System and method for determining postural sleep disorder breathing state

Abstract

A method and system for determining a postural sleep disorder breathing (pSDB) state associated with a breathing apparatus user is disclosed. Airflow data associated with a user is received. The airflow data is analyzed to identify a first period of suspected wakefulness and a second period of suspected wakefulness. A first time segment between the identified first time period and the identified second time period is determined. Airflow data associated with the determined first time period is analyzed to identify (i) an indication of one or more respiratory events and / or (ii) an indication of one or more treatment events. A pSDB status of the user is determined based at least in part on (i) the identified indication of one or more respiratory events and / or (ii) the identified indication of one or more treatment events, where the pSDB status indicates whether the user has pSDB.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims the benefit of and priority to U.S. Provisional Patent Application No. 63 / 362,164, filed on March 30, 2022, which is hereby incorporated by reference in its entirety. Technical Field

[0003] The present disclosure relates generally to systems and methods for sleep monitoring, and more particularly, to systems and methods for determining a positional sleep disordered breathing (pSDB) state associated with a user. Background Art

[0004] Many individuals suffer from sleep-related and / or breathing disorders, such as sleep-disordered breathing (SDB), which may include obstructive sleep apnea (OSA), central sleep apnea (CSA), other types of apnea, such as mixed apnea and hypopnea, respiratory effort-related arousal (RERA) and snoring. In some cases, these disorders may manifest, or may be more obvious, when the individual is in a specific lying / sleeping position. These individuals may also suffer from other health symptoms (which may be referred to as comorbidities), such as insomnia (characterized by, for example, difficulty in starting sleep, frequent or long awakenings after initially falling asleep, and / or early awakenings that cannot restore sleep), periodic limb movement disorder (PLMD), restless legs syndrome (RLS), Cheyne-Stokes breathing (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 behavior (DEB), hypertension, diabetes, stroke and chest wall disorders.

[0005] These individuals are typically treated with a respiratory therapy system (e.g., a continuous positive airway pressure (CPAP) system) that delivers pressurized air to help prevent the individual's airway from narrowing or collapsing during sleep. However, some users find such systems uncomfortable, difficult to use, expensive, aesthetically unattractive, and / or do not realize the benefits associated with using the system. Therefore, when not using respiratory therapy, some users choose not to start using a respiratory therapy system, or stop using a respiratory therapy system without demonstrating the severity of their symptoms. In addition, some individuals who do not use a respiratory therapy system may not realize that they suffer from one or more sleep-related and / or breathing-related disorders. In addition, some users may only suffer from certain symptoms when sleeping in a specific position, and therefore need to detect disorders or symptoms associated with a specific position.

[0006] The present disclosure is directed to addressing these and other issues. Summary of the invention

[0007] According to some implementations of the present disclosure, a method and system for determining a positional sleep disordered breathing (pSDB) state associated with a user of a respiratory therapy device is disclosed as follows. Airflow data associated with a user of a respiratory device is received. The airflow data is analyzed to identify a first period of suspected awakening and a second period of suspected awakening. A first time segment between the identified first period and the identified second period is determined. The airflow data associated with the determined first time segment is analyzed to identify (i) an indication of one or more respiratory events, (ii) an indication of one or more therapeutic events, or (iii) both of the foregoing (i) and (ii). The pSDB state of the user is determined based at least in part on (i) an indication of one or more respiratory events identified, (ii) an indication of one or more therapeutic events identified, or (iii) both of the foregoing (i) and (ii), wherein the pSDB state indicates whether the user suffers from pSDB.

[0008] According to some implementations of the present disclosure, a method and system for determining a pSDB state associated with a user is disclosed as follows. Sensor data associated with the user is received. Such sensor data may include airflow data as described above and later herein. The sensor data is analyzed to identify a first period of suspected awakening and a second period of suspected awakening. A first time segment between the identified first period and the identified second period is determined. The sensor data associated with the determined first time segment is analyzed to identify an indication of one or more respiratory events. Based at least in part on the indication of the identified one or more respiratory events, a pSDB state of the user is determined, wherein the pSDB state indicates whether the user suffers from pSDB.

[0009] According to some implementations of the present disclosure, a system for determining a pSDB state is disclosed. The system includes a control system configured to implement any of the above methods.

[0010] According to some implementations of the present disclosure, a system includes a control system and a memory. The control system includes one or more processors. Machine-readable instructions are stored on the memory. The control system is coupled to the memory. When the machine-executable instructions in the memory are executed by at least one of the one or more processors of the control system, any one of the methods disclosed herein is implemented.

[0011] According to some implementations of the present disclosure, a computer program product includes instructions, which, when executed by a computer, cause the computer to perform any of the methods disclosed herein. In some implementations, the computer program product is a non-transitory computer-readable medium.

[0012] According to some implementations of the present disclosure, a system includes a respiratory therapy device, a memory storing machine-readable instructions, and a control system. The respiratory therapy device is configured to supply pressurized air to a user. The control system includes one or more processors configured to execute machine-readable instructions to receive airflow data associated with a user of the respiratory device. The control system is also configured to analyze the airflow data to identify a first period of suspected awakening and a second period of suspected awakening. The control system is also configured to determine a time segment between the identified first period and the identified second period. The control system is also configured to analyze the airflow data associated with the determined time segment to identify (i) an indication of one or more respiratory events, (ii) an indication of one or more treatment events, or (iii) both of (i) and (ii). Based at least in part on (i) the identified one or more respiratory events, (ii) the identified one or more treatment events, or (iii) both of the foregoing (i) and (ii), the control system is also configured to determine the user's positional sleep disordered breathing (pSDB) state, wherein the pSDB state indicates whether the user suffers from pSDB.

[0013] The above summary of the invention is not intended to represent every implementation or every aspect of the present disclosure. Additional features and benefits of the present disclosure are apparent from the following detailed description and accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] The foregoing and other advantages of the present disclosure will become apparent upon reading the following detailed description and referring to the accompanying drawings.

[0015] Figure 1 is a functional block diagram of a system for determining a positional sleep disordered breathing (pSDB) state associated with a user, according to some implementations of the present disclosure.

[0016] Figure 2 According to some implementations of the present disclosure Figure 1 A stereoscopic view of at least a portion of a system, a user, and a bed partner.

[0017] Figure 3 A flow chart of a method for determining pSDB status using airflow data according to some implementations of the present disclosure is shown.

[0018] Figure 4 A flow chart of a method for determining a pSDB state using sensor data according to some implementations of the present disclosure is shown.

[0019] Figure 5A The average heart rate of the first user before and after awakening is shown according to some implementations of the present disclosure.

[0020] Figure 5BThe average heart rate before and after awakening of the second user is shown in accordance with some implementations of the present disclosure.

[0021] Although the present disclosure is susceptible to various modifications and alternative forms, its specific implementation and implementation have been shown in the accompanying drawings by way of example and will be described in detail herein. However, it should be understood that it is not intended to limit the present disclosure to the specific form disclosed, but rather, the present disclosure should cover all modifications, equivalents and alternatives that fall within the spirit and scope of the present disclosure defined by the appended claims. DETAILED DESCRIPTION

[0022] The present disclosure is described with reference to the accompanying drawings, wherein the same reference numerals are used throughout the drawings to represent similar or equivalent elements. These drawings are not drawn to scale and are only used to illustrate the present disclosure. Several aspects of the present disclosure are described below with reference to exemplary applications for illustration.

[0023] Many individuals suffer from sleep-related and / or breathing disorders, such as sleep-disordered breathing (SDB), such as obstructive sleep apnea (OSA), central sleep apnea (CSA) and other types of apnea, respiratory effort-related arousals (RERA), snoring, Cheyne-Stokes respiration (CSR), respiratory insufficiency, obesity hyperventilation syndrome (OHS), chronic obstructive pulmonary disease (COPD), periodic limb movement disorder (PLMD), restless legs syndrome (RLS), neuromuscular disease (NMD) and chest wall disease. Obstructive sleep apnea (OSA) is a type of sleep-disordered breathing (SDB) characterized by a combination of abnormally small upper airway and normal loss of muscle tone in the tongue, soft palate and posterior oropharyngeal wall area during sleep, resulting in upper airway obstruction or obstruction. Central sleep apnea (CSA) is another form of sleep-disordered breathing. CSA occurs when the brain temporarily stops sending signals to the muscles that control breathing. Other types of apnea include hypopnea, hyperpnea and hypercapnia. In contrast to airway obstruction, hypopnea is typically characterized by slow or shallow breathing caused by airway narrowing. Hyperpnea is typically characterized by increased breathing depth and / or rate. Hypercapnia is typically characterized by elevated or excessive carbon dioxide levels in the bloodstream, typically caused by hypopnea. Respiratory effort-related arousal (RERA) events are typically characterized by increased respiratory effort for ten seconds or longer, resulting in arousal from sleep, and which do not meet the criteria for apnea or hypopnea events. RERA is defined as a series of breaths characterized by increased respiratory effort, resulting in arousal from sleep, but which do not meet the criteria for apnea or hypopnea. These events must meet the following two criteria: (1) a gradually increasing esophageal negative pressure pattern, ending with a sudden change in pressure to a lower negative pressure level and arousal, and (2) the event lasts for ten seconds or longer. In some implementations, the nasal cannula / pressure transducer system is sufficient and reliable in detecting RERA. The RERA detector can be based on a true airflow signal derived from a respiratory therapy device. For example, a measure of airflow limitation can be determined based on the airflow signal. The arousal measure may then be derived from the airflow limitation measure and the ventilation surge measure.One such method is described in WO 2008 / 138040 and US Pat. No. 9,358,353, assigned to ResMed Ltd., the disclosures of each of which are hereby incorporated herein by reference in their entirety.

[0024] Cheyne-Stokes respiration (CSR) is another form of SDB. CSR is a disorder of the patient's respiratory controller in which there are rhythmic alternating periods of ebb and flow of ventilation, called CSR cycles. CSR is characterized by repeated deoxygenation and reoxygenation of arterial blood. OHS is defined as a combination of severe obesity and chronic hypercapnia in wakefulness without other known causes of hypoventilation. Symptoms include dyspnea, morning headaches, and excessive daytime sleepiness. COPD includes any of a group of lower airway diseases with certain common characteristics, such as increased resistance to air movement, prolonged expiratory phase of breathing, and loss of normal elasticity of the lungs. NMDs include many diseases and conditions that impair muscle function directly via intrinsic muscle pathology or indirectly via neuropathology. Chest wall disorders are a group of chest deformities that result in inefficient coupling between the respiratory muscles and the thorax.

[0025] Many of these disorders are characterized by specific events that may occur while an individual is sleeping (e.g., snoring, apnea, hypopnea, restless legs, sleep disturbances, choking, increased heart rate, difficulty breathing, asthma attacks, seizures, epilepsy, or any combination thereof).

[0026] Individuals with diabetes who concurrently use a respiratory therapy system (e.g., to treat SDB) may experience positive and / or negative interactions. For example, use of a respiratory therapy system may impact the effectiveness of the individual's diabetes treatment plan (which may include a diabetes medication plan, a diet plan, an exercise plan, etc.). The impact on the effectiveness of the individual's diabetes treatment plan may be positive or negative, and thus it may be difficult for these individuals to adhere to the respiratory therapy plan while using a respiratory therapy system while also adhering to a diabetes treatment plan that remains in effect. Therefore, it would be advantageous to monitor these individuals and make various adjustments to their diabetes treatment plan and use of a respiratory therapy system to mitigate, optimize, etc., any interactions between the diabetes treatment plan and their respiratory therapy plan.

[0027] 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 a sleep session by the total number of hours of sleep in the sleep session. For example, the event can be an apnea that lasts at least 10 seconds. An AHI of less than 5 is considered normal. An AHI greater than or equal to 5 but less than 15 is considered to indicate mild sleep apnea. An AHI greater than or equal to 15 but less than 30 is considered to indicate moderate sleep apnea. An AHI greater than or equal to 30 is considered to indicate severe sleep apnea. In children, an AHI 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 conjunction with oxygen desaturation levels to indicate the severity of obstructive sleep apnea.

[0028] Everyone has their own sleeping preferences. Whether they sleep completely flat (i.e., in a horizontal position), reclined, or sitting upright; or on their stomach (i.e., in a prone position), on their back (in a supine position), or on their left or right side.

[0029] When an individual lies down, the breathing conditions of his body are different from those when the individual stands. When an individual sits or stands, the individual's airway usually points downward, making breathing and airflow relatively unrestricted. However, when an individual lies down to sleep, the individual's body is forced to breathe in a substantially horizontal position, which means that gravity now acts on the airway. Sleep apnea and snoring may occur when the muscle tissue of the upper airway (or other muscles such as the soft palate, tongue, etc.) relaxes and narrows the airway, and the air breathed by the individual's lungs via the nose or throat is limited. Although the breathing process at night is the same, the tissue around the individual may vibrate, causing the individual to snore. Sometimes relaxed muscles may cause sleep apnea because some airway blockages completely hinder breathing, forcing the individual to wake up in sleep. Therefore, sleeping in a position that best supports the individual's breathing pattern may be beneficial to the individual. For example, some individuals may benefit from sleeping in a reclined position rather than being completely horizontal relative to the ground, or sleeping on the right or left side instead of a supine position.

[0030] Sleeping in the supine position can often be problematic for those with snoring problems, breathing problems or sleep apnea. This occurs because gravity enhances the ability of the jaw, tongue and soft palate to drop back toward the throat. This can narrow or collapse the airway, which can lead to partial or complete cessation of breathing, or other breathing difficulties, snoring, etc.

[0031] Sleeping on your stomach may seem like an alternative to gravity issues, as the downward force pulls the tongue and palate forward. While this is true to a certain extent, when sleeping in this position, the individual's nose and mouth may be blocked by pillows or other bedding, which may affect the individual's breathing. In addition to this, it may also cause neck pain, neck problems, or digestive issues, which in turn affects the individual's sleep quality.

[0032] Some studies have shown that side sleeping may be the most ideal position for people with snoring and sleep apnea. This is because when an individual lies on their side during rest, the airway is more stable and less likely to collapse or restrict airflow. In this position, the individual's body, head, and torso are placed on one side (left or right), the arms are placed under the body or slightly forward or stretched, and the legs are placed one under the other or slightly staggered. Although both sides (left and right) are considered good sleeping positions, the left side position may not be ideal for some people. This is because the internal organs of the body on the chest may face some movement when sleeping on the left side. And the lungs may increase the weight or pressure on the heart. This may affect the function of the heart and retaliate by activating the kidneys, resulting in an increased need to urinate at night. However, the right side puts less pressure on important organs such as the lungs and heart. Sleeping on a specific side may also be ideal if the joints on the other side of the individual (usually the shoulders or hips) cause pain.

[0033] When an individual suffers from sleep apnea or other breathing disorders, getting a good and restful sleep becomes difficult. However, choosing the right sleeping position can help the user feel comfortable and at the same time help overcome or alleviate the breathing problems that individuals commonly face while sleeping. Therefore, according to some implementations of the present disclosure, systems and methods are provided to allow users to change their positions when sleeping in an undesirable body or head position (e.g., supine). Positional therapy can not only provide treatment for users with mild OSA, but also provide a more comfortable and effective option for users who have already received other treatments.

[0034] Studies have also shown that compared with non-pOSA patients, patients with positional OSA (pOSA) have a more posteriorly positioned mandible, lower facial height, longer posterior airway space measurements, and smaller pharyngeal lateral wall tissue volume. Such characteristics of pOSA patients result in larger lateral diameters and elliptical shapes of the upper airway. In addition, pOSA patients tend to have smaller neck circumferences. Therefore, it is suggested that even though the anterior-posterior diameters of pOSA patients and non-pOSA patients are reduced due to the effects of gravity in the supine position, the lateral diameters of pOSA patients are larger, so the airway space can be fully preserved and complete upper airway collapse can be avoided. Therefore, it is advantageous to predict and / or diagnose patients with pOSA and generate treatment plans and / or adjust treatment parameters accordingly. In some implementations, the user's body position is considered when formulating such a treatment plan and / or adjusting such treatment parameters. In some implementations, one or more steps of the method disclosed herein can be incorporated into an application that integrates prediction, screening, diagnosis, and / or treatment.

[0035] refer to Figure 1, shows a system 100 according to some implementations of the present disclosure. The system 100 includes a control system 110, a memory device 114, an electronic interface 119, one or more sensors 130, and optionally one or more user devices 170. In some implementations, the system 100 also includes a respiratory therapy system 120 (which includes a respiratory therapy device 122), a blood pressure device 180, an activity tracker 190, or any combination thereof. The system 100 can be used to monitor an individual using a respiratory therapy system, who may or may not have a pSDB, such as pOSA, positional CSA and other types of positional apnea, positional RERA, positional snoring, positional CSR, positional respiratory insufficiency, positional OHS, positional COPD, etc.

