Method and system for determining a treatment for a sleep disorder for a patient

Abstract

A method for determining a treatment for a sleep disorder for a patient. The method comprises administering a positive airway pressure (PAP) treatment to the patient using a respiratory pressure therapy (RPT) device in a first setting, and acquiring from one or more sensors associated with the patient an initial sleep parameter signal. The PAP treatment is further administered to the patient using the PAP apparatus in a second setting, and subsequent sleep parameter signal is acquired from the one or more sensors. The initial sleep parameter signal and subsequent sleep parameter signal are processed to generate a sleep parameter difference signal. The sleep parameter difference signal is processed to generate a sleep disorder characteristic signal. A sleep disorder treatment for the patient is determined based on the sleep disorder characteristic signal.

Description

METHOD AND SYSTEM FOR DETERMINING A TREATMENT FOR A SLEEP DISORDER FOR A PATIENTTECHNICAL FIELD

[0001] The present disclosure relates generally to systems and methods for determining a treatment for a sleep disorder for a patient, and, more particularly, to a system and method for determining a treatment for a patient experiencing obstructive sleep apnea.BACKGROUND

[0002] Obstructive sleep apnea (OSA) is a disorder characterized by recurrent upper airway collapse (e.g., partial or complete obstructions) during sleep that leads to sleep fragmentation and sympathetic activation. Obstructive Sleep Apnea (OSA) is a common and debilitating disorder. Continuous positive airway pressure (CPAP), when configured correctly is an effective treatment for OSA. Other treatments for OSA include mandibular advancement devices, hypoglossal nerve stimulation, pharyngeal surgery and positional therapy. The present disclosure is directed to providing alternative and possibly improved techniques for more effectively configuring CPAP treatments and determining when nonCP AP treatments could be effective.SUMMARY

[0003] According to some implementations of the present disclosure, there is provided a method for determining a treatment for a sleep disorder for a patient, the method comprising: administering a positive airway pressure (PAP) treatment to the patient using a respiratory pressure therapy (RPT) device in a first setting; acquiring from one or more sensors associated with the patient an initial sleep parameter signal; administering the positive airway pressure (PAP) treatment to the patient using the PAP apparatus in a second setting; acquiring from the one or more sensors a subsequent sleep parameter signal; processing the initial sleep parameter signal and subsequent sleep parameter signal to generate a sleep parameter difference signal; acquiring sleep phenotype data pertaining to the patient; processing the sleep parameter difference signal and sleep phenotype data to generate a sleep disorder characteristic signal; and determining a sleep disorder treatment for the patient based on the sleep disorder characteristic signal.

[0004] In some embodiments the sleep disorder characteristic signal is indicative of an airway collapse characteristic pertaining to a characteristic of an airway collapse in the patient. The characteristic of the airway collapse may comprises one or more of an anatomical site at which the airway collapse occurs, structures of the airway implicated in the airway collapse, extent of the airway collapse and frequency of the airway collapse. The structures of the airway implicated in the airway collapse may comprises one or more pharyngeal structures. In some embodiments, the characteristic of the airway collapse is complete concentric collapse of a soft palate.

[0005] In some embodiments, the first setting comprises a first pressure at which the RPT device delivers air to an upper respiratory tract of the patient, the second setting comprises a second pressure at which the RPT device delivers air to the upper respiratory tract of the patient, wherein the first pressure and the second pressure are different.

[0006] The initial sleep parameter signal and subsequent sleep parameter signal may each comprise one or more of an electroencephalogram signal, electrooculogram signal, electromyogram signal, electrocardiogram signal, nasal airflow signal, oral airflow signal, thoracic effort signal, abdominal effort signal, actigraphy signal, oxygen saturation signal, heart rate signal, pulse rate signal, peripheral arterial tone signal, sleep architecture signal, analyte signal and an audio signal.

[0007] In some embodiments, the sleep phenotype data comprises one or more of physiological measurements, anatomical measurements, genetic data, treatment-response data, treatment compliance data, neurocognitive function data and data derived from a sleep questionnaire.

[0008] In some embodiments, the determined sleep disorder treatment comprises settings for the RPT device. In other embodiments, the determined sleep disorder treatment comprises one or more of a mandibular advancement device, hypoglossal nerve stimulation, pharyngeal surgery, and positional therapy.

[0009] According to another implementation of the present disclosure, there is provided a system for determining a treatment for a sleep disorder for a patient, comprising: a memory storing machine-readable instructions; and one or more processors configured to execute the machine-readable instructions to: acquire from one or more sensors associated with the patient an initial sleep parameter signal indicative of the patient receiving a positive airway pressure (PAP) treatment with a respiratory pressure therapy (RPT) device in a first setting;acquire from one or more sensors associated with the patient a subsequent sleep parameter signal indicative of the patient receiving a PAP treatment with the RPT device in a second setting; process the initial sleep parameter signal and subsequent sleep parameter signal to generate a sleep parameter difference signal; acquire sleep phenotype data pertaining to the patient; process the sleep parameter difference signal and sleep phenotype data to generate a sleep disorder characteristic signal; and determine a sleep disorder treatment for the patient based on the sleep disorder characteristic signal.

[0010] The above summary is not intended to represent each implementation or every aspect of the present disclosure. Additional features and benefits of the present disclosure are apparent from the detailed description and figures set forth below.BRIEF DESCRIPTION OF THE DRAWINGS

[0011] The foregoing and other advantages of the present disclosure will become apparent upon reading the following detailed description and upon reference to the drawings.

[0012] FIG 1 A is a diagram that illustrates an overview of a respiratory system of a patient;

[0013] FIG. IB is a diagram that illustrates an upper airway of the patient of FIG. 1A;

[0014] FIG. 2 is a functional block diagram of a system for determining a treatment for a sleep disorder for a patient, according to some implementations of the present disclosure.

[0015] FIG. 3 is a perspective view of at least a portion of the system of FIG. 2, a patient, and a bed partner, according to some implementations of the present disclosure.

[0016] FIG. 4 illustrates a method for determining a treatment for a sleep disorder for a patient, according to some implementations of the present disclosure.

[0017] While the present disclosure is susceptible to various modifications and alternative forms, specific implementations and embodiments thereof have been shown by way of example in the drawings and will herein be described in detail. It should be understood, however, that it is not intended to limit the present disclosure to the particular forms disclosed, but on the contrary, the present disclosure is to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the present disclosure as defined by the appended claims.DETAILED DESCRIPTION

[0018] Referring to FIG. 1A, an overview of a respiratory system 12 of a patient 10 is shown, which generally includes a nasal cavity, an oral cavity, a larynx, vocal folds, an esophagus, a trachea, a bronchus, lungs, alveolar sacs, a heart, and a diaphragm. More generally, the patient 10 has a throat 20, which includes a region(s) of the respiratory system 12 of the patient 10 generally in the neck area of the patient 10. The diaphragm of the patient 10 is a sheet of muscle that extends across the bottom of the rib cage of the patient 10. The diaphragm generally separates the thoracic cavity 40 of the patient 10, which contains the heart, lungs, and ribs, from the abdominal cavity 40 of the patient 10. As the diaphragm contracts, the volume of the thoracic cavity 40 increases and air is drawn into the lungs.

[0019] Referring to FIG. IB, a view of an upper airway 14 of the patient 10 is shown, which includes the nasal cavity, nasal bone, lateral nasal cartilage, greater alar cartilage, nostrils (one shown), a lip superior, a lip inferior, the larynx, a hard palate, a soft palate, an oropharynx, a tongue, an epiglottis, the vocal folds, the esophagus, and the trachea.

[0020] The respiratory system 12 of the patient 10 facilitates gas exchange. The nose 50 and mouth 60 of the patient 10 form the entrance to the airways of the patient 10. As best shown in FIG. 1A, the airways include a series of branching tubes, which become narrower, shorter, and more numerous as they penetrate deeper into the lungs of the patient 10. The prime function of the lungs is gas exchange, allowing oxygen to move from the inhaled air into the venous blood and carbon dioxide to move in the opposite direction. The trachea divides into right and left main bronchi, which further divide eventually into terminal bronchioles. The bronchi make up the conducting airways, and do not take part in gas exchange. Further divisions of the airways lead to the respiratory bronchioles, and eventually to the alveoli. The alveolated region of the lungs is where the gas exchange takes place, and is referred to as the respiratory zone.

[0021] A range of respiratory disorders exist that can impact the patient 10. Certain disorders are characterized by particular events (e.g., apneas, hypopneas, hyperpneas, or any combination thereof). Examples of sleep-related and / or respiratory disorders include Periodic Limb Movement Disorder (PLMD), Restless Leg Syndrome (RLS), Sleep- Disordered Breathing (SDB), Obstructive Sleep Apnea (OSA), Cheyne-Stokes Respiration (CSR), respiratory insufficiency, Obesity Hyperventilation Syndrome (OHS), ChronicObstructive Pulmonary Disease (COPD), Neuromuscular Disease (NMD), and chest wall disorders.

[0022] Obstructive Sleep Apnea (OSA) is a form of Sleep Disordered Breathing (SDB), and is characterized by events including occlusion or obstruction of the upper air passage during sleep resulting from a combination of an abnormally small upper airway and the normal loss of muscle tone in the region of the tongue, soft palate and posterior oropharyngeal wall. More generally, an apnea generally refers to the cessation of breathing caused by blockage of the air (Obstructive Sleep Apnea) or the stopping of the breathing function (often referred to as central apnea). Other types of apneas include hypopnea, hyperpnea, and hypercapnia. Hypopnea is generally characterized by slow or shallow breathing caused by a narrowed airway, as opposed to a blocked airway. Hyperpnea is generally characterized by an increase depth and / or rate of breathing. Hypercapnia is generally characterized by elevated or excessive carbon dioxide in the bloodstream, typically caused by inadequate respiration.

[0023] Cheyne-Stokes Respiration (CSR) is another form of sleep disordered breathing. CSR is a disorder of a patient's respiratory controller in which there are rhythmic alternating periods of waxing and waning ventilation known as CSR cycles. CSR is characterized by repetitive de-oxygenation and re-oxygenation of the arterial blood. It is possible that CSR is harmful because of the repetitive hypoxia. In some patients, CSR is associated with repetitive arousal from sleep, which causes severe sleep disruption, increased sympathetic activity, and increased afterload.

[0024] Respiratory failure is an umbrella term for respiratory disorders in which the lungs are unable to inspire sufficient oxygen or exhale sufficient CO2 to meet the patient’ s needs. Respiratory failure may encompass some or all of the following disorders. A patient with respiratory insufficiency (a form of respiratory failure) may experience abnormal shortness of breath on exercise.

