Patent Application
20230363700
The present disclosure relates generally to systems and methods for analyzing data generated during an individual's use of a respiratory therapy system, and more particularly, to systems and methods for analyzing data generated during the user's use of a respiratory therapy system to determine a first metric associated with sleep-disordered breathing and a second metric associated with a health condition other sleep-disordered breathing.
Many individuals suffer from sleep-related and/or respiratory disorders such as, for example, Sleep-Disordered Breathing (SDB), which can include Obstructive Sleep Apnea (OSA), Central Sleep Apnea (CSA), other types of apneas such as mixed apneas and hypopneas, and Respiratory Effort Related Arousal (RERA). These individuals may also suffer from other health conditions (which may be referred to as comorbidities), such as insomnia (characterized by, for example, difficult in initiating sleep, frequent or prolonged awakenings after initially falling asleep, and/or an early awakening with an inability to return to sleep), Periodic Limb Movement Disorder (PLMD), Restless Leg Syndrome (RLS), Cheyne-Stokes Respiration (CSR), respiratory insufficiency, Obesity Hyperventilation Syndrome (OHS), Chronic Obstructive Pulmonary Disease (COPD), Neuromuscular Disease (NMD), rapid eye movement (REM) behavior disorder (also referred to as RBD), dream enactment behavior (DEB), hypertension, diabetes, stroke, and chest wall disorders. It can be difficult to accurately identify and monitor these other health conditions. In individuals using a respiratory therapy system to treat the sleep-related disorder, the use of the respiratory therapy system can impact the severity of these health conditions. Thus, it would be advantageous to be able to analyze data associated with the individual's use of the respiratory therapy system to determine the identity, severity, and/or progression of any other health conditions. The present disclosure is directed to systems, devices, and methods to allow for identification and/or monitoring of health conditions using data associated with the individual's use of a respiratory therapy system.
According to some implementations of the present disclosure, a system for monitoring a sleep session of a user comprises a respiratory therapy system, a memory device, and a control system. The respiratory therapy system includes a respiratory therapy device and a user interface. The respiratory therapy device is configured to supply pressurized air. The user interface is coupled to the respiratory therapy device via a conduit, and is configured to engage the user and aid in directing the supplied pressurized air to the airway of the user. The memory device stores machine-readable instructions. The control system is coupled to the memory device, and includes one or more processors configured to execute the machine-readable instructions to: generate data, during a current sleep session, associated with the user; analyze the generated data to determine a value of a first metric that is associated with a sleep disordered breathing (SDB) condition; analyze the generated data to determine a value of a second metric that is associated with a health condition other than the SDB condition; and based at least in part on the determined value of the second metric, cause an action to be performed.
According to some implementations, the one or more processors of the control system are further configured to execute the machine-readable instructions to: track the second metric over a plurality of sleep sessions; determine the value of the second metric for each of the plurality of sleep sessions; determine (i) a duration of use of the respiratory therapy system for each of the plurality of sleep sessions, (ii) values of one or more settings of the respiratory therapy system for each of the plurality of sleep session, (iii) or both; and identify (i) an optimal duration of use of the respiratory therapy system, (ii) optimal values of the one or more settings of the respiratory therapy system, or (iii) both, to treat the health condition.
According to some implementations, the one or more processors of the control system are further configured to execute the machine-readable instructions to: receive historical data associated with one or more prior sleep sessions of the user; analyze the historical data to determine a value of the second metric for each of the one or more prior sleep sessions; and compare the determined value of the second metric for each of the one or more prior sleep sessions to the determined value of the second metric for the current sleep session.
According to some implementations, the one or more processors of the control system are further configured to execute the machine-readable instructions to: determine a severity of the health condition based at least in part on the determined value of the second metric for the current sleep session; generate data during one or more subsequent sleep sessions; determine a value of the second metric for each of the one or more subsequent sleep sessions; determine an updated severity of the health condition based at least in part on the determined values of the second metric for each of the one or more subsequent sleep sessions; and, based at least in part on the updated severity of the health condition, cause the action to be performed
According to some implementations, the one or more processors of the control system are further configured to execute the machine-readable instructions to: receive additional data associated with the user while awake; analyze the additional data to determine a value of a third metric associated with the health condition; and cause the action to be performed based at least in part on the determined value of the third metric associated with the health condition
According to some implementations of the present disclosure, a method of monitoring a sleep session of a user comprises generating data, during a current sleep session, associated with an individual of a respiratory therapy system. The method further includes analyzing the generated data to determine a value of a first metric that is associated with a sleep disordered breathing (SDB) condition. The method further includes analyzing the generated data to determine a value of a second metric that is associated with a health condition other than the SDB condition. The method further includes, based at least in part on the determined value of the second metric, causing an action to be performed.
According to some implementations, the method further includes tracking the second metric over a plurality of sleep sessions. The method further includes determining the value of the second metric for each of the plurality of sleep sessions. The method further includes determining (i) a duration of use of the respiratory therapy system for each of the plurality of sleep sessions, (ii) values of one or more settings of the respiratory therapy system for each of the plurality of sleep session, (iii) or both. The method further includes identifying (i) an optimal duration of use of the respiratory therapy system, (ii) optimal values of the one or more settings of the respiratory therapy system, or (iii) both, to treat the health condition.
According to some implementations, the method further includes receiving historical data associated with one or more prior sleep sessions of the user. The method further includes analyzing the historical data to determine a value of the second metric for each of the one or more prior sleep sessions. The method further includes comparing the determined value of the second metric for each of the one or more prior sleep sessions to the determined value of the second metric for the current sleep session.
According to some implementations, the method further includes determining a severity of the health condition based at least in part on the determined value of the second metric for the current sleep session. The method further includes generating data during one or more subsequent sleep sessions. The method further includes determining a value of the second metric for each of the one or more subsequent sleep sessions. The method further includes determining an updated severity of the health condition based at least in part on the determined values of the second metric for each of the one or more subsequent sleep sessions. The method further includes, based at least in part on the updated severity of the health condition, causing an action to be performed.
According to some implementations, the method further includes receiving additional data associated with the user while awake. The method further includes analyzing the additional data to determine a value of a third metric associated with the health condition. The method further includes causing the action to be performed based at least in part on the determined value of the third metric associated with the health condition.
The above summary is not intended to represent each implementation or every aspect of the present disclosure. Additional features and benefits of the present disclosure are apparent from the detailed description and figures set forth below.
While the present disclosure is susceptible to various modifications and alternative forms, specific implementations and embodiments thereof have been shown by way of example in the drawings and will herein be described in detail. It should be understood, however, that it is not intended to limit the present disclosure to the particular forms disclosed, but on the contrary, the present disclosure is to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the present disclosure as defined by the appended claims.
The present disclosure is described with reference to the attached figures, where like reference numerals are used throughout the figures to designate similar or equivalent elements. The figures are not drawn to scale, and are provided merely to illustrate the instant disclosure. Several aspects of the disclosure are described below with reference to example applications for illustration.
Many individuals suffer from sleep-related and/or respiratory disorders, such as Periodic Limb Movement Disorder (PLMD), Restless Leg Syndrome (RLS), Sleep-Disordered Breathing (SDB) such as Obstructive Sleep Apnea (OSA), Central Sleep Apnea (CSA) and other types of apneas, Respiratory Effort Related Arousal (RERA), Cheyne-Stokes Respiration (CSR), respiratory insufficiency, Obesity Hyperventilation Syndrome (OHS), Chronic Obstructive Pulmonary Disease (COPD), Neuromuscular Disease (NMD), and chest wall disorders. Obstructive Sleep Apnea (OSA), a form of Sleep Disordered Breathing (SDB), is characterized by events including occlusion or obstruction of the upper air passage during sleep resulting from a combination of an abnormally small upper airway and the normal loss of muscle tone in the region of the tongue, soft palate and posterior oropharyngeal wall. Central Sleep Apnea (CSA) is another form of sleep disordered breathing. CSA results when the brain temporarily stops sending signals to the muscles that control breathing. Other types of apneas include hypopnea, hyperpnea, and hypercapnia. Hypopnea is generally characterized by slow or shallow breathing caused by a narrowed airway, as opposed to a blocked airway. Hyperpnea is generally characterized by an increase depth and/or rate of breathing. Hypercapnia is generally characterized by elevated or excessive carbon dioxide in the bloodstream, typically caused by inadequate respiration. A Respiratory Effort Related Arousal (RERA) event is typically characterized by an increased respiratory effort for ten seconds or longer leading to arousal from sleep and which does not fulfill the criteria for an apnea or hypopnea event. RERAs are defined as a sequence of breaths characterized by increasing respiratory effort leading to an arousal from sleep, but which does not meet criteria for an apnea or hypopnea. These events must fulfil both of the following criteria: (1) a pattern of progressively more negative esophageal pressure, terminated by a sudden change in pressure to a less negative level and an arousal, and (2) the event lasts ten seconds or longer. In some implementations, a Nasal Cannula/Pressure Transducer System is adequate and reliable in the detection of RERAs. A RERA detector may be based on a real flow signal derived from a respiratory therapy device. For example, a flow limitation measure may be determined based on a flow signal. A measure of arousal may then be derived as a function of the flow limitation measure and a measure of sudden increase in ventilation. One such method is described in WO 2008/138040 and U.S. Pat. No. 9,358,353, assigned to ResMed Ltd., the disclosure of each of which is hereby incorporated by reference herein in their entireties.
Cheyne-Stokes Respiration (CSR) is a further form of SDB. CSR is a disorder of a patient's respiratory controller in which there are rhythmic alternating periods of waxing and waning ventilation known as CSR cycles. CSR is characterized by repetitive de-oxygenation and re-oxygenation of the arterial blood. OHS is defined as the combination of severe obesity and awake chronic hypercapnia, in the absence of other known causes for hypoventilation. Symptoms include dyspnea, morning headache and excessive daytime sleepiness. COPD encompasses any of a group of lower airway diseases that have certain characteristics in common, such as increased resistance to air movement, extended expiratory phase of respiration, and loss of the normal elasticity of the lung. NMD encompasses many diseases and ailments that impair the functioning of the muscles either directly via intrinsic muscle pathology, or indirectly via nerve pathology. Chest wall disorders are a group of thoracic deformities that result in inefficient coupling between the respiratory muscles and the thoracic cage.