[0036] The control system 110 includes one or more processors 112 (hereinafter referred to as processors 112). The control system 110 is generally used to control (e.g., actuate) 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. Although Figure 1 1 , one processor 112 is shown, but the control system 110 may include any suitable number of processors (e.g., one processor, two processors, five processors, ten processors, etc.), which may be in a single housing, or located remotely from each other. The control system 110 (or any other control system) or a portion of the control system 110, such as the processor 112 (or any other processor or portion of any other control system), may be used to perform one or more steps of any method described and / or claimed herein. The control system 110 may be coupled to and / or located within, for example, a housing of the user device 170, and / or within a housing of one or more of the sensors 130. The control system 110 may be centralized (within one such housing) or decentralized (within two or more such housings that are physically distinct). In such implementations that include two or more housings containing the control system 110, such housings may be located near and / or remotely from each other.

[0037] The memory device 114 stores machine-readable instructions that are executable by the processor 112 of the control system 110. The memory device 114 may be any suitable computer-readable storage device or medium, such as a random or serial access memory device, a hard drive, a solid-state drive, a flash memory device, etc. Figure 11 , but system 100 may include any suitable number of memory devices 114 (e.g., one memory device, two memory devices, five memory devices, ten memory devices, etc.). Memory device 114 may be coupled to and / or positioned within a housing of respiratory therapy device 122 of respiratory therapy system 120, within a housing of user device 170, within a housing of one or more of sensors 130, or any combination thereof. As with control system 110, memory device 114 may be centralized (within one such housing) or distributed (within two or more such housings that are physically distinct).

[0038] In some implementations, the memory device 114 stores a user profile associated with the user. The user profile may include, for example, demographic information associated with the user, biometric information associated with the user, medical information associated with the user, self-reported user feedback, sleep parameters associated with the user (e.g., sleep-related parameters recorded from one or more early sleep sessions), or any combination thereof. The demographic information may include, for example, information indicating the user's age, the user's gender, the user's race, a family medical history (such as a family history of insomnia or sleep apnea), the user's employment status, the user's educational status, the user's socioeconomic status, or any combination thereof. The medical information may include, for example, information indicating one or more medical conditions associated with the user, the user's medication use, or both. The medical information data may also include 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. Self-reported user feedback may include information indicating a self-reported subjective sleep score (e.g., poor, fair, excellent), a self-reported user subjective stress level, a self-reported user subjective fatigue level, a self-reported user subjective health status, life events recently experienced by the user, or any combination thereof.

[0039] The electronic interface 119 is configured to receive data (e.g., physiological data and / or acoustic data) from one or more sensors 130 so 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, a cellular network, any other optical communication protocol, etc.). The electronic interface 119 may include an antenna, a receiver (e.g., an RF receiver), a transmitter (e.g., an RF transmitter), a transceiver, or any combination thereof. The electronic interface 119 may also include one or more processors and / or one or more memory devices that are the same or similar to the processor 112 and the memory device 114 described herein. In some implementations, the electronic interface 119 is coupled to the user device 170 or integrated in the user device. In other implementations, the electronic interface 119 is coupled to the control system 110 and / or the memory device 114 or integrated with the control system and / or the memory device (e.g., in a housing).

[0040] As described above, in some implementations, the system 100 optionally includes a respiratory therapy system 120 (also referred to as a respiratory pressure therapy system). The respiratory therapy system 120 may 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 patient interface), a conduit 126 (also referred to as a tube or 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 supply of air to the airway entrance of a user at a controlled target pressure that is nominally positive relative to atmosphere throughout the user's respiratory cycle (e.g., as opposed to negative pressure therapy such as a tank ventilator or chestpiece). Respiratory therapy system 120 is typically used to treat individuals with one or more sleep-related breathing disorders (e.g., obstructive sleep apnea, central sleep apnea, or mixed sleep apnea), other respiratory diseases (such as COPD), or other conditions that cause respiratory insufficiency, which may occur during sleep or wakefulness.

[0041] The respiratory therapy device 122 is typically used to generate pressurized air that is delivered to the user (e.g., using one or more engines (such as a blower engine) that drive one or more compressors). In some implementations, the respiratory therapy device 122 generates a continuous constant air pressure that is delivered to the user. In other implementations, the respiratory therapy device 122 generates two or more predetermined pressures (e.g., a first predetermined air pressure and a second predetermined air pressure). In yet 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 of H2O, at least about 10 cm of H2O, at least about 20 cm of H2O, between about 6 cm of H2O and about 10 cm of H2O, between about 7 cm of H2O and about 12 cm of H2O, and the like. The respiratory therapy device 122 can also deliver pressurized air at a predetermined flow rate, for example, between about -20 L / min and about 150 L / min, while maintaining a positive pressure (relative to ambient pressure). In some implementations, control system 110 , memory device 114 , electronic interface 119 , or any combination thereof, may be coupled to and / or positioned within a housing of respiratory therapy device 122 .

[0042] 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 help prevent the airway from narrowing and / or collapsing during sleep. This may also increase the user's oxygen intake during sleep. Depending on the treatment to be applied, the user interface 124 can, for example, form a seal with an area or portion of the user's face to facilitate delivery of gas at a pressure sufficiently different from ambient pressure (e.g., at a positive pressure of approximately 10 cm of H2O relative to ambient pressure) to achieve treatment. For other forms of treatment, such as oxygen delivery, the user interface may not include a seal sufficient to facilitate delivery of a gas supply to the airway at a positive pressure of approximately 10 cm of H2O.

[0043] In some implementations, the user interface 124 is or includes a mask that covers the user's nose and mouth (e.g., Figure 2 ). Alternatively, the user interface 124 is or includes a nasal mask that provides air to the user's nose or a nasal pillow mask that delivers air directly to the user's nostrils. The user interface 124 may include a belt assembly having a plurality of straps (e.g., including hook-and-loop fasteners) for positioning and / or stabilizing the user interface 124 or a portion of the user interface 124 at a desired location (e.g., on the face) of the user, and a conformal pad (e.g., silicone, plastic, foam, etc.) that helps provide an airtight seal between the user interface 124 and the user.

[0044] The conduit 126 allows air to flow between two components of the respiratory therapy system 120, such as the respiratory therapy device 122 and the user interface 124. In some implementations, there may be separate conduit limbs for inspiration and exhalation. In other implementations, a single limb conduit is used for both inspiration and exhalation. Typically, the respiratory therapy system 120 forms an air pathway extending between the engine of the respiratory therapy device 122 and the user and / or the user's airway. Therefore, the air pathway typically includes at least the engine of the respiratory therapy device 122, the user interface 124, and the conduit 126.

[0045] 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 may include one or more sensors (e.g., a pressure sensor, a flow rate sensor, or more generally, any other sensor 130 described herein). For example, these one or more sensors may be used to measure the air pressure and / or flow rate of the pressurized air supplied by the respiratory therapy device 122.

[0046] Display device 128 is generally used to display images, including still images, video images, or both, and / or information about respiratory therapy device 122. For example, display device 128 may provide information about the status of respiratory therapy device 122 (e.g., whether respiratory therapy device 122 is on / off, the pressure of air delivered by respiratory therapy device 122, the temperature of air delivered by respiratory therapy device 122, etc.) and / or other information (e.g., a sleep score or a therapy score (such as my sleep score). The display device 128 may include a user's input device, a score, such as described in WO 2016 / 061629 and US2017 / 0311879, each of which is hereby incorporated by reference in its entirety), the current date / time, the user's personal information, the user's questionnaire, etc. In some implementations, the display device 128 acts as a human-machine interface (HMI) that includes a graphical user interface (GUI) configured to display an image as an input interface. The display device 128 may be an LED display, an OLED display, an LCD display, etc. The input interface may be, for example, a touch screen or touch-sensitive substrate, a mouse, a keyboard, or any sensor system configured to sense input made by a human user interacting with the respiratory therapy device 122.

[0047] The humidification tank 129 is coupled to the respiratory therapy device 122 or integrated in the respiratory therapy device and includes a water reservoir that can be used to humidify the pressurized air delivered from the respiratory therapy device 122. The respiratory therapy device 122 may include a heater to heat the water in the humidification tank 129, thereby humidifying the pressurized air provided to the user. In addition, in some implementations, the conduit 126 may also include a heating element (e.g., coupled to the conduit 126 and / or embedded in the conduit) that heats the pressurized air delivered to the user. The humidification tank 129 can be fluidly coupled to the water vapor inlet of the air passage and deliver water vapor to the air passage via the water vapor inlet, or can be formed in line with the air passage as part of the air passage itself. In other implementations, the respiratory therapy device 122 or the conduit 126 may include a waterless humidifier. The waterless humidifier can be combined with a sensor that interfaces with other sensors located elsewhere in the system 100.

[0048] For example, the respiratory therapy system 120 can be used 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 to the user (e.g., determined by a sleep physician). The APAP system automatically changes the air pressure delivered to the user based at least in part on, for example, respiratory data associated with the user. The BPAP or VPAP system is configured to deliver a first predetermined pressure (e.g., inspiratory positive airway pressure or IPAP) and a second predetermined pressure (e.g., expiratory positive airway pressure or EPAP) that is lower than the first predetermined pressure.

[0049] refer to Figure 2 , showing a system 100 ( Figure 1 ) part of the respiratory therapy system 120. A user 210 and a bed partner 220 of the respiratory therapy system 120 are positioned on a bed 230 and lying on a mattress 232. A user interface 124 (e.g., a full mask) can be worn by the user 210 during a sleep session. The user interface 124 is fluidly coupled and / or connected to the respiratory therapy device 122 via a conduit 126. In turn, the respiratory therapy device 122 delivers pressurized air to the user 210 via the conduit 126 and the user interface 124 to increase the air pressure in the throat of the user 210, thereby helping to prevent the airway from closing and / or narrowing during sleep. The respiratory therapy device 122 may include a display device 128, which may allow a user to interact with the respiratory therapy device 122. The respiratory therapy device 122 may also include a humidification tank 129, which stores water for humidifying the pressurized air. As Figure 2As shown, respiratory therapy device 122 can be positioned on a bedside table 240 directly adjacent to bed 230, or more generally, on any surface or structure generally adjacent to bed 230 and / or user 210. The user can also wear blood pressure device 180 and activity tracker 190 while lying on mattress 232 on bed 230.

[0050] Return to reference Figure 1 The one or more sensors 130 of the system 100 include a pressure sensor 132, a flow rate sensor 134, a temperature sensor 136, a motion sensor 138, a microphone 140, a speaker 142, a radio frequency (RF) receiver 146, an RF transmitter 148, a camera 150, an infrared (IR) sensor 152, a phototransmissive smoke map (PPG) sensor 154, an electrocardiogram (ECG) sensor 156, an electroencephalogram (EEG) sensor 158, a capacitive sensor 160, a force sensor 162, a strain gauge sensor 164, an electromyogram (EMG) sensor 166, an oxygen sensor 168, an analyte sensor 174, a humidity sensor 176, a light detection and ranging (LiDAR) sensor 178, a blood glucose monitor 182, or any combination thereof. In general, each of the one or more sensors 130 is configured to output sensor data, which is received and stored in the memory device 114 or one or more other memory devices. The sensor 130 may also include an electrooculogram (EOG) sensor, a peripheral oxygen saturation (SpO2) sensor, a galvanic skin response (GSR) sensor, a carbon dioxide (CO2) sensor, or any combination thereof.

[0051] Although one or more sensors 130 are shown and described as including each of pressure sensor 132, flow rate sensor 134, temperature sensor 136, motion sensor 138, microphone 140, speaker 142, RF receiver 146, RF transmitter 148, camera 150, IR sensor 152, PPG sensor 154, ECG sensor 156, EEG sensor 158, capacitive sensor 160, force sensor 162, strain gauge sensor 164, EMG sensor 166, oxygen sensor 168, analyte sensor 174, humidity sensor 176, and LiDAR sensor 178, more generally, one or more sensors 130 may include any combination and any number of each sensor described and / or shown herein.

[0052] The one or more sensors 130 may be used to generate, for example, physiological data, acoustic data, or both, which is associated with a user of the respiratory therapy system 120, such as Figure 2The physiological data generated by one or more of the sensors 130 may be used by the control system 110 to determine a sleep wakefulness signal and one or more sleep-related parameters associated with the user during a sleep session. The sleep wakefulness signal may indicate one or more sleep stages (sometimes referred to as sleep states), including sleep, wakefulness, relaxed wakefulness, micro-arousals, or different sleep stages, such as a rapid eye movement (REM) stage (which may include both typical REM stages and atypical REM stages), a first non-REM stage (commonly referred to as “N1”), a second non-REM stage (commonly referred to as “N2”), a third non-REM stage (commonly referred to as “N3”), or any combination thereof. For example, methods of determining sleep stages based on physiological data generated by one or more of sensors (such as sensor 130) are described in WO 2014 / 047310, US10,492,720, US10,660,563, US2020 / 0337634, WO 2017 / 132726, WO 2019 / 122413, US2021 / 0150873, WO 2019 / 122414, US2020 / 0383580, each of which is hereby incorporated by reference in its entirety. Another method of determining sleep stages based on airflow data generated by one or more of sensors (such as pressure sensor 132 and / or flow rate sensor 134) is described in WO2022 / 091005A1, which is hereby incorporated by reference in its entirety.

[0053] The sleep wake signal may also be time stamped to indicate the time the user entered the bed, the time the user left the bed, the time the user attempted to fall asleep, etc. During a sleep session, the sleep wake signal of one or more of the sensors 130 may be measured at a predetermined sampling rate (e.g., one sample per second, one sample per 30 seconds, or one sample per minute, etc.). During a sleep session, examples of one or more sleep-related parameters that may be determined for the user based at least in part on the sleep wake signal include total time in bed, total sleep time, total wake time, sleep onset delay, wake time parameter after sleep onset, sleep efficiency, fragmentation index, sleep onset time, consistency of breathing rate, sleep onset time, wake time, sleep disturbance rate, number of movements, or any combination thereof.

[0054] Physiological data and / or acoustic data generated by one or more sensors 130 may also be used to determine a respiratory signal associated with the user during a sleep session. The respiratory signal generally indicates the user's breathing or respiration during a sleep session. The respiratory signal may indicate, for example, respiratory rate, respiratory rate variability, inspiratory amplitude, expiratory amplitude, inspiratory expiratory amplitude ratio, inspiratory expiratory duration ratio, number of events per hour, event pattern, pressure setting of the respiratory therapy device 122, or any combination thereof. Events may include snoring, apnea, central apnea, obstructive apnea, mixed apnea, hypopnea, RERA, airflow limitation (e.g., events in which airflow is not increased despite an increase in negative intrathoracic pressure, indicating an increase in workload), mask leak (e.g., from the user interface 124), restless legs, sleep disorders, choking, increased heart rate, heart rate variability, dyspnea, asthma attack, seizure, epilepsy, fever, coughing, sneezing, snoring, wheezing, the presence of illnesses such as the common cold or flu, increased stress levels, etc. Events may be detected by any means known in the art, such as described, for example, in US 5,245,995, US 6,502,572, WO 2018 / 050913, WO 2020 / 104465, each of which is incorporated herein by reference in its entirety.

[0055] 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., an atmospheric pressure sensor) that generates sensor data indicating the breathing (e.g., inhalation and / or exhalation) and / or ambient pressure of a user of the respiratory therapy system 120. In such an implementation, the pressure sensor 132 can be coupled to the respiratory therapy device 122 or integrated in the respiratory therapy device. 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 the blood pressure of the user.

[0056] 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 the air flow rate from the respiratory therapy device 122, the air flow rate through the conduit 126, the 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 into 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 a rotary flow meter (e.g., a Hall effect flow meter), a turbine flow meter, an orifice flow meter, an ultrasonic flow meter, a hot wire sensor, a vortex sensor, a membrane sensor, or any combination thereof.

[0057] The temperature sensor 136 outputs temperature data that may be stored in the memory device 114 and / or analyzed by the processor 112 of the control system 110. In some implementations, the temperature sensor 136 generates temperature data indicative of the core body temperature of the user, the skin temperature of the user 210, the temperature of the air flowing from the respiratory therapy device 122 and / or through the conduit 126, the temperature in the user interface 124, the ambient temperature, or any combination thereof. The temperature sensor 136 may be, for example, a thermocouple sensor, a thermistor sensor, a silicon bandgap temperature sensor or a semiconductor-based sensor, a resistance temperature detector, or any combination thereof.

[0058] The motion sensor 138 outputs motion data that can be stored in the memory device 114 and / or analyzed by the processor 112 of the control system 110. The motion sensor 138 can be used to detect the movement of the user during a sleep session, and / or detect the movement of any component of the respiratory therapy system 120 (such as the respiratory therapy device 122, the user interface 124, or the catheter 126). The motion sensor 138 may 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 user's face and near the user interface 124), and is configured to detect characteristics of pulse shape, speed, amplitude, or volume. In some implementations, the motion sensor 138 generates one or more signals representing the user's body movement, for example, via the user's respiratory movement, from which a signal representing the user's sleep stage / state can be obtained.

