions can also relate to the user's respiration, such as respiratory rate and respiratory amplitude. In some embodiments, block 608A can also utilize the detected sounds from block 606C and the detected light level from block 606D.

[0132] At block 608B, specific audio events are marked as relevant. For example, if algorithm 600 determines that specific audio events identified during the vocalization detection of block 604C are generated by the user's bed partner, those audio events can be discarded. The identified audio events determined to be from the user can be retained for further use. The identified audio events can include any vocalizations of interest, such as speech, crying, silence, choking, coughing, etc. In particular, speech with recognizable pitch, intensity, and verbal content can be marked, as these types of events often occur in users with RBD. In some embodiments, block 608B can also utilize the detected motion from block 606A, the detected breathing from block 606B, and the detected light level from block 606D.

[0133] At block 608C, the area in which the user is located during the sleep period is identified as a light or dark environment. Based on the light level in that area (e.g., the user's bedroom), block 608C determines whether it is light or dark, which in turn can be used to determine whether the user is experiencing an atypical REM sleep stage. In some embodiments, block 608C can also utilize the detected motion from block 606A, the detected breathing from block 606B, and the detected sound from block 606C.

[0134] The recognizable motion of block 608A, the recognizable audio events of block 608B, and the determined lightness or darkness of the user's environment of block 608C can also be used as inputs at block 610A, which performs real-time sleep period analysis. Real-time sleep period analysis identifies the various sleep stages as they occur during the sleep period.

[0135] One of the outputs of the real-time sleep period analysis is the detection of the occurrence of a DEB. When a DEB is detected, real-time DEB mitigation can occur at block 612A. As discussed herein, DEB mitigation can occur in a variety of ways, including using an audible alarm, a light, a bed adjustment mechanism that moves or vibrates the user's bed, a physical contact mechanism that contacts the user, or a protective piece or barrier that mitigates any harm that the DEB might cause to the user or the user's bed partner.

[0136] The results of the real-time sleep period analysis at block 610A are also used as input to block 610B, which typically performs a more refined sleep analysis after the sleep period ends. Generally, the refined sleep analysis at block 610B is performed by one or more trained algorithms or models. Thus, the sleep period analysis at block 610B can utilize more data to identify the various sleep stages of the sleep period. The output of the sleep period analysis at block 610B is a sleep period summary at block 612B. The sleep period summary identifies all the sleep stages the user experienced during the sleep period, and how long each sleep stage lasted. In some embodiments, the sleep period summary is in the form of a hypnogram, as further described herein.

[0137] In the retraining component 652, the trained algorithms / models that perform the real-time sleep period analysis at block 610A and the refined sleep period analysis at block 610B can be updated. At block 654, feedback regarding the sleep period analysis can be provided. In some examples, the user provides feedback regarding the sleep period summary, which can include confirmation of the various sleep stages, the start and end times of the sleep period, the occurrence of DEB, or other aspects of the sleep period summary. Based on the sleep period summary at block 612B and the feedback at block 654, a verified template can be created or updated. A verified motion template is generated at block 656A and includes data indicative of user-specific motions that tend to re-occur during the sleep period. The verified motion template generated at block 656A typically can also include any type of confirmed data or information related to motion that can be used to update the trained algorithms at blocks 610A and 610B. Similarly, a verified audio template is generated at block 656B, which includes data indicative of vocalizations (e.g., speech or non-speech) confirmed by the user, or any type of confirmed data or information related to audio that can generally be used to update the trained algorithms at blocks 610A and 610B. Both templates can be used as input to the retraining block 658, where the trained model can be retrained, for example, using user-specific information. Thus, the refined sleep period analysis can be made more accurate for future sleep periods.

[0138] At block 660A, an updated detection threshold is created for real-time sleep period analysis of block 610A. The detection threshold generally tells the trained algorithm what types of data indicate various sleep stages and when a DEB occurs. At block 660B, an updated mitigation threshold is created for DEB mitigation of block 612A. The mitigation threshold generally indicates when a certain type of DEB mitigation should be performed. For example, some DEBs may occur that are not severe enough to require any mitigation. By updating the mitigation threshold at block 660B, algorithm 600 can more accurately determine when the severity of a DEB is sufficient to warrant mitigation. The thresholds generated at blocks 660A and 660B can be generated by any suitable mechanism, including updating various algorithms and models with new user-specific data, new data of people similar to the user (e.g., people in the same demographic group as the user), or any other data.

[0139] Figure 7A 、 7B and 7C show different sleep graphs that can be created for users with different levels of RBD. Each sleep graph shows four different sleep cycles. Figure 7A Sleep graph 700A shows the sleep period of a user without RBD. It can be seen that the REM sleep stage of each sleep cycle is the typical REM sleep stage 702A. Figure 7B Sleep graph 700B of shows an overview of the sleep period of a user with mild to moderate RBD. As shown, the REM sleep stage of the first cycle is the typical REM sleep stage 702A. However, the user begins to experience atypical REM sleep stages in subsequent sleep cycles. The REM sleep stages of the second and third sleep cycles include the typical REM sleep stage 702A and the atypical REM sleep stage 702B. Finally, Figure 7C Sleep graph 700C of shows an overview of the sleep period of a user with RBD who has started to exhibit DEB. In the first sleep cycle, the REM sleep stage includes only the typical REM sleep stage 702A. In the second and third sleep cycles, the REM sleep stages include the typical REM sleep stage 702A and the atypical REM sleep stage 702B, similar to Figure 7B sleep graph 700B in. In the fourth sleep cycle, the user experiences DEB, so the REM sleep stage of the fourth sleep cycle includes only the DEB stage 702C.

[0140] Refer to Figure 8, shows a method 800 for monitoring a sleep period. Generally, a control system (e.g., the control system 110 of system 100) or a part of the control system (e.g., a processor (e.g., one or more processors 112)) is configured to perform the various steps of method 800 (or other methods disclosed herein). A memory device (e.g., the memory device 114 of system 100) can be used to store any type of data utilized in the steps of method 800 (or other methods). Method 800 is a specific implementation of example algorithms 500 and 600, which can be used with a variety of different methods.

[0141] Step 802 of method 800 includes receiving data associated with a user's sleep period. The received data can include various different types of data, such as respiratory data, motion data indicating the movement of an individual during the sleep period, audio data indicating vocalizations detected during the sleep period, optical data indicating light in the area where the individual is located during the sleep period, or any combination thereof. The data can also include time data representing the time elapsed during the sleep period. For example, any one or more of the sensors 130 can be used to generate the data.