Many of these disorders are characterized by particular events (e.g., snoring, an apnea, a hypopnea, a restless leg, a sleeping disorder, choking, an increased heart rate, labored breathing, an asthma attack, an epileptic episode, a seizure, or any combination thereof) that can occur when the individual is sleeping. A wide variety of types of data can be used to monitor the health of individuals having any of the above types of sleep-related and/or respiratory disorders (or other disorders). However, it is often difficult to collect accurate data in a manner that does not interrupt or disturb the user's sleep, or interfere with any treatment the user may be undergoing during sleep. Thus, it is advantageous to utilize a system for treatment that includes various sensors to generate and collect data, without disturbing the user, the user's sleep, or the user's treatment.
The Apnea-Hypopnea Index (AHI) is an index used to indicate the severity of sleep apnea during a sleep session. The AHI is calculated by dividing the number of apnea and/or hypopnea events experienced by the user during the sleep session by the total number of hours of sleep in the sleep session. The event can be, for example, a pause in breathing that lasts for at least 10 seconds. An AHI that is less than 5 is considered normal. An AHI that is greater than or equal to 5, but less than 15 is considered indicative of mild sleep apnea. An AHI that is greater than or equal to 15, but less than 30 is considered indicative of moderate sleep apnea. An AHI that is greater than or equal to 30 is considered indicative of severe sleep apnea. In children, an AHI that is greater than 1 is considered abnormal. Sleep apnea can be considered “controlled” when the AHI is normal, or when the AHI is normal or mild. The AHI can also be used in combination with oxygen desaturation levels to indicate the severity of Obstructive Sleep Apnea.
Referring to
The control system 110 includes one or more processors 112 (hereinafter, processor 112). The control system 110 is generally used to control (e.g., actuate) the 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 or special purpose processor or microprocessor. While one processor 112 is shown in
The memory device 114 stores machine-readable instructions that are executable by the processor 112 of the control system 110. The memory device 114 can be any suitable computer readable storage device or media, such as, for example, a random or serial access memory device, a hard drive, a solid state drive, a flash memory device, etc. While one memory device 114 is shown in
In some implementations, the memory device 114 stores a user profile associated with the user. The user profile can 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 sessions), or any combination thereof. The demographic information can include, for example, information indicative of an age of the user, a gender of the user, a race of the user, a family medical history (such as a family history of insomnia or sleep apnea), an employment status of the user, an educational status of the user, a socioeconomic status of the user, or any combination thereof. The medical information can include, for example, information indicative of one or more medical conditions associated with the user, medication usage by the user, or both. The medical information data can further include a fall risk assessment associated with the user (e.g., a fall risk score using the Morse fall scale), a multiple sleep latency test (MSLT) result or score and/or a Pittsburgh Sleep Quality Index (PSQI) score or value. The self-reported user feedback can include information indicative of a self-reported subjective sleep score (e.g., poor, average, excellent), a self-reported subjective stress level of the user, a self-reported subjective fatigue level of the user, a self-reported subjective health status of the user, a recent life event experienced by the user, or any combination thereof.
The electronic interface 119 is configured to receive data (e.g., physiological data and/or acoustic data) from the 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 the 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, over a cellular network, over 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 one more processors and/or one more memory devices 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 or integrated in the user device 170. In other implementations, the electronic interface 119 is coupled to or integrated (e.g., in a housing) with the control system 110 and/or the memory device 114.
As noted 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 device), a user interface 124 (also referred to as a mask or a patient interface), a conduit 126 (also referred to as a tube or an air circuit), a display device 128, a humidification tank 129, or any combination thereof. In some implementations, the control system 110, the memory device 114, the display device 128, one or more of the sensors 130, and the humidification tank 129 are part of the respiratory therapy device 122. Respiratory pressure therapy refers to the application of a supply of air to an entrance to a user's airways at a controlled target pressure that is nominally positive with respect to atmosphere throughout the user's breathing cycle (e.g., in contrast to negative pressure therapies such as the tank ventilator or cuirass). The respiratory therapy system 120 is generally used to treat individuals suffering from one or more sleep-related respiratory disorders (e.g., obstructive sleep apnea, central sleep apnea, or mixed sleep apnea), other respiratory disorders such as COPD, or other disorders leading to respiratory insufficiency, that may manifest either during sleep or wakefulness.
The respiratory therapy device 122 is generally used to generate pressurized air that is delivered to a user (e.g., using one or more motors (such as a blower motor) that drive one or more compressors). In some implementations, the respiratory therapy device 122 generates continuous constant air pressure that is delivered to the user. In other implementations, the respiratory therapy device 122 generates two or more predetermined pressures (e.g., a first predetermined air pressure and a second predetermined air pressure). In still other implementations, the respiratory therapy device 122 is configured to generate a variety of different air pressures within a predetermined range. For example, the respiratory therapy device 122 can deliver at least about 6 cm H2O, at least about 10 cm H2O, at least about 20 cm H2O, between about 6 cm H2O and about 10 cm H2O, between about 7 cm H2O and about 12 cm H2O, etc. The respiratory therapy device 122 can also deliver pressurized air at a predetermined flow rate between, for example, about −20 L/min and about 150 L/min, while maintaining a positive pressure (relative to the ambient pressure). In some implementations, the control system 110, the memory device 114, the electronic interface 119, or any combination thereof can be coupled to and/or positioned within a housing of the respiratory therapy device 122.
The user interface 124 engages a portion of the user's face and delivers pressurized air from the respiratory therapy device 122 to the user's airway to aid in preventing the airway from narrowing and/or collapsing during sleep. This may also increase the user's oxygen intake during sleep. Depending upon the therapy to be applied, the user interface 124 may form a seal, for example, with a region or portion of the user's face, to facilitate the delivery of gas at a pressure at sufficient variance with ambient pressure to effect therapy, for example, at a positive pressure of about 10 cm H2O relative to ambient pressure. For other forms of therapy, such as the delivery of oxygen, the user interface may not include a seal sufficient to facilitate delivery to the airways of a supply of gas at a positive pressure of about 10 cmH2O.
In some implementations, the user interface 124 is or includes a facial mask that covers the nose and mouth of the user (as shown, for example, in
The conduit 126 allows the flow of air between two components of a respiratory therapy system 120, such as the respiratory therapy device 122 and the user interface 124. In some implementations, there can be separate limbs of the conduit for inhalation and exhalation. In other implementations, a single limb conduit is used for both inhalation and exhalation. Generally, the respiratory therapy system 120 forms an air pathway that extends between a motor of the respiratory therapy device 122 and the user and/or the user's airway. Thus, the air pathway generally includes at least a motor of the respiratory therapy device 122, the user interface 124, and the conduit 126.
One or more of the respiratory therapy device 122, the user interface 124, the conduit 126, the display device 128, and the humidification tank 129 can contain one or more sensors (e.g., a pressure sensor, a flow rate sensor, or more generally any of the other sensors 130 described herein). These one or more sensors can be used, for example, to measure the air pressure and/or flow rate of pressurized air supplied by the respiratory therapy device 122.
The display device 128 is generally used to display image(s) including still images, video images, or both and/or information regarding the respiratory therapy device 122. For example, the display device 128 can provide information regarding the status of the respiratory therapy device 122 (e.g., whether the respiratory therapy device 122 is on/off, the pressure of the air being delivered by the respiratory therapy device 122, the temperature of the air being delivered by the respiratory therapy device 122, etc.) and/or other information (e.g., a sleep score or a therapy score (such as a myAir® score, such as described in WO 2016/061629 and US 2017/0311879, each of which is hereby incorporated by reference herein in its entirety), the current date/time, personal information for the user, a questionnaire for the user, etc.). In some implementations, the display device 128 acts as a human-machine interface (HMI) that includes a graphic user interface (GUI) configured to display the image(s) as an input interface. The display device 128 can be an LED display, an OLED display, an LCD display, or the like. The input interface can be, for example, a touchscreen or 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.
The humidification tank 129 is coupled to or integrated in the respiratory therapy device 122 and includes a reservoir of water 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 tank 129 in order to humidify the pressurized air provided to the user. Additionally, in some implementations, the conduit 126 can also include a heating element (e.g., coupled to and/or imbedded in the conduit 126) that heats the pressurized air delivered to the user. The humidification tank 129 can be fluidly coupled to a water vapor inlet of the air pathway and deliver water vapor into the air pathway via the water vapor inlet, or can be formed in-line with the air pathway as part of the air pathway itself. In other implementations, the respiratory therapy device 122 or the conduit 126 can include a waterless humidifier. The waterless humidifier can incorporate sensors that interface with other sensor positioned elsewhere in system 100.
The respiratory therapy system 120 can be used, for example, as a ventilator or a positive airway pressure (PAP) system, such as a continuous positive airway pressure (CPAP) system, an automatic positive airway pressure system (APAP), a bi-level 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 varies the air pressure delivered to the user based at least in part on, for example, respiration data associated with the user. The BPAP or VPAP system is configured to deliver a first predetermined pressure (e.g., an inspiratory positive airway pressure or IPAP) and a second predetermined pressure (e.g., an expiratory positive airway pressure or EPAP) that is lower than the first predetermined pressure.