[0059] The microphone 140 outputs acoustic data that can be stored in the memory device 114 and / or analyzed by the processor 112 of the control system 110. The acoustic data generated by the microphone 140 can be reproduced as one or more sounds (e.g., sounds from the user, sounds associated with the user's movements, components of the respiratory therapy system (e.g., a catheter), or both) during a sleep session to determine (e.g., using the control system 110) one or more sleep-related parameters, such as the user's arousal, as described in further detail herein. The acoustic data from the microphone 140 can also be used (e.g., using the control system 110) to identify events experienced by the user during the sleep session, as described in further detail herein. In an implementation, the acoustic data from the microphone 140 represents noise associated with the respiratory therapy system 120. In some implementations, the system 100 includes multiple microphones (e.g., two or more microphones and / or a microphone array with beamforming) such that the sound data generated by each of the multiple microphones can be used to distinguish the sound data generated by another of the multiple microphones. Microphone 140 can be generally coupled to respiratory therapy system 120 (or system 100) or integrated therein in any configuration. For example, microphone 140 can be arranged inside respiratory therapy device 122, user interface 124, conduit 126 or other components. Microphone 140 can also be positioned adjacent to or coupled to the outside of respiratory therapy device 122, the outside of user interface 124, the outside of conduit 126 or the outside of any other component. Microphone 140 can also be a component of user device 170 (for example, microphone 140 is the microphone of a smart phone). Microphone 140 can be integrated in user interface 124, conduit 126, respiratory therapy device 122 or any combination. Generally, microphone 140 can be located at any point in or near the air passage of respiratory therapy system 120, which air passage includes at least the engine, user interface 124 and conduit 126 of respiratory therapy device 122. Therefore, air passage can also be referred to as acoustic passage.

[0060] Speaker 142 outputs sound waves that are generally audible to a user. In one or more implementations, the sound waves may be audible to a user of system 100 or inaudible to a user of the system (e.g., ultrasound). Speaker 142 may be used, for example, as an alarm clock or to play an alarm or message to a user (e.g., in response to an event). In some implementations, speaker 142 may be used to transmit acoustic data generated by microphone 140 to a user. Speaker 142 may be coupled to or integrated into respiratory therapy device 122, user interface 124, conduit 126, or user device 170.

[0061] 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, for example, in WO 2018 / 050913 and WO 2020 / 104465, each of which is hereby incorporated herein by reference in its entirety. In such an implementation, the speaker 142 generates or emits sound waves at predetermined intervals and / or frequencies, and the microphone 140 detects reflections of the sound waves emitted from the speaker 142. The sound waves generated or emitted by the speaker 142 have a frequency that is inaudible to the human ear (e.g., below 20 Hz or above approximately 18 kHz) so as not to disturb the user or the user's bed partner (such as Figure 2 Based at least in part on data from microphone 140 and / or speaker 142, control system 110 may determine the position of the user and / or one or more sleep-related parameters described herein, such as a breathing signal, a breathing rate, an inspiratory amplitude, an expiratory amplitude, an inspiratory-expiratory ratio, a number of events per hour, an event pattern, a sleep stage, a pressure setting of a respiratory therapy device 122, a mouth leak state, or any combination thereof. In this case, the sonar sensor may be understood to involve active acoustic sensing, such as generating / transmitting ultrasonic or low-frequency ultrasonic sensing signals through the air (e.g., in a frequency range of about 17kHz to 23kHz, 18kHz to 22kHz, or 17kHz to 18kHz). Such a system may be considered in conjunction with WO 2018 / 050913 and WO 2020 / 104465 described above. In some implementations, speaker 142 is a bone conduction speaker. In some implementations, one or more sensors 130 include (i) a first microphone that is the same as or similar to microphone 140 and integrated into acoustic sensor 141, and (ii) a second microphone that is the same as or similar to microphone 140 but separate and different from the first microphone integrated into acoustic sensor 141.

[0062] The RF transmitter 148 generates and / or transmits radio waves having a predetermined frequency and / or predetermined amplitude (e.g., within a high frequency band, within a low frequency band, a long wave signal, a short wave signal, etc.). The RF receiver 146 detects reflections of the radio waves transmitted from the RF transmitter 148, and this data can be analyzed by the control system 110 to determine the user's position and / or one or more sleep-related parameters described herein. The RF receiver (RF receiver 146 and RF transmitter 148 or another RF pair) can also be used for wireless communication between the control system 110, the respiratory therapy device 122, one or more sensors 130, the user device 170, or any combination thereof. Although the RF receiver 146 and the RF transmitter 148 are in Figure 1146 and 148 are shown as separate and distinct elements, but in some implementations, the RF receiver 146 and the RF transmitter 148 are combined as part of the RF sensor 147 (e.g., a RADAR sensor). In some such implementations, the RF sensor 147 includes a control circuit. The specific format of the RF communication can be WiFi, Bluetooth, etc.

[0063] In some implementations, the RF sensor 147 is part of a mesh system. An example of a mesh system is a WiFi mesh system, which may include mesh nodes, mesh routers, and mesh gateways, each of which may be mobile / removable or fixed. In such an implementation, 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 includes an RF sensor that is the same or similar to the RF sensor 147. The WiFi router and the satellite communicate with each other continuously using WiFi signals. The WiFi mesh system can be used to generate motion data based at least in part on changes in WiFi signals between routers and satellites (e.g., differences in received signal strength) that partially obstruct signal generation due to the movement of an object or person. The motion data may indicate motion, breathing, heart rate, gait, falls, behavior, etc., or any combination thereof.

[0064] The camera 150 outputs image data that can be reproduced as one or more images (e.g., still images, video images, thermal images, or a combination thereof) that can be stored in the memory device 114. The control system 110 can use the image data from the camera 150 to determine one or more sleep-related parameters described herein. For example, the image data from the camera 150 can be used to identify the location of the user to determine whether the user has entered the user's bed (such as Figure 2 The camera 150 may also be used to track the user's position, which may affect the duration and / or severity of apnea episodes for a user with positional obstructive sleep apnea.

[0065] IR sensor 152 outputs infrared image data that can be reproduced as one or more infrared images (e.g., still images, video images, or both) that can be stored in memory device 114. Infrared data from IR sensor 152 can be used to determine one or more sleep-related parameters during a sleep session, including the user's temperature and / or the user's movement. IR sensor 152 can also be used in conjunction with camera 150 when measuring the user's presence, position, and / or movement. For example, IR sensor 152 can detect infrared light having a wavelength between about 700 nm and about 1 mm, while camera 150 can detect visible light having a wavelength between about 380 nm and about 740 nm.

[0066] IR sensor 152 outputs infrared image data that can be reproduced as one or more infrared images (e.g., still images, video images, or both) that can be stored in memory device 114. Infrared data from IR sensor 152 can be used to determine one or more sleep-related parameters during a sleep session, including the user's temperature and / or the user's movement. IR sensor 152 can also be used in conjunction with camera 150 when measuring the user's presence, position, and / or movement. For example, IR sensor 152 can detect infrared light having a wavelength between about 700 nm and about 1 mm, while camera 150 can detect visible light having a wavelength between about 380 nm and about 740 nm.

[0067] The PPG sensor 154 outputs physiological data associated with the user, which can be used to determine one or more sleep-related parameters, such as heart rate, heart rate pattern, heart rate variability, cardiac cycle, respiratory rate, inspiratory amplitude, expiratory amplitude, inspiratory-expiratory ratio, estimated blood pressure parameters, or any combination thereof. The PPG sensor 154 can be worn by the user, embedded in clothing and / or fabric worn by the user, embedded and / or coupled to the user interface 124 and / or its associated headband (e.g., belt, etc.), etc.

[0068] The ECG sensor 156 outputs physiological data associated with the electrical activity of the user's heart. In some implementations, the ECG sensor 156 includes one or more electrodes that are positioned on or around a portion of the user during a sleep session. For example, the physiological data from the ECG sensor 156 can be used to determine one or more sleep-related parameters described herein.

[0069] The EEG sensor 158 outputs physiological data associated with the electrical activity of the user's brain. In some implementations, the EEG sensor 158 includes one or more electrodes that are positioned on or around the user's scalp during a sleep session. For example, physiological data from the EEG sensor 158 can be used to determine the sleep stage of the user at any given time during a sleep session. In some implementations, the EEG sensor 158 can be integrated into the user interface 124 and / or an associated headband (e.g., a belt, etc.).

[0070] 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 sleep-related parameters described herein. The EMG sensor 166 outputs physiological data associated with the electrical activity generated by one or more muscles. The oxygen sensor 168 outputs oxygen data indicating the oxygen concentration of the 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 oximeter sensor, or any combination thereof.

[0071] The analyte sensor 174 can be used to detect the presence of an analyte in the user's exhaled gas. The data output by the analyte sensor 174 can be stored in the memory device 114 and used by the control system 110 to determine the identity and concentration of any analyte in the user's breath. In some implementations, the analyte sensor 174 is positioned near the user's mouth to detect the analyte in the breath exhaled from the user's mouth. For example, when the user interface 124 is a mask covering the user's nose and mouth, the analyte sensor 174 can be positioned in the mask to monitor the user's 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 user's nose to detect the analyte in the breath exhaled through the user's nose. In yet another implementation, when the user interface 124 is a nasal mask or a nasal pillow mask, the analyte sensor 174 can be positioned near the user's mouth. In this implementation, the analyte sensor 174 can be used to detect whether any air leaks inadvertently from the user's mouth. In some implementations, the analyte sensor 174 is a volatile organic compound (VOC) sensor, which can be used to detect carbon-based chemicals or compounds, such as carbon dioxide. In some implementations, the analyte sensor 174 can also be used to detect whether the user breathes through the nose or mouth. For example, if the data output by the analyte sensor 174 positioned near the user's mouth or within the mask (in implementations where the user interface 124 is a mask) detects the presence of an analyte, the control system 110 can use the data as an indication that the user is breathing through their mouth.

[0072] The humidity sensor 176 outputs data that can be stored in the memory device 114 and used by the control system 110. The humidity sensor 176 can be used to detect the humidity of various areas around the user (e.g., inside the conduit 126 or 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.). Therefore, in some implementations, the humidity sensor 176 can be coupled to or integrated into the user interface 124 or conduit 126 to monitor the humidity of the pressurized air from the respiratory therapy device 122. In other implementations, the humidity sensor 176 is placed near any area where the humidity level needs to be monitored. The humidity sensor 176 can also be used to monitor the humidity of the user's surrounding environment (e.g., the air in the user's bedroom). The humidity sensor 176 can also be used to track the user's biometric response to environmental changes.

[0073] One or more LiDAR sensors 178 can be used for depth sensing. This type of optical sensor (e.g., a laser sensor) can be used to detect objects and build a three-dimensional (3D) map of the surrounding environment (such as a living space). LiDAR can usually use a pulsed laser for time-of-flight measurement. LiDAR is also called 3D laser scanning. In an example using such a sensor, a fixed or mobile device (such as a smartphone) with a LiDAR sensor 178 can measure and map an area 5 meters or more away from the sensor. For example, LiDAR data can be fused with point cloud data estimated by an electromagnetic RADAR sensor. The LiDAR sensor 178 can also use artificial intelligence (AI) to automatically geo-fence the RADAR system by detecting and classifying features in the space that may cause problems for the RADAR system, such as glass windows (which may be highly reflective to the RADAR). For example, LiDAR can also be used to provide a person's height estimate, as well as changes in height when a person sits down or falls. LiDAR can be used to form a 3D mesh representation of the environment. In further use, for solid surfaces through which radio waves pass (e.g., radio translucent materials), LiDAR may reflect from such surfaces, allowing classification of different types of obstacles.

[0074] Blood glucose monitor 182 can be used to measure the glucose concentration in the user's blood. Blood glucose monitor 182 can be implemented in various different ways. In some implementations, blood glucose monitor 182 is an independent blood glucose monitor that analyzes blood samples (e.g., via optical analysis, electrochemical analysis, and / or other analytical techniques) to perform point measurements of the user's blood glucose (e.g., single-point time measurement). In other implementations, blood glucose monitor 182 is a continuous blood glucose monitor, also referred to as CGM. A continuous blood glucose monitor is capable of performing continuous measurements of the user's blood glucose. In some examples, a continuous blood glucose monitor includes a small needle that can be inserted under the user's skin (e.g., the skin of the user's upper arm) for continuous analysis of body fluid samples (e.g., blood, interstitial fluid, etc.) and measurement of the user's blood glucose (e.g., via optical analysis, electrochemical analysis, and / or other analytical techniques).

[0075] In yet another implementation, the blood glucose monitor 182 may include other types of devices and / or sensors (via point measurement and / or continuous measurement) for measuring the user's blood glucose. In one example, the blood glucose monitor 182 measures blood glucose through the user's skin or other body parts (e.g., via optical analysis techniques such as spectroscopy, polarization measurement, etc.). In another example, the blood glucose monitor 182 measures blood glucose via sweat. In another example, the blood glucose monitor 182 measures blood glucose via the user's breath, in which case the blood glucose monitor 182 may be the same or similar to the analyte sensor 174. Typically, the blood glucose monitor 182 may include any suitable number of blood glucose monitors. For example, in some implementations, the blood glucose monitor 182 of the system 100 may include only devices / sensors, such as time point blood glucose monitors or continuous blood glucose meters. In other implementations, the blood glucose monitor 182 of the system 100 may include multiple devices and / or sensors, such as continuous blood glucose meters and devices / sensors that measure the user's blood glucose via sweat analysis and / or breath analysis.

[0076] Although in Figure 1 124, the user interface 124, the catheter 126, the humidification tank 129, the control system 110, the user device 170, or any combination thereof. For example, the acoustic sensor 141 and / or the RF sensor 147 can be integrated into and / or coupled to the user device 170. In such an implementation, according to some aspects of the present disclosure, the user device 170 can be viewed as an auxiliary device that generates additional or auxiliary data for use by the system 100 (e.g., the control system 110). In some implementations, the pressure sensor 132 and / or the flow rate sensor 134 are integrated into and / or coupled to the respiratory therapy device 122. In some implementations, at least one of the one or more sensors 130 is not coupled to the respiratory therapy device 122, the control system 110, or the user device 170, and is generally positioned adjacent to the user during a sleep session (e.g., positioned on or in contact with a portion of the user, worn by the user, coupled to or positioned on a nightstand, coupled to a mattress, coupled to a ceiling, etc.). More generally, the one or more sensors 130 may be positioned at any suitable location relative to the user such that the one or more sensors 130 may generate physiological data associated with the user and / or bed partner 220 during one or more sleep sessions.

[0077] Data from one or more sensors 130 may be analyzed to determine one or more sleep-related parameters, which may include a respiratory signal, a respiratory rate, a respiratory pattern, an inspiratory amplitude, an expiratory amplitude, an inspiratory-expiratory ratio, the occurrence of one or more events, the number of events per hour, an event pattern, an average duration of an event, a range of event durations, a ratio between different numbers of events, a sleep stage, an apnea-hypopnea index (AHI), or any combination thereof. One or more events may include snoring, apnea, central apnea, obstructive apnea, mixed apnea, hypopnea, intentional user interface leakage, unintentional user interface leakage, mouth leakage, coughing, restless legs, sleep disorders, choking, increased heart rate, dyspnea, asthma attack, epileptic seizure, epilepsy, increased blood pressure, hyperventilation, or any combination thereof. Many of these sleep-related parameters are physiological parameters, although some sleep-related parameters may be considered non-physiological parameters. Other types of physiological and non-physiological parameters may also be determined based on data from one or more sensors 130 or based on other types of data.

[0078] User device 170 includes display device 172. For example, user device 170 may be a mobile device such as a smartphone, a tablet, a laptop, a game console, a smart watch, etc. Alternatively, user device 170 may be an external sensing system, a television (e.g., a smart TV), or another smart home device (e.g., a smart speaker such as Google Home TM , Google Nest TM , Amazon Echo TM , Amazon Echo Alexa support TM Devices, etc.). In some implementations, the user device 170 is a wearable device (e.g., a smart watch). The display device 172 is typically used to display images including still images, video images, or both. In some implementations, the display device 172 acts as a human-machine interface (HMI) that includes a graphical user interface (GUI) configured to display images and an input interface. The display device 172 can be an LED display, an OLED display, an LCD display, etc. The input interface can be, for example, a touch screen or touch-sensitive substrate, a mouse, a keyboard, or any sensor system configured to sense input made by a human user interacting with the user device 170. In some implementations, one or more user devices 170 can be used by the system 100 and / or included in the system.

[0079] Blood pressure device 180 is generally used to facilitate generation of physiological data to determine one or more blood pressure measurements associated with a user. Blood pressure device 180 may include at least one of one or more sensors 130 to measure, for example, a systolic pressure component and / or a diastolic pressure component.

[0080] In some implementations, blood pressure device 180 is a blood pressure monitor that includes an inflatable cuff that can be worn by a user and a pressure sensor (e.g., pressure sensor 132 described herein). Figure 2 As shown in the example of , the blood pressure device 180 can be worn on the upper arm of the user. In such an implementation where the blood pressure device 180 is a sphygmomanometer, the blood pressure device 180 also includes a pump (e.g., a manually operated light bulb) for inflating the cuff. In some implementations, the blood pressure device 180 is coupled to a respiratory therapy device 122 of the respiratory therapy system 120, which in turn delivers pressurized air to inflate the cuff. More generally, the blood pressure device 180 can be communicatively coupled and / or physically integrated (e.g., within a housing) with the control system 110, the memory device 114, the respiratory therapy system 120, the user device 170, and / or the activity tracker 190.