[0025] Obesity Hyperventilation Syndrome (OHS) is defined as the combination of severe obesity and awake chronic hypercapnia, in the absence of other known causes for hypoventilation. Symptoms include dyspnea, morning headache and excessive daytime sleepiness.

[0026] Chronic Obstructive Pulmonary Disease (COPD) encompasses any of a group of lower airway diseases that have certain characteristics in common, such as increasedresistance to air movement, extended expiratory phase of respiration, and loss of the normal elasticity of the lung. Examples of COPD are emphysema and chronic bronchitis. COPD is caused by chronic tobacco smoking (primary risk factor), occupational exposures, air pollution and genetic factors. Symptoms include: dyspnea on exertion, chronic cough and sputum production.

[0027] Neuromuscular Disease (NMD) encompasses many diseases and ailments that impair the functioning of the muscles either directly via intrinsic muscle pathology, or indirectly via nerve pathology. Some patients suffering from NMD are characterized by progressive muscular impairment leading to loss of ambulation, being wheelchair-bound, swallowing difficulties, respiratory muscle weakness and, eventually, death from respiratory failure. Neuromuscular disorders can be divided into rapidly progressive and slowly progressive: (i) rapidly progressive disorders: characterized by muscle impairment that worsens over months and results in death within a few years (e.g. amyotrophic lateral sclerosis (ALS) and duchenne muscular dystrophy (DMD) in teenagers); (ii) variable or slowly progressive disorders: characterized by muscle impairment that worsens over years and only mildly reduces life expectancy (e.g. limb girdle, Facioscapulohumeral and myotonic muscular dystrophy). Symptoms of respiratory failure in NMD include: increasing generalized weakness, dysphagia, dyspnea on exertion and at rest, fatigue, sleepiness, morning headache, and difficulties with concentration and mood changes.

[0028] Chest wall disorders are a group of thoracic deformities that result in inefficient coupling between the respiratory muscles and the thoracic cage. The disorders are usually characterized by a restrictive defect and share the potential of long term hypercapnic respiratory failure. Scoliosis and / or kyphoscoliosis may cause severe respiratory failure. Symptoms of respiratory failure include: dyspnea on exertion, peripheral edema, orthopnea, repeated chest infections, morning headaches, fatigue, poor sleep quality and loss of appetite.

[0029] These other disorders are characterized by particular events (e.g., snoring, an apnea, a hypopnea, a restless leg, a sleeping disorder, choking, an increased heart rate, labored breathing, an asthma attack, an epileptic episode, a seizure, or any combination thereof) that occur when the individual is sleeping. While these other sleep-related disorders may have similar symptoms as insomnia, distinguishing these other sleep-related disorders from insomnia is useful for tailoring an effective treatment plan distinguishing characteristicsthat may call for different treatments. For example, fatigue is generally a feature of insomnia, whereas excessive daytime sleepiness is a characteristic feature of other disorders (e.g., PLMD) and reflects a physiological propensity to fall asleep unintentionally.

[0030] 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 patient during the sleep session by the total number of hours of sleep in the sleep session. The event can be, for example, a pause in breathing that lasts for at least 10 seconds. An AHI that is less than 5 is considered normal. An AHI that is greater than or equal to 5, but less than 15 is considered indicative of mild sleep apnea. An AHI that is greater than or equal to 15, but less than 40 is considered indicative of moderate sleep apnea. An AHI that is greater than or equal to 40 is considered indicative of severe sleep apnea. In children, an AHI that is greater than 1 is considered abnormal. Sleep apnea can be considered “controlled” when the AHI is normal, or when the AHI is normal or mild. The AHI can also be used in combination with oxygen desaturation levels to indicate the severity of Obstructive Sleep Apnea.

[0031] The present disclosure is concerned with methods and system for determining treatments for one or more of the sleep-related and / or respiratory disorders discussed above. According to one embodiment, the present disclosure provides a method and system for determining a treatment for OSA. As discussed below, the determined treatment can involve continuous positive airway pressure (CPAP) treatment using specific settings or another modality such as treatment with a mandibular advancement device, hypoglossal nerve stimulation, pharyngeal surgery or positional therapy.

[0032] Referring to FIG. 2, a system 100, according to some implementations of the present disclosure, is illustrated. The system 100 includes a control system 110, a memory device 114, an electronic interface 119, one or more sensors 130, and one or more user devices 170. In some implementations, the system 100 further includes a respiratory therapy system 120, and an activity tracker 180.

[0033] The control system 110 includes one or more processors 112 (hereinafter, processor 112). The control system 110 is generally used to control (e.g., actuate) the various components of the system 100 and / or analyze data obtained and / or generated by the components of the system 100. The processor 112 can be a general or special purposeprocessor or microprocessor. 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(s) or portion(s) of any other control system), can be used to carry out one or more steps of any of the methods described and / or claimed herein. The control system 110 can include any suitable number of processors (e.g., one processor, two processors, five processors, ten processors, etc.) that can be in a single housing, or located remotely from each other. The control system 110 can be coupled to and / or positioned within, for example, a housing of the user device 170, a portion (e.g., a housing) of the respiratory therapy system 120, and / or within a housing of one or more of the sensors 130. The control system 110 can be centralized (within one such housing) or decentralized (within two or more of such housings, which are physically distinct). In such implementations including two or more housings containing the control system 110, such housings can be located proximately and / or remotely from each other.

[0034] The memory device 114 stores machine-readable instructions that are executable by the processor 112 of the control system 110. The memory device 114 can be any suitable computer readable storage device or media, such as, for example, a random or serial access memory device, a hard drive, a solid state drive, a flash memory device, etc. While one memory device 114 is shown in FIG. 1, the system 100 can include any suitable number of memory devices 114 (e.g., one memory device, two memory devices, five memory devices, ten memory devices, etc.). The memory device 114 can be coupled to and / or positioned within a housing of a respiratory therapy device 122 of the respiratory therapy system 120, within a housing of the user device 170, within a housing of one or more of the sensors 130, or any combination thereof. Like the control system 110, the memory device 114 can be centralized (within one such housing) or decentralized (within two or more of such housings, which are physically distinct).

[0035] In some implementations, the memory device 114 stores a user profile associated with the patient. The user profile can include sleep phenotype data pertaining to the patient, for example, demographic information associated with the patient, biometric information associated with the patient, medical information associated with the patient, physiological measurements associated with the patient, anatomical measurements associated with the patient, genetic data associated with the patient, treatment response data associated with the patient, treatment compliance data associated with the patient, neurocognitive functiondata associated with the patient, data derived from a sleep questionnaire, self-reported patient feedback, sleep parameters associated with the patient (e.g., sleep-related parameters recorded from one or more earlier sleep sessions), or any combination thereof. The anatomical measurements may include measurements such as the patient’s pharyngeal width. The physiological measurements of the patient may include measurements such as the patient’s polysomnography (PSG) data. The demographic information can include, for example, information indicative of an age of the patient, a gender of the patient, a race of the patient, a geographic location of the patient, a relationship status, a family history of insomnia or sleep apnea, an employment status of the patient, an educational status of the patient, a socioeconomic status of the patient, or any combination thereof. The medical information can include, for example, information indicative of one or more medical conditions associated with the patient, medication usage by the patient, or both. The medical information data can further include a multiple sleep latency test (MSLT) result or score and / or a Pittsburgh Sleep Quality Index (PSQI) score or value. The self-reported user feedback can include information indicative of a self-reported patientive sleep score (e.g., poor, average, excellent), a self-reported patientive stress level of the user, a selfreported patientive fatigue level of the user, a self-reported patientive health status of the user, a recent life event experienced by the user, or any combination thereof.

[0036] The electronic interface 119 is configured to receive data (e.g., sleep parameter data including physiological data and / or audio data) from the one or more sensors 130 such that the data can be stored in the memory device 114 and / or analyzed by the processor 112 of the control system 110. The electronic interface 119 can communicate with the one or more sensors 130 using a wired connection or a wireless connection (e.g., using an RF communication protocol, a Wi-Fi communication protocol, a Bluetooth communication protocol, over a cellular network, etc.). The electronic interface 119 can include an antenna, a receiver (e.g., an RF receiver), a transmitter (e.g., an RF transmitter), a transceiver, or any combination thereof. The electronic interface 119 can also include one more processors and / or one more memory devices that are the same as, or similar to, the processor 112 and the memory device 114 described herein. In some implementations, the electronic interface 119 is coupled to or integrated in the user device 170. In other implementations, the electronic interface 119 is coupled to or integrated (e.g., in a housing) with the control system 110 and / or the memory device 114.

[0037] As noted above, in some implementations, the system 100 includes a respiratory therapy system 120 (also referred to as a respiratory therapy system). The respiratory therapy system 120 can include a respiratory pressure therapy (RPT) device 122 (referred to herein as respiratory therapy device 122), a user interface 124, a conduit 126 (also referred to as a tube or an air circuit), a display device 128, a humidification tank 129 or any combination thereof. In some implementations, the control system 110, the memory device 114, the display device 128, one or more of the sensors 130, and the humidification tank 129 are part of the respiratory therapy device 122. Respiratory pressure therapy refers to the application of a supply of air to an entrance to a patient’s airways at a controlled target pressure that is nominally positive with respect to atmosphere throughout the user’s breathing cycle (e.g., in contrast to negative pressure therapies such as the tank ventilator or cuirass). The respiratory therapy system 120 is generally used to treat individuals suffering from one or more sleep-related respiratory disorders (e.g., obstructive sleep apnea, central sleep apnea, or mixed sleep apnea).

[0038] The respiratory therapy device 122 is generally used to generate pressurized air that is delivered to a patient (e.g., using one or more motors that drive one or more compressors). In some implementations, the respiratory therapy device 122 generates continuous constant air pressure that is delivered to the user. In other implementations, the respiratory therapy device 122 generates two or more predetermined pressures (e.g., a first predetermined air pressure and a second predetermined air pressure). In still other implementations, the respiratory therapy device 122 is configured to generate a variety of different air pressures within a predetermined range. For example, the respiratory therapy device 122 can deliver at least about 6 crnHzO, at least about 10 crnHzO, at least about 20 crnHzO, between about 6 cmHzO and about 10 crnHzO, between about 7 cmHzO and about 12 crnHzO, etc. The respiratory therapy device 122 can also deliver pressurized air at a predetermined flow rate between, for example, about -20 L / min and about 150 L / min, while maintaining a positive pressure (relative to the ambient pressure).