Referring to
Referring back to
While the one or more sensors 130 are shown and described as including each of the pressure sensor 132, the flow rate sensor 134, the temperature sensor 136, the motion sensor 138, the microphone 140, the speaker 142, the RF receiver 146, the RF transmitter 148, the camera 150, the IR sensor 152, the PPG sensor 154, the ECG sensor 156, the EEG sensor 158, the capacitive sensor 160, the force sensor 162, the strain gauge sensor 164, the EMG sensor 166, the oxygen sensor 168, the analyte sensor 174, the moisture sensor 176, and the LiDAR sensor 178, more generally, the one or more sensors 130 can include any combination and any number of each of the sensors described and/or shown herein.
The one or more sensors 130 can be used to generate, for example physiological data, acoustic data, or both, that is associated with a user of the respiratory therapy system 120 (such as the user 210 of
The sleep-wake signal can also be timestamped to indicate a time that the user enters the bed, a time that the user exits the bed, a time that the user attempts to fall asleep, etc. The sleep-wake signal can be measured one or more of the sensors 130 during the sleep session at a predetermined sampling rate, such as, for example, one sample per second, one sample per 30 seconds, one sample per minute, etc. Examples of the one or more sleep-related parameters that can be determined for the user during the sleep session based at least in part on the sleep-wake signal include a total time in bed, a total sleep time, a total wake time, a sleep onset latency, a wake-after-sleep-onset parameter, a sleep efficiency, a fragmentation index, an amount of time to fall asleep, a consistency of breathing rate, a fall asleep time, a wake time, a rate of sleep disturbances, a number of movements, or any combination thereof.
Physiological data and/or acoustic data generated by the one or more sensors 130 can also be used to determine a respiration signal associated with the user during a sleep session. The respiration signal is generally indicative of respiration or breathing of the user during the sleep session. The respiration signal can be indicative of, for example, a respiration rate, a respiration rate variability, an inspiration amplitude, an expiration amplitude, an inspiration-expiration amplitude ratio, an inspiration-expiration duration ratio, a number of events per hour, a pattern of events, pressure settings of the respiratory therapy device 122, or any combination thereof. The event(s) can include snoring, apneas, central apneas, obstructive apneas, mixed apneas, hypopneas, RERAs, a flow limitation (e.g., an event that results in the absence of the increase in flow despite an elevation in negative intrathoracic pressure indicating increased effort), a mask leak (e.g., from the user interface 124), a restless leg, a sleeping disorder, choking, an increased heart rate, a heart rate variation, labored breathing, an asthma attack, an epileptic episode, a seizure, a fever, a cough, a sneeze, a snore, a gasp, the presence of an illness such as the common cold or the flu, an elevated stress level, etc. Events can be detected by any means known in the art such as described in, for example, U.S. Pat. Nos. 5,245,995, 6,502,572, WO 2018/050913, WO 2020/104465, each of which is incorporated by reference herein in its entirety.
The pressure sensor 132 outputs pressure data that can be stored in the memory device 114 and/or analyzed by the processor 112 of the control system 110. In some implementations, the pressure sensor 132 is an air pressure sensor (e.g., barometric pressure sensor) that generates sensor data indicative of the respiration (e.g., inhaling and/or exhaling) of the user of the respiratory therapy system 120 and/or ambient pressure. In such implementations, the pressure sensor 132 can be coupled to or integrated in the respiratory therapy device 122. The pressure sensor 132 can be, for example, a capacitive 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 a blood pressure of the user.
The flow rate sensor 134 outputs flow rate data that can be stored in the memory device 114 and/or analyzed by the processor 112 of the control system 110. In some implementations, the flow rate sensor 134 is used to determine an air flow rate from the respiratory therapy device 122, an air flow rate through the conduit 126, an air flow rate through the user interface 124, or any combination thereof. In such implementations, the flow rate sensor 134 can be coupled to or integrated in the respiratory therapy device 122, the user interface 124, or the conduit 126. The flow rate sensor 134 can be a mass flow rate sensor such as, for example, a rotary flow meter (e.g., Hall effect flow meters), 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.
The temperature sensor 136 outputs temperature data that can be stored in the memory device 114 and/or analyzed by the processor 112 of the control system 110. In some implementations, the temperature sensor 136 generates temperatures data indicative of a core body temperature of the user, a skin temperature of the user 210, a temperature of the air flowing from the respiratory therapy device 122 and/or through the conduit 126, a temperature in the user interface 124, an ambient temperature, or any combination thereof. The temperature sensor 136 can be, for example, a thermocouple sensor, a thermistor sensor, a silicon band gap temperature sensor or semiconductor-based sensor, a resistance temperature detector, or any combination thereof.
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 movement of the user during the sleep session, and/or detect movement of any of the components of the respiratory therapy system 120, such as the respiratory therapy device 122, the user interface 124, or the conduit 126. The motion sensor 138 can include one or more inertial sensors, such as accelerometers, gyroscopes, and magnetometers. The motion sensor 138 can be used to detect motion or acceleration associated with arterial pulses, such as pulses in or around the face of the user and proximal to the user interface 124, and configured to detect features of the pulse shape, speed, amplitude, or volume. In some implementations, the motion sensor 138 alternatively or additionally generates one or more signals representing bodily movement of the user, from which may be obtained a signal representing a sleep state of the user; for example, via a respiratory movement of the user.
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 is reproducible as one or more sound(s) during a sleep session (e.g., sounds from the user) 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) an event experienced by the user during the sleep session, as described in further detail herein. In other implementations, the acoustic data from the microphone 140 is representative of noise associated with the respiratory therapy system 120. In some implementations, the system 100 includes a plurality of microphones (e.g., two or more microphones and/or an array of microphones with beamforming) such that sound data generated by each of the plurality of microphones can be used to discriminate the sound data generated by another of the plurality of microphones. The microphone 140 can be coupled to or integrated in the respiratory therapy system 120 (or the system 100) generally 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 outside of any other components. The microphone 140 could also be a component of the user device 170 (e.g., the microphone 140 is a 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. In general, the microphone 140 can be located at any point within or adjacent to the air pathway of the respiratory therapy system 120, which includes at least the motor of the respiratory therapy device 122, the user interface 124, and the conduit 126. Thus, the air pathway can also be referred to as the acoustic pathway.
The speaker 142 outputs sound waves that are typically audible to the user. In one or more implementations, the sound waves can be audible to a user of the system 100 or inaudible to the user of the system (e.g., ultrasonic sound waves). 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 implementations, the speaker 142 can be used to communicate the acoustic data generated by the microphone 140 to the user. The speaker 142 can be coupled to or integrated in the respiratory therapy device 122, the user interface 124, the conduit 126, or the user device 170.
The microphone 140 and the speaker 142 can be used as separate devices. In some implementations, 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, WO 2018/050913 and WO 2020/104465, each of which is hereby incorporated by reference herein in its entirety. In such implementations, the speaker 142 generates or emits sound waves at a predetermined interval and/or frequency, and the microphone 140 detects the reflections of the emitted sound waves from the speaker 142. The sound waves generated or emitted by the speaker 142 have a frequency that is not audible to the human ear (e.g., below 20 Hz or above around 18 kHz) so as not to disturb the sleep of the user or a bed partner of the user (such as bed partner 220 in
The RF transmitter 148 generates and/or emits 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 signals, short wave signals, etc.). The RF receiver 146 detects the reflections of the radio waves emitted from the RF transmitter 148, and this data can be analyzed by the control system 110 to determine a location of the user and/or one or more of the sleep-related parameters described herein. An RF receiver (either the RF receiver 146 and the RF transmitter 148 or another RF pair) can also be used for wireless communication between the control system 110, the respiratory therapy device 122, the one or more sensors 130, the user device 170, or any combination thereof. While the RF receiver 146 and RF transmitter 148 are shown as being separate and distinct elements in
In some implementations, the RF sensor 147 is a part of a mesh system. One example of a mesh system is a WiFi mesh system, which can include mesh nodes, mesh router(s), and mesh gateway(s), each of which can be mobile/movable or fixed. In such implementations, the WiFi mesh system includes a WiFi router and/or a WiFi controller and one or more satellites (e.g., access points), each of which include an RF sensor that the is the same as, or similar to, the RF sensor 147. The WiFi router and satellites continuously communicate with one another using WiFi signals. The WiFi mesh system can be used to generate motion data based at least in part on changes in the WiFi signals (e.g., differences in received signal strength) between the router and the satellite(s) due to an object or person moving partially obstructing the signals. The motion data can be indicative of motion, breathing, heart rate, gait, falls, behavior, etc., or any combination thereof.
The camera 150 outputs image data reproducible 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 a location of the user, to determine a time when the user enters the user's bed (such as bed 230 in
The IR sensor 152 outputs infrared image data reproducible 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 the sleep session, including a temperature of the user and/or movement of the user. The IR sensor 152 can also be used in conjunction with the camera 150 when measuring the presence, location, and/or movement of the user. The IR sensor 152 can detect infrared light having a wavelength between about 700 nm and about 1 mm, for example, while the camera 150 can detect visible light having a wavelength between about 380 nm and about 740 nm.
The IR sensor 152 outputs infrared image data reproducible 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 the sleep session, including a temperature of the user and/or movement of the user. The IR sensor 152 can also be used in conjunction with the camera 150 when measuring the presence, location, and/or movement of the user. The IR sensor 152 can detect infrared light having a wavelength between about 700 nm and about 1 mm, for example, while the camera 150 can detect visible light having a wavelength between about 380 nm and about 740 nm.
The PPG sensor 154 outputs physiological data associated with the user that can be used to determine one or more sleep-related parameters, such as, for example, a heart rate, a heart rate pattern, a heart rate variability, a cardiac cycle, respiration rate, an inspiration amplitude, an expiration amplitude, an inspiration-expiration ratio, estimated blood pressure parameter(s), or any combination thereof. The PPG sensor 154 can be worn by the user, embedded in clothing and/or fabric that is worn by the user, embedded in and/or coupled to the user interface 124 and/or its associated headgear (e.g., straps, etc.), etc.