[0081] Activity tracker 190 is typically used to help generate physiological data to determine activity measurements associated with a user. Activity measurements may include, for example, number of steps, distance traveled, number of steps climbed, duration of physical activity, type of physical activity, intensity of physical activity, time standing, breathing rate, average breathing rate, resting breathing rate, maximum breathing rate, breathing rate variability, heart rate, average heart rate, resting heart rate, maximum heart rate, heart rate variability, number of calories burned, blood oxygen saturation, skin electrode activity (also known as skin conductance or skin electrode response), or any combination thereof. Activity tracker 190 includes one or more sensors 130 described herein, such as motion sensor 138 (e.g., one or more accelerometers and / or gyroscopes), PPG sensor 154, and / or ECG sensor 156.

[0082] In some implementations, activity tracker 190 is a wearable device that can be worn by a user, such as a smart watch, wristband, ring, or patch. Figure 2 , activity tracker 190 is worn on the wrist of the user. Activity tracker 190 may also be coupled to or integrated with one or more pieces of clothing worn by the user. Alternatively still, activity tracker 190 may also be coupled to user device 170 or integrated into the user device (e.g., within the same housing). More generally, activity tracker 190 may be communicatively coupled or physically integrated (e.g., within a housing) with control system 110, memory device 114, respiratory therapy system 120, user device 170, and / or blood pressure device 180.

[0083] Although the control system 110 and the memory device 114 are Figure 1 100 as separate and distinct components of system 100, but in some implementations, control system 110 and / or memory device 114 are integrated into user device 170 and / or respiratory therapy device 122. Alternatively, in some implementations, control system 110 or a portion thereof (e.g., processor 112) may be located in the cloud (e.g., integrated in a server, integrated in an Internet of Things (IoT) device, connected to the cloud, subject to edge cloud processing, etc.), located in one or more servers (e.g., remote servers, local servers, etc., or any combination thereof).

[0084] Although system 100 is shown as including all of the above components, more or fewer components may be included in the system for determining catheter length according to implementations of the present disclosure. For example, a first alternative system includes a control system 110, a memory device 114, and at least one of one or more sensors 130. As another example, a second alternative system includes a control system 110, a memory device 114, at least one of one or more sensors 130, and a user device 170. As yet another example, a third alternative system includes a control system 110, a memory device 114, a respiratory therapy system 120, at least one of one or more sensors 130, and a user device 170. As another example, a fourth alternative system includes a control system 110, a memory device 114, a respiratory therapy system 120, at least one of one or more sensors 130, a user device 170, and a blood pressure device 180 and / or an activity tracker 190. Therefore, any one or more portions of the components shown and described herein and / or in combination with one or more other components may be used to form various systems for modifying pressure settings.

[0085] Reference again Figure 2 In some implementations, any one of the control system 110, the memory device 114, the one or more sensors 130, or a combination thereof can be located on and / or in any surface and / or structure generally adjacent to the bed 230 and / or the user 210. For example, in some implementations, at least one of the one or more sensors 130 can be located at a first location on and / or in one or more components of the respiratory therapy system 120 adjacent to the bed 230 and / or the user 210. The one or more sensors 130 can be coupled to the respiratory therapy system 120, the user interface 124, the conduit 126, the display device 128, the humidification tank 129, or a combination thereof.

[0086] Alternatively or additionally, at least one of the one or more sensors 130 may be located at a second location on and / or in the bed 230 (e.g., the one or more sensors 130 are coupled to and / or integrated into the bed). Also, alternatively or additionally, at least one of the one or more sensors 130 may be located at a third location on and / or in the mattress 232 adjacent to the bed 230 and / or the user 210 (e.g., the one or more sensors 130 are coupled to and / or integrated into the mattress). Alternatively or additionally, at least one of the one or more sensors 130 may be located at a fourth location on and / or in the pillow generally adjacent to the bed 230 and / or the user 210.

[0087] Alternatively or additionally, at least one of the one or more sensors 130 can be located at a fifth location 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 location such that at least one of the one or more sensors 130 is coupled to and / or positioned on the user 210 (e.g., the one or more sensors 130 are embedded 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 located 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.

[0088] In some implementations, the primary sensor (such as microphone 140) is configured to generate acoustic data associated with the user 210 during a sleep session. The acoustic data can be based on, for example, acoustic signals in the conduit 126 of the respiratory therapy system 120. For example, one or more microphones (with Figure 1 The microphone 140 (same or similar to the microphone 140 of the respiratory therapy device 122) can be integrated into and / or coupled to (i) a circuit board of the respiratory therapy device 122, (ii) the conduit 126, (iii) a connector between components of the respiratory therapy system 120, (iv) the user interface 124, (v) a headband (e.g., a belt) associated with the user interface, or (vi) a combination of these. In some implementations, the microphone 140 is fluidly connected to an airflow pathway (e.g., an airflow pathway between a flow generator / motor and a distal end of a conduit). By fluidly connected, it is also intended to include configurations in which the microphone is acoustically connected to the airflow pathway without necessarily being in direct or physical contact with the airflow. For example, in some implementations, the microphone is positioned on a circuit board and is optionally fluidly connected to the airflow pathway via a conduit sealed by a membrane.

[0089] In some implementations, one or more auxiliary sensors may be used in addition to the primary sensor to generate additional data. In some such implementations, the one or more auxiliary sensors include: a microphone (e.g., microphone 140 of system 100), a flow rate sensor (e.g., flow rate sensor 134 of system 100), a pressure sensor (e.g., pressure sensor 132 of system 100), a temperature sensor (e.g., temperature sensor 136 of system 100), a camera (e.g., camera 150 of 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.

[0090] Additionally or alternatively, one or more microphones (with Figure 1 The microphone 140 (same or similar to the microphone 140 of the embodiment of the present invention) can be integrated into and / or coupled to a co-located smart device, such as the user device 170, a television, a watch (e.g., a mechanical watch or another smart device worn by a user), a pendant, a mattress 232, a bed 230, bedding placed on the bed 230, a pillow, a speaker (e.g., Figure 1 The co-located smart device may be any smart device within range for detecting sounds emitted by the user, the respiratory therapy system 120, and / or any part of the system 100. In some implementations, the co-located smart device is a smart device that is located in the same room as the user during a sleep session.

[0091] Additionally or alternatively, in some implementations, one or more microphones (with Figure 1 The microphone 140 (same or similar) can be located away from the system 100 ( Figure 1 ) and / or user 210( Figure 2 ), as long as there is an air path that allows the acoustic signal to propagate to the one or more microphones. For example, the one or more microphones can be located in a room different from the room containing the system 100.

[0092] As used herein, a sleep session can be defined in a variety of ways based at least in part on, for example, an initial start time and an end time. In some implementations, a sleep session is a duration that a user falls asleep, i.e., a sleep session has a start time and an end time, and during the sleep session, the user does not wake up until the end time. That is, any period during which the user is awake is not included in the sleep session. According to this first definition of a sleep session, if the user wakes up and falls asleep multiple times in the same night, each sleep interval separated by a wake interval is a sleep session.

[0093] Alternatively, in some implementations, a sleep session has a start time and an end time, and during a sleep session, the user can wake up without the sleep session ending as long as the continuous duration of the user's wakefulness is below a wakefulness duration threshold. The wakefulness duration threshold can be defined as a percentage of the sleep session. The wakefulness duration threshold can be, for example, about 20 percent of the sleep session, about 15 percent of the sleep session duration, about 10 percent of the sleep session duration, about 5 percent of the sleep session duration, about 2 percent of the sleep session duration, etc., or any other threshold percentage. In some implementations, the wakefulness duration threshold is defined as a fixed amount of time, such as about one hour, about thirty minutes, about fifteen minutes, about ten minutes, about five minutes, about two minutes, etc., or any other amount of time.

[0094] In some implementations, a sleep session is defined as the entire time between the night that the user first enters the bed and the next morning that the user last leaves the bed. In other words, a sleep session can be defined as a period starting at a first time (e.g., 10:00 p.m.) on a first date (e.g., Monday, January 6, 2020), when the user first enters the bed with the intention of going to sleep (e.g., if the user does not plan to watch TV or play with a smartphone before going to bed, this is not the case), which can be referred to as the current night, and ending at a second time (e.g., 7:00 a.m.) on a second date (e.g., Tuesday, January 7, 2020), when the user first leaves the bed without intending to go back to sleep the next morning, which can be referred to as the next morning.

[0095] In some implementations, a user can manually define the start of a sleep session and / or manually terminate a sleep session. For example, a user can select (e.g., by clicking or tapping) a button displayed on the user device 170 ( Figure 1 )'s display device 172 to manually initiate or terminate a sleep session.

[0096] Although the control system 110 and the memory device 114 are Figure 1 100 as separate and distinct components of system 100, but in some implementations, control system 110 and / or memory device 114 are integrated into user device 170 and / or respiratory therapy device 122. Alternatively, in some implementations, control system 110 or a portion thereof (e.g., processor 112) may be located in the cloud (e.g., integrated in a server, integrated in an Internet of Things (IoT) device, connected to the cloud, subject to edge cloud processing, etc.), located in one or more servers (e.g., remote servers, local servers, etc., or any combination thereof).

[0097] Although system 100 is shown as including all of the above components, more or fewer components may be included in the system depending on the implementation of the present disclosure. For example, a first alternative system includes control system 110, memory device 114, and at least one of one or more sensors 130, and does not include respiratory therapy system 120. As another example, a second alternative system includes control system 110, memory device 114, at least one of one or more sensors 130, and user device 170. As yet another example, a third alternative system includes control system 110, memory device 114, respiratory therapy system 120, at least one of one or more sensors 130, and optionally user device 170. Therefore, various systems may be formed using any one or more portions of the components shown and described herein and / or in combination with one or more other components.

[0098] Figure 3 1 shows a flow chart of a method 300 for determining a pSDB state using airflow data according to some implementations of the present disclosure. Positional obstructive sleep apnea may include position-related snoring, position-related RERA, position-related hypopnea, positional obstructive sleep apnea, etc. The airflow data may be obtained from a respiratory therapy device such as respiratory therapy device 122 ( Figure 1 ))generate.

[0099] Method 300 begins at step 310 by receiving airflow data associated with a user of a respiratory device. The airflow data may include flow rate data associated with a respiratory therapy system, pressure data associated with a respiratory therapy system, or both.

[0100] At step 320, the airflow data is analyzed to identify a first period of suspected arousal and a second period of suspected arousal. The first period and / or the second period can each be a time point, a duration, or both. In some implementations, the suspected arousal indicates a body movement of the user and is indicated by one or more features in the airflow data. In other words, the body movement of the user is inferred from the suspected arousal of the user, which can be indicated by one or more features in the airflow data. In some implementations, the suspected arousal is associated with a change in the user's body position.

[0101] In some implementations, the first time period may be associated with a first motion event, and the second time period may be associated with a second motion event. In some other such implementations, the first motion event and the second motion event are different types of events.

[0102] At step 330, method 300 further provides for determining a first time segment between the identified first time segment and the identified second time segment. At step 340, method 300 further provides for analyzing airflow data associated with the determined first time segment to identify an indication of one or more respiratory events and / or an indication of one or more therapy events.

[0103] For example, analyzing the airflow data associated with the user at step 340 may include: processing the airflow data to identify one or more features indicative of suspected arousal. Such one or more features may include an increase in the amplitude of the flow rate signal, an increase in the change in the breathing rate, a respiratory cessation, an increase in the noise of the flow rate signal, an increase in the amplitude of the flow rate at a reduced breathing rate (relative to a previous and / or subsequent flow rate, e.g., a previous flow rate and / or a subsequent flow rate) (e.g., corresponding to a larger number of breaths relative to an average respiratory volume), followed by a decrease in the amplitude of the flow rate signal at an increased breathing rate (relative to a previous and / or subsequent flow rate, e.g., a previous flow rate and / or a subsequent flow rate), or any combination thereof. For example, respiratory cessation can be used to distinguish between breath holding and apnea by analyzing the duration (such as holding a breath for 1 to 3 seconds versus experiencing apnea for 10 seconds or more).

[0104] Examples of one or more respiratory events include snoring, airflow limitation, residual airflow limitation, residual apnea, residual hypopnea, respiratory effort related arousal (RERA) events, or any combination thereof. Examples of indications of the identified one or more respiratory events may include severity of the one or more respiratory events, number of occurrences of the one or more respiratory events, number of occurrences of the one or more respiratory events relative to a threshold, type of the one or more respiratory events, apnea hypopnea index (AHI), or any combination thereof.

[0105] Examples of one or more treatment events include an increase in treatment pressure, a decrease in treatment pressure, a rate of change in treatment pressure, or any combination thereof. A change in treatment pressure may be an AutoSet TM If an apnea event is detected, the AutoSet TM The feature automatically increases the treatment pressure and, if a predetermined duration passes without a detected apnea event, reduces the pressure again. An indication of one or more treatment events can include a change in treatment pressure triggered by a detected respiratory event or a predicted respiratory event. A respiratory event that would otherwise occur is prevented by the increased pressure and detected or counted. Thus, in some implementations, including both respiratory events and treatment events can provide a more accurate assessment of the user's pSDB status.

[0106] For example, step 340 may include a metric that combines treatment events (e.g., changes in treatment pressure) and respiratory events (e.g., the rate of respiratory events). For example, a "severity metric" may be determined using k1P+k2AHI, where P is a treatment pressure event, k1 is a coefficient associated with the treatment pressure event, and k2 is a coefficient associated with the AHI, e.g., the number of apnea events per hour. Such a metric may take into account the effect of treatment pressure on the rate of events. For example, a severity metric of an AHI of 4 at a treatment pressure of 4 cm of H2O may be equivalent to an AHI of 1 at 10 cm of H2O. As will be appreciated, the AHI may be replaced by any event rate, such as snoring, airflow limitation, etc.

[0107] Based at least in part on the indication of the identified one or more respiratory events and / or the indication of the identified one or more therapeutic events, the pSDB status of the user is determined at step 350. In some implementations, the pSDB status indicates whether the user suffers from pSDB. In some implementations, the pSDB status includes a probability that the user suffers from pSDB, a classification of pSDB, or both. For example, the classification of pSDB includes determining whether the pSDB is positional obstructive sleep apnea (pOSA), positional central sleep apnea (pCSA), positional respiratory effort-related arousals (RERA), positional hypopnea, positional snoring, or any combination thereof.

[0108] In some implementations, the airflow data associated with the determined first time segment is further analyzed to identify a body position or a change in body position at step 360. In some such implementations, the pSDB status indicates whether the user suffers from pSDB in the identified body position.

[0109] In some implementations, a machine learning model can be used to identify body position based on airflow data. Examples of inputs for the machine learning model include patterns in the airflow waveform, the shape of airflow-limited breathing, and the duration of exhalation determined based on the airflow data. Examples of outputs for the machine learning model include body position or body position changes, or the possibility of body position or body position changes. The machine learning model may have been trained using historical airflow data and reference data, wherein the reference data may include data indicating body position and / or body position changes. For example, in some implementations, the reference data includes accelerometer data, observer scoring data, or both. As another example, in some implementations, PAP pressure data, sleep staging (to allow potential REM-dominated sleep apnea, as further described below) and scored airflow data (e.g., scored to highlight respiratory events and / or body position or body position changes) can be used to train the machine learning model.

[0110] In this example, features such as respiratory rate, residual AHI residual index of other events, apnea index (AI), hypopnea index (HI), snoring breath percentage, airflow limited breath percentage, airflow limitation type (e.g., a recognizable shape of the inspiratory airflow waveform), expiratory and / or inspiratory duration, treatment pressure (e.g., mean, peak, median and / or range), sleep state estimation (e.g., REM, non-REM, etc.), etc. can be used to train any machine learning model, such as but not limited to support vector machines, convolutional neural networks, etc.

[0111] In some implementations, method 300 further determines, at step 370, whether the body position determined at step 360 is associated with a pSDB status indicating that the user suffers from pSDB in the identified body position. In some such implementations, if the pSDB status indicates that the user suffers from pSDB in the identified body position, then, at step 372, an increase or modification of a pressure setting of the respiratory therapy device is determined when the user is in the identified body position.

[0112] In some examples, the pressure supplied to the user is gradually increased. In some such examples, the steps of method 300 are repeated until the maximum necessary pressure limit for the identified body position is reached. For example, at a certain point, the pressure supplied to the user is so high for the user that the user is unlikely to experience any respiratory events. Therefore, the maximum necessary pressure limit is associated with a maximum pressure limit, exceeding which there is no additional improvement in the user's respiratory events. In other words, the maximum necessary pressure limit is associated with the highest pressure at which the user's SDB is adequately treated (such that the severity is at or below a predetermined threshold, e.g., an AHI less than 5, 4, 3, 2, or 1, or within a threshold range, e.g., an AHI of 0 to 5, or 1 to 5), and no additional benefit is obtained by exceeding this maximum pressure.