[0142] Step 804 of method 800 includes analyzing the received data to determine whether the user is experiencing DEB during the sleep period. In some embodiments, a machine learning tool (such as a trained DEB algorithm) is used to determine whether the user is experiencing DEB. The trained DEB algorithm is configured to make various determinations, including whether a sound detected during the sleep period is a vocalization made by the user or the user's bed partner, and whether a vocalization made by the user is speech or non-speech. In some embodiments, the DEB algorithm analyzes additional audio data of the user speaking outside of the sleep period to help determine whether a vocalization detected during the sleep period is speech made by the user. In further embodiments, the DEB algorithm determines whether the detected sound is from a separate source, such as an electronic device in the user's room. The DEB algorithm can also analyze motion data, respiratory data, and optical data to help determine whether the user is experiencing DEB. The DEB algorithm can also identify the verbal content in any detected vocalizations. Based on the identified verbal content, system 100 can identify the user's mood, such as fear or terror, which can indicate DEB. Generally, in order to analyze the data and determine whether the user is experiencing DEB, a minimum amount of data must be received. In some embodiments, the minimum amount of data received is the data generated during a 30-second time period within the sleep period. In other embodiments, the minimum amount of data received is the data generated during a two-minute time period within the sleep period.

[0143] Step 806 of method 800 includes performing an action to mitigate a user's DEB. The action can include activating a light source, activating an audible alarm, moving the bed on which the user lies, moving the individual's body, taking steps to prevent harm to the user or the user's bed partner, or any combination thereof. In some embodiments, the action is configured to help end the user's DEB by waking the user or causing them to exit an atypical REM sleep stage. In other embodiments, the action is configured to mitigate the severity of the DEB itself, such as by preventing movement of the user and / or the user's limbs, or by protecting the user and / or the user's bed partner. Generally, steps 804 and 808 are performed continuously in real time during a sleep period to ensure that whenever a user experiences a DEB, the DEB can be detected and mitigated.

[0144] In step 808, the received data is analyzed to determine the various different sleep stages that the individual experiences during the sleep period. These sleep stages can include light sleep stages, deep sleep stages, typical REM sleep stages, atypical REM sleep stages, and wake states. In contrast to steps 804 and 806, step 808 can be performed at any point during or after the sleep period. If performed during the sleep period, the data can be continuously analyzed to determine the various sleep stages that the user experiences. However, since merely experiencing an atypical REM sleep stage does not require real-time intervention (as opposed to experiencing a DEB which does not occur during every atypical REM sleep stage), the data can be analyzed after the sleep period to determine the different sleep stages that the user experienced during the sleep period. In some embodiments, trained machine learning algorithms are used to analyze the data and determine the various different sleep stages that the user experiences during the sleep period. These trained algorithms can be the same as or different from the trained DEB algorithms used to determine in real time whether the user is experiencing a DEB.

[0145] In some embodiments, different types of data are viewed separately to determine what sleep stage the user is in. For example, detected movement above a particular threshold can indicate that the user is experiencing intentional and complex movements common to both an atypical REM sleep stage and / or a DEB, such as pointing, kicking, thrashing, etc. In another example, detected vocalizations made by the user during the sleep period can indicate that the user is in an atypical REM sleep stage or is experiencing a DEB. In yet another example, optical data can be analyzed to determine the light level in the area where the sleep period occurs, which is typically the user's bedroom. A light level above a particular threshold can help determine that the user is in a wake state, while a light level below the threshold can help determine that the user is in some type of sleep stage, such as light, deep, typical REM, or atypical REM.

[0146] Different types of data can be combined to reveal insights about a user's sleep period, such as whether the user is experiencing an atypical REM sleep stage and / or is experiencing a DEB. In particular, since movement detection can occur in both the wake state and an atypical REM sleep stage, it is useful to cross-check movement data with other data to determine whether the user is truly in an atypical REM sleep stage or is simply waking up and moving around. In one example, time data and movement data are combined. The time data can be analyzed to determine the time elapsed during the sleep period, for example, by using a timer starting from the beginning of the sleep period or by comparing the current time with the time the sleep period started. The movement data is analyzed to determine whether the user is moving. Typically, the REM sleep stage occurs later during the sleep period, usually in the second half. Thus, movement that occurs after a first amount of time (which can be, for example, a maximum of 4 hours) indicates that the movement is due to the user being in the wake state, while movement that occurs when a second amount of time (which can be, for example, a minimum of 4 hours) has elapsed indicates that the movement is due to the user currently being in an atypical REM sleep stage. The time data can also be combined with audio data. If the audio data indicates that the user is speaking after the first amount of time has passed, the system 100 can generally determine that the speech is because the user is in the wake state. If the audio data shows that the user is speaking when the second amount of time has passed, the system 100 can determine that the speech is because the user is in an atypical REM sleep stage or is experiencing a DEB. In other embodiments, the time elapsed can be combined with knowledge of the sleep stages the user has experienced during the sleep period. Typically, if it is determined that the user has experienced a certain previous sleep stage at a particular elapsed time within the sleep period, the system 100 can determine what sleep stage the user is currently in, or the probability percentage that the current sleep stage is a particular one of the sleep stages.

[0147] In another example, the motion data is combined with the respiration data. The respiration data is analyzed to determine the respiration rate variability. Typically, the user's respiration rate variability is high during both typical and atypical REM sleep stages. Determining that the user has a respiration rate variability above a threshold during motion indicates that the user is experiencing DEB or is in an atypical REM sleep stage. Determining that the user has a respiration rate variability above the same threshold, but when no motion is occurring, indicates that the user is in a typical REM sleep stage. The respiration rate can also be used instead of the respiration rate variability. In another example, the motion data is combined with optical data, and the optical data is analyzed to determine the light level in the area where the user is during the sleep period. Detecting movement when the light level is above a specific threshold (e.g., the bedroom light or lights are on) indicates that the user is awake. Conversely, detecting movement when the light level is below the threshold (e.g., the lights are off) indicates that the user is not awake, and the detected movement is due to the user being in an atypical REM sleep stage or experiencing DEB. In yet another example, the same light level analysis can be combined with vocalizations detected during the sleep period. Detecting that the user is speaking when the light level is below the threshold can indicate that the user is in an atypical REM sleep stage or experiencing DEB. Detecting that the user is speaking when the light level is above the threshold can indicate that the user is in a waking state.