The ECG sensor 156 outputs physiological data associated with electrical activity of the heart of the user. In some implementations, the ECG sensor 156 includes one or more electrodes that are positioned on or around a portion of the user during the sleep session. The physiological data from the ECG sensor 156 can be used, for example, to determine one or more of the sleep-related parameters described herein.
The EEG sensor 158 outputs physiological data associated with electrical activity of the brain of the user. In some implementations, the EEG sensor 158 includes one or more electrodes that are positioned on or around the scalp of the user during the sleep session. The physiological data from the EEG sensor 158 can be used, for example, to determine a sleep stage of the user at any given time during the sleep session. In some implementations, the EEG sensor 158 can be integrated in the user interface 124 and/or the associated headgear (e.g., straps, etc.).
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 associated with electrical activity produced by one or more muscles. The oxygen sensor 168 outputs oxygen data indicative of an oxygen concentration of 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 electrical oxygen sensor, a chemical oxygen sensor, an optical oxygen sensor, or any combination thereof. In some implementations, the one or more sensors 130 also include a galvanic skin response (GSR) sensor, a blood flow sensor, a respiration sensor, a pulse sensor, a sphygmomanometer sensor, an oximetry sensor, or any combination thereof.
The analyte sensor 174 can be used to detect the presence of an analyte in the exhaled breath of the user. 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 implementations, the analyte sensor 174 is positioned near a mouth of the user to detect analytes in breath exhaled from the user's mouth. For example, when the user interface 124 is a facial mask that covers the nose and mouth of the user, the analyte sensor 174 can be positioned within the facial mask to monitor the user mouth breathing. In other implementations, such as when the user interface 124 is a nasal mask or a nasal pillow mask, the analyte sensor 174 can be positioned near the nose of the user to detect analytes in breath exhaled through the user's nose. In still other implementations, the analyte sensor 174 can be positioned near the user's mouth when the user interface 124 is a nasal mask or a nasal pillow mask. In this implementation, the analyte sensor 174 can be used to detect whether any air is inadvertently leaking from the user's mouth. In some implementations, the analyte sensor 174 is a volatile organic compound (VOC) sensor that can be used to detect carbon-based chemicals or compounds, such as carbon dioxide. In some implementations, the analyte sensor 174 can also be used to detect whether the user is breathing through their nose or mouth. For example, if the data output by an analyte sensor 174 positioned near the mouth of the user or within the facial mask (in implementations where the user interface 124 is a facial mask) detects the presence of an analyte, the control system 110 can use this data as an indication that the user is breathing through their mouth.
The moisture sensor 176 outputs data that can be stored in the memory device 114 and used by the control system 110. The moisture sensor 176 can be used to detect moisture in various areas surrounding the user (e.g., inside the conduit 126 or 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 implementations, the moisture sensor 176 can be coupled to or integrated into the user interface 124 or in the conduit 126 to monitor the humidity of the pressurized air from the respiratory therapy device 122. In other implementations, the moisture sensor 176 is placed near any area where moisture levels need to be monitored. The moisture sensor 176 can also be used to monitor the humidity of the ambient environment surrounding the user, for example the air inside the user's bedroom. The moisture sensor 176 can also be used to track the user's biometric response to environmental changes.
One or more LiDAR sensors 178 can be used for depth sensing. This type of optical sensor (e.g., laser sensor) can be used to detect objects and build three dimensional (3D) maps of the surroundings, such as of a living space. LiDAR can generally utilize a pulsed laser to make time of flight measurements. LiDAR is also referred to as 3D laser scanning. In an example of use of such a sensor, a fixed or mobile device (such as a smartphone) having a LiDAR sensor 178 can measure and map an area extending 5 meters or more away from the sensor. The LiDAR data can be fused with point cloud data estimated by an electromagnetic RADAR sensor, for example. The LiDAR sensor 178 may also use artificial intelligence (AI) to automatically geofence RADAR systems by detecting and classifying features in a space that might cause issues for RADAR systems, such a glass windows (which can be highly reflective to RADAR). LiDAR can also be used to provide an estimate of the height of a person, as well as changes in height when the person sits down, or falls down, for example. LiDAR may be used to form a 3D mesh representation of an environment. In a further use, for solid surfaces through which radio waves pass (e.g., radio-translucent materials), the LiDAR may reflect off such surfaces, thus allowing a classification of different type of obstacles.
While shown separately in
The data from the one or more sensors 130 can be analyzed to determine one or more sleep-related parameters, which can include a respiration signal, a respiration rate, a respiration pattern, an inspiration amplitude, an expiration amplitude, an inspiration-expiration ratio, an occurrence of one or more events, a number of events per hour, a pattern of events, an average duration of events, a range of event durations, a ratio between the number of different events, a sleep stage, an apnea-hypopnea index (AHI), or any combination thereof. The one or more events can include snoring, apneas, central apneas, obstructive apneas, mixed apneas, hypopneas, an intentional user interface leak, an unintentional user interface leak, a mouth leak, a cough, a restless leg, a sleeping disorder, choking, an increased heart rate, labored breathing, an asthma attack, an epileptic episode, a seizure, increased blood pressure, hyperventilation, or any combination thereof. Many of these sleep-related parameters are physiological parameters, although some of the sleep-related parameters can be considered to be non-physiological parameters. Other types of physiological and non-physiological parameters can also be determined, either from the data from the one or more sensors 130, or from other types of data.
The user device 170 includes a display device 172. The user device 170 can be, for example, a mobile device such as a smart phone, a tablet, a laptop, a gaming console, a smart watch, or the like. Alternatively, the user device 170 can be an external sensing system, a television (e.g., a smart television) or another smart home device (e.g., a smart speaker(s) such as Google Home®, Google Nest®, Amazon Echo®, Amazon Echo Show®, Alexa®-enabled devices, etc.). In some implementations, the user device 170 is a wearable device (e.g., a smart watch). The display device 172 is generally used to display image(s) including still images, video images, or both. In some implementations, the display device 172 acts as a human-machine interface (HMI) that includes a graphic user interface (GUI) configured to display the image(s) and an input interface. The display device 172 can be an LED display, an OLED display, an LCD display, or the like. The input interface can be, for example, a touchscreen or touch-sensitive substrate, a mouse, a keyboard, or any sensor system configured to sense inputs made by a human user interacting with the user device 170. In some implementations, one or more user devices 170 can be used by and/or included in the system 100.
The blood pressure device 180 is generally used to aid in generating 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.
In some implementations, the blood pressure device 180 is a sphygmomanometer including 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
The activity tracker 190 is generally used to aid in generating physiological data for determining an activity measurement associated with the user. The activity measurement can include, for example, a number of steps, a distance traveled, a number of steps climbed, a duration of physical activity, a type of physical activity, an intensity of physical activity, time spent standing, a respiration rate, an average respiration rate, a resting respiration rate, a maximum respiration rate, a respiration rate variability, a heart rate, an average heart rate, a resting heart rate, a maximum heart rate, a heart rate variability, a number of calories burned, blood oxygen saturation, electrodermal activity (also known as skin conductance or galvanic skin response), or any combination thereof. The activity tracker 190 includes one or more of the sensors 130 described herein, such as, for example, the motion sensor 138 (e.g., one or more accelerometers and/or gyroscopes), the PPG sensor 154, and/or the ECG sensor 156.
In some implementations, the activity tracker 190 is a wearable device that can be worn by the user, such as a smartwatch, a wristband, a ring, or a patch. For example, referring to
While the control system 110 and the memory device 114 are described and shown in
While system 100 is shown as including all of the components described above, more or fewer components can be included in a system for identifying a user interface used by a user, according to implementations of the present disclosure. For example, a first alternative system includes the control system 110, the memory device 114, and at least one of the one or more sensors 130. As another example, a second alternative system includes the control system 110, the memory device 114, at least one of the one or more sensors 130, and the user device 170. As yet another example, a third alternative system includes the control system 110, the memory device 114, the respiratory therapy system 120, at least one of the one or more sensors 130, and the user device 170. As a further example, a fourth alternative system includes the control system 110, the memory device 114, the respiratory therapy system 120, at least one of the one or more sensors 130, the user device 170, and the blood pressure device 180 and/or activity tracker 190. Thus, various systems for modifying pressure settings can be formed using any portion or portions of the components shown and described herein and/or in combination with one or more other components.
Referring again to
Alternatively, or additionally, at least one of the one or more sensors 130 can be located at a second position on and/or in the bed 230 (e.g., the one or more sensors 130 are coupled to and/or integrated in the bed 230). Further, alternatively or additionally, at least one of the one or more sensors 130 can be located at a third position on and/or in the mattress 232 that is adjacent to the bed 230 and/or the user 210 (e.g., the one or more sensors 130 are coupled to and/or integrated in the mattress 232). Alternatively, or additionally, at least one of the one or more sensors 130 can be located at a fourth position on and/or in a pillow that is generally adjacent to the bed 230 and/or the user 210.
Alternatively, or additionally, at least one of the one or more sensors 130 can be located at a fifth position on and/or in the nightstand 240 that is generally adjacent to the bed 230 and/or the user 210. Alternatively, or additionally, at least one of the one or more sensors 130 can be located at a sixth position such that the at least one of the one or more sensors 130 are coupled to and/or positioned on the user 210 (e.g., the one or more sensors 130 are embedded in or coupled to fabric, clothing, and/or a smart device worn by the user 210). More generally, at least one of the one or more sensors 130 can be positioned at any suitable location relative to the user 210 such that the one or more sensors 130 can generate sensor data associated with the user 210.
In some implementations, a primary sensor, such as the microphone 140, is configured to generate acoustic data associated with the user 210 during a sleep session. For example, one or more microphones (the same as, or similar to, the microphone 140 of
In some implementations, one or more secondary sensors may be used in addition to the primary sensor to generate additional data. In some such implementations, the one or more secondary sensors include: a microphone (e.g., the microphone 140 of the system 100), a flow rate sensor (e.g., the flow rate sensor 134 of the system 100), a pressure sensor (e.g., the pressure sensor 132 of the system 100), a temperature sensor (e.g., the temperature sensor 136 of the system 100), a camera (e.g., the camera 150 of the system 100), a vane sensor (VAF), a hot wire sensor (MAF), a cold wire sensor, a laminar flow sensor, an ultrasonic sensor, an inertial sensor, or a combination thereof.