[0113] In some implementations, if the pSDB status indicates that the user does not have pSDB in the identified position, a reduction or modification of the pressure setting of the respiratory therapy device is determined at step 374 when the user is in the identified position. In some examples, the pressure supplied to the user is gradually reduced. In some such examples, the steps of method 300 are repeated until the minimum necessary pressure limit for the identified position is reached. In some such examples, the minimum necessary pressure limit is associated with the lowest pressure limit before the user begins to experience respiratory events indicative of OSA in the position. In other words, the minimum necessary pressure limit is associated with the lowest pressure at which the user's SDB is adequately treated (so that the severity is at or below a predetermined threshold, e.g., AHI is less than 5, 4, 3, 2, or 1, or within a threshold range, e.g., AHI is 0 to 5, or 1 to 5), and below which an SDB event occurs so that the severity is at or above a predetermined threshold or above a threshold range.

[0114] In some implementations, a portion of the airflow data is discarded at step 312 prior to analyzing to identify a first period of suspected arousal and a second period of suspected arousal at step 320, wherein the portion of the airflow data is associated with the airflow supplied to the user at a pressure setting of the respiratory therapy device that exceeds a predetermined threshold. In some such implementations, the predetermined threshold is associated with a maximum treatment pressure that will suppress any pSDB events. In other words, in these implementations, the method 300 analyzes data only from lower pressures at which pSDB events are more likely to be detected. For example, in some such implementations, the predetermined threshold is about 8 cm of H2O, about 9 cm of H2O, about 10 cm of H2O, about 11 cm of H2O, or about 12 cm of H2O. Additionally or alternatively, in some such implementations, the predetermined threshold is a percentage threshold of the user's maximum pressure as determined by a medical provider (e.g., a user's physician).

[0115] In some implementations, the treatment adjustment may be made to the first time segment after a period of time, or to a later time segment after a period of time that we know is similar to the particular type of time segment determined at step 330. For example, in some such implementations, a pattern associated with the supine position may be identified early in a treatment session or during a previous treatment session, and the supine position signature may then be identified and relied upon to modify treatment, such as to modify a pressure response parameter.

[0116] In some implementations, a portion of the airflow data may be discarded during and after determining the first period of suspected arousal and the second period of suspected arousal. For example, in some such implementations, after determining the first time segment at step 330, the airflow data is analyzed and then discarded if (i) the airflow data within the first time segment exceeds the pressure threshold, (ii) a REM sleep stage is detected, or (iii) both (i) and (ii).

[0117] In some implementations, heart rate data associated with the user of the respiratory device is also received at step 380. The received heart rate data can be analyzed to identify or confirm suspected arousals. For example, analyzing the received heart rate data can include determining a heart rate, a change in heart rate, or both, and confirming the suspected arousal by analyzing the determined heart rate, the determined change in heart rate, or both. In some such examples, an increase in heart rate indicates a suspected arousal.

[0118] Heart rate (e.g., cardiac activity) may also indicate arousal, such as described in U.S. Publication No. 2008 / 0045813, U.S. Publication No. 2015 / 0182713A1, and WO 2005 / 079897, each of which is incorporated herein by reference in its entirety. Heart rate may change upon arousal; therefore, by using a heart rate sensor, arousal may be identified. In some implementations, when an increase in signal noise is detected for a heart rate signal or a respiratory signal, motion may be inferred due to motion artifacts in the signal. Changes in heart rate have also been found to be associated with different sleep stages. For example, heart rate variability is greater during REM sleep in particular than during other sleep stages.

[0119] Brief reference FIG. 5A to FIG. 5B According to some implementations of the present disclosure, Figure 5A shows the average heart rate of the first user before and after awakening, and Figure 5B The average heart rate of the second user before and after awakening is shown. As shown, the heart rates of both users increase upon awakening.

[0120] Return to reference Figure 3In some implementations, in addition to or as an alternative to receiving heart rate data, acoustic data associated with a user of the respiratory device is also received at step 380. The received acoustic data is analyzed to further determine or confirm suspected awakening. In some implementations, if the pSDB state indicates that the user suffers from pSDB, the received acoustic data associated with the determined first time segment is analyzed to identify the location of an obstacle associated with the user. For example, the location can be a point along the user's airway and / or a distance from a user interface worn by a user of the respiratory therapy device. In some such implementations, the acoustic data includes acoustic reflections of the user's airway, the inside of the user's mouth, or both. For example, the acoustic reflection can be represented by an acoustic impedance or a distance of an acoustic impedance. The use of acoustic data to identify physical features or obstacles in the airway of a user of a respiratory therapy system is described in PCT / IB2021 / 053603, which is incorporated herein in its entirety.

[0121] Acoustic data may additionally or alternatively be used to detect motion indicative of arousal. For example, a combination of a breathless flow as detected by, for example, a flow sensor and a noise signal as detected by, for example, a microphone may indicate that the user is holding their breath while tumbling, while the noise alone may indicate other forms of motion.

[0122] In some implementations, the airflow data received at step 310 is analyzed to identify (i) one or more sleep stages of the user and (ii) one or more respiratory events experienced by the user. The one or more sleep stages of the user are correlated with the one or more respiratory events experienced by the user to determine whether the user is experiencing a rapid eye movement (REM)-dominated respiratory event.

[0123] In some such implementations, the airflow data associated with the determined first time segment is further analyzed at step 340 to identify the user's sleep stage. At step 342, the airflow data associated with the determined first time segment and when the identified user's sleep stage is REM sleep is discarded. Discarding these data ensures that REM-dominated respiratory events are not confused with pSDB-related events. Additionally or alternatively, sleep stage data associated with the user during the treatment session is received from another source (i.e., not the airflow data received at step 310) such as a wearable sensor, a sonar sensor, or a radar sensor. The sleep stage is determined at least in part based on the sleep stage data, and the pSDB state is associated with the sleep stage. Sleep stages include awake, drowsy, falling asleep, light sleep, deep sleep, N1 sleep, N2 sleep, N3 sleep, REM sleep, or a combination thereof.

[0124] In some implementations, the method 400 further includes: providing a control signal to a respiratory device. In response to the pSDB state determined at step 350, a modification of the pressure setting of the respiratory device is determined. In some implementations, the method 400 further includes: providing a control signal to a smart pillow. In response to the pSDB state determined at step 350, the smart pillow can be adjusted so that the smart pillow prompts the user to change the position of the user's head. In some implementations, the method 400 further includes: providing a control signal to a smart bed or a smart mattress. As will be understood, a "smart" pillow, a "smart" bed, or a "smart" mattress refers to an adjustable pillow, bed, or mattress, respectively. The adjustable pillow, bed, or mattress can be connected to and / or controlled by a user device or other such device, either wired or wirelessly, for providing a signal to the pillow, bed, and mattress based on the user's input or action, or can be automatically adjusted based on sensed data (e.g., data related to the user's body position or body position change, data related to an SDB event experienced by the user, etc.). In response to the pSDB state determined at step 350, the smart bed or smart mattress is adjusted so that the smart bed or smart mattress prompts the user to change the user's body position. In some implementations, the method 400 also includes: providing a control signal to a wearable device. The wearable device can be coupled to a body part of the user, and in response to the pSDB state determined at step 350, the wearable device can be adjusted so that the wearable device stimulates the user to change the user's body position.

[0125] In some implementations, in response to the pSDB state determined at step 350, a notification is provided to a user or a third party (e.g., a doctor, a home medical equipment provider (HME), etc.) via an electronic device, so that an alert of the pSDB state is issued to the user. In some such implementations, the electronic device is an electronic display device, and providing the notification includes: displaying a message on the electronic display device. Additionally or alternatively, the electronic device includes a speaker, and providing the notification includes: playing a sound via the speaker. In some such implementations, the sound is an alarm to wake up the user. Additionally or alternatively, the electronic device includes a tactile device worn by and / or in contact with the user, and in response to the pSDB state determined at step 350, the tactile device prompts the user to change position.

[0126] In some implementations, if it is detected that the user has not changed position, the frequency of the transmitted sound or vibration may rise. In some implementations, the frequency of the transmitted sound or vibration is adjusted in proportion to the user's sleep stage. For example, if the user is lightly asleep, the stimulation may wake the user. In some implementations, prompting the user to change position requires one or both of the following conditions to be met: (i) the user is in a position associated with pSDB, and (ii) one or more respiratory events such as snoring, airflow limitation, hypopnea, and apnea are detected.

[0127] In an implementation, method 300 includes: analyzing a respiratory waveform including an inspiratory waveform and / or an expiratory waveform, and determining a deviation from a normal respiratory waveform. The normal respiratory waveform can be understood based on, for example, a respiratory flow model, such as a numerical model, such as a half-sine wave with scaled amplitude and length to fit the inspiratory (or expiratory) period and amplitude of a specific breath of a specific user, or an average value of the number of breaths. In some implementations, a normal respiratory waveform for a specific user can be learned, such as an average value of the number of breaths or breaths during a period in which the patient is determined to have good airway patency, such as during a wakeful period, during a period in which the user is in a specific sleep stage, during a period in which the user is in a specific body position, or during a period in which the respiratory signal lacks any indication of airway obstruction (such as airflow limitation, snoring or apnea or hypopnea). Therefore, as will be understood, the normal respiratory waveform can take the form of, for example, a curve or function, so that the respiratory flow can be represented as a function of time, and the deviation between the user's inspiratory respiratory flow and the normal waveform can be defined by any known method of quantifying the fit between two functions or curves. For example, this can be quantified by a root mean square (RMS) error, where the larger the error, the larger the deviation. Alternatively, the deviation can be quantified as the amount of air, such as the amount of air inhaled by a user exceeding the normal inhalation amount, expressed as the area between two curves. In some cases, it may be desirable to normalize the deviation to the user's breathing volume. For example, the deviation can be expressed as the amount between the curves, i.e., the percentage of the inhaled volume. Therefore, in some implementations, method 300 may include a step of fitting a half-sine wave to an inhalation waveform. For example, a half-sine wave can be fitted between three points, i.e., two zero crossings marking the beginning and end of inhalation, and the maximum flow value therebetween. A fitting metric, such as a fitting RMS error, can then be determined. In this way, a value is obtained for each inhalation. This value can be understood as a deviation from an inhalation sine wave model, which can be considered an approximation of a normal inhalation. Therefore, method 300 can provide a measure of the deviation from a normal inhalation flow. The metric can be calculated for each patient's breathing and tracked over multiple breaths, or tracked throughout a sleep and / or treatment session, or over multiple sessions, or even over a longer period of time to determine longitudinal changes in breathing, such as changes caused by disease progression, including, for example, respiratory diseases such as the development or worsening of bronchitis, the development or worsening of COPD, or COPD exacerbations, the development or worsening of SBD (such as OSA) or other sleep and / or breathing disorders. In some cases, a step change in the fit metric (such as the RMS error of the fit) can be used as an indication of suspected arousal and / or position change. Additionally or alternatively, such a step change can be used as an indication of a change in sleep state. Such a step change can include exceeding a threshold (such as 5%, 10%, 20%, or 30%) of the breathing volume.In some implementations, the threshold can be dynamically adjusted to take into account baseline respiratory changes, for example, the threshold can be set at multiple standard deviations of multiple respiratory deviations. Alternatively or additionally, the running deviation metric can be low-pass filtered, for example, using a moving average filter, to remove some of the noise of each breath, and the step change of the deviation can be evaluated based on exceeding the threshold in the low-pass filtered signal. In this way, the identification of step changes is unlikely to be triggered by abnormal events (such as coughing or sneezing alone). In some cases, it may be desirable to track the breath-by-breath changes of the deviation according to statistical characteristics (such as running standard deviation) to identify relatively stable or unstable periods of breathing. In other cases, the oscillation of the fit metric (such as the fit RMS error) may indicate the oscillation of respiratory control, and may be more sensitive than alternative parameters such as flow amplitude or ventilation or minute ventilation. Such oscillations in respiratory control can include, for example, oscillations in respiratory drive, fluctuations in the amplitude or rate of respiratory effort, and thus oscillations in respiratory amplitude or rate.

[0128] In some implementations, it may be desirable to classify deviations between a normal breathing model and measured breathing according to certain parameters of the breathing, such as the location of the inspiratory peak relative to the normal breathing model. For example, the inspiratory peak may occur near, substantially before, or substantially after the model peak, which for a half-sine wave model would correspond to half the inspiratory time.

[0129] In some implementations, a respiratory therapy system (such as, in particular, a respiratory therapy device control circuit) can automatically adjust therapy pressure to normalize inspiratory flow shape.

[0130] Similar steps described above can be used to identify a second time segment associated with a second pSDB state. The second time segment can also be associated with a different body position than the first time segment. For example, if the user experiences certain respiratory events during the first time segment, but not during the second time segment, the user may be experiencing pSDB for the body position at the first time segment.

[0131] To determine the second time segment, the airflow data is analyzed to identify a third period of suspected arousal and a fourth period of suspected arousal. In some implementations, the first period, the second period, the third period, and the fourth period are different periods. In other implementations, the second period is the same as the third period, or the fourth period is the same as the first period. A second time segment is then determined between the identified third period and the identified fourth period.

[0132] Analyze the airflow data associated with the determined second time segment to identify another (i) indication of one or more respiratory events, (ii) indication of one or more therapeutic events, or (iii) both of the foregoing (i) and (ii). The identified indication associated with the first time segment includes a first number and / or type of respiratory events, therapeutic events, or both. The identified indication associated with the second time segment includes a second number and / or type of respiratory events, therapeutic events, or both. In this example, the step of determining the user's pSDB state at 350 also includes: comparing the first number and / or type of respiratory events, therapeutic events, or both with the second number and / or type of respiratory events, therapeutic events, or both.

[0133] Although method 300 involves analyzing airflow data generated by a respiratory device, similar steps may be performed using devices other than respiratory devices. Figure 4 A flow chart of a method 400 for determining a pSDB state using sensor data is shown. In some implementations, the steps of method 400 can be the same or similar to the steps of method 300, where like reference numerals represent similar steps.

[0134] Method 400 may begin by receiving sensor data associated with a user at step 410. In some implementations, the sensor data is obtained from a motion sensor (e.g., an accelerometer). In some implementations, the motion sensor is worn on the user's body or is an environmental sensor (e.g., a radar sensor, a camera, etc.) that is not worn by the user. In some implementations, the motion sensor is coupled to or integrated into the user's respiratory device. In some other implementations, the motion sensor is coupled to or integrated into a mobile device. In some implementations, the sensor data is received from a diagnostic device. In some such implementations, the breathing signal can originate from a non-sealed interface of a diagnostic device (such as a nasal cannula or a breathing effort belt), from an accelerometer mounted on the body (e.g., chest, head, etc.), a contact sensor (e.g., EEG sensor, PPG sensor, and other sensors that may be included in, for example, smart watches, wristbands, etc.), a non-contact sensor (such as a radar sensor, sonar sensor, or lidar sensor, such as described herein), or an acoustic sensor (such as acoustic sensor 141 described herein, or a microphone for passive acoustic sensing, which may be included in, for example, a smart home device), or any combination thereof.

[0135] At step 420, the sensor data is analyzed to identify a first period of suspected arousal and a second period of suspected arousal. The suspected arousal indicates a body movement of the user and is indicated by one or more features in the sensor data. In some implementations, the suspected arousal is associated with a change in body position. In some implementations, the first period is associated with a first motion event and the second period is associated with a second motion event. In other such implementations, the first motion event and the second motion event are different types of events.

[0136] At step 430, a first time segment between the identified first time period and the identified second time period is determined. At step 440, sensor data associated with the determined first time segment is analyzed to identify indications of one or more respiratory events. For example, in some implementations, analyzing sensor data associated with the user includes: processing sensor data to identify one or more features indicating suspected awakening. One or more respiratory events may include snoring, airflow limitation, residual airflow limitation, apnea, residual apnea, hypopnea, residual hypopnea, respiratory effort related awakening event (RERA) event, residual RERA event, or any combination thereof. The indication of the identified one or more respiratory events may include the severity of one or more respiratory events, the number of occurrences of one or more respiratory events, the number of occurrences of one or more respiratory events relative to a threshold, the type of one or more respiratory events, an apnea hypopnea index (AHI), or any combination thereof.

[0137] Based at least in part on the indication of one or more respiratory events identified at step 440, a pSDB status of the user is determined at step 450. The pSDB status indicates whether the user suffers from pSDB. For example, the pSDB status may include a probability that the user suffers from pSDB, a classification of pSDB, or both. For example, the probability that the user suffers from pSDB may include having more severe SDB (e.g., a higher AHI) in a particular body position. For example, the classification of pSDB includes determining that the pSDB is positional obstructive sleep apnea (pOSA), positional central sleep apnea (pCSA), positional respiratory effort-related arousals (RERA), positional hypopnea, positional snoring, or any combination thereof.

[0138] In some implementations, at step 460, the sensor data associated with the determined first time segment is further analyzed to identify a body position or a change in body position. In some such implementations, a machine learning model is used to identify body position, which model can be trained using historical sensor data and reference data (e.g., accelerometer data, observer scoring data, or both). For example, the reference data can include data indicating body position and / or a change in body position. In some such implementations, the pSDB state determined at step 450 also indicates whether the user suffers from pSDB in the body position identified at step 460.