[0039] A trained technician (or in some cases the patient 10) can adjust certain settings of the respiratory therapy device 122. These adjustable settings include the pressure level of the air that the respiratory therapy device 122 delivers to the user 10, the length of a ramp time over which the pressure level increases from a starting pressure to the target pressure and the level of humidification applied to the air prior to its supply to the patient’s airways.

[0040] The user interface 124 engages a portion of the patient’s face and delivers pressurized air from the respiratory therapy device 122 to the patient’s airway to aid in preventing the airway from narrowing and / or collapsing during sleep. This may also increase the patient’s oxygen intake during sleep. Generally, the user interface 124 engages the patient’s face such that the pressurized air is delivered to the patient’s airway via the patient’s mouth, the patient’s nose, or both the patient’s mouth and nose. Together, the respiratory therapy device 122, the user interface 124, and the conduit 126 form an air pathway fluidly coupled with an airway of the patient. The pressurized air also increases the patient’s oxygen intake during sleep.

[0041] Depending upon the therapy to be applied, the user interface 124 may form a seal, for example, with a region or portion of the patient’s face, to facilitate the delivery of gas at a pressure at sufficient variance with ambient pressure to effect therapy, for example, at a positive pressure of about 10 crnHzO relative to ambient pressure. For other forms of therapy, such as the delivery of oxygen, the user interface may not include a seal sufficient to facilitate delivery to the airways of a supply of gas at a positive pressure of about 10 cmHzO.

[0042] As shown in FIG. 3, in some implementations, the user interface 124 is a facial mask (e.g., a full face mask) that covers the nose and mouth of the patient. Alternatively, the user interface 124 is a nasal mask that provides air to the nose of the patient or a nasal pillow mask that delivers air directly to the nostrils of the patient. Thus, for the purpose of this disclosure the term “user interface” 124 is used interchangeably with the term “CPAP mask” or “PAP mask”. The user interface 124 can include a plurality of straps (e.g., including hook and loop fasteners) forming, for example, a headgear for aiding in positioning and / or stabilizing the interface on a portion of the patient (e.g., the face) and a conformal cushion (e.g., silicone, plastic, foam, etc.) that aids in providing an air-tight seal between the user interface 124 and the patient. The user interface 124 can also include one or more vents for permitting the escape of carbon dioxide and other gases exhaled by the patient 210. In other implementations, the user interface 124 includes a mouthpiece (e.g., a night guard mouthpiece molded to conform to the teeth of the patient, a mandibular repositioning device, etc.).

[0043] The conduit 126 (also referred to as an air circuit or tube) allows the flow of air between two components of a respiratory therapy system 120, such as the respiratorytherapy device 122 and the user interface 124. In some implementations, there can be separate limbs of the conduit for inhalation and exhalation. In other implementations, a single limb conduit is used for both inhalation and exhalation.

[0044] One or more of the respiratory therapy device 122, the user interface 124, the conduit 126, the display device 128, and the humidification tank 129 can contain one or more sensors (e.g., a pressure sensor, a flow rate sensor, or more generally any of the other sensors 130 described herein). These one or more sensors can be used, for example, to measure the air pressure and / or flow rate of pressurized air supplied by the respiratory therapy device 122.

[0045] The display device 128 is generally used to display image(s) including still images, video images, or both and / or information regarding the respiratory therapy device 122. For example, the display device 128 can provide information regarding the status of the respiratory therapy device 122 (e.g., whether the respiratory therapy device 122 is on / off, the pressure of the air being delivered by the respiratory therapy device 122, the temperature of the air being delivered by the respiratory therapy device 122, etc.) and / or other information (e.g., a sleep score and / or a therapy score, also referred to as a my Air™ score, such as described in WO 2016 / 061629, which is hereby incorporated by reference herein in its entirety; the current date / time; personal information for the patient 210; etc.). In some implementations, the display device 128 acts as a human-machine interface (HMI) that includes a graphic user interface (GUI) configured to display the image(s) as an input interface. The display device 128 can be an LED display, an OLED display, an LCD display, or the like. The input interface can be, for example, a touchscreen or touch- sensitive substrate, a mouse, a keyboard, or any sensor system configured to sense inputs made by a human user interacting with the respiratory therapy device 122.

[0046] The humidification tank 129 is coupled to or integrated in the respiratory therapy device 122, and includes a reservoir of water that can be used to humidify the pressurized air delivered from the respiratory therapy device 122. The respiratory therapy device 122 can include a heater to heat the water in the humidification tank 129 in order to humidify the pressurized air provided to the user. Additionally, in some implementations, the conduit 126 can also include a heating element (e.g., coupled to and / or imbedded in the conduit 126) that heats the pressurized air delivered to the user. The humidification tank 129 can be fluidly coupled to a water vapor inlet of the air pathway and deliver water vapor intothe air pathway via the water vapor inlet, or can be formed in-line with the air pathway as part of the air pathway itself.

[0047] In some implementations, the system 100 can be used to deliver a substance from a receptacle to the air pathway the patient based at least in part on the physiological data, the sleep-related parameters, other data or information, or any combination thereof. Generally, modifying the delivery of the substance into the air pathway can include (i) initiating the delivery of the substance into the air pathway, (ii) ending the delivery of the substance into the air pathway, (iii) modifying an amount of the substance delivered into the air pathway, (iv) modifying a temporal characteristic of the delivery of the substance into the air pathway, (v) modifying a quantitative characteristic of the delivery of the substance into the air pathway, (vi) modifying any parameter associated with the delivery of the substance into the air pathway, or (vii) a combination of (i)-(vi).

[0048] Modifying the temporal characteristic of the delivery of the substance into the air pathway can include changing the rate at which the substance is delivered, starting and / or finishing at different times, continuing for different time periods, changing the time distribution or characteristics of the delivery, changing the amount distribution independently of the time distribution, etc. The independent time and amount variation ensures that, apart from varying the frequency of the release of the substance, one can vary the amount of substance released each time. In this manner, a number of different combination of release frequencies and release amounts (e.g., higher frequency but lower release amount, higher frequency and higher amount, lower frequency and higher amount, lower frequency and lower amount, etc.) can be achieved. Other modifications to the delivery of the portion of the substance into the air pathway can also be utilized.

[0049] The respiratory therapy system 120 can be used, for example, as a ventilator or as a positive airway pressure (PAP) system such as a continuous positive airway pressure (CPAP) system, an automatic positive airway pressure system (APAP), a bi-level or variable positive airway pressure system (BPAP or VPAP), or any combination thereof. The CPAP system delivers a predetermined air pressure (e.g., determined by a sleep physician) to the user. The APAP system automatically varies the air pressure delivered to the user based on, for example, respiration data associated with the user. The BPAP or VPAP system is configured to deliver a first predetermined pressure (e.g., an inspiratorypositive airway pressure or IPAP) and a second predetermined pressure (e.g., an expiratory positive airway pressure or EPAP) that is lower than the first predetermined pressure.

[0050] Referring to FIG. 3, a portion of the system 100 (FIG. 2), according to some implementations, is illustrated. A patient 210 of the respiratory therapy system 120 and a bed partner 220 are located in a bed 230 and are laying on a mattress 232.

[0051] The user interface 124 (also referred to herein as a mask or CPAP mask, e.g., a full face mask, a nasal mask, a nasal pillows mask, etc.) can be worn by the patient 210 during a sleep session. The sleep session can be conducted in a hospital setting or in the environment illustrated in FIG. 3 by utilizing a home sleep apnea test, such as the NightOwl system, described in, for example, WO 2022 / 238492, WO 2022 / 063874, WO 2021 / 260190, and WO 2021 / 260192, each of which is hereby incorporated by reference herein in its entirety. The NightOwl system includes a sensor (that fulfills the role of sensors 130) that is placed on the patient’s fingertip and a cloud-based analytics platform. The sensor acquires accelerometer data and reflectance-based photoplethysmographic (PPG). The analytics platform derives actigraphy from the accelerometer, and blood oxygen saturation, peripheral arterial tonometry (PAT) and pulse rate among other features, from the sensor.

[0052] The user interface 124 is fluidly coupled and / or connected to the respiratory therapy device 122 via the 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 to aid in preventing the airway from closing and / or narrowing during sleep. The respiratory therapy device 122 can be positioned on a nightstand 240 that is directly adjacent to the bed 230 as shown in FIG. 3, or more generally, on any surface or structure that is generally adjacent to the bed 230 and / or the user 210.

[0053] Referring to back to FIG. 1, the one or more sensors 130 of the system 100 include a pressure sensor 132, a flow rate sensor 134, temperature sensor 136, a motion sensor 138, a microphone 140, a speaker 142, a radio-frequency (RF) receiver 146, a RF transmitter 148, a camera 150, an infrared sensor 152, a photoplethysmogram (PPG) sensor 154, an electrocardiogram (ECG) sensor 156, an electroencephalography (EEG) sensor 158, a capacitive sensor 160, a force sensor 162, a strain gauge sensor 164, an electromyography (EMG) sensor 166, an oxygen sensor 168, an analyte sensor 174, a moisture sensor 176, aLiDAR sensor 178, or any combination thereof. Generally, each of the one or more sensors 130 are configured to output sensor data that is received and stored in the memory device 114 or one or more other memory devices.

[0054] While the one or more sensors 130 are shown and described as including each of the pressure sensor 132, the flow rate sensor 134, the temperature sensor 136, the motion sensor 138, the microphone 140, the speaker 142, the RF receiver 146, the RF transmitter 148, the camera 150, the infrared sensor 152, the photoplethysmogram (PPG) sensor 154, the electrocardiogram (ECG) sensor 156, the electroencephalography (EEG) sensor 158, the capacitive sensor 160, the force sensor 162, the strain gauge sensor 164, the electromyography (EMG) sensor 166, the oxygen sensor 168, the analyte sensor 174, the moisture sensor 176, and the LiDAR sensor 178 more generally, the one or more sensors 130 can include any combination and any number of each of the sensors described and / or shown herein.

[0055] As described herein, the system 100 generally can be used to generate physiological data associated with a patient (e.g., a user of the respiratory therapy system 120 shown in FIG. 2) such as during a sleep session. A technician can also remotely modify one or more settings of the respiratory therapy system 120 during the sleep session and generate updated physiological data associated with the patient. As described below, the present disclosure utilizes signal processing techniques to generate a sleep parameter difference signal from the physiological data and updated physiological data. The generated sleep parameter difference signal is algorithmically combined with sleep phenotype data pertaining to the patient to generate a sleep disorder characteristic signal which can be used to determine a treatment modality for the patient.