[0148] Other data can also be used to help determine which sleep stage the user is in. In some embodiments, system 100 can access the user's medical history. Data from the medical history can reveal specific conditions or diseases that the user has that can provide additional insight into the sleep period. For example, the medical history can indicate that the user has been treated or diagnosed with RBD in the past, which can indicate that detected movements or vocalizations are more likely due to the user being in an atypical REM sleep stage. In another example, the user's medical history can provide other reasons for why the user may be moving or speaking during the sleep period, which can indicate that the user is not in an atypical REM sleep stage, despite detecting movement and / or vocalizations during the sleep period. A questionnaire can also be provided to the user, which can reveal information that helps determine which sleep stage the user is in. For example, if the user indicates on the questionnaire that they have a condition such as insomnia or periodic limb movement (PLM), a trained algorithm can more accurately determine whether the movement is due to the user being in an atypical REM sleep stage or due to the user's insomnia or periodic limb movement. Additionally, the algorithm can include additional modules that are capable of determining potential other conditions or sleep disorders, for example, based on the respiration signal, detected vocalizations, or movement. In one example, PLM can be detected based on the periodicity of the movement pattern. In another example, OSA can be detected based on the respiration signal and detected snoring. Thus, these modules can eliminate confounding factors in atypical REM and / or DEB detection.

[0149] Other data can also be historical data associated with one or more previous sleep periods. System 100 can analyze the historical data to determine if there are similar aspects in the data from the current sleep period. If the historical data associates any characteristics, trends, etc. with a particular sleep stage, System 100 can use these correlations to help determine when similar characteristics or trends from the current sleep period indicate that the user is in a particular sleep stage or is experiencing DEB.

[0150] Finally, at step 810 of method 800, a summary of the sleep period is generated. In some embodiments, the summary is presented as a sleep graph, as Figure 7A - 7C shown. In other embodiments, the summary comprises or consists of data indicating the time spent in each sleep stage during the sleep period. In either embodiment, the summary can include the number of different atypical REM sleep stages the user experienced during the sleep period, and / or the total time spent in atypical REM sleep stages during the current sleep period. In some embodiments, the historical summary of a previous sleep period can be compared to the summary of the current sleep period. This comparison can be used to help confirm the number of atypical REM sleep stages the user experienced during the sleep period, as well as the time spent in atypical REM sleep stages during the sleep period. These comparisons can also be used to monitor the user's long-term trends. For example, if comparisons of sleep periods over time indicate that the user is experiencing an increasing number of atypical REM sleep stages, System 100 can notify the user or a third party and provide recommendations.

[0151] Reference Figure 9 , shows a method 900 for monitoring a sleep period. Generally, a control system (e.g., control system 110 of system 100) or a part of the control system (e.g., a processor (e.g., one or more processors 112)) is configured to perform the various steps of method 900 (or other methods disclosed herein). A memory device (e.g., memory device 114 of system 100) can be used to store any type of data utilized in the steps of method 900 (or other methods). Method 900 is a particular embodiment of example algorithms 500 and 600, which can be used with a variety of different methods.

[0152] Step 902 of method 900 includes receiving data associated with a user's sleep period, similar to step 802 of method 800. The received data can include various different types of data, such as respiratory data, motion data indicating the movement of an individual during the sleep period, audio data indicating vocalizations detected during the sleep period, optical data indicating light in the area where the individual is located during the sleep period, or any combination thereof. The data can also include time data representing the time elapsed during the sleep period. For example, any one or more of the sensors 130 can be used to generate the data.

[0153] Step 904 of method 900 includes analyzing the received data to identify the sleep stages experienced by the user during the sleep period. The sleep stages can include a light sleep stage, a deep sleep stage, a typical REM sleep stage, an atypical REM sleep stage, and a wake state. During the sleep period, the user will typically cycle through the various sleep stages in a periodic or semi-periodic manner. Thus, in some embodiments, the sleep period can be divided into a plurality of smaller sleep cycles, where each sleep cycle includes at least one light sleep stage, at least one deep sleep stage, and at least one REM sleep stage, which can be typical, atypical, or both. In some embodiments, one or more trained algorithms are used to identify the sleep stages of the sleep period. Generally, any of the techniques described herein can be used at step 904, including the techniques described with respect to step 808 of method 800.

[0154] At step 906, the number of atypical REM sleep stages experienced by the user during the sleep period, and the total time spent in the atypical REM sleep stages during the sleep period, are determined. At step 908, an action is performed based on these determinations. In some embodiments, if the number of atypical REM sleep stages or the amount of time spent in the atypical REM sleep stages exceeds a corresponding threshold, the action is caused to be performed.

[0155] The action caused to be performed can generally be any action related to the occurrence of the atypical REM sleep stage. In some embodiments, the action includes causing a notification or message to be sent to the user or a third party. The notification can include an indication that the number of atypical REM sleep stages or the time spent in the atypical REM sleep stages has exceeded its corresponding threshold, a recommendation for the next step to be taken by the user (such as treatment or cure, taking medication, seeing a doctor, etc.), or any other suitable notification or message. The third party can be the user's healthcare provider (such as a doctor or nurse), a family member of the user, a friend of the user, a caregiver of the user, or any other suitable third party. In additional embodiments, the action includes updating the user's medical record with information about the number of atypical REM sleep stages and the time spent in the atypical REM sleep stages during the sleep period.

[0156] In some embodiments, user feedback can be utilized to update training algorithms for identifying sleep stages and, in particular, for identifying atypical REM sleep stages. For example, if it is determined that a user has experienced one or more atypical REM sleep stages during a sleep period, the user (or a third party, such as the user's bed partner) can be prompted to confirm that the user has indeed experienced one or more atypical REM sleep stages during the sleep period. In response to the confirmation or lack thereof, the trained algorithms can be updated such that they can more accurately identify atypical REM sleep stages during subsequent sleep periods. Audio and / or video data can also be used to confirm whether a user has experienced one or more atypical REM sleep stages during a sleep period. The audio data can indicate whether the user has made any vocalizations or other sounds indicative of an atypical REM sleep stage, and the video data can indicate whether the user has experienced any movements indicative of an atypical REM sleep stage.

[0157] In some embodiments, the time spent in atypical REM sleep stages during the current sleep period can be compared to the time spent in atypical REM sleep stages during one or more previous sleep periods. In one example, the average amount of time spent in atypical REM sleep stages for each sleep period is determined and then compared to the total time spent in atypical REM sleep stages during the current cycle. If the user spends more time in the current sleep stage than the average time in atypical REM sleep stages, the action can be performed in step 908. Additionally, the average amount of time spent in atypical REM sleep stages for each cycle can be updated with data from the current sleep period. If the updated average time spent in atypical REM sleep stages for each sleep period meets a threshold amount of time, the action in step 908 can also be performed.

[0158] In other embodiments, a similar method can be utilized for analyzing the number of atypical REM sleep stages experienced during a sleep period. Thus, the average number of atypical REM sleep stages experienced for each sleep period spanning one or more previous sleep periods is determined and then compared to the number of atypical REM sleep stages in the current sleep period. If the number of atypical REM sleep stages in the current sleep period is high, the action can be performed. The average number of atypical REM sleep stages for each sleep period can also be updated with data from the current sleep period, and if the updated average is above a threshold, the action can be caused to be performed.