[0139] In some implementations, additional sensor data, such as heart rate data and / or acoustic data, may be received at step 480. The received additional sensor data is analyzed to determine or confirm suspected arousals. For example, analyzing the received heart rate data includes determining a heart rate, a change in heart rate, or both, and confirming the suspected arousal by analyzing the determined heart rate, the determined change in heart rate, or both. An increase in heart rate may indicate a suspected arousal.

[0140] In some implementations, the sensor data received at step 410 is analyzed to identify (i) one or more sleep stages of the user and (ii) one or more respiratory events experienced by the user. The one or more sleep stages of the user are correlated with the one or more respiratory events experienced by the user to determine whether the user is experiencing a rapid eye movement (REM) dominated respiratory event. The sensor data associated with the determined first time segment is further analyzed at step 440 to identify the sleep stage of the user, and the sensor data associated with the determined first time segment and when the identified sleep stage of the user is REM sleep is discarded at step 442.

[0141] Similar steps described above can be used to identify a second time segment associated with a second pSDB state. The second time segment can also be associated with a different body position than the first time segment. For example, if the user experiences certain respiratory events during the first time segment, but not during the second time segment, the user may be experiencing pSDB for the body position at the first time segment.

[0142] To determine the second time segment, the sensor data received at step 410 is analyzed to identify a third period of suspected awakening and a fourth period of suspected awakening. A second time segment between the identified third period and the identified fourth period is then determined. The sensor data associated with the determined second time segment is analyzed to identify another indication of one or more respiratory events. The indication of the identified one or more respiratory events associated with the first time segment includes a first number and / or type of respiratory events. Another indication of the identified one or more respiratory events associated with the second time segment includes a second number and / or type of respiratory events. Step 450 of determining the user's pSDB state also includes comparing the first number and / or type of respiratory events with the second number and / or type of respiratory events.

[0143] In some implementations, one or more steps of the methods disclosed herein can be incorporated into a distributed system for pSDB prediction, screening, diagnosis, and / or treatment. In one example, a first user device (such as a smartwatch) can pick up the user's heart rate or any other physiological parameter as disclosed herein. For example, a separate sensor (such as an accelerometer) of the first user device on the user's chest and / or head can be activated to determine the torso and / or head position. An analysis is then performed to determine whether the head position or torso position or both are important to the user. In some such implementations, the user device can also be configured to generate a notification (e.g., a buzz, sound, etc.) as needed to remind the user.

[0144] In general, methods 300 and 400 can be implemented using a system having a control system with one or more processors and a memory storing machine-readable instructions. The control system can be coupled to the memory; when the machine-readable instructions are executed by at least one processor of the control system, methods 300 and 400 can be implemented. Methods 300 and 400 can also be implemented using a computer program product (such as a non-transitory computer-readable medium) that includes instructions that, when executed by a computer, cause the computer to perform the steps of methods 300 and 400.

[0145] Although the system 100 and methods 300 and 400 have been described herein with reference to a single user, more generally, the system 100 and methods 300 and 400 may be used simultaneously with multiple users (e.g., two users, five users, 10 users, 20 users, etc.). For example, the system 100 and methods 300 and 400 may be used in a cloud monitoring setting.

[0146] Although some examples of system 100 and methods 300 and 400 have been described herein with reference to determining pSDB status, more generally, system 100 and methods 300 and 400 may be used to determine one or more other health-related issues, such as any disease or condition that increases sympathetic nerve activity, examples of which include COPD, CVD, somatic syndromes, etc.

[0147] In some implementations, multiple treatment modes can be combined. For example, positional therapy can be combined with positive airway pressure therapy so that the pressure requirements of positive airway pressure therapy can be reduced in certain body positions. In some implementations, a position monitoring application can be combined with positive airway pressure therapy so that the user's position is incorporated into the algorithm for determining the target treatment pressure. For example, the target treatment can be increased when the user transitions to a horizontal position, or the target pressure can be increased when the user transitions from a prone or lateral position (or any other position) to a supine position. Similarly, the target pressure may be reduced when the user transitions from a supine position. In some implementations, demographic data and / or historical treatment data can be used to estimate the magnitude of the change in the target pressure applied at a particular position transition.

[0148] Other implementations of the present disclosure include:

[0149] 1. A method for determining a positional sleep disordered breathing (pSDB) state associated with a user of a respiratory therapy device, the method comprising:

[0150] receiving airflow data associated with a user of the respiratory device;

[0151] analyzing the airflow data to identify a first period of suspected arousal and a second period of suspected arousal;

[0152] determining a first time period between the identified first time period and the identified second time period;

[0153] analyzing the airflow data associated with the determined first time segment to identify (i) an indication of one or more respiratory events, (ii) an indication of one or more therapy events, or (iii) both; and

[0154] A pSDB status of the user is determined based at least in part on (i) an indication of one or more identified respiratory events, (ii) an indication of one or more identified therapy events, or (iii) both (i) and (ii), the pSDB status indicating whether the user suffers from pSDB.

[0155] 2. The method according to implementation mode 1, wherein the airflow data includes flow rate data, pressure data, or both.

[0156] 3. The method according to implementation mode 1 or implementation mode 2, wherein the suspected arousal is indicated by one or more features in the airflow data.

[0157] 4. The method according to any one of implementations 1 to 3, wherein the suspected awakening indicates a body movement of the user.

[0158] 5. The method according to any one of implementations 1 to 4, wherein the suspected arousal is associated with a change in body position.

[0159] 6. The method according to any one of implementations 1 to 5, wherein the first time period is associated with a first motion event, and the second time period is associated with a second motion event.

[0160] 7. A method according to any one of implementations 1 to 6, wherein the one or more respiratory events include snoring, airflow limitation, residual airflow limitation, apnea, residual apnea, hypopnea, residual hypopnea, respiratory effort related arousal (RERA) events, or any combination thereof.

[0161] 8. The method of any one of implementations 1 to 7, wherein the one or more treatment events include an increase in treatment pressure, a decrease in treatment pressure, a rate of change of treatment pressure, or any combination thereof.

[0162] 9. The method according to any one of implementations 1 to 8, wherein the airflow data associated with the determined first time segment is further analyzed to identify a body position or a change in body position.

[0163] 10. A method according to implementation 9, wherein a machine learning model is used to identify body position.

[0164] 11. A method according to implementation 10, wherein the machine learning model is trained using historical airflow data and reference data.

[0165] 12. The method of implementation 11, wherein the reference data comprises accelerometer data, observer rating data, or both.

[0166] 13. The method according to any one of implementations 9 to 12, wherein the pSDB status indicates whether the user suffers from pSDB in the identified body position.

[0167] 14. The method of implementation 13, further comprising: if the pSDB status indicates that the user suffers from pSDB in the identified body position, determining an increase or modification to a pressure setting of the respiratory therapy device when the user is in the identified body position.

[0168] 15. The method according to implementation 14, wherein the steps of the method are repeated until a maximum necessary pressure limit for the identified body position is reached.

[0169] 16. The method of implementation 13 further includes: if the pSDB status indicates that the user does not have a pSDB in the identified body position, determining a reduction or modification of a pressure setting of the respiratory therapy device when the user is in the identified body position.

[0170] 17. The method according to implementation 16, wherein the steps of the method are repeated until a minimum necessary pressure limit for the identified body position is reached.

[0171] 18. A method according to any one of implementations 1 to 17, wherein the indication of the identified one or more respiratory events includes the severity of the one or more respiratory events, the number of occurrences of the one or more respiratory events, the number of occurrences of the one or more respiratory events relative to a threshold, the type of the one or more respiratory events, an apnea-hypopnea index (AHI), or any combination thereof.

[0172] 19. The method according to any one of implementations 1 to 18 further includes: before analyzing to identify the first period of suspected arousal and the second period of suspected arousal, discarding a portion of the airflow data associated with the airflow supplied to the user at a pressure setting of the respiratory therapy device that exceeds a predetermined threshold.

[0173] 20. According to any one of implementations 1 to 19, the method further includes: analyzing at least a portion of the airflow data associated with the determined first time period, wherein the portion decreases as the pressure setting of the respiratory therapy device decreases to identify (i) an indication of one or more respiratory events, (ii) an indication of one or more therapy events, or (iii) both of the foregoing (i) and (ii).

[0174] 21. The method according to any one of implementations 1 to 20, further comprising:

[0175] analyzing the airflow data to identify a third period of suspected arousal and a fourth period of suspected arousal;

[0176] determining a second time period between the identified third time period and the identified fourth time period; and

[0177] Analyze the airflow data associated with the determined second time period to identify (i) an indication of one or more respiratory events, (ii) an indication of one or more therapeutic events, or (iii) both of (i) and (ii), wherein the therapeutic pressure associated with the second time period is lower than the therapeutic pressure associated with the first time period.

[0178] 22. The method according to any one of implementations 1 to 21 further includes: before analyzing the airflow data associated with the determined first time period, discarding a portion of the airflow data associated with the airflow supplied to the user at a pressure setting of the respiratory therapy device that exceeds a predetermined threshold.

[0179] 23. The method according to implementation 19 or implementation 22, wherein the predetermined threshold is approximately 10 cmH2O.

[0180] 24. The method of any one of implementations 1 to 23, wherein the pSDB status comprises a probability that the user suffers from pSDB, a classification of pSDB, or both.

[0181] 25. A method according to implementation 24, wherein the classification of pSDB includes determining whether the pSDB is positional obstructive sleep apnea (pOSA), positional central sleep apnea (pCSA), positional respiratory effort-related arousals (RERA), positional hypopnea, positional snoring, or any combination thereof.

[0182] 26. The method according to any one of implementations 1 to 25, further comprising:

[0183] receiving heart rate data associated with a user of the respiratory device; and

[0184] The received heart rate data is analyzed to confirm suspected arousals.

[0185] 27. The method of implementation 26, wherein analyzing the received heart rate data comprises determining the heart rate, the heart rate change, or both, and confirming the suspected awakening based on the determined heart rate, the determined heart rate change, or both.

[0186] 28. The method of implementation 27, wherein an increase in heart rate indicates suspected arousal.

[0187] 29. The method according to any one of implementations 1 to 28, further comprising:

[0188] receiving acoustic data associated with a user of the respiratory device; and

[0189] The received acoustic data is analyzed to identify or confirm a first period of suspected arousal and / or a second period of suspected arousal.

[0190] 30. The method of implementation 29, wherein analyzing the received acoustic data comprises detecting sounds associated with body movements of the user and / or changes in body position of the user.

[0191] 31. The method according to any one of implementations 1 to 30, further comprising:

[0192] receiving acoustic data associated with a user of the respiratory device; and

[0193] If the pSDB status indicates that the user suffers from pSDB, the received acoustic data associated with the determined first time period is analyzed to identify a location of an obstruction associated with the user.

[0194] 32. The method of implementation 31, wherein the location is a point or area along the user's airway.

[0195] 33. The method of implementation 31 or implementation 32, wherein the location is a distance from a user interface worn by a user of the respiratory therapy device.

[0196] 34. The method of any one of implementations 31 to 33, wherein the acoustic data includes acoustic reflections of the user's airway, the inside of the user's mouth, or both.

[0197] 35. The method according to implementation 34, wherein the acoustic reflection is represented by acoustic impedance or a distance of acoustic impedance.

[0198] 36. The method according to any one of implementations 1 to 35, further comprising:

[0199] receiving acoustic data associated with a user of the respiratory device; and

[0200] The received acoustic data is analyzed to further determine or confirm the suspected arousal.

[0201] 37. The method according to any one of implementations 1 to 36, further comprising:

[0202] analyzing the airflow data to identify (i) one or more sleep stages of the user and (ii) one or more respiratory events experienced by the user; and

[0203] One or more sleep stages of the user are correlated with one or more respiratory events experienced by the user to determine whether the user is experiencing a rapid eye movement (REM) dominated respiratory event.

[0204] 38. The method according to implementation manner 37 further includes:

[0205] analyzing the airflow data associated with the determined first time segment to further identify a sleep stage of the user; and

[0206] Airflow data associated with the determined first time segment and when the identified sleep stage of the user is REM sleep is discarded.

[0207] 39. The method of any one of implementations 1 to 38, wherein analyzing airflow data associated with the user comprises: processing the airflow data to identify one or more features indicative of suspected arousal.

[0208] 40. A method according to implementation 39, wherein one or more characteristics include an increased amplitude of the flow rate signal, an increased change in the respiratory rate, respiratory arrest, an increase in noise in the flow rate signal, an increase in the flow rate amplitude at a decreased respiratory rate with a decrease in the amplitude of the flow rate signal at a relatively increased respiratory rate, or any combination thereof.

[0209] 41. The method according to any one of implementations 1 to 40, wherein analyzing the airflow data comprises: detecting a breathing waveform including an inspiratory waveform and / or an expiratory waveform, and determining a deviation from a normal breathing waveform.

[0210] 42. The method according to implementation 41, wherein determining the deviation from a normal breathing waveform comprises: analyzing changes in the user's respiratory flow over time, and quantifying a fit metric between the inhalation waveform and / or the exhalation waveform and the normal breathing waveform.

[0211] 43. The method according to implementation 41 or 42, wherein determining the deviation from a normal breathing waveform comprises: fitting a half-sine wave to the inspiratory waveform, and determining a fit metric of the half-sine wave to the inspiratory waveform.

[0212] 44. The method of implementation 42, wherein the fit metric is a root mean square (RMS) error of the fit.

[0213] 45. The method of implementation 42, wherein the fit metric describes the deviation from normal inspiratory flow.

[0214] 46. ​​The method of implementation 45, wherein deviations from normal inspiratory airflow are monitored over one or more respiratory therapy sessions, wherein the deviations are indicative of development or worsening of a respiratory disorder.

[0215] 47. The method of implementation 46, wherein the respiratory disease is one or more of bronchitis, COPD, and sleep-related disorders.

[0216] 48. The method of any one of implementations 42 to 47, wherein a step change in the fit metric indicates a change in the user's body position or sleep state.

[0217] 49. A method according to any one of implementations 41 to 48, wherein the normal breathing waveform is determined during a period in which the user is determined to have good airway patency, a period in which the user is in a particular sleep stage, or a period in which there is no sign of airway obstruction.

[0218] 50. The method according to any one of implementations 1 to 49, further comprising:

[0219] Provide control signals to the respiratory equipment, and

[0220] Responsive to the pSDB state, a modification to a pressure setting of the respiratory device associated with pressurized air supplied to the airway of the user is determined.

[0221] 51. The method according to any one of implementations 1 to 50, further comprising:

[0222] providing control signals to the smart pillow; and

[0223] Responsive to the pSDB state, a modification to the smart pillow is determined such that the modification to the smart pillow is implemented in a manner that prompts a user to change a position of the user's head.

[0224] 52. The method according to any one of implementations 1 to 51, further comprising:

[0225] Providing control signals to a smart bed or smart mattress; and

[0226] Responsive to the pSDB state, a modification to the smart bed or to the smart mattress is determined such that the modification to the smart bed or to the smart mattress is implemented in a manner that prompts the user to change the user's body position.

[0227] 53. The method according to any one of implementations 1 to 52, further comprising:

[0228] providing a control signal to a wearable device that can be coupled to a body part of a user; and

[0229] Responsive to the pSDB state, a modification to the wearable device is determined such that the modification to the wearable device is implemented in a manner that stimulates the user to change a body position of the user.

[0230] 54. The method according to any one of implementations 1 to 53 further includes: in response to the pSDB status, causing a notification to be provided to a user or a third party via an electronic device, so that an alert of the pSDB status is issued to the user or the third party.

[0231] 55. The method according to implementation 54, wherein the electronic device is an electronic display device, and providing the notification includes: displaying a message on the electronic display device.

[0232] 56. The method according to implementation 54 or implementation 55, wherein the electronic device includes a speaker, and providing the notification includes: playing a sound via the speaker.

[0233] 57. The method according to implementation 56, wherein the sound is an alarm to wake up the user.

[0234] 58. The method according to any one of implementations 1 to 57, further comprising:

[0235] receiving sleep stage data associated with the user during a respiratory therapy session;

[0236] determining a sleep stage based at least in part on the sleep stage data; and

[0237] Associate pSDB states with sleep stages.

[0238] 59. A method according to implementation 58, wherein the sleep stage includes awake, drowsy, asleep, light sleep, deep sleep, N1 sleep, N2 sleep, N3 sleep, REM sleep, or a combination thereof.

[0239] 60. The method according to any one of implementations 1 to 59, further comprising:

[0240] analyzing the airflow data to identify a third period of suspected arousal and a fourth period of suspected arousal;

[0241] determining a second time period between the identified third time period and the identified fourth time period; and

[0242] analyzing the airflow data associated with the determined second time segment to identify another of (i) one or more indications of respiratory events, (ii) one or more indications of therapy events, or (iii) both (i) and (ii),

[0243] wherein (i) an indication of one or more identified respiratory events, (ii) an indication of one or more identified therapy events, or (iii) both (i) and (ii) include a first number and / or type of respiratory events, therapy events, or both, associated with the first time segment,

[0244] wherein (i) another indication of the one or more identified respiratory events, (ii) another indication of the one or more identified therapy events, or (iii) both (i) and (ii) include a second number and / or type of respiratory events, therapy events, or both, and

[0245] Wherein, determining the pSDB state of the user includes: comparing a first number and / or type of respiratory events, therapy events, or both with a second number and / or type of respiratory events, therapy events, or both.