Additionally, or alternatively, one or more microphones (the same as, or similar to, the microphone 140 of
Additionally, or alternatively, in some implementations, one or more microphones (the same as, or similar to, the microphone 140 of
As used herein, a sleep session can be defined in a number of ways based at least in part on, for example, an initial start time and an end time. In some implementations, a sleep session is a duration where the user is asleep, that is, the sleep session has a start time and an end time, and during the sleep session, the user does not wake until the end time. That is, any period of the user being awake is not included in a sleep session. From this first definition of sleep session, if the user wakes ups and falls asleep multiple times in the same night, each of the sleep intervals separated by an awake interval is a sleep session.
Alternatively, in some implementations, a sleep session has a start time and an end time, and during the sleep session, the user can wake up, without the sleep session ending, so long as a continuous duration that the user is awake is below an awake duration threshold. The awake duration threshold can be defined as a percentage of a sleep session. The awake duration threshold can be, for example, about twenty percent of the sleep session, about fifteen percent of the sleep session duration, about ten percent of the sleep session duration, about five percent of the sleep session duration, about two percent of the sleep session duration, etc., or any other threshold percentage. In some implementations, the awake duration threshold is defined as a fixed amount of time, such as, for example, about one hour, about thirty minutes, about fifteen minutes, about ten minutes, about five minutes, about two minutes, etc., or any other amount of time.
In some implementations, a sleep session is defined as the entire time between the time in the evening at which the user first entered the bed, and the time the next morning when user last left the bed. Put another way, a sleep session can be defined as a period of time that begins on a first date (e.g., Monday, Jan. 6, 2020) at a first time (e.g., 10:00 PM), that can be referred to as the current evening, when the user first enters a bed with the intention of going to sleep (e.g., not if the user intends to first watch television or play with a smart phone before going to sleep, etc.), and ends on a second date (e.g., Tuesday, Jan. 7, 2020) at a second time (e.g., 7:00 AM), that can be referred to as the next morning, when the user first exits the bed with the intention of not going back to sleep that next morning.
In some implementations, the user can manually define the beginning of a sleep session and/or manually terminate a sleep session. For example, the user can select (e.g., by clicking or tapping) one or more user-selectable element that is displayed on the display device 172 of the user device 170 (
Referring to
The enter bed time bed is associated with the time that the user initially enters the bed (e.g., bed 230 in
The go-to-sleep time (GTS) is associated with the time that the user initially attempts to fall asleep after entering the bed (tbed). For example, after entering the bed, the user may engage in one or more activities to wind down prior to trying to sleep (e.g., reading, watching TV, listening to music, using the user device 170, etc.). The initial sleep time (tsleep) is the time that the user initially falls asleep. For example, the initial sleep time (tsleep) can be the time that the user initially enters the first non-REM sleep stage.
The wake-up time twake is the time associated with the time when the user wakes up without going back to sleep (e.g., as opposed to the user waking up in the middle of the night and going back to sleep). The user may experience one of more unconscious microawakenings (e.g., microawakenings MA1 and MA2) having a short duration (e.g., 5 seconds, 10 seconds, 30 seconds, 1 minute, etc.) after initially falling asleep. In contrast to the wake-up time twake, the user goes back to sleep after each of the microawakenings MA1 and MA2. Similarly, the user may have one or more conscious awakenings (e.g., awakening A) after initially falling asleep (e.g., getting up to go to the bathroom, attending to children or pets, sleep walking, etc.). However, the user goes back to sleep after the awakening A. Thus, the wake-up time twake can be defined, for example, based at least in part on a wake threshold duration (e.g., the user is awake for at least 15 minutes, at least 20 minutes, at least 30 minutes, at least 1 hour, etc.).
Similarly, the rising time trise is associated with the time when the user exits the bed and stays out of the bed with the intent to end the sleep session (e.g., as opposed to the user getting up during the night to go to the bathroom, to attend to children or pets, sleep walking, etc.). In other words, the rising time trise is the time when the user last leaves the bed without returning to the bed until a next sleep session (e.g., the following evening). Thus, the rising time trise can be defined, for example, based at least in part on a rise threshold duration (e.g., the user has left the bed for at least 15 minutes, at least 20 minutes, at least 30 minutes, at least 1 hour, etc.). The enter bed time tbed time for a second, subsequent sleep session can also be defined based at least in part on a rise threshold duration (e.g., the user has left the bed for at least 4 hours, at least 6 hours, at least 8 hours, at least 12 hours, etc.).
As described above, the user may wake up and get out of bed one more times during the night between the initial tbed and the final trise. In some implementations, the final wake-up time twake and/or the final rising time trise that are identified or determined based at least in part on a predetermined threshold duration of time subsequent to an event (e.g., falling asleep or leaving the bed). Such a threshold duration can be customized for the user. For a standard user which goes to bed in the evening, then wakes up and goes out of bed in the morning any period (between the user waking up (twake) or raising up (trise), and the user either going to bed (tbed), going to sleep (tGTS) or falling asleep (tsleep) of between about 12 and about 18 hours can be used. For users that spend longer periods of time in bed, shorter threshold periods may be used (e.g., between about 8 hours and about 14 hours). The threshold period may be initially selected and/or later adjusted based at least in part on the system monitoring the user's sleep behavior.
The total time in bed (TIB) is the duration of time between the time enter bed time tbed and the rising time trise. The total sleep time (TST) is associated with the duration between the initial sleep time and the wake-up time, excluding any conscious or unconscious awakenings and/or micro-awakenings therebetween. Generally, the total sleep time (TST) will be shorter than the total time in bed (TIB) (e.g., one minute short, ten minutes shorter, one hour shorter, etc.). For example, referring to the timeline 300 of
In some implementations, the total sleep time (TST) can be defined as a persistent total sleep time (PTST). In such implementations, the persistent total sleep time excludes a predetermined initial portion or period of the first non-REM stage (e.g., 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 sustained sleep, and smooths the sleep-wake hypnogram. For example, when the user is initially falling asleep, the user may be in the first non-REM stage for a very short time (e.g., about 30 seconds), then back into the wakefulness stage for a short period (e.g., one minute), and then goes back to the first non-REM stage. In this example, the persistent total sleep time excludes the first instance (e.g., about 30 seconds) of the first non-REM stage.
In some implementations, the sleep session is defined as starting at the enter bed time (teed) and ending at the rising time (trise), i.e., the sleep session is defined as the total time in bed (TIB). In some implementations, a sleep session is defined as starting at the initial sleep time (tsleep) and ending at the wake-up time (twake). In some implementations, the sleep session is defined as the total sleep time (TST). In some implementations, a sleep session is defined as starting at the go-to-sleep time (tGTS) and ending at the wake-up time (twake). In some implementations, a sleep session is defined as starting at the go-to-sleep time (tGTS) and ending at the rising time (trise). In some implementations, a sleep session is defined as starting at the enter bed time (teed) and ending at the wake-up time (twake). In some implementations, a sleep session is defined as starting at the initial sleep time (tsleep) and ending at the rising time (trise).
Referring to
The sleep-wake signal 401 can be generated based at least in part 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 be indicative of one or more sleep stages, including wakefulness, relaxed wakefulness, microawakenings, a REM stage, a first non-REM stage, a second non-REM stage, a third non-REM stage, or any combination thereof. In some implementations, one or more of the first non-REM stage, the second non-REM stage, and the third non-REM stage can be grouped together and categorized as a light sleep stage or a deep sleep stage. For example, the light sleep stage can include the first non-REM stage and the deep sleep stage can include the second non-REM stage and the third non-REM stage. While the hypnogram 400 is shown in
The hypnogram 400 can be used to determine one or more sleep-related parameters, such as, for example, a sleep onset latency (SOL), wake-after-sleep onset (WASO), a sleep efficiency (SE), a sleep fragmentation index, sleep blocks, or any combination thereof.
The sleep onset latency (SOL) is defined as the time between the go-to-sleep time (tGTS) and the initial sleep time (tsleep). In other words, the sleep onset latency is indicative of the time that it took the user to actually fall asleep after initially attempting to fall asleep. In some implementations, the sleep onset latency is defined as a persistent sleep onset latency (PSOL). The persistent sleep onset latency differs from the sleep onset latency in that the persistent sleep onset latency is defined as the duration time between the go-to-sleep time and a predetermined amount of sustained sleep. In some implementations, the predetermined amount of sustained sleep can 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 requires up to, for example, 8 minutes of sustained sleep within the second non-REM stage, the third non-REM stage, and/or the REM stage. In other implementations, the predetermined amount of sustained sleep can 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 subsequent to the initial sleep time. In such implementations, the predetermined amount of sustained sleep can exclude any micro-awakenings (e.g., a ten second micro-awakening does not restart the 10-minute period).
The wake-after-sleep onset (WASO) is associated with the total duration of time that the user is awake between the initial sleep time and the wake-up time. Thus, the wake-after-sleep onset includes short and micro-awakenings during the sleep session (e.g., the micro-awakenings MA1 and MA2 shown in
The sleep efficiency (SE) is determined as a ratio of the total time in bed (TIB) and the total sleep time (TST). For example, if the total time in bed is 8 hours and the total sleep time is 7.5 hours, the sleep efficiency for that sleep session is 93.75%. The sleep efficiency is indicative of the sleep hygiene of the user. For example, if the user enters the bed and spends time engaged in other activities (e.g., watching TV) before sleep, the sleep efficiency will be reduced (e.g., the user is penalized). In some implementations, the sleep efficiency (SE) can be calculated based at least in part on the total time in bed (TIB) and the total time that the user is attempting to sleep. In such implementations, the total time that the user is attempting to sleep is defined as the duration between the go-to-sleep (GTS) time and the rising time described herein. For example, if the total sleep time is 8 hours (e.g., between 11 PM and 7 AM), the go-to-sleep time is 10:45 PM, and the rising time is 7:15 AM, in such implementations, the sleep efficiency parameter is calculated as about 94%.