[0056] The physiological data can be analyzed to generate one or more sleep-related parameters, which can include any parameter, measurement, etc. related to the user during the sleep session. The one or more sleep-related parameters that can be determined for the user 210 during the sleep session include, for example, an Apnea-Hypopnea Index (AHI) score, a sleep score, a flow signal, a respiration signal, a respiration rate, an inspiration amplitude, an expiration amplitude, an inspiration-expiration ratio, a number of events per hour, a pattern of events, a stage, pressure settings of the respiratory therapy device 122, a heart rate, a heart rate variability, movement of the user 210, temperature, EEG activity,EMG activity, arousal, snoring, choking, coughing, whistling, wheezing, or any combination thereof.

[0057] The one or more sensors 130 can be used to generate, for example, physiological data, audio data, or both. Physiological data generated by one or more of the sensors 130 can be used by the control system 110 to determine a sleep-wake signal associated with the user 210 (FIG. 2) during the sleep session and one or more sleep-related parameters. The sleep-wake signal can be indicative of one or more sleep states, including wakefulness, relaxed wakefulness, micro-awakenings, or distinct sleep stages (or sleep architecture) such as, for example, a rapid eye movement (REM) stage, a first non-REM stage (often referred to as “Nl”), a second non-REM stage (often referred to as “N2”), a third non- REM stage (often referred to as “N3”), or any combination thereof. Methods for determining sleep states and / or sleep stages from physiological data generated by one or more sensors, such as the one or more sensors 130, are described in, for example, WO 2014 / 047310, US 2014 / 0088373, WO 2017 / 132726, WO 2019 / 122413, and WO 2019 / 122414, each of which is hereby incorporated by reference herein in its entirety.

[0058] In some implementations, the sleep-wake signal described herein can be timestamped to indicate a time that the patient enters the bed, a time that the patient exits the bed, a time that the patient attempts to fall asleep, etc. The sleep-wake signal can be measured by the one or more sensors 130 during the sleep session at a predetermined sampling rate, such as, for example, one sample per second, one sample per 30 seconds, one sample per minute, etc. In some implementations, the sleep-wake signal can also be indicative of a respiration signal, a respiration rate, an inspiration amplitude, an expiration amplitude, an inspiration-expiration ratio, a number of events per hour, a pattern of events, pressure settings of the respiratory therapy device 122, or any combination thereof during the sleep session. The event(s) can include snoring, apneas, central apneas, obstructive apneas, mixed apneas, hypopneas, a mask leak (e.g., from the user interface 124), a restless leg, a sleeping disorder, choking, an increased heart rate, labored breathing, an asthma attack, an epileptic episode, a seizure, or any combination thereof. The one or more sleep- related parameters that can be determined for the patient during the sleep session based on the sleep-wake signal include, for example, a total time in bed, a total sleep time, a sleep onset latency, a wake-after-sleep-onset parameter, a sleep efficiency, a fragmentation index, or any combination thereof. As described in further detail herein, the physiologicaldata and / or the sleep-related parameters can be analyzed to determine one or more sleep- related scores as well as treatment modalities.

[0059] Physiological data and / or audio data generated by the one or more sensors 130 can also be used to determine a respiration signal associated with a patient during a sleep session. The respiration signal is generally indicative of respiration or breathing of the patient during the sleep session. The respiration signal can be indicative of and / or analyzed to determine (e.g., using the control system 110) one or more sleep-related parameters, such as, for example, a respiration rate, a respiration rate variability, an inspiration amplitude, an expiration amplitude, an inspiration-expiration ratio, an occurrence of one or more events, a number of events per hour, a pattern of events, a sleep state, a sleet stage, an apnea-hypopnea index (AHI), pressure settings of the respiratory therapy device 122, or any combination thereof. The one or more events can include snoring, apneas, central apneas, obstructive apneas, mixed apneas, hypopneas, a mask leak (e.g., from the user interface 124), a cough, a restless leg, a sleeping disorder, choking, an increased heart rate, labored breathing, an asthma attack, an epileptic episode, a seizure, increased blood pressure, or any combination thereof. Many of the described sleep-related parameters are physiological parameters, although some of the sleep-related parameters can be considered to be non-physiological parameters. Other types of physiological and / or non-physiological parameters can also be determined, either from the data from the one or more sensors 130, or from other types of data

[0060] As used herein, a sleep session can be defined in multiple ways. For example, a sleep session can be defined by an initial start time and an end time. In some implementations, a sleep session is a duration where the patient is asleep, that is, the sleep session has a start time and an end time, and during the sleep session, the patient does not wake until the end time. That is, any period of the patient being awake is not included in a sleep session. From this first definition of sleep session, if the patient wakes ups and falls asleep multiple times in the same night, each of the sleep intervals separated by an awake interval is a sleep session.

[0061] Alternatively, in some implementations, a sleep session has a start time and an end time, and during the sleep session, the patient can wake up, without the sleep session ending, so long as a continuous duration that the patient is awake is below an awake duration threshold. The awake duration threshold can be defined as a percentage of a sleepsession. The awake duration threshold can be, for example, about twenty percent of the sleep session, about fifteen percent of the sleep session duration, about ten percent of the sleep session duration, about five percent of the sleep session duration, about two percent of the sleep session duration, etc., or any other threshold percentage. In some implementations, the awake duration threshold is defined as a fixed amount of time, such as, for example, about one hour, about thirty minutes, about fifteen minutes, about ten minutes, about five minutes, about two minutes, etc., or any other amount of time.

[0062] In some implementations, a sleep session is defined as the entire time between the time in the evening at which the patient first entered the bed, and the time the next morning when the patient last left the bed. Put another way, a sleep session can be defined as a period of time that begins on a first date (e.g., Monday, January 6, 2020) at a first time (e.g., 10:00 PM), that can be referred to as the current evening, when the patient first enters a bed with the intention of going to sleep (e.g., not if the user intends to first watch television or play with a smart phone before going to sleep, etc.), and ends on a second date (e.g., Tuesday, January 7, 2020) at a second time (e.g., 7:00 AM), that can be referred to as the next morning, when the patient first exits the bed with the intention of not going back to sleep that next morning.

[0063] In some implementations, the patient can manually define the beginning of a sleep session and / or manually terminate a sleep session. For example, the patient can select (e.g., by clicking or tapping) one or more user-selectable element that is displayed on the display 172 of the user device 170 (FIG. 3) to manually initiate or terminate the sleep session.

[0064] Generally, the sleep session includes any point in time after the patient 210 has laid or sat down in the bed 230 (or another area or object on which they intend to sleep), and has turned on the respiratory therapy device 122 and donned the user interface 124. The sleep session can thus include time periods (i) when the patient 210 is using the CPAP system but before the patient 210 attempts to fall asleep (for example when the patient 210 lays in the bed 230 reading a book); (ii) when the patient 210 begins trying to fall asleep but is still awake; (iii) when the patient 210 is in a light sleep (also referred to as stage 1 and stage 2 of non-rapid eye movement (NREM) sleep); (iv) when the patient 210 is in a deep sleep (also referred to as slow- wave sleep, SWS, or stage 3 of NREM sleep); (v) when the patient 210 is in rapid eye movement (REM) sleep; (vi) when the patient 210 isperiodically awake between light sleep, deep sleep, or REM sleep; or (vii) when the patient 210 wakes up and does not fall back asleep.

[0065] The sleep session is generally defined as ending once the patient 210 removes the user interface 124, turns off the respiratory therapy device 122, and gets out of bed 230. In some implementations, the sleep session can include additional periods of time, or can be limited to only some of the above-disclosed time periods. For example, the sleep session can be defined to encompass a period of time beginning when the respiratory therapy device 122 begins supplying the pressurized air to the airway or the patient 210, ending when the respiratory therapy device 122 stops supplying the pressurized air to the airway of the patient 210, and including some or all of the time points in between, when the patient 210 is asleep or awake.

[0066] The pressure sensor 132 outputs pressure data that can be stored in the memory device 114 and / or analyzed by the processor 112 of the control system 110. In some implementations, the pressure sensor 132 is an air pressure sensor (e.g., barometric pressure sensor) that generates sensor data indicative of the respiration (e.g., inhaling and / or exhaling) of the user of the respiratory therapy system 120 and / or ambient pressure. In such implementations, the pressure sensor 132 can be coupled to or integrated in the respiratory therapy device 122. The pressure sensor 132 can be, for example, a capacitive sensor, an electromagnetic sensor, a piezoelectric sensor, a strain-gauge sensor, an optical sensor, a potentiometric sensor, or any combination thereof.

[0067] 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. Examples of flow rate sensors (such as, for example, the flow rate sensor 134) are described in International Publication No. WO 2012 / 012835, which is hereby incorporated by reference herein in its entirety. In some implementations, the flow rate sensor 134 is used to determine an air flow rate from the respiratory therapy device 122, an air flow rate through the conduit 126, an air flow rate through the user interface 124, or any combination thereof. In such implementations, the flow rate sensor 134 can be coupled to or integrated in the respiratory therapy device 122, the user interface 124, or the conduit 126. The flow rate sensor 134 can be a mass flow rate sensor such as, for example, a rotary flow meter (e.g., Hall effect flow meters), a turbine flow meter, an orifice flow meter, an ultrasonic flow meter, a hot wire sensor, a vortex sensor, a membrane sensor, or any combination thereof.In some implementations, the flow rate sensor 134 is configured to measure a vent flow (e.g., intentional “leak”), an unintentional leak (e.g., mouth leak and / or mask leak), a patient flow (e.g., air into and / or out of lungs), or any combination thereof. In some implementations, the flow rate data can be analyzed to determine cardiogenic oscillations of the patient. In one example, the pressure sensor 132 can be used to determine a blood pressure of a patient.

[0068] The temperature sensor 136 outputs temperature data that can be stored in the memory device 114 and / or analyzed by the processor 112 of the control system 110. In some implementations, the temperature sensor 136 generates temperatures data indicative of a core body temperature of the patient 210 (FIG. 3), a skin temperature of the patient 210, a temperature of the air flowing from the respiratory therapy device 122 and / or through the conduit 126, a temperature in the user interface 124, an ambient temperature, or any combination thereof. The temperature sensor 136 can be, for example, a thermocouple sensor, a thermistor sensor, a silicon band gap temperature sensor or semiconductor-based sensor, a resistance temperature detector, or any combination thereof.

[0069] The motion sensor 138 outputs motion data that can be stored in the memory device 114 and / or analyzed by the processor 112 of the control system 110. The motion sensor 138 can be used to detect movement of the patient 210 during the sleep session, and / or detect movement of any of the components of the respiratory therapy system 120, such as the respiratory therapy device 122, the user interface 124, or the conduit 126. The motion sensor 138 can include one or more inertial sensors, such as accelerometers, gyroscopes, and magnetometers. In some implementations, the motion sensor 138 alternatively or additionally generates one or more signals representing bodily movement of the patient, from which may be obtained a signal representing a sleep state of the user; for example, via a respiratory movement of the user. In some implementations, the motion data from the motion sensor 138 can be used in conjunction with additional data from another sensor 130 to determine the sleep state of the patient.