[0246] 61. The method according to implementation mode 60, wherein the second time period is the same as the third time period.

[0247] 62. The method according to implementation 60, wherein the fourth time period is the same as the first time period. Still another implementation of the present disclosure includes:

[0248] 63. A method for determining a positional sleep disordered breathing (pSDB) state associated with a user, the method comprising:

[0249] receiving sensor data associated with a user;

[0250] analyzing the sensor data to identify a first period of suspected arousal and a second period of suspected arousal;

[0251] determining a first time period between the identified first time period and the identified second time period;

[0252] analyzing sensor data associated with the determined first time period to identify indications of one or more respiratory events; and

[0253] Based at least in part on the identified indication of one or more respiratory events, a pSDB status of the user is determined, the pSDB status indicating whether the user suffers from pSDB.

[0254] 64. The method of implementation 63, wherein the sensor data is obtained from one or more sensors selected from body-mounted accelerometers, contact sensors, non-contact sensors, acoustic sensors, or any combination thereof.

[0255] 65. The method of implementation 63 or 64, wherein the sensor data is obtained from a diagnostic device.

[0256] 66. The method according to implementation 65, wherein the breathing signal originates from a non-sealed interface or a breathing effort belt of the diagnostic device.

[0257] 67. The method of implementation 66, wherein the non-sealing interface is a nasal cannula.

[0258] 68. The method of any one of implementations 63 to 67, wherein the sensor data is obtained from a motion sensor.

[0259] 69. The method of implementation 68, wherein the motion sensor comprises an accelerometer.

[0260] 70. The method according to implementation 68 or implementation 69, wherein the motion sensor is worn on the user's body or is an environmental sensor not worn by the user.

[0261] 71. The method according to implementation 68 or implementation 69, wherein the motion sensor is coupled to or integrated into the user's respiratory device.

[0262] 72. The method according to implementation 68 or implementation 69, wherein the motion sensor is coupled to the mobile device or integrated in the mobile device.

[0263] 73. The method according to any one of implementations 63 to 72, wherein the breathing signal is derived from sensor data.

[0264] 74. A method according to any one of implementations 63 to 73, wherein the suspected awakening is indicated by one or more features in the sensor data.

[0265] 75. The method of any one of implementations 63 to 74, wherein the suspected awakening indicates a body movement of the user.

[0266] 76. The method of any one of implementations 63 to 75, wherein the suspected arousal is associated with a change in body position.

[0267] 77. The method of any one of implementations 63 to 76, wherein the first time period is associated with a first motion event and the second time period is associated with a second motion event.

[0268] 78. A method according to any one of implementations 63 to 77, wherein the one or more respiratory events include snoring, airflow limitation, residual airflow limitation, residual apnea, residual hypopnea, respiratory effort related arousal (RERA) events, or any combination thereof.

[0269] 79. The method of any one of implementations 63 to 78, wherein the sensor data associated with the determined first time period is further analyzed to identify a body position or a change in body position.

[0270] 80. A method according to implementation 79, wherein a machine learning model is used to identify body position.

[0271] 81. A method according to implementation 80, wherein the machine learning model is trained using historical sensor data and reference data.

[0272] 82. The method of implementation 81, wherein the reference data comprises accelerometer data, observer rating data, or both.

[0273] 83. The method of any one of implementations 79 to 82, wherein the pSDB status indicates whether the user suffers from pSDB in the identified body position.

[0274] 84. A method according to any one of implementations 63 to 83, wherein the indication of the identified one or more respiratory events includes a severity of one or more respiratory events, a number of occurrences of one or more respiratory events, a number of occurrences of one or more respiratory events relative to a threshold, a type of one or more respiratory events, an apnea-hypopnea index (AHI), or any combination thereof.

[0275] 85. The method of any one of implementations 63 to 84, wherein the pSDB status comprises a probability that the user suffers from pSDB, a classification of pSDB, or both.

[0276] 86. A method according to implementation 85, wherein the classification of pSDB includes determining whether the pSDB is positional obstructive sleep apnea (pOSA), positional central sleep apnea (pCSA), positional respiratory effort-related arousals (RERA), positional hypopnea, positional snoring, or any combination thereof.

[0277] 87. The method according to any one of implementations 63 to 86, further comprising:

[0278] receiving heart rate data associated with the user; and

[0279] The received heart rate data is analyzed to determine or confirm suspected arousals.

[0280] 88. The method of implementation 87, wherein analyzing the received heart rate data comprises determining the heart rate, the heart rate change, or both, and confirming the suspected awakening by analyzing the determined heart rate, the determined heart rate change, or both.

[0281] 89. The method of implementation 88, wherein an increase in heart rate indicates suspected arousal.

[0282] 90. The method according to any one of implementations 63 to 89, further comprising:

[0283] receiving acoustic data associated with a user; and

[0284] The received acoustic data is analyzed to determine or confirm suspected arousals.

[0285] 91. The method according to any one of implementations 63 to 90, further comprising:

[0286] analyzing the sensor data to identify (i) one or more sleep stages of the user and (ii) one or more respiratory events experienced by the user; and

[0287] One or more sleep stages of the user are correlated with one or more respiratory events experienced by the user to determine whether the user is experiencing a rapid eye movement (REM) dominated respiratory event.

[0288] 92. The method according to implementation manner 91 further includes:

[0289] analyzing sensor data associated with the determined first time period to further identify a sleep stage of the user; and

[0290] Sensor data associated with the determined first time period and when the identified sleep stage of the user is REM sleep is discarded.

[0291] 93. A method according to any of implementations 63 to 92, wherein analyzing sensor data associated with the user includes: processing the sensor data to identify one or more features indicative of suspected arousal.

[0292] 94. The method according to any one of implementations 63 to 92, wherein analyzing the sensor data comprises: detecting a breathing waveform including an inspiratory waveform and / or an expiratory waveform, and determining a deviation from a normal breathing waveform.

[0293] 95. The method according to implementation 94, wherein determining the deviation from a normal breathing waveform comprises analyzing changes in the user's respiratory flow over time, and quantifying a fit metric between the inhalation waveform and / or the exhalation waveform and the normal breathing waveform.

[0294] 96. The method according to implementation 94 or 95, wherein determining the deviation from a normal breathing waveform comprises: fitting a half-sine wave to the inspiratory waveform, and determining a fit metric of the half-sine wave to the inspiratory waveform.

[0295] 97. The method of implementation 96, wherein the fit metric is a root mean square (RMS) error of the fit.

[0296] 98. The method of implementation 96, wherein the fit metric describes the deviation from normal inspiratory flow.

[0297] 99. The method of implementation 97, wherein deviations from normal inspiratory airflow are monitored over one or more respiratory therapy sessions, wherein the deviations are indicative of development or worsening of a respiratory disorder.

[0298] 100. The method of implementation 98, wherein the respiratory disease is one or more of bronchitis, COPD, and a sleep-related disorder.

[0299] 101. The method of implementations 96 to 100, wherein a step change in the fit metric indicates a change in the user's body position or sleep state.

[0300] 102. A method according to any one of implementations 94 to 101, wherein the normal breathing waveform is determined during a period in which the user is determined to have good airway patency, a period in which the user is in a particular sleep stage, or a period in which there is no sign of any airway obstruction.

[0301] 103. The method according to any one of implementations 63 to 102, further comprising:

[0302] Provide control signals to the breathing apparatus; and

[0303] Responsive to the pSDB state, a modification to a pressure setting of the respiratory device associated with pressurized air supplied to the airway of the user is determined.

[0304] 104. The method according to any one of implementations 63 to 103, further comprising:

[0305] providing control signals to the smart pillow; and

[0306] In response to the pSDB state, the smart pillow is adjusted so that the smart pillow prompts the user to change the position of the user's head.

[0307] 105. The method according to any one of implementations 63 to 104, further comprising:

[0308] Providing control signals to a smart bed or smart mattress; and

[0309] In response to the pSDB state, the smart bed or smart mattress is adjusted so that the smart bed or smart mattress prompts the user to change the user's body position.

[0310] 106. The method according to any one of implementations 63 to 105, further comprising:

[0311] providing a control signal to a wearable device that can be coupled to a body part of a user; and

[0312] In response to the pSDB state, the wearable device is adjusted such that the wearable device stimulates the user to change the user's body position.

[0313] 107. The method according to any one of implementations 63 to 106 further includes: in response to the pSDB status, causing a notification to be provided to a user or a third party via an electronic device, so that an alert of the pSDB status is issued to the user.

[0314] 108. The method according to implementation manner 107, wherein the electronic device is an electronic display device, and providing the notification includes: displaying a message on the electronic display device.

[0315] 109. The method according to implementation 107 or implementation 108, wherein the electronic device includes a speaker, and providing the notification includes: playing a sound via the speaker.

[0316] 110. The method according to implementation 109, wherein the sound is an alarm to wake up the user.

[0317] 111. The method according to any one of implementations 63 to 109, further comprising:

[0318] receiving sleep stage data associated with the user during a therapy session;

[0319] determining a sleep stage based at least in part on the sleep stage data; and

[0320] Associate pSDB states with sleep stages.

[0321] 112. A method according to implementation 111, wherein the sleep stage includes awake, drowsy, asleep, light sleep, deep sleep, N1 sleep, N2 sleep, N3 sleep, REM sleep, or a combination thereof.

[0322] 113. The method according to any one of implementations 63 to 112, further comprising:

[0323] analyzing the sensor data to identify a third period of suspected arousal and a fourth period of suspected arousal;

[0324] determining a second time period between the identified third time period and the identified fourth time period; and

[0325] analyzing sensor data associated with the determined second time period to identify another indication of one or more respiratory events,

[0326] wherein the indication of the identified one or more respiratory events associated with the first time segment includes a first number and / or type of respiratory events,

[0327] wherein the further indication of the identified one or more respiratory events associated with the second time segment comprises a second number and / or type of respiratory events, and

[0328] Wherein, determining the pSDB state of the user includes: comparing a first number and / or type of respiratory events with a second number and / or type of respiratory events.

[0329] 114. The method according to implementation mode 113, wherein the second time period is the same as the third time period.

[0330] 115. The method according to implementation mode 113, wherein the fourth time period is the same as the first time period.

[0331] One or more elements or aspects or steps or any portion thereof from one or more of any of the above claims may be combined with one or more elements or aspects or steps or any portion thereof from one or more of any other claims or combinations thereof to form one or more additional implementations and / or claims of the present disclosure.

[0332] Although the present disclosure has been described with reference to one or more specific 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 their obvious variations are considered to fall 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 implementation described herein.

Claims

1. A system for determining a positional sleep disordered breathing (pSDB) state associated with a user of a respiratory device, the system comprising a control system configured to: receiving airflow data associated with a user of the respiratory device; analyzing the airflow data to identify a first period of suspected arousal and a second period of suspected arousal; determining a first time period between the identified first time period and the identified second time period; analyzing the airflow data associated with the determined first time segment to identify (i) an indication of one or more respiratory events, (ii) an indication of one or more therapy events, or (iii) both; and Determining a pSDB status of the user based at least in part on (i) an indication of one or more identified respiratory events, (ii) an indication of one or more identified therapeutic events, or (iii) both (i) and (ii), the pSDB status indicating whether the user suffers from pSDB.

2. The system according to claim 1, in, The air flow data includes flow rate data, pressure data, or both.

3. The system according to claim 1 or 2, in, The suspected arousal is indicated by one or more features in the airflow data.

4. The system according to any one of claims 1 to 3, in, The suspected awakening indicates body movement of the user.

5. The system according to any one of claims 1 to 4, in, The suspected arousal is associated with a change in body position.

6. The system according to any one of claims 1 to 5, in, The first time period is associated with a first motion event and the second time period is associated with a second motion event.

7. The system according to any one of claims 1 to 6, in, The one or more respiratory events include snoring, airflow limitation, residual airflow limitation, apnea, residual apnea, hypopnea, residual hypopnea, respiratory effort related arousal (RERA) events, or any combination of the foregoing.

8. The system according to any one of claims 1 to 7, in, The one or more treatment events include an increase in treatment pressure, a decrease in treatment pressure, a rate of change in treatment pressure, or any combination of the foregoing.

9. The system according to any one of claims 1 to 8, in, The airflow data associated with the determined first time period is further analyzed to identify a body position or a change in body position.

10. The system according to claim 9, in, A machine learning model is used to identify the body position.

11. The system according to claim 10, in, The machine learning model is trained using historical airflow data and reference data.

12. The system according to claim 11, in, The reference data includes accelerometer data, observer rating data, or both.

13. A system according to any one of claims 9 to 12, in, The pSDB status indicates whether the user suffers from pSDB in the identified body position.

14. The system according to claim 13, in, The control system is further configured to determine an increase or modification to a pressure setting of a respiratory therapy device when the user is in the identified body position if the pSDB status indicates that the user suffers from pSDB in the identified body position.

15. The system according to claim 14, in, The steps of the method are repeated until the maximum necessary pressure limit for the identified body position is reached.

16. The system according to claim 13, in, The control system is further configured to determine an increase or modification to a pressure setting of a respiratory therapy device when the user is in the identified body position if the pSDB status indicates that the user suffers from pSDB in the identified body position.

17. The system according to claim 16, in, The receiving, analyzing, determining, analyzing, and determining are repeated until the minimum necessary pressure limit for the identified body position is reached.

18. The system according to any one of claims 1 to 17, in, The indication of the identified one or more respiratory events includes a severity of the one or more respiratory events, a number of occurrences of the one or more respiratory events, a number of occurrences of the one or more respiratory events relative to a threshold, a type of the one or more respiratory events, an apnea-hypopnea index (AHI), or any combination of the foregoing.

19. The system according to any one of claims 1 to 18, in, The control system is further configured to discard a portion of the airflow data associated with airflow supplied to the user at a pressure setting of a respiratory therapy device that exceeds a predetermined threshold prior to analyzing to identify the first period of suspected arousal and the second period of suspected arousal.

20. The system according to any one of claims 1 to 19, in, The control system is also configured to analyze at least a portion of the airflow data associated with the determined first time period, wherein the portion decreases as a pressure setting of the respiratory therapy device decreases, to identify (i) an indication of one or more respiratory events, (ii) an indication of one or more therapy events, or (iii) both of the foregoing (i) and (ii).

21. The system according to any one of claims 1 to 20, in, The control system is also configured to: analyzing the airflow data to identify a third period of suspected arousal and a fourth period of suspected arousal; determining a second time period between the identified third time period and the identified fourth time period; and Analyze the airflow data associated with the determined second time period to identify (i) an indication of one or more respiratory events, (ii) an indication of one or more therapeutic events, or (iii) both of (i) and (ii), wherein a therapeutic pressure associated with the second time period is lower than a therapeutic pressure associated with the first time period.

22. The system according to any one of claims 1 to 21, in, The control system is further configured to discard a portion of the airflow data associated with airflow supplied to the user at a pressure setting of a respiratory therapy device that exceeds a predetermined threshold before analyzing the airflow data associated with the determined first time period.

23. A system according to claim 19 or claim 22, in, The predetermined threshold is about 10 cmH 2 O.

24. A system according to any one of claims 1 to 23, in, The pSDB status includes a probability that the user suffers from pSDB, a classification of pSDB, or both.

25. The system according to claim 24, in, The classification of the pSDB includes determining whether the pSDB is positional obstructive sleep apnea (pOSA), positional central sleep apnea (pCSA), positional respiratory effort-related arousals (RERA), positional hypopnea, positional snoring, or any combination of the foregoing.

26. The system according to any one of claims 1 to 25, in, The control system is also configured to: receiving heart rate data associated with a user of the respiratory device; and The received heart rate data is analyzed to confirm the suspected arousal.

27. The system according to claim 26, in, Analyzing the received heart rate data includes determining a heart rate, a change in heart rate, or both, and confirming the suspected arousal based on the determined heart rate, the determined change in heart rate, or both.

28. The system according to claim 27, in, An increase in the heart rate indicates the suspected arousal.

29. The system according to any one of claims 1 to 28, in, The control system is also configured to: receiving acoustic data associated with a user of the respiratory device; and The received acoustic data is analyzed to identify or confirm the first period of suspected arousal and / or the second period of suspected arousal.

30. The system according to claim 29, in, Analyzing the received acoustic data includes detecting sounds associated with body movements of the user and / or changes in body position of the user.

31. A system according to any one of claims 1 to 30, in, The control system is also configured to: receiving acoustic data associated with a user of the respiratory device; and If the pSDB status indicates that the user suffers from pSDB, the received acoustic data associated with the determined first time period is analyzed to identify a location of an obstacle associated with the user.

32. The system according to claim 31, in, The location is a point or area along the user's airway.

33. A system according to claim 31 or claim 32, in, The location is a distance from a user interface worn by a user of the respiratory therapy device.

34. A system according to any one of claims 31 to 33, in, The acoustic data includes acoustic reflections of the user's airway, the inside of the user's mouth, or both.