[0159] Accordingly, method 900 can be used to monitor a user over a longer period of time to determine that the user is developing RBD by looking at the number of atypical REM sleep stages and the time spent in atypical REM sleep stages. If the user starts experiencing more atypical REM sleep stages or starts spending a longer amount of time in atypical REM sleep stages, certain actions can be taken, which can include notifying the user that they may be developing or have RBD, or providing relevant information to a third party.

[0160] Reference Figure 10 Method 1000 for monitoring a sleep period is shown. Generally, a control system (e.g., control system 110 of system 100) or a portion of a control system (e.g., a processor (e.g., one or more processors 112)) is configured to perform the various steps of method 1000 (or other methods disclosed herein). A memory device (e.g., memory device 114 of system 100) can be used to store any type of data utilized in the steps of method 1000 (or other methods). Method 1000 is a specific implementation of example algorithms 500 and 600, which can be used with a variety of different methods.

[0161] Step 1002 of method 1000 includes determining a historical parameter associated with the severity of atypical REM sleep stages experienced by the user during a previous sleep period. Generally, the severity of an atypical REM sleep stage can be quantified with reference to, for example, the movement of the user during the atypical REM sleep stage or the vocalizations of the user during the atypical REM sleep stage. For example, an atypical REM sleep stage characterized by violent and rapid movement or movement with a large range of motion can be considered more severe. In contrast, an atypical REM sleep stage characterized by slower and smaller movement (while still being an intentional movement of RBD) can be considered less severe. In another example, an atypical REM sleep stage characterized by loud vocalizations (verbal or non-verbal) can be considered more severe than an atypical REM sleep stage characterized by quieter vocalizations (verbal or non-verbal). Monitoring the severity of atypical REM sleep stages (or the severity of DEB experienced during an atypical REM sleep stage) can show the development of RBD in the user over a longer period of time.

[0162] Step 1004 of method 1000 includes receiving data associated with the current sleep period. Step 1004 may be similar to step 802 of method 800 and step 902 of method 900. Step 1006 of method 1000 includes determining a current parameter associated with the severity of an atypical REM sleep stage experienced during the current sleep period. Generally, the parameter associated with the severity of the atypical REM sleep stage during the current sleep period is measured in the same manner as the historical parameter. In some embodiments, the historical and current parameters may be measured on a scale from 0 to 10.

[0163] Step 1008 of method 1000 includes comparing the current parameter with the historical parameter. Step 1010 of method 1000 includes performing an action based on the comparison. In some embodiments, if the comparison indicates that the atypical REM sleep stage experienced during the current sleep period is more severe than the atypical REM sleep stage experienced during one or more previous sleep periods, then this may result in the performance of the action. The action of step 1010 may include notifying the user or a third party that the atypical REM sleep stage is becoming more severe, which generally indicates that the user's RBD is progressing. The action may also include steps to reduce the user's movement or vocalizations during the atypical REM sleep stage. The action may be a real-time mitigation such as DEB mitigation described at least with reference to step 806 of method 800. The action may also include suggesting to the user that they modify their sleep period to attempt to reduce the severity of the atypical REM sleep stage. The modification may be something as simple as tilting the bed slightly or may be something more drastic, such as partially restraining themselves during the sleep period.

[0164] In some embodiments, the severity of any atypical REM sleep stage is based on the user's movement during the sleep period and / or the vocalizations made by the user during the sleep period. In some embodiments, if a part of the user that is moving (e.g., the user's arm) moves at a threshold speed, at a threshold acceleration, for a time amount greater than a predetermined threshold time amount, or any combination thereof, according to a predetermined movement pattern, then the severity of the atypical REM sleep stage is considered to have increased. Thus, if the user moves faster or with greater acceleration, starts moving with an intentional movement (as opposed to a more random movement), or moves for a longer time amount, the atypical REM sleep stage becomes more severe. Other movement characteristics may also be analyzed to determine if the atypical REM sleep stage is becoming more severe. Vocalizations made by the user may also be used to monitor the severity of the atypical REM sleep stage. For example, if the vocalizations produced during the atypical REM sleep stage are louder than a threshold volume, have a particular pitch / frequency, or occur for a longer time amount, then it may be said that the atypical REM sleep stage has become more severe.

[0165] ReferenceFigure 11 illustrates a method 1100 for monitoring a sleep period. Generally, a control system (such as the control system 110 of system 100) or a part of the control system (such as a processor (e.g., one or more processors 112)) is configured to perform the various steps of method 1100 (or other methods disclosed herein). A memory device (such as the memory device 114 of system 100) can be used to store any type of data utilized in the steps of method 1100 (or other methods). Method 1100 is a specific implementation of example algorithms 500 and 600, which can be used with a variety of different methods.

[0166] Step 1102 of method 1100 includes receiving data associated with the current sleep period. Step 1102 can be similar to step 802 of method 800, step 902 of method 900, and step 1004 of method 1000. Step 1104 of method 1100 includes inputting some or all of the received data into a trained DEB algorithm to determine whether an individual is experiencing DEB during the sleep period. In some embodiments, the DEB algorithm is trained using training data associated with previous sleep periods in which a known user experienced DEB. Thus, motion data indicating motion during DEB, respiratory data indicating respiration during DEB, and audio data indicating vocalizations detected during DEB (or other sounds detected during previous sleep periods) can all be used to train the DEB algorithm. The DEB algorithm can also be trained using audio data or other data indicating vocalizations (speech or non-speech) made by the user while awake.

[0167] In some embodiments, population-level training data is used to additionally or alternatively train the DEB algorithm. Generally, the user will be a member of a particular group of interest, which can be characterized by a variety of factors, including demographic factors such as age, sex, gender, underlying medical conditions, etc. Respiratory, motion, and audio data associated with other members of the user group can be used to train the DEB algorithm, and in particular, data associated with known instances of DEB experienced by group members.

[0168] In some embodiments, a neural network is used to train the DEB algorithm. In some embodiments, the DEB algorithm is trained by extracting features from various sleep metrics such as respiratory rate and motion level and inputting these features into a classifier such as support vector machine (SVM) regression.