[0070] The microphone 140 outputs sound data that can be stored in the memory device 114 and / or analyzed by the processor 112 of the control system 110. The audio data generated by the microphone 140 is reproducible as one or more sound(s) during a sleep session (e.g., sounds from the patient 210). The audio data form the microphone 140 canalso be used to identify (e.g., using the control system 110) an event experienced by the user during the sleep session, as described in further detail herein. The microphone 140 can be coupled to or integrated in the respiratory therapy device 122, the user interface 124, the conduit 126, or the user device 170. In some implementations, the system 100 includes a plurality of microphones (e.g., two or more microphones and / or an array of microphones with beamforming) such that sound data generated by each of the plurality of microphones can be used to discriminate the sound data generated by another of the plurality of microphones.

[0071] The speaker 142 outputs sound waves that are audible to a user of the system 100 (e.g., the patient 210 of FIG. 3). The speaker 142 can be used, for example, as an alarm clock or to play an alert or message to the user 210 (e.g., in response to an event). In some implementations, the speaker 142 can be used to communicate the audio data generated by the microphone 140 to the patient. The speaker 142 can be coupled to or integrated in the respiratory therapy device 122, the user interface 124, the conduit 126, or the user device 170.

[0072] The microphone 140 and the speaker 142 can be used as separate devices. In some implementations, the microphone 140 and the speaker 142 can be combined into an acoustic sensor 141 (e.g., a SONAR sensor), as described in, for example, WO 2018 / 050913 and WO 2020 / 104465, each of which is hereby incorporated by reference herein in its entirety. In such implementations, the speaker 142 generates or emits sound waves at a predetermined interval and the microphone 140 detects the reflections of the emitted sound waves from the speaker 142. The sound waves generated or emitted by the speaker 142 have a frequency that is not audible to the human ear (e.g., below 20 Hz or above around 18 kHz) so as not to disturb the sleep of the patient 210 or the bed partner 220 (FIG. 3). Based at least in part on the data from the microphone 140 and / or the speaker 142, the control system 110 can determine a location of the patient 210 (FIG. 3) and / or one or more of the sleep-related parameters described in herein, such as, for example, a respiration signal, a respiration rate, an inspiration amplitude, an expiration amplitude, an inspiration-expiration ratio, a number of events per hour, a pattern of events, a sleep state, a sleep stage, pressure settings of the respiratory therapy device 122, or any combination thereof. In such a context, a sonar sensor may be understood to concern an active acoustic sensing, such as by generating and / or transmitting ultrasound and / or low frequencyultrasound sensing signals (e.g., in a frequency range of about 17-23 kHz, 18-22 kHz, or 17-18 kHz, for example), through the air. Such a system may be considered in relation to WO 2018 / 050913 and WO 2020 / 104465 mentioned above, each of which is hereby incorporated by reference herein in its entirety.

[0073] In some implementations, the sensors 130 include (i) a first microphone that is the same as, or similar to, the microphone 140, and is integrated in the acoustic sensor 141 and (ii) a second microphone that is the same as, or similar to, the microphone 140, but is separate and distinct from the first microphone that is integrated in the acoustic sensor 141.

[0074] The RF transmitter 148 generates and / or emits radio waves having a predetermined frequency and / or a predetermined amplitude (e.g., within a high frequency band, within a low frequency band, long wave signals, short wave signals, etc.). The RF receiver 146 detects the reflections of the radio waves emitted from the RF transmitter 148, and this data can be analyzed by the control system 110 to determine a location of the patient 210 (FIG. 2) and / or one or more of the sleep-related parameters described herein. An RF receiver (either the RF receiver 146 and the RF transmitter 148 or another RF pair) can also be used for wireless communication between the control system 110, the respiratory therapy device 122, the one or more sensors 130, the user device 170, or any combination thereof. While the RF receiver 146 and RF transmitter 148 are shown as being separate and distinct elements in FIG. 1, in some implementations, the RF receiver 146 and RF transmitter 148 are combined as a part of an 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 Wi-Fi, Bluetooth, or the like.

[0075] In some implementations, the RF sensor 147 is a part of a mesh system. One example of a mesh system is a Wi-Fi mesh system, which can include mesh nodes, mesh router(s), and mesh gateway(s), each of which can be mobile / movable or fixed. In such implementations, the Wi-Fi mesh system includes a Wi-Fi router and / or a Wi-Fi controller and one or more satellites (e.g., access points), each of which include an RF sensor that the is the same as, or similar to, the RF sensor 147. The Wi-Fi router and satellites continuously communicate with one another using Wi-Fi signals. The Wi-Fi mesh system can be used to generate motion data based on changes in the Wi-Fi signals (e.g., differences in received signal strength) between the router and the satellite(s) due to an object or person movingpartially obstructing the signals. The motion data can be indicative of motion, breathing, heart rate, gait, falls, behavior, etc., or any combination thereof.

[0076] The camera 150 outputs image data reproducible as one or more images (e.g., still images, video images, thermal images, or any combination thereof) that can be stored in the memory device 114. The image data from the camera 150 can be used by the control system 110 to determine one or more of the sleep-related parameters described herein, such as, for example, one or more events (e.g., periodic limb movement or restless leg syndrome), a respiration signal, a respiration rate, an inspiration amplitude, an expiration amplitude, an inspiration-expiration ratio, a number of events per hour, a pattern of events, a sleep state, a sleep stage, or any combination thereof. Further, the image data from the camera 150 can be used to, for example, identify a location of the patient, to determine chest movement of the patient 210 (FIG. 3), to determine air flow of the mouth and / or nose of the patient 210, to determine a time when the patient 210 enters the bed 230 (FIG. 3), and to determine a time when the patient 210 exits the bed 230. In some implementations, the camera 150 includes a wide angle lens or a fish eye lens.

[0077] The infrared (IR) sensor 152 outputs infrared image data reproducible as one or more infrared images (e.g., still images, video images, or both) that can be stored in the memory device 114. The infrared data from the IR sensor 152 can be used to determine one or more sleep-related parameters during a sleep session, including a temperature of the patient 210 and / or movement of the patient 210. The IR sensor 152 can also be used in conjunction with the camera 150 when measuring the presence, location, and / or movement of the patient 210. The IR sensor 152 can detect infrared light having a wavelength between about 700 nm and about 1 mm, for example, while the camera 150 can detect visible light having a wavelength between about 380 nm and about 740 nm.

[0078] The PPG sensor 154 outputs physiological data associated with the patient 210 (FIG. 2) that can be used to determine one or more sleep-related parameters, such as, for example, a heart rate, a heart rate variability, a cardiac cycle, respiration rate, an inspiration amplitude, an expiration amplitude, an inspiration-expiration ratio, estimated blood pressure parameter(s), or any combination thereof. The PPG sensor 154 can be worn by the patient 210, embedded in clothing and / or fabric that is worn by the patient 210, embedded in and / or coupled to the user interface 124 and / or its associated headgear (e.g., straps, etc.), etc.

[0079] The ECG sensor 156 outputs physiological data associated with electrical activity of the heart of the patient 210. In some implementations, the ECG sensor 156 includes one or more electrodes that are positioned on or around a portion of the patient 210 during the sleep session. The physiological data from the ECG sensor 156 can be used, for example, to determine one or more of the sleep-related parameters described herein.

[0080] The EEG sensor 158 outputs physiological data associated with electrical activity of the brain of the patient 210. In some implementations, the EEG sensor 158 includes one or more electrodes that are positioned on or around the scalp of the patient 210 during the sleep session. The physiological data from the EEG sensor 158 can be used, for example, to determine a sleep state and / or a sleep stage of the patient 210 at any given time during the sleep session. In some implementations, the EEG sensor 158 can be integrated in the user interface 124 and / or the associated headgear (e.g., straps, etc.).

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

[0082] The analyte sensor 174 can be used to detect the presence of an analyte in the exhaled breath of the patient 210. The data output by the analyte sensor 174 can be stored in the memory device 114 and used by the control system 110 to determine the identity and concentration of any analytes in the breath of the patient 210. In some implementations, the analyte sensor 174 is positioned near a mouth of the patient 210 to detect analytes in breath exhaled from the patient 210’s mouth. For example, when the user interface 124 is a facial mask that covers the nose and mouth of the patient 210, the analyte sensor 174 can be positioned within the facial mask to monitor the patient 210’s mouthbreathing. In other implementations, such as when the user interface 124 is a nasal mask or a nasal pillow mask, the analyte sensor 174 can be positioned near the nose of the patient 210 to detect analytes in breath exhaled through the patient’s nose. In still other implementations, the analyte sensor 174 can be positioned near the patient 210’s mouth when the user interface 124 is a nasal mask or a nasal pillow mask. In this implementation, the analyte sensor 174 can be used to detect whether any air is inadvertently leaking from the patient 210’s mouth. In some implementations, the analyte sensor 174 is a volatile organic compound (VOC) sensor that can be used to detect carbon-based chemicals or compounds. In some implementations, the analyte sensor 174 can also be used to detect whether the patient 210 is breathing through their nose or mouth. For example, if the data output by an analyte sensor 174 positioned near the mouth of the patient 210 or within the facial mask (in implementations where the user interface 124 is a facial mask) detects the presence of an analyte, the control system 110 can use this data as an indication that the patient 210 is breathing through their mouth.

[0083] The moisture sensor 176 outputs data that can be stored in the memory device 114 and used by the control system 110. The moisture sensor 176 can be used to detect moisture in various areas surrounding the patient (e.g., inside the conduit 126 or the user interface 124, near the user 210’s face, near the connection between the conduit 126 and the user interface 124, near the connection between the conduit 126 and the respiratory therapy device 122, etc.). Thus, in some implementations, the moisture sensor 176 can be coupled to or integrated in the user interface 124 or in the conduit 126 to monitor the humidity of the pressurized air from the respiratory therapy device 122. In other implementations, the moisture sensor 176 is placed near any area where moisture levels need to be monitored. The moisture sensor 176 can also be used to monitor the humidity of the ambient environment surrounding the patient 210, for example, the air inside the bedroom.