35. The system according to claim 34, in, The acoustic reflection is represented by an acoustic impedance or a distance of the acoustic impedance.

36. A system according to any one of claims 1 to 35, in, The control system is also configured to: receiving acoustic data associated with a user of the respiratory device; and The received acoustic data is analyzed to further determine or confirm the suspected arousal.

37. A system according to any one of claims 1 to 36, in, The control system is also configured to: analyzing the airflow data to identify (i) one or more sleep stages of the user and (ii) the one or more respiratory events experienced by the user; and One or more sleep stages of the user are correlated with one or more respiratory events experienced by the user to determine whether the user experiences a rapid eye movement (REM) dominated respiratory event.

38. The system according to claim 37, in, The control system is also configured to: analyzing the airflow data associated with the determined first time period to further identify a sleep stage of the user; and The airflow data associated with the determined first time segment and when the identified sleep stage of the user is REM sleep is discarded.

39. The system according to any one of claims 1 to 38, in, Analyzing airflow data associated with the user includes processing the airflow data to identify one or more features indicative of the suspected arousal.

40. The system according to claim 39, in, The one or more characteristics include an increased amplitude of the flow rate signal, an increased change in the respiratory rate, respiratory cessation, an increase in noise in the flow rate signal, an increase in the flow rate amplitude at a decreased respiratory rate with a decrease in the amplitude of the flow rate signal at a relatively increased respiratory rate, or any combination of the foregoing.

41. A system according to any one of claims 1 to 40, in, Analyzing the airflow data includes detecting a breathing waveform including an inspiratory waveform and / or an expiratory waveform, and determining a deviation from a normal breathing waveform.

42. The system according to claim 41, in, Determining the deviation from the normal breathing waveform includes analyzing changes in the user's respiratory flow over time and quantifying a measure of fit between an inspiratory waveform and / or an expiratory waveform and the normal breathing waveform.

43. The system according to claim 41 or 42, in, Determining the deviation from the normal breathing waveform includes fitting a half-sine wave to the inspiratory waveform and determining a measure of the fit of the half-sine wave to the inspiratory waveform.

44. The system according to claim 43, in, The fit metric is the root mean square (RMS) error of the fit.

45. The system according to claim 43 or 44, in, The fit metric describes the deviation from normal inspiratory flow.

46. ​​The system according to claim 45, in, Deviations from the normal inspiratory flow are monitored over one or more respiratory therapy sessions, wherein the deviations are indicative of development or worsening of a respiratory disorder.

47. The system according to claim 46, in, The respiratory disease is one or more of bronchitis, COPD and sleep related disorders.

48. A system according to any one of claims 43 to 47, in, A step change in the fit metric indicates a change in the user's body position or sleep state.

49. A system according to any one of claims 41 to 48, in, The normal breathing waveform is determined during a period when the user is determined to have good airway patency, a period when the user is in a particular sleep stage, or a period without any signs of airway obstruction.

50. The system according to any one of claims 1 to 49, in, The control system is also configured to: providing a control signal to the breathing apparatus; and Responsive to the pSDB state, a modification to a pressure setting of the respiratory apparatus is determined, the pressure setting associated with pressurized air supplied to the airway of the user.

51. A system according to any one of claims 1 to 50, in, The control system is also configured to: providing control signals to the smart pillow; and Responsive to the pSDB state, a modification to the smart pillow is determined such that the modification to the smart pillow is implemented in a manner that prompts the user to change the position of the user's head.

52. A system according to any one of claims 1 to 51, in, The control system is also configured to: Providing control signals to a smart bed or smart mattress; and In response to the pSDB state, a modification to the smart bed or the smart mattress is determined, such that the modification to the smart bed or the smart mattress is implemented in a manner that prompts the user to change the body position of the user.

53. A system according to any one of claims 1 to 52, in, The control system is also configured to: providing a control signal to a wearable device, the wearable device being coupleable to a body part of the user; and Responsive to the pSDB state, a modification to the wearable device is determined such that the modification to the wearable device is implemented in a manner that stimulates the user to change a body position of the user.

54. A system according to any one of claims 1 to 53, in, The control system is further configured to cause a notification to be provided to the user or a third party via an electronic device in response to the pSDB status, such that the user or the third party is alerted to the pSDB status.

55. The system according to claim 54, in, The electronic device is an electronic display device, and providing the notification includes displaying a message on the electronic display device.

56. A system according to claim 54 or claim 55, in, The electronic device includes a speaker, and providing the notification includes playing a sound via the speaker.

57. The system according to claim 56, in, The sound is an alarm to wake up the user.

58. A system according to any one of claims 1 to 57, in, The control system is also configured to: receiving sleep stage data associated with the user during a respiratory therapy session; determining a sleep stage based at least in part on the sleep stage data; and The pSDB state is associated with the sleep stage.

59. The system according to claim 58, in, The sleep stages include awake, drowsy, asleep, light sleep, deep sleep, N1 sleep, N2 sleep, N3 sleep, REM sleep, or a combination thereof.

60. The system according to any one of claims 1 to 59, in, The control system is also configured to: analyzing the airflow data to identify a third period of suspected arousal and a fourth period of suspected arousal; determining a second time period between the identified third time period and the identified fourth time period; and analyzing the airflow data associated with the determined second time segment to identify another of (i) one or more indications of respiratory events, (ii) one or more indications of therapy events, or (iii) both (i) and (ii), wherein (i) an indication of one or more identified respiratory events, (ii) an indication of one or more identified therapeutic events, or (iii) both (i) and (ii) include a first number and / or type of respiratory events, therapeutic events, or both, associated with the first time segment; wherein associated with the second time segment is (i) another indication of the one or more identified respiratory events, (ii) another indication of the one or more identified therapy events, or (iii) both (i) and (ii) a second number and / or type of respiratory events, therapy events, or both, and Wherein, determining the pSDB state of the user includes: comparing the first number and / or type of respiratory events, treatment events, or both with the second number and / or type of respiratory events, treatment events, or both.

61. The system according to claim 60, in, The second time period is the same as the third time period.

62. The system according to claim 60, in, The fourth time period is the same as the first time period.

63. A computer program product comprising instructions which, when executed by a computer, cause the computer to: receiving airflow data associated with a user of the respiratory device; analyzing the airflow data to identify a first period of suspected arousal and a second period of suspected arousal; determining a first time period between the identified first time period and the identified second time period; analyzing the airflow data associated with the determined first time segment to identify (i) an indication of one or more respiratory events, (ii) an indication of one or more therapy events, or (iii) both; and Determining a postural sleep dyspnea (pSDB) status of the user based at least in part on (i) an indication of one or more identified respiratory events, (ii) an indication of one or more identified therapeutic events, or (iii) both (i) and (ii), the pSDB status indicating whether the user suffers from pSDB.

64. The computer program product according to claim 63, in, The computer program product is a non-transitory computer-readable medium.

65. A system for determining a positional sleep disordered breathing (pSDB) state associated with a user, the system comprising a control system configured to: receiving sensor data associated with the user; analyzing the sensor data to identify a first period of suspected arousal and a second period of suspected arousal; determining a first time period between the identified first time period and the identified second time period; analyzing sensor data associated with the determined first time period to identify indications of one or more respiratory events; and Based at least in part on the identified indication of one or more respiratory events, a pSDB status of the user is determined, the pSDB status indicating whether the user suffers from pSDB.

66. The system according to claim 65, in, The sensor data is obtained from one or more sensors selected from body mounted accelerometers, contact sensors, contactless sensors, acoustic sensors, or any combination of the foregoing.

67. The system according to claim 65 or 66, in, The sensor data is obtained from a diagnostic device.

68. The system according to claim 67, in, The breathing signal originates from a non-sealed interface or a breathing effort belt of the diagnostic device.

69. The system according to claim 68, in, The non-sealing interface is a nasal cannula.

70. The system according to any one of claims 65 to 69, in, The sensor data is obtained from a motion sensor.

71. The system according to claim 70, in, The motion sensor includes an accelerometer.

72. A system according to claim 70 or claim 71, in, The motion sensor is worn on the user's body or is an environmental sensor not worn by the user.

73. A system according to claim 70 or claim 71, in, The motion sensor is coupled to or integrated into the user's respiratory device.

74. A system according to claim 70 or claim 71, in, The motion sensor is coupled to or integrated into the mobile device.

75. A system according to any one of claims 65 to 74, in, The breathing signal is derived from the sensor data.

76. A system according to any one of claims 65 to 75, in, The suspected arousal is indicated by one or more features in the sensor data.

77. A system according to any one of claims 65 to 76, in, The suspected awakening indicates body movement of the user.

78. A system according to any one of claims 65 to 77, in, The suspected arousal is associated with a change in body position.

79. A system according to any one of claims 65 to 78, in, The first time period is associated with a first motion event and the second time period is associated with a second motion event.

80. The system according to any one of claims 65 to 79, in, The one or more respiratory events include snoring, airflow limitation, residual airflow limitation, residual apnea, residual hypopnea, respiratory effort related arousal (RERA) events, or any combination of the foregoing.

81. The system according to any one of claims 65 to 80, in, The sensor data associated with the determined first time period is further analyzed to identify a body position or a change in body position.

82. The system according to claim 81, in, A machine learning model is used to identify the body position.

83. The system according to claim 82, in, The machine learning model is trained using historical sensor data and reference data.

84. The system according to claim 83, in, The reference data includes accelerometer data, observer rating data, or both.

85. A system according to any one of claims 81 to 84, in, The pSDB status indicates whether the user suffers from pSDB in the identified body position.

86. A system according to any one of claims 65 to 85, in, The indication of the identified one or more respiratory events includes a severity of the one or more respiratory events, a number of occurrences of the one or more respiratory events, a number of occurrences of the one or more respiratory events relative to a threshold, a type of the one or more respiratory events, an apnea-hypopnea index (AHI), or any combination of the foregoing.

87. A system according to any one of claims 65 to 86, in, The pSDB status includes a probability that the user suffers from pSDB, a classification of pSDB, or both.

88. The system according to claim 87, in, The classification of the pSDB includes determining whether the pSDB is positional obstructive sleep apnea (pOSA), positional central sleep apnea (pCSA), positional respiratory effort-related arousals (RERA), positional hypopnea, positional snoring, or any combination of the foregoing.

89. A system according to any one of claims 65 to 88, in, The control system is also configured to: receiving heart rate data associated with the user; and The received heart rate data is analyzed to determine or confirm the suspected arousal.

90. The system according to claim 89, in, Analyzing the received heart rate data includes determining a heart rate, a change in heart rate, or both, and confirming the suspected arousal by analyzing the determined heart rate, the determined change in heart rate, or both.

91. The system according to claim 90, in, An increase in the heart rate indicates the suspected arousal.

92. A system according to any one of claims 65 to 91, in, The control system is also configured to: receiving acoustic data associated with the user; and The received acoustic data is analyzed to determine or confirm the suspected arousal.

93. A system according to any one of claims 65 to 91, in, The control system is also configured to: analyzing the sensor data to identify (i) one or more sleep stages of the user and (ii) one or more respiratory events experienced by the user; as well as One or more sleep stages of the user are correlated with one or more respiratory events experienced by the user to determine whether the user experiences a rapid eye movement (REM) dominated respiratory event.

94. The system according to claim 93, in, The control system is also configured to: analyzing the sensor data associated with the determined first time period to further identify a sleep stage of the user; and The sensor data associated with the determined first time period and when the identified sleep stage of the user is REM sleep is discarded.

95. The system according to any one of claims 65 to 94, in, Analyzing sensor data associated with the user includes processing the sensor data to identify one or more features indicative of the suspected arousal.

96. A system according to any one of claims 65 to 95, in, Analyzing the sensor data includes detecting a breathing waveform including an inspiratory waveform and / or an expiratory waveform, and determining deviations from a normal breathing waveform.

97. The system according to claim 96, in, Determining the deviation from the normal breathing waveform includes analyzing changes in the user's respiratory flow over time and quantifying a measure of fit between an inspiratory waveform and / or an expiratory waveform and the normal breathing waveform.

98. The system according to claim 96 or 97, in, Determining the deviation from the normal breathing waveform includes fitting a half-sine wave to the inspiratory waveform and determining a measure of the fit of the half-sine wave to the inspiratory waveform.

99. The system according to claim 97, in, The fit metric is the root mean square (RMS) error of the fit.

100. The system according to claim 98, in, The fit metric describes the deviation from normal inspiratory flow.

101. The system according to claim 100, in, Deviations from the normal inspiratory flow are monitored over one or more respiratory therapy sessions, wherein the deviations are indicative of development or worsening of a respiratory disorder.

102. The system according to claim 101, in, The respiratory disease is one or more of bronchitis, COPD and sleep related disorders.

103. The system according to any one of claims 97 to 102, in, A step change in the fit metric indicates a change in the user's body position or sleep state.

104. The system according to any one of claims 96 to 103, in, The normal breathing waveform is determined during a period when the user is determined to have good airway patency, a period when the user is in a particular sleep stage, or a period without any signs of airway obstruction.

105. The system according to any one of claims 65 to 104, in, The control system is also configured to: Provide control signals to the breathing apparatus; and Responsive to the pSDB state, a modification to a pressure setting of the respiratory apparatus is determined, the pressure setting associated with pressurized air supplied to the airway of the user.

106. The system according to any one of claims 65 to 105, in, The control system is also configured to: providing control signals to the smart pillow; and In response to the pSDB state, the smart pillow is adjusted so that the smart pillow prompts the user to change the position of the user's head.

107. The system according to any one of claims 65 to 106, in, The control system is also configured to: Providing control signals to a smart bed or smart mattress; and In response to the pSDB state, the smart bed or the smart mattress is adjusted so that the smart bed or the smart mattress prompts the user to change the body position of the user.

108. A system according to any one of claims 65 to 107, in, The control system is also configured to: providing a control signal to a wearable device, the wearable device being coupleable to a body part of the user; and In response to the pSDB state, the wearable device is adjusted so that the wearable device stimulates the user to change the user's body position.

109. The system according to any one of claims 65 to 108, in, The control system is further configured to cause a notification to be provided to the user or a third party via an electronic device in response to the pSDB status such that the user is alerted to the pSDB status.

110. The system according to claim 109, in, The electronic device is an electronic display device, and providing the notification includes displaying a message on the electronic display device.

111. The system of claim 109 or claim 110, in, The electronic device includes a speaker, and providing the notification includes playing a sound via the speaker.

112. The system according to claim 111, in, The sound is an alarm to wake up the user.

113. A system according to any one of claims 65 to 112, in, The control system is also configured to: receiving sleep stage data associated with the user during a therapy session; determining a sleep stage based at least in part on the sleep stage data; and The pSDB state is associated with the sleep stage.

114. The system according to claim 113, in, The sleep stages include awake, drowsy, asleep, light sleep, deep sleep, N1 sleep, N2 sleep, N3 sleep, REM sleep, or a combination thereof.

115. A system according to any one of claims 65 to 114, in, The control system is also configured to: analyzing the sensor data to identify a third period of suspected arousal and a fourth period of suspected arousal; determining a second time period between the identified third time period and the identified fourth time period; and analyzing sensor data associated with the determined second time period to identify another indication of one or more respiratory events, wherein the indication of the identified one or more respiratory events associated with the first time segment comprises a first number and / or type of respiratory events, wherein the further indication of the identified one or more respiratory events associated with the second time period comprises a second number and / or type of respiratory events, and Wherein, determining the pSDB state of the user comprises: comparing the first number and / or type of respiratory events with the second number and / or type of respiratory events.

116. The system according to claim 115, in, The second time period is the same as the third time period.

117. The system according to claim 115, in, The fourth time period is the same as the first time period.

118. A computer program product comprising instructions which, when executed by a computer, cause the computer to: receiving sensor data associated with a user; analyzing the sensor data to identify a first period of suspected arousal and a second period of suspected arousal; determining a first time period between the identified first time period and the identified second time period; analyzing sensor data associated with the determined first time period to identify indications of one or more respiratory events; and Based at least in part on the identified indication of one or more respiratory events, a positional sleep disordered breathing (pSDB) status of the user is determined, the pSDB status indicating whether the user suffers from pSDB.

119. The computer program product according to claim 118, in, The computer program product is a non-transitory computer-readable medium.

120. A system, include: a respiratory therapy device configured to supply pressurized air to a user; a memory storing machine-readable instructions; as well as A control system comprising one or more processors configured to execute the machine-readable instructions to: receiving airflow data associated with a user of the respiratory device; analyzing the airflow data to identify a first period of suspected arousal and a second period of suspected arousal; determining a time period between the identified first time period and the identified second time period; analyzing the airflow data associated with the determined time segment to identify (i) an indication of one or more respiratory events, (ii) an indication of one or more therapy events, or (iii) both; and Based at least in part on (i) one or more identified respiratory events, (ii) an indication of one or more identified treatment events, or (iii) both (i) and (ii), a positional sleep dysbiosis (pSDB) status of the user is determined, the pSDB status indicating whether the user suffers from pSDB.

121. The system according to claim 120, in, The air flow data includes flow rate data, pressure data, or both.

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