[0169] Step 1106 of method 1100 includes performing an action to mitigate DEB in response to determining that the user is experiencing DEB during a sleep period. Step 1106 is thus similar to step 806 of method 800. Step 1108 of method 1100 includes confirming to the user that the user did experience DEB during the sleep period. The confirmation can include simply asking the user if they experienced DEB. The confirmation can also include showing the user a video or audio of the sleep period and having the user indicate that they experienced DEB when they heard or saw the DEB. Any vocalizations recorded during the sleep period can be played back to the user and then the user can be prompted to confirm if they made the vocalizations. Step 1110 of method 1100 includes updating the trained DEB algorithm based on the confirmation to improve the accuracy of the DEB algorithm. The DEB algorithm can then be updated based on the user's response, which thus helps to improve the ability of the DEB algorithm to distinguish between vocalizations made by the user and other sounds. In some embodiments, additional data can be used to help the user confirm that the user made a vocalization and respond to the prompt. For example, video data of the user during the sleep period can be used to show the user a video from the time point during the sleep period when the sound was detected. This allows the user to easily confirm that the sound was a vocalization (verbal or non-verbal) made by the user.

[0170] In some embodiments, the thresholds mentioned herein (e.g., amount of movement, respiratory rate, respiratory rate variability, light level, elapsed time, number of atypical REM sleep stages, amount of time spent in atypical REM sleep stages, etc.) can generally be determined by pre-training any of the algorithms or modules discussed herein, including algorithm 500, algorithm 600, the DEB algorithm, and the algorithm for identifying sleep stages. In some embodiments, the algorithms and modules can be pre-trained using existing clinical data from patients known to exhibit RBD, and there is an available suitable data source for the clinical data (such as polysomnogram (PSG) data). These thresholds can be re-trained (e.g., updated) by using user-specific information. For example, if RBD is detected (e.g., by identifying DEB or atypical REM sleep stages) in a previous sleep period recorded by the user, then the various thresholds used to identify DEB or atypical REM sleep stages can be adjusted. The thresholds can be adjusted based on the user's confirmation of DEB or atypical REM sleep stages, based on data (such as sound data) that confirms the user specifically experienced DEB or atypical REM sleep stages, or based on any other suitable mechanism.

[0171] The trained DEB algorithm can also be updated using one or more sleep maps generated from a user's previous sleep sessions. The sleep map can indicate the atypical REM sleep stages that the user experienced during the sleep session, as well as the total number of atypical REM sleep stages that the user experienced during the sleep session. Since the sleep map does not need to be generated until the end of the sleep session, the sleep map can typically utilize more data to identify the various different sleep stages that the user was in during the sleep session. Thus, compared to real-time detection employed during the sleep session, the sleep map can include a more accurate retrospective view of the DEB that the user experienced during the sleep session. Accordingly, the sleep map can be used to update the DEB algorithm to improve the accuracy of the DEB algorithm. In some embodiments, the DEB algorithm can initially be trained using sleep maps from the user or from other users.

[0172] In other embodiments, the threshold can also be determined based on user input. For example, the user can indicate to the system when the light level in the room is at its normal level during the sleep session, and when the user wakes up before or after the sleep session. These light levels can then be used to determine the threshold light level for any of the methods or algorithms described herein. In other embodiments, the system can detect when the user is awake or asleep by some other means (user confirmation, data from sensors, etc.), and detect the values of various parameters to determine the thresholds for these parameters.

[0173] Generally, any of methods 800, 900, 1000, and 1100 can be implemented using a system having a control system that has one or more processors and a memory storing machine-readable instructions. The control system can be coupled to the memory, and when the machine-readable instructions are executed by at least one processor of the control system, methods 800 - 1100 can be implemented. Methods 800 - 1100 can also be implemented using a computer program product (such as a non-transitory computer-readable medium) that includes instructions that, when executed by a computer, cause the computer to perform the steps of methods 800 - 1100.

[0174] One or more elements or aspects or steps or any part thereof from one or more of claims 1 - 83 below can be combined with one or more elements or aspects or steps or any part thereof from one or more of the other claims 1 - 83 or a combination thereof to form one or more additional embodiments and / or claims of the present invention.

[0175] Although the present invention has been described with reference to one or more specific embodiments or implementations, those skilled in the art will recognize that many changes can be made thereto without departing from the spirit and scope of the present invention. Each of these embodiments and its obvious variations are considered to fall within the spirit and scope of the present invention. Additional embodiments in accordance with aspects of the present invention are also contemplated that may combine any number of features from any of the embodiments described herein.

Claims

1. A method for monitoring an individual's sleep period, the method comprising: Receiving data associated with the individual's current sleep period, the data including time data associated with the time characteristics of the current sleep period; Analyzing at least a portion of the received data to identify one or more sleep stages experienced by the individual during the current sleep period, the one or more sleep stages including a light sleep stage, a deep sleep stage, a typical rapid eye movement (REM) sleep stage, an atypical REM sleep stage, a wake stage, or any combination thereof; And Generating a summary of the current sleep period, the summary including: (i) a plurality of atypical REM sleep stages experienced by the individual during the current sleep period, (ii) the time spent in atypical REM sleep stages during the current sleep period, or (iii) both (i) and (ii), Wherein the analysis includes distinguishing one or more atypical REM sleep stages and one or more wake stages at least partially based on the time data.

2. The method according to claim 1, wherein the received data further comprises: (i) Respiratory data, (ii) motion data indicating the movement of the individual during the current sleep period, (iii) audio data indicating sounds detected during the current sleep period, (iv) optical data indicating light in the area where the individual is located during the sleep period, or (v) any combination thereof.

3. The method according to claim 2, further comprising: Tracking the time elapsed since the start of the current sleep period at least partially based on the time data; Analyzing the motion data to detect whether the individual is currently moving; Determining that the individual is currently in the wake state in response to a first amount of time having elapsed since the start of the current sleep period and detecting that the individual is currently moving; And / or Determining that the individual is currently in the atypical REM sleep stage in response to a second amount of time having elapsed since the start of the current sleep period and detecting that the individual is currently moving, the second amount of time being greater than the first amount of time.

4. The method according to claim 2 or 3, further comprising: Determining the amount of movement of the individual during the sleep period based on the motion data; Determining the respiratory rate or respiratory rate variability of the individual during the sleep period based on the respiratory data; Determining that the individual is not in the typical REM sleep stage in response to determining that the amount of movement of the individual is less than a threshold amount of movement and the respiratory rate or respiratory rate variability of the individual is greater than a threshold respiratory rate or threshold respiratory rate variability; and / or Determining that the individual is in the atypical REM sleep stage in response to determining that the amount of movement of the individual is greater than the threshold amount of movement and the respiratory rate or respiratory rate variability of the individual is greater than the threshold respiratory rate or threshold respiratory rate variability.