[0084] The Light Detection and Ranging (LiDAR) sensor 178 can be used for depth sensing. This type of optical sensor (e.g., laser sensor) can be used to detect objects and build three dimensional (3D) maps of the surroundings, such as of a living space. LiDAR can generally utilize a pulsed laser to make time of flight measurements. LiDAR is also referred to as 3D laser scanning. In an example of use of such a sensor, a fixed or mobile device (such as a smartphone) having a LiDAR sensor 166 can measure and map an area extending 5 meters or more away from the sensor. The LiDAR data can be fused with pointcloud data estimated by an electromagnetic RADAR sensor, for example. The LiDAR sensor(s) 178 can also use artificial intelligence (Al) to automatically geofence RADAR systems by detecting and classifying features in a space that might cause issues for RADAR systems, such a glass windows (which can be highly reflective to RADAR). LiDAR can also be used to provide an estimate of the height of a person, as well as changes in height when the person sits down, or falls down, for example. LiDAR may be used to form a 3D mesh representation of an environment. In a further use, for solid surfaces through which radio waves pass (e.g., radio-translucent materials), the LiDAR may reflect off such surfaces, thus allowing a classification of different type of obstacles.

[0085] While shown separately in FIG. 1, any combination of the one or more sensors 130 can be integrated in and / or coupled to any one or more of the components of the system 100, including the respiratory therapy device 122, the user interface 124, the conduit 126, the humidification tank 129, the control system 110, the user device 170, the activity tracker 180, or any combination thereof. For example, microphone 140 and the speaker 142 can be integrated in and / or coupled to the user device 170; and the pressure sensor 130 and / or flow rate sensor 132 are integrated in 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 positioned generally adjacent to the patient 210 during the sleep session (e.g., positioned on or in contact with a portion of the user 210, worn by the patient 210, coupled to or positioned on the nightstand, coupled to the mattress, coupled to the ceiling, etc.).

[0086] The user device 170 (FIG. 2) includes a display device 172. The user device 170 can be, for example, a mobile device such as a smart phone, a tablet, a gaming console, a smart watch, a laptop, or the like. Alternatively, the user device 170 can be an external sensing system, a television (e.g., a smart television) or another smart home device (e.g., a smart speaker(s) such as Google Home, Amazon Echo, Alexa etc.). In some implementations, the user device is a wearable device (e.g., a smart watch). The display device 172 is generally used to display image(s) including still images, video images, or both. In some implementations, the display device 172 acts as a human-machine interface (HMI) that includes a graphic user interface (GUI) configured to display the image(s) and an input interface. The display device 172 can be an LED display, an OLED display, an LCD display, or the like. The input interface can be, for example, a touchscreen or touch-sensitive substrate, a mouse, a keyboard, or any sensor system configured to sense inputs made by a human user interacting with the user device 170. In some implementations, one or more user devices can be used by and / or included in the system 100.

[0087] In some implementations, the system 100 also includes an activity tracker 180. The activity tracker 180 is generally used to aid in generating phenotype data (including physiological data) associated with the patient. The activity tracker 180 can include one or more of the sensors 130 described herein, such as, for example, the motion sensor 138 (e.g., one or more accelerometers and / or gyroscopes), the PPG sensor 154, and / or the ECG sensor 156. The physiological data from the activity tracker 180 can be used to determine, for example, a number of steps, a distance traveled, a number of steps climbed, a duration of physical activity, a type of physical activity, an intensity of physical activity, time spent standing, a respiration rate, an average respiration rate, a resting respiration rate, a maximum he respiration art rate, a respiration rate variability, a heart rate, an average heart rate, a resting heart rate, a maximum heart rate, a heart rate variability, a number of calories burned, blood oxygen saturation, electrodermal activity (also known as skin conductance or galvanic skin response), or any combination thereof. In some implementations, the activity tracker 180 is coupled (e.g., electronically or physically) to the user device 170.

[0088] In some implementations, the activity tracker 180 is a wearable device that can be worn by the patient, such as a smartwatch, a wristband, a ring, or a patch. For example, referring to FIG. 3, the activity tracker 180 is worn on a wrist of the patient 210. The activity tracker 180 can also be coupled to or integrated a garment or clothing that is worn by the patient. Alternatively still, the activity tracker 180 can also be coupled to or integrated in (e.g., within the same housing) the user device 170. More generally, the activity tracker 180 can be communicatively coupled with, or physically integrated in (e.g., within a housing), the control system 110, the memory 114, the respiratory system 120, and / or the user device 170.

[0089] While the control system 110 and the memory device 114 are described and shown in FIG. 1 as being a separate and distinct component of the system 100, in some implementations, the control system 110 and / or the memory device 114 are integrated in the user device 170 and / or the respiratory therapy device 122. Alternatively, in some implementations, the control system 110 or a portion thereof (e.g., the processor 112) can be located in a cloud (e.g., integrated in a server, integrated in an Internet of Things (loT)device, connected to the cloud, be patient to edge cloud processing, etc.), located in one or more servers (e.g., remote servers, local servers, etc., or any combination thereof.

[0090] While system 100 is shown as including all of the components described above, more or fewer components can be included in a system according to implementations of the present disclosure. For example, a first alternative system includes the control system 110, the memory device 114, and at least one of the one or more sensors 130 and does not include the respiratory therapy system 120. As another example, a second alternative system includes the control system 110, the memory device 114, at least one of the one or more sensors 130, and the user device 170. As yet another example, a third alternative system includes the control system 110, the memory device 114, the respiratory therapy system 120, at least one of the one or more sensors 130, and the user device 170. Thus, various systems can be formed using any portion or portions of the components shown and described herein and / or in combination with one or more other components.

[0091] Referring to FIG. 4, method 400 for determining a treatment for a sleep disorder for the patient 210 is disclosed, according to some implementations of the present disclosure. The method finds particular utility in characterizing the pharyngeal collapse associated with OSA such as in terms of the anatomical site at which the collapse occurs, structures of the airway implicated in the collapse, the extent of the collapse and the frequency of the collapse. These characterizations can be used to quantify the severity of OSA and determine a treatment.

[0092] For example, those skilled in the art will appreciate that some treatment alternatives to CPAP (such as mandibular advancement devices (MAD), hypoglossal nerve stimulation (HGNS), pharyngeal surgery, and positional therapy) seem to work better for tongue and epiglottic collapse compared to oropharyngeal lateral walls (OPLW) collapse. Studies also indicate that complete concentric collapse (CCC) of the velopharyngeal airway is an exclusion category for HGNS. The pharyngeal collapse characterizations that the present disclosure provides are useful both in tailoring PAP therapy such as but not limited to CPAP therapy and in selecting an alternative treatment modality. Where CPAP is referenced in this application, it is intended as a non-limiting example of PAP therapy, unless stated to the contrary.

[0093] The present disclosure is also a useful non-surgical alternative to the conventional method for determining the site of pharyngeal collapse through drug-induced sleependoscopy (DISE). Owing to the expensive an invasive nature of DISE it is rarely performed in initial assessments of OSA, and treatment decisions are seldom made with knowledge of the site of obstruction. Having knowledge of the site of obstruction can be valuable in early consideration of treatment modalities.

[0094] At step 402, CPAP is administered to the patient 210 with the respiratory therapy device 122 in a first setting. In the illustrated embodiment, the setting is the pressure of the pressurized air that the respiratory therapy device 122 delivers to the patient 122’ s airway.

[0095] At step 404, the control system 110 operates one or more of the sensors 130 to collect respective sensor data and output the collected data to the memory device 114 for storage. The processor 112 of the control system 110 accesses the collected data from the memory device 114 and processes the collected data to generate an initial sleep parameter signal. The initial sleep parameter signal is indicative of the sensed sleep parameters when the respiratory therapy device is operating in the first pressure setting. In the illustrated embodiment, the initial sleep parameter signal includes one or more of an electroencephalogram signal, electrooculogram signal, electromyogram signal, electrocardiogram signal, nasal airflow signal, oral airflow signal, thoracic effort signal, abdominal effort signal, actigraphy signal, oxygen saturation signal, heart rate signal, pulse rate signal, peripheral arterial tone signal, sleep architecture signal, analyte signal and an audio signal.

[0096] At step 406, after a predetermined time interval, CPAP is administered to the patient 210 with the respiratory therapy device 122 in a second setting. The technician conducting the sleep study can remotely change the respiratory therapy device 122 into the second setting by remotely accessing either the user interface 124 of the respiratory therapy device 120 or the user device 170. Alternatively, the respiratory therapy device can act in response to an automated sleep study module stored in the memory device 114 and executed by the processor 112 to automatically change the respiratory therapy device 122 into the second setting. In the illustrated embodiment, the second setting is a different pressure of pressurized air that the respiratory therapy device 122 delivers to the patient 122’s airway.

[0097] At step 408, At step 404, the control system 110 operates one or more of the sensors 130 to collect respective sensor data and output the collected data to the memory device114 for storage. The processor 112 of the control system 110 accesses the collected data from the memory device 114 and processes the collected data to generate a subsequent sleep parameter signal. The subsequent sleep parameter signal is indicative of the sensed sleep parameters when the respiratory therapy device is operating in the second pressure setting. Typically, the subsequent sleep parameter signal contains the same sleep parameters that are present in the initial sleep parameter signal. As with the initial sleep parameter signal, in the present embodiment, the subsequent sleep parameter signal includes one or more of an electroencephalogram signal, electrooculogram signal, electromyogram signal, electrocardiogram signal, nasal airflow signal, oral airflow signal, thoracic effort signal, abdominal effort signal, actigraphy signal, oxygen saturation signal, heart rate signal, pulse rate signal, peripheral arterial tone signal, sleep architecture signal, analyte signal and an audio signal.

[0098] In some embodiments, at step 410, the processor 112 of the control system 110 may execute programming stored in the memory device 114 that processes the initial sleep parameter signal(s) and subsequent sleep parameter signal(s) to generate a sleep parameter difference signal(s) and / or to determine a change or trend over a time frame (e.g., two weeks). The programming that the processor 112 executes to generate the sleep parameter difference signal in the illustrated embodiment includes signal processing routines that highlight the variations and changes between the initial sleep parameter signal and subsequent sleep parameter signal. These variations and changes are indicative of the impact of the changed air pressure on the patient 124’s airway.

[0099] The sleep parameter being determined may include one or more apnea-hypopnea indices (AHI) for the user. Prior to a user starting therapy he or she will have an initial AHI. During therapy, the AHI is expected to be reduced, but there may still be a residual AHI. The AHI determination may be made before therapy to get the initial AHI. It may be made during therapy delivery as mentioned, to obtain the residual AHI. Additionally or alternatively, the AHI determination may be made during a test window of time when the PAP device such as CPAP device is off, to obtain an off-therapy AHI. This off time may be a controlled off time where the PAP therapy is not being delivered. The test window may be initiated on the basis of a detection that the user is not wearing the CPAP mask, or it may be initiated by the controller to temporarily interrupt the therapy.