The fragmentation index is determined based at least in part on the number of awakenings during the sleep session. For example, if the user had two micro-awakenings (e.g., micro-awakening MA1 and micro-awakening MA2 shown in
The sleep blocks are associated with a transition between any stage of sleep (e.g., the first non-REM stage, the second non-REM stage, the third non-REM stage, and/or the REM) and the wakefulness stage. The sleep blocks can be calculated at a resolution of, for example, 30 seconds.
In some implementations, the systems and methods described herein can include generating or analyzing a hypnogram including a sleep-wake signal to determine or identify the enter bed time (tbed), the go-to-sleep time (tGTS), the initial sleep time (tsleep), one or more first micro-awakenings (e.g., MA1 and MA2), the wake-up time (twake), the rising time (trise), or any combination thereof based at least in part on the sleep-wake signal of a hypnogram.
In other implementations, one or more of the sensors 130 can be used to determine or identify the enter bed time (tbed), the go-to-sleep time (tGTS), the initial sleep time (tsleep), one or more first micro-awakenings (e.g., MA1 and MA2), the wake-up time (twake), the rising time (trise), or any combination thereof, which in turn define the sleep session. For example, the enter bed time teed can be determined based at least in part on, for example, data generated by the motion sensor 138, the microphone 140, the camera 150, or any combination thereof. The go-to-sleep time can be determined based at least in part on, for example, data from the motion sensor 138 (e.g., data indicative of no movement by the user), data from the camera 150 (e.g., data indicative of no movement by the user and/or that the user has turned off the lights), data from the microphone 140 (e.g., data indicative of the using turning off a TV), data from the user device 170 (e.g., data indicative of the user no longer using the user device 170), data from the pressure sensor 132 and/or the flow rate sensor 134 (e.g., data indicative of the user turning on the respiratory therapy device 122, data indicative of the user donning the user interface 124, etc.), or any combination thereof.
Referring to
Step 502 of method 500 includes generating data during a current sleep session that is associated with a user of a respiratory therapy system. The data can be generated by any suitable source, including a number of different sensors (such as sensor(s) 130). The sensors can be located within a housing of a respiratory therapy device (such as respiratory therapy device 122) used with the respiratory therapy system, or outside of the housing of the respiratory therapy device. The data can also be generated by a number of different devices, such as a smart watch, an activity tracker, a mobile phone, any number of external medical measurement devices, etc. The respiratory therapy system can include a respiratory therapy device that supplies pressurized air to the user's airway via, for example, a conduit and a user interface. The generated data can include data that is indicative of the pressure of the pressurized air, the flow rate of the pressurized air, and other properties of the pressurized air. The user interface can include a full facial mask that covers the user's mouth and nose, a nasal mask, or other types of user interfaces.
Step 504 of method 500 includes analyzing the generated data to determine the value of a first metric associated with sleep-disordered breathing (SDB). Step 506 of method 500 includes analyzing the generated data to determine the value of a second metric associated with a health condition of the user other than SDB. The first metric can include metrics that are used to analyze the user's sleep session. For example, if the respiratory therapy system is being used as a CPAP system, the first metric can be associated with using the CPAP system to minimize events, such as apnea events.
The first metric (and in some implementations the second metric) can include a respiration signal, a respiration rate, a respiration pattern, an inspiration amplitude, an expiration amplitude, an inspiration-expiration ratio, an occurrence of one or more events, a number of events per hour, a pattern of events, an average duration of events, a range of event durations, a ratio between the number of different events, a sleep stage, a time spent in each of a plurality of sleep stages, a pattern of sleep stages, an apnea-hypopnea index (AHI), a sleep score, a therapy score, a total sleep time, a total time in bed, a wake-up time, a rising time, a hypnogram, a total light sleep time, a total deep sleep time, a total REM sleep time, a total sleep time on therapy, a total sleep time off therapy a number of awakenings, a sleep-onset latency, a stress level, or any combination thereof.
In some implementations, the first metric is associated only with SDB and the second metric is associated only with the health condition. In some implementations, the first metric is associated with SDB and the health condition, and the second metric is associated only with the health condition. In some implementations, the first metric is associated only with SDB, and the second metric is associated with the health condition and SDB. In some implementations, the first metric is associated with SDB and the health condition. In some implementations, the second metric is associated with the health condition and SDB.
In some implementations, the second metric is associated with the presence of a cardiac condition. For example, the second metric can be associated with heart failure, a heart murmur, an increased heart rate, a heart attack, atrial fibrillation, heart arrhythmia, myocarditis, cardiac autonomic neuropathy, chronic inflammatory conditions such as atherosclerosis (the repetitive drops in nocturnal blood oxygen levels caused by SDB can result in intermittent hypoxia, which has been shown to be associated with systemic inflammation), and others. The second metric can include a heart rate variability (a lack of a decrease in heart rate variability during a sleep session can indicate that the heart is stressed and not reacting correctly to being asleep); a standard deviation in beat-to-beat intervals (also known as SDNN, and can be related to the heart's recovery capability); root mean square of successive differences between adjacent beats (also known as RMSSD, and can be related to the heart's recovery capability); a cardiac output (a decreased cardiac output (e.g., smaller amount of blood pumped per time interval) can be indicative of hypertension and/or heart failure); a structure of the atrial chambers (a modified structure can be indicative of a risk of atrial fibrillation); a tidal volume (instability in tidal volume can be indicative of heart failure); a duration of apnea, a duration of expiration, a gradient of expiration, an envelope of the flow signal (which can show the level of stability in the user's breathing), a respiration rate; and others.
In some implementations, the second metric is associated with the user's clinical stress level, or sympathetic activity. For example, the second metric can include a heart rate, a heart rate variability, a skin conductance (e.g., a galvanic skin response), an arterial pulse speed, an arterial pulse shape, an arterial pulse volume, an arterial pulse amplitude, or any combination thereof. Further, one or more of these various measurements can be used to quantify the stress level of the user, so that the second metric itself includes a quantifiable measure of the user's stress level. The second metric can also be a measure of the user's stress response to various different types of apnea events. For example, the second metric for one user can indicate that the user experiences elevated stress levels (which could be measured using heart rate, heart rate variability, skin conductance, arterial pulse characteristics, etc.) only in response to longer apnea events and/or apnea events with a significant reduction in inspiration volume. However, the second metric for a different user can indicate that that user experiences elevated stress levels in response to shorter apnea events and/or apnea events with a minor reduction in inspiration volume, as well as apnea events having longer durations and/or a significant reduction in inspiration volume. The second metric could also indicate how much the stress levels of that user increase as the severity of the apnea event increases. Thus, the second metric in some implementations can include a link between the user's stress response and various different types of apnea events, a percentage of different types of apnea events that result in different stress responses, and others.
In further implementations, the respiratory therapy system may be configured to learn an association between the second metric and the respiratory therapy system therapy (e.g., CPAP pressure). In yet further implementations, a respiratory therapy level (e.g., CPAP pressure level) may be varied to augment learning the association between therapy and the second metric, and in further implementations still, a therapy level may be adjusted to control the second metric (e.g., CPAP pressure may be increased or decreased to reduce a metric associated with stress or sympathetic activity).
In some implementations, the second metric is associated with the user's breathing during the sleep session. For example, the second metric can include the cadence of the user's breathing (e.g., how quickly or slowly the user breathes, and/or if there are any starts and stops in the user's breathing), an amplitude of the breathing of the user (e.g., to the volume of air that the user breathes in and/or breathes out), a time constant of expiration (e.g., the time constant of the exponential decay of the lung volume during expiration), a shape of expiration (e.g., a shape of a plot of the lung volume over time during expiration), a magnitude of expiration (e.g., a volume or force of expiration), a speed of expiration (e.g., how quickly the lung volume decreases during expiration), a time constant of inspiration (e.g., the time constant of the exponential increase of the lung volume during inspiration), a shape of inspiration (e.g., a shape of a plot of the lung volume over time during inspiration), a magnitude of inspiration (e.g., a volume and/or force of inspiration), a speed of inspiration (e.g., how quickly the lung volume increases during inspiration), a tidal volume, a rate of the breathing, or other breathing metrics (e.g., respiratory-associated metrics).
These breathing metrics can be associated with any number of different health conditions. For example, the cadence of the user's breathing and/or the tidal volume can be associated with obesity, COPD, pneumonia, asthma, and other conditions. A user being short of breath (e.g., the cadence of the user's breathing is rapid and/or the tidal volume is shallow) can indicate that the user is obese, or has COPD, pneumonia, asthma, etc. In another example, the shape of inspiration and/or expiration can be indicative of the presence of an obstruction in the user's airway. In a further example, the tidal volume can be associated with heart failure. Instability in the tidal volume (e.g., different volumes for different breaths) can be indicative of the presence of heart failure, or the risk of developing failure. In a further example, any one or more of (i) the cadence of breathing, (ii) the shape, speed, and magnitude of inspiration, and (iii) the shape, speed, and magnitude of expiration can be indicative of the presence of Cheyne-Stokes respiration.