5. The method according to claim 2 or 3, further comprising: Determine the amount of movement of the individual during the sleep period based on the movement data; Determine the light level of the area where the individual is located during the sleep period based on the optical data; In response to determining that the amount of movement of the individual is greater than a threshold amount of movement and the light level of the area where the individual is located is greater than a threshold light level, determine that the individual is in a wake state; and / or In response to determining that the amount of movement of the individual is greater than the threshold amount of movement and the light level of the area where the individual is located is less than the threshold light level, determine that the individual is in an atypical REM sleep stage.

6. The method according to claim 2 or 3, further comprising: Determine whether the sound detected during the current sleep period is speech uttered by the individual; Determine the light level of the area where the individual is located during the sleep period based on the optical data; In response to determining that the detected sound is speech uttered by the individual and the light level of the area where the individual is located is greater than a threshold light level, determine that the individual is in a wake state; and / or In response to determining that the detected sound is speech uttered by the individual and the light level of the area where the individual is located is less than the threshold light level, determine that the individual is in an atypical REM sleep stage.

7. The method according to claim 2 or 3, further comprising: Determine whether the sound detected during the current sleep period is speech uttered by the individual; Determine the time elapsed during the current sleep period based at least in part on the time data; In response to determining that the detected sound is speech uttered by the individual and the time elapsed during the current sleep period is less than a threshold time, determine that the individual is in a wake state; and / or In response to determining that the detected sound is speech uttered by the individual and the time elapsed during the current sleep period is greater than the threshold time, determine that the individual is in an atypical REM sleep stage.

8. The method according to claim 2 or 3, further comprising: Determine the amount of movement of the individual during the sleep period based on the movement data; Determine the time elapsed during the current sleep period based at least in part on the time data; In response to determining that the amount of movement of the individual is greater than a threshold amount of movement and the time elapsed during the current sleep period is less than a threshold time, determine that the individual is in a wake state; and / or In response to determining that the amount of movement of the individual is greater than the threshold amount of movement and the time elapsed during the current sleep period is greater than the threshold time, determine that the individual is in an atypical REM sleep stage.

9. The method according to claim 2 or 3, further comprising determining the amount of movement of the individual during the sleep period based on the movement data, wherein the amount of movement of the individual being higher than a threshold amount of movement indicates that the individual is in an atypical REM sleep stage.

10. The method according to claim 2 or 3, further comprising determining whether the sound detected during the current sleep period is a vocalization made by the individual, by the individual's bed partner, or by a separate source.

11. The method according to claim 10, wherein determining that the sound detected during the current sleep period is a vocalization made by the individual or the individual's bed partner is at least partially based on: (i) additional audio data indicative of vocalizations made by the individual when the individual wakes up, (ii) additional audio data indicative of confirmed vocalizations made by the individual during one or more previous sleep periods, or (iii) both (i) and (ii).

12. The method according to claim 10, further comprising determining whether the vocalization made by the individual during the current sleep period is speech or non-speech.

13. The method according to claim 12, wherein determining that the vocalization made by the individual is speech is at least partially based on: (i) additional audio data indicative of vocalizations made by the individual when the individual wakes up, (ii) additional audio data indicative of confirmed vocalizations made by the individual during one or more previous sleep periods, or (iii) both (i) and (ii).

14. The method according to claim 2 or 3, further comprising determining whether the detected sound is speech made by the individual, the detected sound being speech made by the individual indicating that the individual is in an atypical REM sleep stage.

15. The method according to claim 2 or 3, further comprising determining the light level in the area where the individual is located during the sleep period, the determined light level being greater than a threshold light level indicative of the individual being in a waking state.

16. The method according to claim 1 or 2, further comprising receiving data indicative of the medical history of the individual, the medical history of the individual assisting in identifying the one or more sleep stages experienced by the individual during the current sleep period.

17. The method according to claim 1 or 2, wherein a first portion of the received data includes audio data indicative of the sound detected during the current sleep period.

18. The method according to claim 1 or 2, wherein a second portion of the received data includes motion data indicative of the movement of the individual during the current sleep period and audio data indicative of the sound detected during the current sleep period.

19. The method according to claim 1 or 2, further comprising: determining the time elapsed during the current sleep period at least partially based on the time data; and identifying the current sleep stage based on the one or more previous sleep stages experienced by the individual during the sleep period and the time elapsed during the current sleep period.

20. The method according to claim 1 or 2, further comprising: receiving historical data associated with one or more previous sleep periods of the individual; Generate a historical summary of the one or more previous sleep periods of the individual, the historical summary including a plurality of atypical REM sleep stages experienced by the individual during the one or more previous sleep periods, and the time spent in atypical REM sleep stages during the one or more previous sleep periods; and Compare the historical summary of the one or more previous sleep periods with the summary of the current sleep period to assist in confirming the number of atypical REM sleep stages experienced by the individual during the current sleep period, and the time spent in atypical REM sleep stages during the current sleep period.

21. The method according to claim 1, further comprising: Analyze at least the portion of the received data using a trained dream enactment behavior (DEB) algorithm to determine whether the individual is experiencing DEB during the current sleep period; and Cause an action to be performed in response to determining that the individual is experiencing DEB.

22. The method according to claim 21, further comprising: Receive historical data associated with one or more previous sleep periods of the individual, the historical data including data related to confirmed instances of the individual experiencing DEB during at least one of the one or more previous sleep periods; and Compare the historical data associated with the one or more previous sleep periods with the data associated with the current sleep period to assist in determining whether the individual is experiencing DEB during the current sleep period.

23. The method according to claim 21 or 22, wherein the action is configured to: (i), assist in ending the DEB, (ii), assist in alleviating the impact of the DEB on the individual, (iii), assist in alleviating the impact of the DEB on the individual's bed partner, or (iv), any combination thereof.

24. The method according to claim 21 or 22, wherein the action includes activating a light source, activating an audible alarm, causing the bed on which the individual lies to move, or causing the individual's body to move.

25. The method according to claim 24, wherein the audible alarm includes a ringing noise, a siren, music, a natural sound, or an uttered word.

26. The method according to claim 1 or 2, further comprising performing an action in response to the summary indicating: (i), the number of atypical REM sleep stages experienced by the individual during the current sleep period meets a threshold number, (ii), the time spent in atypical REM sleep stages during the current sleep period is greater than a threshold time, or (iii), both (i) and (ii).

27. The method according to claim 1, wherein Use one or more trained algorithms to perform an analysis of at least a portion of the received data to identify the one or more sleep stages, and wherein the method further comprises: Determine the total number of atypical REM sleep stages experienced by the individual during the sleep period; Determine the total amount of time that the individual experiences the atypical REM sleep stage during the sleep period; and In response to (i) the total number of the atypical REM sleep stages meeting a first threshold, cause an action to be performed, (ii) the total amount of time meeting a second threshold, cause the action to be performed, or (iii) both (i) and (ii).