[0100] Accordingly, the sleep parameter signal(s) may comprise a signal which can be analyzed to determine the AHI, such as but not limited to the oxygen level signal or an electrocardiogram (ECG) signal. There are other examples. For instance, the sleep parameter signal may additionally or alternatively comprise a breathing signal which can be measured to determine whether there is a breathing pause (e.g., accelerometer signal), where a breathing pause of a sufficient length may indicate an apnea event, and thus the analysis of the breathing signal can determine AHI. These examples are not intended to be limiting. Other examples of measurable signals have been mentioned in this disclosure, and the skilled person can ascertain whether they can be used to determine the AHI. Two or more types of signals may be included, for a more accurate determination of the AHI and to reduce false positive determinations of apnea events.

[0101] The signals may be obtained by sensor(s) included in a device worn by the user. For example, it may be included in a wearable device such as a smart watch or a smart ring. These may be the activity tracker 180 in the overall system 100. The sensor may instead be attached by a mask or harness providing the user interface 124 for a PAP therapy system 120. In some PAP therapy devices, the controller is configurable to measure a residual AHI of the user, i.e., the user’s AHI whilst the therapy is being delivered. This may also directly provide the sleep parameter. In embodiments where one or more wearable devices such smart watches or rings are used, the control system 110 may be configured to receive physiological and / or phenotype data such as BMI, weight, height, heart rate, etc., from the memory of the one or more wearable devices.

[0102] The sleep parameter being determined may include a leakage pressure measured during delivery of the PAP therapy. In an embodiment, the sleep parameter being determined includes a treatment on / off data indicating whether the patient is using the PAP therapy device. This signal may change throughout the user’s sleep time, for example when the user takes off the mask and stops using it in the middle of the night.

[0103] The sleep parameter being determined may include two or more different parameters, including but not limited to the aforementioned parameters.

[0104] At step 412, the processor 112 of the control system 110 executes programming to access sleep phenotype data pertaining to the patient 124 that is stored in the memory device. In the exemplified embodiment, the sleep phenotype data comprises one or more of physiological measurements, anatomical measurements, genetic data, treatmentresponse data, treatment compliance data, neurocognitive function data and data derived from a sleep questionnaire. The control system 110 may also have access to data in relation to whether the patient is on any medication which may cause a change in the phenotype data such as the BMI. The data may also include a dosage data in relation to the medication. For instance, the users may be taking medication such as glucagon-like peptide 1 (GLP-1) agonists or sodium glucose cotransporter 2 (SGLT-2) inhibitors. The control system 110 may also have access to phenotype data such as but not limited to weight, body measurements, BMI. Such data may be data included in the sleep questionnaire, or may be provided by the patient 124 through an electronic interface, such as an online portal for a patient management platform or a human machine interface (HMI) provided via a mobile application, or an HMI provided on the PAP.

[0105] At step 414, the processor 112 of the control system 110 executes programming stored in the memory device 114 that processes one or more of the sleep parameter signal, the sleep parameter difference signal, and sleep phenotype data, to generate a sleep disorder characteristic signal and / or a therapy adjustment signal. The therapy adjustment signal, and if applicable, the present mode of therapy (e.g., CPAP) will be used to determine the sleep disorder treatment. For instance, in relation to processing data include the sleep phenotype data to determine a sleep adjustment signal, the sleep phenotype data may be used to determine a measure for upper airway collapsibility, and may be used in generating the signals in relation to whether and / or how the therapy should be adjusted to better suit the patient given the possibility of upper airway collapse for the individual. For example, in an individual where the sleep phenotype data correlates with a low upper airway collapsibility and the patient is currently undergoing PAP treatment, the adjustment may include adjustment to the PAP treatment settings, or the adjustment may include utilization of another intervention such as using neurostimulation to stimulate the respiratory drive, e.g., if there is a higher than expected degree of apnea given the upper airway collapsibility.

[0106] In embodiments where the sleep parameter signals include signals indicating AHI, the sleep disorder characteristic signal may be generated on the basis of the determined AHI. The sleep disorder characteristic signal may relate to an increase or decrease in apnea severity. A sleep parameter difference signal showing there has been an increase in the sleep parameter signal may cause a change in the sleep disorder characteristic signal. The sleep disorder characteristic signal may be an apnea severity level, and an increase in thesleep disorder characteristic signal indicates the apnea severity has increased or is higher than previously determined, whereas a decrease indicates the apnea severity has decreased or is lower than previously determined. The determined increase or decrease may be used to generate a therapy adjustment signal. For example, if there is a reduction in measured off-therapy AHI or measured residual AHI over time, then the sleep disorder characteristic signal may indicate a reduced severity of apnea. This may be fed back to the control system to generate a therapy adjustment signal, to adjust the PAP therapy pressure to reduce the pressure. The amount of adjustment may be based on whether the processing of the recorded sleep parameter signals determines that the measured AHI is below a threshold.

[0107] The sleep disorder characteristic signal may be a signal obtained on the basis of a comparison between the measured AHI and one or more thresholds. For example, if the measured AHI includes a residual AHI and / or an AHI measured when the user is off therapy (“off-therapy AHI”), then the threshold may be set by the amount of change in residual AHI or off-therapy AHI which triggers a change in the therapy pressure. If the measured residual AHI or off-therapy AHI is at, or at or above, the threshold, then a therapy pressure is increased. There may be one or more such thresholds, each being associated with an amount of therapy pressure adjustment. The amount of therapy pressure adjustment may be an absolute quantity of pressure, or it may be a proportional quantity in relation to a predetermined therapy pressure. If the adjusted therapy pressure is still within a recommended PAP pressure range, this may be indicative of the recommended therapy still being PAP. Thus, the sleep disorder treatment in this case comprises the application of PAP at the adjusted therapy pressure.

[0108] In the embodiments contemplated in the current disclosure, the therapy adjustment may be different types of adjustments. For example, the therapy adjustment may be an adjustment to one or more settings of the PAP therapy, additional to or in place of a change in the therapy pressure. For example an adjustment to an EPR (expiratory pressure relief) setting may be made to increase the exhalation relief, if the measured AHI is reduced, but is not reduced by a sufficient amount to trigger a change of the therapy pressure. In another example, if the measured AHI is between two threshold levels corresponding to two therapy pressure levels, then the adjusted therapy pressure may be set to the higher pressure level, but with the EPR setting adjusted to increase the exhalation relief.

[0109] The therapy adjustment may be to move from one type of PAP therapy to a different type of PAP therapy. For example, if the sleep parameter difference signal shows statistically significant variations over periods of observation, but which do not demonstrate a trend, the therapy adjustment signal may involve a change of therapy mode, to adjust to therapy delivering a variable pressure therapy rather than a therapy which delivers a predetermined pressure, such as CPAP.

[0110] The sleep parameter signals may include signals indicating a leakage pressure during therapy, and the sleep parameters in this case include a leakage pressure. The leakage pressure may be monitored over time to determine a difference parameter, e.g., change or a rate of change, or trend, in leakage pressure, which may be used to establish a leakage pressure profile. The leakage pressure may be compared against a threshold to determine whether the leakage pressure is large enough that a therapy adjustment in the form of refitting the therapy device, for example to use a different PAP mask or strap type, or sizing, is required. The amount of leakage and or a leakage pressure profile may be used to determine the recommended PAP mask or strap type. Preferably, the leakage pressure parameter is determined using signals measured over an observation window, to ensure the calculated leakage pressure is representative of the patient’s sleep parameter, to minimize effects due to transient movements (e.g., user pushing a CPAP mask off his or her nose) or other forms of “noise”. In this case, the therapy adjustment signal relates to recommended refitting, such as a change in mask type, mask sizing. In a non-limiting example, if the leakage pressure is at or above the threshold over a measurement window, and from the sleep parameter difference signal (i.e., leakage parameter difference signal) it is determined that the leakage pressure is not changing statistically significantly, then a therapy adjustment comprising a refitting recommendation signal is generated. The refitting recommendation may be obtained by comparing the leakage pressure with one or more thresholds, each being associated with a refitting value. The generation of the refitting recommendation signal may further take into account the current AHI sleep parameter for the patient. The sleep disorder treatment then comprises the application of PAP with the refitting recommendation.[OHl] The therapy adjustment may be determined on the basis of one, or two or more sleep parameters. In another non-limiting example, both AHI and leakage pressure may be used. For example, an off-therapy AHI is compared against a predetermined threshold, andthe leakage pressure is compared against a threshold associated with a minimum refitting value corresponding to a smallest sizing of the mask. The predetermined threshold may be selected on the basis of clinical data in relation to a low to moderate degree of apnea. If the AHI is below the predetermined threshold and the leakage parameter is at or above the threshold corresponding to the minimum refitting value, then the sleep disorder treatment may be a therapy mode adjustment, for a therapy change from e.g., CPAP to an alternative therapy, such as, an intraoral device, mandibular device, neurostimulation, positional therapy device, etc. Thus the sleep disorder treatment in this case comprises the alternative therapy.

[0112] An initial sleep disorder therapy treatment, if the patient is not already undergoing any sleep therapy, or a sleep disorder therapy treatment determined on the basis of the therapy adjustment if the patient is currently on therapy, may be determined on the basis of one or more sleep therapy parameters and the phenotype data. For example, for a patient who is taking medication which is expected to lead to weight-loss, such as GLP-1 agonist class drugs, the phenotype data including a weight of the patient is expected to reduce over time. The expected weight change profile may be dependent on the particular medication taken. For instance, the expected profile of weight change may be a more or less linear weight loss profile, in comparison with a patient who is undergoing weight loss on the basis of an exercise or diet regimen. This data may be used to determine when to initiate tests to measure sleep parameters such as off-therapy AHI, and / or leakage pressure, or flow data. The data may also be used to determine a frequency of obtaining the sleep parameters and / or a duration of the observation period or window over which the sleep parameter signals are acquired.

[0113] Given an expected phenotype or physiology change profile such as the expected weight-loss profile, it is also possible to determine whether the measured sleep parameters behave in a manner which statistically corresponds with the phenotype change profile(s). For instance, sleep parameter changes for certain types, or known physiological causes, of apnea, may be expected to change in a way which correlates with a weight-loss. Thus, by determining whether the expected changes corresponding to a particular apnea characteristic are observed, an assessment of the patient’s sleep disorder characteristic may be made.