In another example, the second metric can be associated with when the user is awake or asleep during the sleep session, and can include the amount of time it took to fall asleep, the total amount of time spent awake, the total amount of time spent asleep, the time that the user fell asleep, the time that the user woke up, a consistency in the user's breathing rate, and others. The metrics can indicate that the user was awake for a longer time than desired, was asleep for a shorter time than desired, that the user's breathing did not slow down and become consistent (indicating that the user is asleep) until later in the sleep session than desired, etc., which can all indicate the user suffers from insomnia. Information related to the sleep staging, such as a hypnogram (e.g., hypnogram 400 of
These metrics or other metrics can also be indicative of the presence of other neurological conditions, such as anxiety, claustrophobia, stress, and others. In general, the second metric can be associated with cardiac conditions, respiratory conditions, neurological conditions, and other types of conditions. In general, the metrics associated with neurological conditions can include heart rate (high heart rate can be indicative of anxiety); blood pressure (high blood pressure can be indicative of anxiety); sleep onset latency (low sleep onset latency can be indicative of anxiety); an arousal threshold (e.g., how easily the user wakes up—the arousal threshold being low can be indicative of anxiety); an amount of physical movement during sleep session (e.g., uncontrolled leg movement can be indicative of restless leg syndrome); metrics related to awakenings during the sleep session (erratic sleep-wake patterns can be indicative of disruption of circadian rhythms and can lead to morning depression, and can also be indicative of nocturnal epilepsy); interface leak rate (a high rate of leaks from the user's interface, such as user interface 124, can indicate that the user interface is being worn looser as a result of claustrophobia); a number of times the user interface is removed during the sleep session (a large amount can be indicative of claustrophobia); a various metrics related to the user's answers to a questionnaire, as discussed in more detail below.
Step 508 of method 500 includes causing some action to be performed, based at least in part on the determined value of the second metric. In some implementations, the second metric is indicative of the identity, presence, and/or severity of the health condition in the user, and thus can be used to determine the identity, presence, and/or severity of the health condition in the user. A number of different actions can then be taken.
In some implementations, based at least in part on the determined value of the second metrics, one or more settings of the respiratory therapy system can be adjusted. In one example, the respiratory therapy system is initially operated as a positive airway pressure system, such as a CPAP system. In response to the value of the second metric indicating the presence of a cardiac condition (such as heart failure) or an increased risk of developing a cardiac condition (such as heart failure), one or more settings of the respiratory therapy device can be adjusted (manually or automatically by the control system) so that the respiratory therapy system operates as an adaptive servo-ventilation system. Cardiac conditions (such as heart failure) often result in apneas that are not treatable with a CPAP system, and the use of a CPAP system can often worsen the occurrence and/or severity of these apneas. When the respiratory therapy system operates as an adaptive servo-ventilation system, the respiratory therapy system can monitor the user's breathing, and add volume to the user's breaths to ensure tidal volume stability, and reduce the occurrence and/or severity of these apneas.
In another example, settings related to the supply of the pressurized air can be modified, such as the pressure of the air, the flow rate of the air, the ramp time of the pressurized air (e.g., how long it takes for the pressure of the air to increase to the desired pressure (e.g., the desired therapy pressure) from the beginning of the use of the respiratory therapy system), a humidity of the pressurized air, and others. A medicament or other substance can also be supplied and/or injected into user's airway via the pressurized air. The medicament can be configured to aid the user in falling asleep. The substance could be a scent configured to calm the user and aid the user in falling asleep. In one example, the volume of the respiratory therapy system when operating at high pressure can impact a user's insomnia, and in some cases worsen the user's insomnia. If the second metric indicates that the user has insomnia, the ramp time can be increased, which gives the user a longer time to fall asleep until the respiratory therapy system achieves the desired pressure. In a further example, if the second metric indicates the presence of insomnia, the therapy response sensitivity ramp time can be increased, and/or the air pressure can be maintained at a relatively low level (e.g., less than the desired therapy, which can increase faster in the presence of milder events such as snore or flow limitation), until the respiratory therapy system detects that the user has fallen asleep (e.g., based at least in part on the sleep-wake signal).
In another example, the humidity of the pressurized air can be increased for users suffering from COPD, asthma, allergies, a sinus infection, rhinitis, or other respiratory-related conditions. In a further example, if the second metric indicates that the user is suffering from a condition that can be treated by inhaled medication, a medicament can be supplied and/or injected into the user's airway. In some of these implementations, the value of the first metric related to SDB may impact how the settings of the respiratory therapy system are adjusted. In still other implementations, adjusting the settings can include changing the type of user interface currently being used. The microphone 140 and/or the speaker 142 can be used to characterize the user interface, for example by directing an acoustic signal toward the user interface, and then analyzing acoustic data associated with a reflection of the acoustic signal. Once the type of user interface currently being used is determined, a recommendation can be generated to change the type of user interface, based at least in part on the determined value of the second metric. In general, the settings of the respiratory therapy system have an intended therapy effect. The settings of the respiratory therapy system can be adjusted as needed to modify the intended therapy effect.
In a further example, adjusting the settings of the respiratory therapy system can include adjusting the pressure of the pressurized air to account for the CO2 level around the interface. If the user's interface is leaking and/or if the user's ventilation rate is low, the pressure of the pressurized air can be decreased.
In some implementations, a notification and/or report can be generated and transmitted to the user and/or a third party. The third party could include a friend, family member, spouse, significant other, caretaker, healthcare provider, etc. The notification and/or report can identify the condition that was detected and/or the severity of the detected condition. The notification and/or report can also recommend future therapy to help treat the condition. For example, of the second metric indicates the presence of insomnia, the notification and/or report can suggest cognitive behavioral therapy. The notification and/or report can be displayed to the user on an electronic display device, such as display device 172 of user device 170.
In some implementations, the techniques of method 500 can be used to monitor the efficacy of the use of the respiratory therapy system in treating the health condition. In these implementations, the second metric is tracked over multiple sleep sessions, and the value of the second metric is determined for each sleep session. The settings of the respiratory therapy system for each sleep session can be determined, along with the duration of use of the respiratory therapy system for each sleep session. The settings and duration of use of the respiratory therapy system can be correlated with any change in the presence and/or severity of the health condition. The optimal settings and/or duration of use to treat the health condition can then be determined.
In some implementations, the second metric is related to the stress level of the user, as discussed above. In these implementations, the action can include adjusting various settings of the respiratory therapy system, based on the determined stress level of the user. In one example, the stress level of the user is used to adjust in real-time how the respiratory therapy system responds to an apnea event. The respiratory therapy system may be operated as a positive airway pressure system that supplies pressurized air to the user's airway at a constant pressure (a CPAP system) or at two different constant pressures for the user's inspiration and exhalation (a BiPAP system or a VPAP system). If the user is experiencing elevated stress levels, the respiratory therapy system can increase the pressure of the supplied air when a severe apnea event is detected. Increasing the pressure of the air can aid in ending severe apnea events more quickly, and/or minimizing the inspiration reduction that occurs during the severe apnea event. Increasing the pressure can have negative side effects, such as drying out the user's airway, and potentially waking the user up, and thus is generally not done. However, if the user experiences elevated stress levels during the sleep session (and particularly during the severe apnea event), the benefit of ending the severe apnea event more quickly by increasing the pressure of the air can outweigh any drawbacks from the increased pressure. Thus, the respiratory therapy system in these implementations can increase the pressure of the air in response to a severe apnea event only if the user's stress levels are elevated. If the user's stress levels are not elevated, the respiratory therapy system can maintain the same pressure level, even when a severe apnea event occurs.
In some implementations, the pressure is increased in response to a severe apnea event if the user is experiencing elevated stress levels during the sleep session generally. Once the severe apnea event ends, the pressure can be decreased back to its standard level. In other implementations, the pressure is increased in response to a severe apnea event if the user experiences elevated stress levels during the severe apnea event. Once the severe apnea event ends and/or the user's stress levels return to normal, the pressure can be decreased back to its standard level. In further implementations, this increase in pressure could occur during mild apnea events, not just severe apnea events. Thus, in some implementations, the respiratory therapy system can increase the pressure of the air delivered to the user's airway in response to an apnea event (severe or mild), but only if the second metric indicates that the user's stress levels are increased. If the second metric does not indicate that the user's stress levels are elevated during the apnea event, the respiratory therapy system can continue operating at its standard pressure(s).
Referring to
Step 602 of method 600 includes generating data during a current sleep session associated with a user of a respiratory therapy system. Step 602 of method 600 is the same as or similar to method 502 of method 500. Step 604 includes receiving historical data associated with one or more prior sleep sessions. Generally, the historical data can be the same type as the data generated during the current sleep session, except that the historical data is associated with one or more prior sleep sessions of the user.
Step 606 of method 600 includes analyzing the generated data to determine the value of a metric associated with a health condition of the user other than SDB. Step 606 of method 600 is the same as or similar to step 506 of method 500. Thus, the metric for which the value is determined can be the second metric for which the value is determined in step 506. Step 608 of method 600 includes analyzing the historical data to determine a value of the metric for each of the one or more prior sleep sessions. Step 610 of method 600 includes comparing the determined value of the metric for the one or more prior sleep sessions to the determined value of the metric for the current sleep session. The comparison can reveal whether the severity of the condition in the user increased or decreased over time.
Finally, step 612 of method 600 includes causing an action to be performed based at least in part on the comparison. If the comparison of step 610 indicates that the severity of the health condition has increased, a variety of different actions can be taken. In some implementations, a notification can be sent to the user or a third party (such as a friend, family member, spouse, partner, caretaker, or healthcare provider). The notification can provide information about the increased severity of the health condition. The notification can be displayed to the user on an electronic display device, such as user device 170. In another implementation, one or more settings of the respiratory therapy system can be modified to better treat the health condition.
In yet another implementation, a recommendation for future therapy can be transmitted to the user. The recommendation for future therapy can include a recommendation to visit a doctor or a dentist, a recommendation to undergo a medication regimen or a medical regimen, a recommendation to modify settings of the respiratory therapy system, a recommendation to use a separate device to treat the health condition (such as using a portable oxygen concentrator), or a recommendation to use a separate device to confirm the increased severity of the health condition.