28. The method according to claim 27, wherein the action comprises: Cause a notification to be sent to the individual or a third party, cause the notification to be displayed on an electronic display device, assist in waking up the individual when the individual is currently experiencing one of the atypical REM sleep stages, or any combination thereof.

29. The method according to claim 27 or 28, wherein the sleep period includes a plurality of sleep cycles.

30. The method according to claim 29, wherein each of the plurality of sleep cycles includes the one or more sleep stages.

31. The method according to claim 30, wherein each of the plurality of sleep cycles comprises: (i) at least one light sleep stage, (ii) at least one deep sleep stage, and (iii) at least one typical REM sleep stage or at least one atypical REM sleep stage.

32. The method according to claim 27 or 28, further comprising: In response to the one or more trained algorithms determining that the individual has experienced one or more atypical REM sleep stages during the sleep period, confirm that the individual has experienced the one or more atypical REM sleep stages during the sleep period after the current sleep period; And In response to the confirmation, update the one or more trained algorithms at least in part based on the response.

33. The method according to claim 32, wherein the confirmation includes prompting the individual to indicate whether the individual has experienced the one or more atypical REM sleep stages during the current sleep period.

34. The method according to claim 27 or 28, further comprising determining the average amount of time that the individual experiences the atypical REM sleep stage in each sleep period during at least one or more previous sleep periods.

35. The method according to claim 34, wherein in response to the total amount of time that the individual experiences the atypical REM sleep stage during the sleep period being greater than a threshold amount compared to the average amount of time that the individual experiences the atypical REM sleep stage, cause the action to be performed.

36. The method according to claim 34, further comprising updating the average amount of time that the individual experiences the atypical REM sleep stage in each sleep period based on the total amount of time that the individual experiences the atypical REM sleep stage during the sleep period.

37. The method according to claim 36, further comprising causing the action to be performed in response to the updated average amount of time that the individual experiences the atypical REM sleep stage in each sleep period being greater than a threshold amount.

38. The method according to claim 27 or 28, further comprising determining the average number of atypical REM sleep stages that the individual experiences in each sleep period during at least one or more previous sleep periods.

39. The method according to claim 38, wherein the action is performed in response to the total number of atypical REM sleep stages experienced by the individual during the sleep period being greater than a threshold amount than the average number of atypical REM sleep stages experienced by the individual.

40. The method according to claim 38, further comprising updating the average number of atypical REM sleep stages for each sleep period based on the total number of atypical REM sleep stages experienced by the individual during the sleep period.

41. The method according to claim 40, further comprising performing the action in response to the updated average number of atypical REM sleep stages experienced by the individual in each sleep period being greater than a threshold amount.

42. The method according to claim 27 or 28, wherein the action comprises sending a message to the individual, sending a message to a third party, updating the individual's medical record, or any combination thereof.

43. The method according to claim 42, wherein the third party comprises the individual's healthcare provider, the individual's family member, the individual's friend, the individual's caregiver, or any combination thereof.

44. The method according to claim 1, further comprising: determining a value of a historical sleep parameter associated with the severity of atypical REM sleep stages experienced by the individual during the plurality of previous sleep periods, at least in part based on historical physiological data associated with the plurality of previous sleep periods of the individual; determining a value of a current sleep parameter associated with the severity of one or more atypical REM sleep stages experienced by the individual during the current sleep period, the determination being at least in part based on the received data associated with the current sleep period; comparing the value of the historical parameter with the value of the current parameter; and performing an action in response to the comparison indicating that the severity of at least one of the one or more atypical REM sleep stages experienced by the individual during the current sleep period is greater than the historical severity of the atypical REM sleep stages experienced by the individual.

45. The method according to claim 44, wherein the severity of one of the one or more atypical REM sleep stages experienced by the individual is determined based on (i) movement of at least a portion of the individual during one of the one or more atypical REM sleep stages experienced by the individual, (ii) sounds emitted by the individual during one of the one or more atypical REM sleep stages experienced by the individual, or (iii) both (i) and (ii).

46. The method according to claim 45, wherein when the part (i) of the individual moves at a speed that meets a predetermined threshold speed, (ii) moves with an acceleration that meets a predetermined threshold acceleration, (iii) moves according to a predetermined movement pattern, (iv) moves for a time period that meets a predetermined threshold time amount, (v) both (i) and (iii), (vi) both (ii) and (iii), (vii) both (i) and (iv), (viii) both (ii) and (iv), or (ix) any combination of (i) - (viii), the severity of one of the one or more atypical REM sleep stages experienced by the individual during the current sleep period is greater than the historical severity of the atypical REM sleep stages experienced by the individual.

47. The method according to claim 45 or 46, wherein when the sound emitted by the individual (i) is speech, (ii) is louder than a predetermined threshold volume, (iii) has a pitch equal to a predetermined pitch, (iv) occurs for a time period greater than a predetermined threshold time amount, (v) both (i) and (ii), (vi) both (i) and (iv), (vii) both (ii) and (iii), (viii) both (ii) and (iv), or (ix) any combination of (i) - (viii), the severity of one of the one or more atypical REM sleep stages experienced by the individual during the current sleep period is greater than the historical severity of the atypical REM sleep stages experienced by the individual.

48. The method according to claim 45 or 46, wherein the action comprises: (i) Reduce the movement of the part of the individual during one of the one or more atypical REM sleep stages, (ii) reduce the sound generated by the individual during one of the one or more atypical REM sleep stages, or (iii) both (i) and (ii).

49. The method according to claim 44 or 45, wherein the value of the current sleep parameter associated with the severity of the one or more atypical REM sleep stages experienced by the individual during the current sleep period is measured on a scale from 0 to 10.

50. The method according to claim 44 or 45, wherein the historical parameter indicates (i) the movement of the individual during the atypical REM sleep stages experienced during the plurality of previous sleep periods, or (ii) the sound of the individual during the one or more atypical REM sleep stages experienced during the plurality of previous sleep periods.

51. The method according to claim 44 or 45, wherein the current parameter indicates (i) the movement of the individual during the atypical REM sleep stages experienced during the current sleep period, or (ii) the sound of the individual during the one or more atypical REM sleep stages experienced during the current sleep period.