The separate device can be any device that may be used to confirm the increased severity. For example, the device may be a medical measurement device that the user can use to take a measurement of some property or parameter that is indicative of the severity of the health condition. The separate device can include a pulse oximeter configured to measure an oxygen saturation of the user, a blood pressure monitor configured to monitor a blood pressure of the user, a heart rate monitor configured to measure a heart rate of the user, a blood glucose meter configured to measure a blood glucose level of the user, an electroencephalography (EEG) device configured to measure brain activity (to monitor central arousals and/or sleep stages during the sleep session), an electrooculography (EOG) device configured to detect eye movements during the sleep session, a wearable device to detect movements during the sleep session, a mattress sensor to detect movements during the sleep session, or other devices.
In still other implementations, the comparison between data from the current sleep session and the past sleep sessions can indicate that the severity of the health condition has increased, decrease, or has not changed, or has decreased. If the severity of the health condition has increased, the action can include recommending to the user that they adjust the settings and/or use of the respiratory therapy system to increase the intended therapy effect of the respiratory therapy system, commensurate with the increased severity of the health condition. If the severity of the health condition has decreased or stayed the same, the action can include recommending to the user that they either continue using the respiratory therapy system with the current settings, or to adjust settings and/or use of the respiratory therapy system to decrease the intended therapy effect of the respiratory therapy system commensurate with the unchanged or decreased severity of the health condition. Other types of actions can also be taken.
In additional implementations, the data from the prior sleep session can be analyzed to predict the future status of various health conditions of the use. This can include predicting a suitable time in the future for any type of intervention or therapy change, and sending a notification and/or report regarding predict future intervention or therapy change to the user or to a third party. The prediction could also be continually updated as more data is obtained from each sleep session.
In some implementations, the historical data from the one or more prior sleep sessions is analyzed to track the user's stress levels over time, and the second metric includes a measure of the user's stress levels during the current sleep session. If a comparison indicates that the user's stress levels during sleep sessions are not decreased, the settings of the respiratory therapy system can be adjusted to aid in reducing the user's stress levels. For example, the settings of the respiratory therapy system could be adjusted to increase the ramp-up time of the pressurized air delivered to the user's airway or to decrease the pressure of the pressurized air delivered to the user's airway. Generally, when the user initially begins to use the respiratory therapy system at the beginning of the sleep session, the respiratory therapy system ramps up the pressure of the air supplied to the user's airway from a low initial pressure to a higher working pressure over a first time period (which could be 5 minutes, 10 minutes, 30 minutes, 1 hour, etc.). The time it takes for the pressure to increase from the initial pressure to the working pressure can be referred to as the ramp-up time. If the user's stress levels are elevated (for example in response to initially donning the user interface), the action can include increasing the ramp-up time of the respiratory therapy system, such that the pressure of the air supplied to the user increases from the initial pressure to the working pressure over a second time period that is greater than the first time period. The action could additionally or alternatively include decreasing the working pressure of the respiratory therapy system. In these implementations, the working pressure could be modified to be a modified working pressure that is less than the original working pressure.
In some implementations, the historical data can be used to determine the user's baseline stress levels during different types of apnea events, and the second metric can include a measurement of the user's stress levels during the current sleep session. If the second metric indicates that the user is experiencing elevated stress levels (as compared to the baseline) when experiencing certain apnea events, the pressure of the pressurized air delivered to the user's airway can be increased to aid in reducing the duration and/or severity of the apnea event, even if an apnea event of that duration and/or severity would normally be treated with an increased pressure. Similarly, if the second metric indicates that the user is not experiencing elevated stress levels when experiencing certain apnea events, the pressure of the pressurized air delivered to the user's airway can be kept constant, even if an apnea event of that duration and/or severity would normally be treated with an increased pressure.
While method 600 generally refers to comparing data from the current sleep session to data from prior sleep sessions, method 600 is generally applicable to any comparison between different sets of data generated during different sleep sessions. Thus, data generated during a current sleep session can also be compared to data generated during subsequent sleep sessions, in order to determine the value of the second metric for the current and subsequent sleep sessions, and take some action based at least in part on the comparison.
Referring to
Step 702 of method 700 includes generating data during a current sleep session that is associated with a user of the respiratory therapy system. Step 702 of method 700 is generally the same as or similar to step 602 of method 600 and/or step 502 of method 500. Step 704 of method 700 includes analyzing the generated data to determine the value of a first metric associated with sleep-disordered breathing (SDB). Step 704 of method 700 is generally the same as or similar to step 504 of method 500. Step 706 of method 700 includes analyzing the generated data to determine the value of a second metric that is associated with a health condition other than SDB. Step 706 of method 700 is generally the same as or similar to step 506 of method 500 and step 606 of method 600.
Step 708 of method 700 includes receiving additional data that is associated with the user while the user is awake. Generally, the additional data is associated with the user, but not associated with the user's use of the respiratory therapy system. This data can include demographic information such as an age of the user, a sex of the user, a gender of the user, a geographic location of the user, a height of the user, a weight of the user, a neck size of the user, and an occupation of the user; medical information associated with the user, such as a smoking status of the user; audio data associated with the user, such as answers of the user to a questionnaire or other data related to the user speaking; and other types of additional data.
The questionnaire can include questions that are designed to elicit further information from the user about themselves. The questions can be related to the quality and/or amount of the user's sleep, the user's health, the user recent activities, the user's physical activity/exercise, the user's stress levels, the user's physical health, the user's mental health, other data, or any combination thereof. The data associated with the user's answers to the questionnaire can include the content of the user's answers.
However, in some implementations, the data can also include how the user actually answers the questions. Properties including the tone of the user's voice, the cadence of the user's speech, the structure of the provided answers (e.g., a full sentence versus single words), the speed and accuracy of the user's answers, etc., can provide insight into different conditions, including neurological conditions such as anxiety, depression, etc. For example, a faster cadence, a monotone/limited tone, high ratio of breathing sounds to voice during the answer, a large separation between words, and/or any tremors in the user's voice can indicate the presence of neurological conditions such as anxiety and/or depression.
In some implementations, the questions can be displayed to the user via the display device 128 of the respiratory therapy system 120, and/or the display device 172 of the user device 170. In other implementations, the questions can be transmitted to the user (e.g., played out loud) via the speaker 142. The user's answers to the questionnaire can be manually inputted (for example, via the display device 128 of the respiratory therapy system 128, or the user device 170). However, the user can also speak their answers aloud, and a sensor (such as microphone 140) can generate audio data indicative of the user's answers. The audio data is representative of properties or features in the user's answers indicative of health conditions of the user.
The additional data can also include audio data other than just the user's answers to the questionnaire. For example, the user could be recorded during the day (for example using microphone 142 or other devices). Audio data associated with the user's speech and other sounds made by the user (e.g., coughing, wheezing, etc.) can be indicative of a variety of different conditions, including respiratory conditions (e.g., asthma, pneumonia, allergies, respiratory infection, etc.) and neurological conditions (depression, anxiety, bi-polar disorder, etc.).
Step 710 of method 700 includes analyzing the received additional data (the data associated with the user while awake) to determine a value of a third metric that is associated with the health condition. Step 712 of method 700 includes causing an action to be performed based at least in part on the determined value of the third metric.
Generally, the third metric associated with the health condition is different than the second metric associated with the health condition. By determining the value of the third metric from the additional data associated with the user while awake, a more accurate representation of the health condition can be obtained, and an action can be taken.
In some implementations, the value of the second metric can be adjusted based at least in part on the value of the third metric. In these implementations, the precision of the determined value of the second metric may be limited when based at least in part on only the data associated with use of the respiratory therapy system. However, the value of the second metric may be more precisely determined by taking into account the data associated with the user while awake, and/or the value of the third metric.
In other implementations, the value of the second metric may indicate that the user could have a plurality of different health conditions. However, it may be impossible to determine which of the plurality of health conditions the user is actually suffering from, based at least in part on only the value of the second metric. The value of the third metric may provide more details, that enables which of the plurality of health conditions the user is suffering from to be determined. In still other implementations, the reverse may be true. The value of the third metric may indicate the possibility the user is suffering from a plurality of different health conditions, and the value of the second metric can be then used to determine which of the plurality of health conditions the user is suffering from.
In still other implementations, only the identity of the health condition the user is suffering from can be determined from the second metric (or the third metric). The value of the third metric (or the second metric) can then be used to determine the severity of the health condition.
Generally, any of methods 500, 600, and 700 can be implemented using a system having a control system with one or more processors, and a memory device storing machine readable instructions. The controls system can be coupled to the memory device, and methods 500, 600, and 700 can be implemented when the machine readable instructions are executed by at least one of the processors of the control system. Methods 500, 600, and 700 can also be implemented using a computer program product (such as a non-transitory computer readable medium) comprising instructions that when executed by a computer, cause the computer to carry out the steps of methods 500, 600, and 700.
One or more elements or aspects or steps, or any portion(s) thereof, from one or more of any of claims 1-125 below can be combined with one or more elements or aspects or steps, or any portion(s) thereof, from one or more of any of the other claims 1-125 or combinations thereof, to form one or more additional implementations and/or claims of the present disclosure.
While the present disclosure has been described with reference to one or more particular embodiments or implementations, those skilled in the art will recognize that many changes may be made thereto without departing from the spirit and scope of the present disclosure. Each of these implementations and obvious variations thereof is contemplated as falling within the spirit and scope of the present disclosure. It is also contemplated that additional implementations according to aspects of the present disclosure may combine any number of features from any of the implementations described herein.
This application claims the benefit of, and priority to, U.S. Provisional Patent Application No. 63/080,344 filed on Sep. 18, 2020, which is hereby incorporated by reference herein in its entirety.
| Filing Document | Filing Date | Country | Kind |
|---|---|---|---|
| PCT/IB2021/058513 | 9/17/2021 | WO |
| Number | Date | Country | |
|---|---|---|---|
| 63080344 | Sep 2020 | US |