52. The method according to claim 1, Among them, the received data associated with the individual's current sleep period includes: (i) respiratory data, (ii) motion data indicating the individual's motion during the current sleep period, (iii) audio data indicating sounds detected during the current sleep period, (iv) optical data indicating light in the area where the individual is located during the sleep period, or (v) any combination thereof, and wherein the method further comprises: inputting at least a portion of the received data into a trained dream enactment behavior (DEB) algorithm to determine whether the individual is experiencing DEB during the current sleep period; and in response to determining that the individual is experiencing DEB, causing an action to be performed to: (i) assist in ending the DEB, (ii) assist in alleviating the impact of the DEB on the individual, (iii) assist in alleviating the impact of the DEB on the individual's bed partner, or (iv) any combination thereof.

53. The method according to claim 52, wherein the action includes activating a light source, activating an audible alarm, causing the bed on which the individual lies to move, causing the individual's body to move, or providing a barrier between the individual and the individual's bed partner.

54. The method according to claim 53, wherein the audible alarm includes a ringing noise, a siren, music, a natural sound, or an uttered word.

55. The method according to claim 52 or 53, wherein training data associated with a plurality of previous sleep periods of the individual is used to train the DEB algorithm, the training data including: (i) respiratory data indicating the individual's respiration during at least a portion of the plurality of previous sleep periods during which the individual experienced DEB, (ii) motion data indicating the individual's motion during at least a portion of the plurality of previous sleep periods during which the individual experienced DEB, (iii) audio data indicating sounds detected during at least a portion of the plurality of previous sleep periods during which the individual experienced DEB, (iv) audio data indicating speech generated by the individual when the individual wakes up, or (v) any combination thereof.

56. The method according to claim 52 or 53, wherein the individual is a member of a population of individuals, and wherein the DEB algorithm is trained using training data associated with a plurality of previous sleep periods of the members of the population of individuals, the training data comprising: (i) respiratory data indicating the respiration of the members of the group during at least a portion of the plurality of previous sleep periods, (ii) motion data indicating the motion of the members of the group during at least a portion of the plurality of previous sleep periods, (iii) audio data indicating sounds detected during at least a portion of the plurality of previous sleep periods, (iv) audio data indicating speech emitted by the members of the group when the individual wakes up, or (v) any combination thereof.

57. The method according to claim 52 or 53, further comprising, after the current sleep period, analyzing the received data associated with the current sleep period and generating a sleep graph that indicates (i) the number of atypical REM sleep stages experienced by the individual during the current sleep period, (ii) the amount of time of the atypical REM sleep stages experienced by the individual during the current sleep period, or (iii) both (i) and (ii).

58. The method according to claim 57, further comprising using at least a portion of the sleep graph to maintain the trained DEB algorithm.

59. The method according to claim 52 or 53, further comprising: in response to the trained DEB algorithm determining that the individual is experiencing DEB, confirming after the current sleep period that the individual experienced DEB during the sleep period; and in response to the confirmation, updating the trained DEB algorithm at least in part based on the response.

60. The method according to claim 59, wherein the confirmation includes prompting the individual to indicate whether the individual experienced DEB during the current sleep period.

61. The method according to claim 52 or 53, further comprising: receiving audio data associated with an individual's current sleep period; in response to determining that the individual is experiencing DEB, prompting the individual to confirm that a portion of the audio data corresponding to the DEB experienced by the individual during the current sleep period is a sound associated with the individual; and in response to the individual's response to the prompt, updating the trained DEB algorithm at least in part based on the response.

62. The method according to claim 61, further comprising: receiving video data indicating a video of the individual during the sleep period; and displaying the video of the individual during the sleep period to assist the individual in responding to the prompt.

63. The method according to claim 52 or 53, wherein data indicating the movement of the individual during one or more previous sleep periods is used to train the DEB algorithm before the current sleep period, and wherein updating the trained DEB algorithm helps to improve the ability of the DEB algorithm to determine whether an audio detected during a sleep period is a sound emitted by the individual.

64. The method according to claim 1, wherein, The analysis includes: selecting one or more unrecognized sleep stages; for each respective unrecognized sleep stage, determining the time elapsed within the sleep period based on a portion of the time data associated with the respective unrecognized sleep stage; and determining whether each respective unrecognized sleep stage is an awake stage or an atypical REM sleep stage based on the time elapsed within the sleep period of the respective unrecognized sleep stage.

65. The method according to claim 64, wherein, Selecting one or more unrecognized sleep stages includes determining whether each respective unrecognized sleep stage is an awake stage or an atypical REM sleep stage based on non-time data.

66. The method according to claim 65, wherein The non-time data is movement data or audio data.

67. The method according to claim 1, wherein Data associated with the current sleep period of the individual also includes motion data, and wherein the analysis includes: analyzing the motion data to determine whether one or more unrecognized sleep stages are wake stages or atypical REM sleep stages; and determining the time elapsed within the sleep period of each respective unrecognized sleep stage based on a portion of the time data associated with the respective unrecognized sleep stage data to determine whether each respective unrecognized sleep stage is a wake stage or an atypical REM sleep stage.

68. The method according to claim 1, wherein Data associated with the current sleep period of the individual also includes audio data, and wherein the analysis includes: analyzing the audio data to determine whether one or more unrecognized sleep stages are wake stages or atypical REM sleep stages; and determining the time elapsed within the sleep period of each respective unrecognized sleep stage based on a portion of the time data associated with the respective unrecognized sleep stage data to determine whether each respective unrecognized sleep stage is a wake stage or an atypical REM sleep stage.

69. A system for monitoring an individual's sleep period, the system comprising: a control system including one or more processors; and a memory having machine-readable instructions stored thereon; wherein the control system is coupled to the memory and, when the machine-executable instructions in the memory are executed by at least one of the one or more processors of the control system, implements the method according to any one of claims 1 to 68.

70. A system for monitoring an individual's sleep period, the system comprising a control system configured to implement the method according to any one of claims 1 to 68.

71. A computer program product comprising instructions that, when executed by a computer, cause the computer to execute the method according to any one of claims 1 to 68.

72. The computer program product according to claim 71, wherein the computer program product is a non-transitory computer-readable medium.

73. A system comprising: an electronic interface configured to receive data associated with an individual's sleep period, the data including time data associated with the time characteristics of the current sleep period; a memory storing machine-readable instructions; and a control system including one or more processors configured to execute the machine-readable instructions to: analyze at least a portion of the received data to identify one or more sleep stages experienced by the individual during the current sleep period, the one or more sleep stages including light sleep stages, deep sleep stages, typical rapid eye movement (REM) sleep stages, atypical REM sleep stages, wake stages, or any combination thereof; and generate a summary of the current sleep period, the summary including: (i), a plurality of atypical REM sleep stages experienced by the individual during the current sleep period, (ii), the time spent in atypical REM sleep stages during the current sleep period, or (iii), both (i) and (ii), Wherein, the analysis includes differentiating one or more atypical REM sleep stages and one or more wakefulness stages based at least in part on the time data.

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