[0114] In the exemplified embodiment, the sleep disorder characteristic signal is indicative of an airway collapse characteristic pertaining to a characteristic of an airway collapse in the patient, such as an anatomical site at which the airway collapse occurs, structures of the airway implicated in the airway collapse, extent of the airway collapse and frequency of the airway collapse. The structures of the airway implicated in the airway collapse comprises one or more pharyngeal structures. The characteristic of the airway collapse is complete concentric collapse of a soft palate.

[0115] At step 416, the processor 112 of the control system 110 executes programming to determine a sleep disorder treatment for the patient based on the sleep disorder characteristic signal. In the illustrated embodiment, the determined treatment can involve specific settings (such as pressure settings) for the respiratory therapy device 122. The determined sleep disorder treatment can also involve an alternative to CPAP such as a mandibular advancement device, neurostimulation such as hypoglossal nerve stimulation or stimulation of a respiratory drive, pharyngeal surgery and positional therapy.

[0116] When the patient is undergoing another treatment or regimen to try to result in a change in phenotype (e.g., weight, BMI) or a physiological change (e.g., metrics related to cardiovascular fitness such as blood oxygen level), it may be possible to use at least a subset of the signals and parameters mentioned above to determine an efficacy of the treatment or regimen. In the case of a patient being on GLP-1 medication, the sleep parameter difference signal and / or the sleep parameters which have a correlation with a weight loss may be monitored over an observation period. For example, a weight loss, over time, may be expected to result in an increase in the leakage pressure. As another example, weight loss may also affect the severity of apnea, possibly affecting metrics such as the off-therapy and / or the residual therapy. Data in relation to the relevant sleep parameter or parameters, such the observed parameter values, and the rate of change in the parameter values as either an absolute quantity or relative to the initial observed parameter values, may be used as feedback data to support the treatment or regimen. The initiation, duration, and frequency of data monitoring may be dependent on the data in relation to the treatment or regime. The feedback data may be used to determine the efficacy and effectiveness of the weight loss treatment.

[0117] The determination of the sleep disorder therapy from the sleep parameter signals may be made using a neural network approach. The neural network may be implementedwith different architectures, such as but not limited to feed-forward neural network. A hybridized model may be utilized, such as but not limited to including a portion with a recurrent architecture in a feed-forward architecture, or vice versa. For example, the architecture may include one or more recurrent portions in order to compute temporal patterns in the sleep parameter signals or sleep parameter difference signals. The layers of the neural network may include the sleep parameter signals at an input layer. The sleep parameter(s) determined from the sleep parameter signals at sleep parameter layer, where each sleep parameter is a layer node. Embodiments which utilize difference parameters may include a differential layer, for calculation of quantities such as difference(s), rates of change, etc. The outputs at the differential layer and / or outputs at the sleep parameter layer may be used to determine one or more sleep disorder characteristics (e.g., severity level, apnea type, etc.). The determined sleep disorder therapy may include a therapy mode and / or a therapy strength.

[0118] Further data including a current therapy type, current therapy settings, current phenotype and / or medication data, may also provide input nodes, for the data to be used at the computation at one or more of the higher layers. For instance, the weight and medication data, as well as the calculated difference parameters, may be used for determination of sleep disorder characteristics at a sleep disorder characteristic layer.

[0119] The present disclosure is described with reference to the attached figures, where like reference numerals are used throughout the figures to designate similar or equivalent elements. The figures are not drawn to scale, and are provided merely to illustrate the instant disclosure. Several aspects of the disclosure are described herein with reference to example applications for illustration.

[0120] One or more elements or aspects or steps, or any portion(s) thereof, from one or more of any of embodiments can be combined with one or more elements or aspects or steps, or any portion(s) thereof, from one or more of any of the other embodiments or combinations thereof, to form one or more additional implementations and / or embodiments of the present disclosure.

[0121] While the present disclosure has been described with reference to one or more particular embodiments or implementations, those skilled in the art will recognize that many changes may be made thereto without departing from the spirit and scope of the present disclosure. Each of these implementations and obvious variations thereof iscontemplated as falling within the spirit and scope of the present disclosure. It is also contemplated that additional implementations according to aspects of the present disclosure may combine any number of features from any of the implementations described herein.

[0122] It is to be understood that, if any prior art is referred to herein, such reference does not constitute an admission that the prior art forms a part of the common general knowledge in the art, in any country.

[0123] In the claims which follow and in the preceding description of the invention, except where the context requires otherwise due to express language or necessary implication, the word “comprise” or variations such as “comprises” or “comprising” is used in an inclusive sense, i.e. to specify the presence of the stated features but not to preclude the presence or addition of further features in various embodiments of the invention.

Claims

CLAIMS1. A method for determining a treatment for a sleep disorder for a patient, the method comprising: administering a positive airway pressure (PAP) treatment to the patient using a respiratory pressure therapy (RPT) device in a first setting; acquiring from one or more sensors associated with the patient an initial sleep parameter signal; administering the positive airway pressure (PAP) treatment to the patient using the PAP apparatus in a second setting; acquiring from the one or more sensors a subsequent sleep parameter signal; processing the initial sleep parameter signal and subsequent sleep parameter signal to generate a sleep parameter difference signal; processing the sleep parameter difference signal to generate a sleep disorder characteristic signal; and determining a sleep disorder treatment for the patient based on the sleep disorder characteristic signal.

2. A method according to claim 1, further comprising acquiring sleep phenotype data and / or medication data pertaining to the patient.

3. A method according to claim 2, wherein the sleep disorder characteristic signal is determined by processing the sleep parameter difference signal and the sleep phenotype data.

4. A method according to claim 2, wherein the sleep disorder treatment is determined by processing the sleep disorder characteristic signal, and sleep phenotype data and / or medication data.

5. A method according to any one of claims 1 to 4, wherein the sleep disorder characteristic signal is indicative of an airway collapse characteristic pertaining to a characteristic of an airway collapse in the patient.

6. A method according to claim 5, wherein the characteristic of the airway collapse comprises one or more of an anatomical site at which the airway collapse occurs, structure(s) of the airway implicated in the airway collapse, extent of the airway collapse and frequency of the airway collapse.

7. A method according to claim 6, wherein the structure(s) of the airway implicated in the airway collapse comprises one or more pharyngeal structures.

8. A method according to claim 7, wherein the characteristic of the airway collapse is complete concentric collapse of a soft palate.

9. A method according to any one of claims 1 to 8, wherein the first setting comprises a first pressure at which the RPT device delivers air to an upper respiratory tract of the patient, the second setting comprises a second pressure at which the RPT device delivers air to the upper respiratory tract of the patient, wherein the first pressure and the second pressure are different.

10. A method according to any one of claims 1 to 9, wherein the initial sleep parameter signal and subsequent sleep parameter signal each comprise one or more of an electroencephalogram signal, electrooculogram signal, electromyogram signal, electrocardiogram signal, nasal airflow signal, oral airflow signal, thoracic effort signal, abdominal effort signal, actigraphy signal, oxygen saturation signal, heart rate signal, pulse rate signal, peripheral arterial tone signal, sleep architecture signal, analyte signal and an audio signal.

11. A method according to claim 2 or any one of claims 3 to 10 when dependent from claim 2, wherein the sleep phenotype data comprises one or more of physiological measurements, anatomical measurements, genetic data, treatment response data, treatment compliance data, neurocognitive function data and data derived from a sleep questionnaire.

12. A method according to any one of claims 1 to 11, wherein the determined sleep disorder treatment comprises settings for the RPT device.

13. A method according to any one of claims 1 to 12, wherein the determined sleep disorder treatment comprises one or more of a mandibular advancement device, hypoglossal nerve stimulation, pharyngeal surgery, and positional therapy.

14. A system for determining a treatment for a sleep disorder for a patient, comprising: a memory storing machine-readable instructions; and one or more processors configured to execute the machine-readable instructions to:acquire from one or more sensors associated with the patient an initial sleep parameter signal indicative of the patient receiving a positive airway pressure (PAP) treatment with a respiratory pressure therapy (RPT) device in a first setting; acquire from one or more sensors associated with the patient a subsequent sleep parameter signal indicative of the patient receiving a PAP treatment with the RPT device in a second setting; process the initial sleep parameter signal and subsequent sleep parameter signal to generate a sleep parameter difference signal; process the sleep parameter difference signal to generate a sleep disorder characteristic signal; and determine a sleep disorder treatment for the patient based on the sleep disorder characteristic signal.

15. A system according to claim 14, wherein the one or more processors is or are configured to execute the machine-readable instructions to acquire sleep phenotype data and / or medication data pertaining to the patient.

16. A system according to claim 15, wherein the sleep disorder characteristic signal is determined by processing the sleep parameter difference signal and the sleep phenotype data.

17. A system according to claim 15, wherein the sleep disorder treatment is determined by processing the sleep disorder characteristic signal and sleep phenotype data and / or medication data.

18. A system according to any one of claims 14 to 17, wherein the sleep disorder characteristic signal is indicative of an airway collapse characteristic pertaining to a characteristic of an airway collapse in the patient.

19. A system according to claim 18, wherein the characteristic of the airway collapse comprises one or more of an anatomical site at which the airway collapse occurs, structures of the airway implicated in the airway collapse, extent of the airway collapse and frequency of the airway collapse.

20. A system according to claim 19, wherein the structures of the airway implicated in the airway collapse comprises one or more pharyngeal structures.

21. A system according to claim 20 wherein the characteristic of the airway collapse is complete concentric collapse of a soft palate.

22. A system according to any one of claims 14 to 21, wherein the first setting comprises a first pressure at which the RPT device delivers air to an upper respiratory tract of the patient, the second setting comprises a second pressure at which the RPT device delivers air to the upper respiratory tract of the patient, wherein the first pressure and the second pressure are different.

23. A system according to any one of claims 14 to 22, wherein the initial sleep parameter signal and subsequent sleep parameter signal each comprise one or more of an electroencephalogram signal, electrooculogram signal, electromyogram signal, electrocardiogram signal, nasal airflow signal, oral airflow signal, thoracic effort signal, abdominal effort signal, actigraphy signal, oxygen saturation signal, heart rate signal, pulse rate signal, peripheral arterial tone signal, sleep architecture signal, analyte signal and an audio signal.

24. A system according to claim 15 or any one of claims 16 to 23 when dependent on clam 15, wherein the sleep phenotype data comprises one or more of physiological measurements, anatomical measurements, genetic data, treatment response data, treatment compliance data, neurocognitive function data and data derived from a sleep questionnaire.

25. A system according to any one of claims 14 to 24, wherein the determined sleep disorder treatment comprises settings for the RPT device.

26. A system according to any one of claims 14 to 25, wherein the determined sleep disorder treatment comprises one or more of a mandibular advancement device, hypoglossal nerve stimulation, pharyngeal surgery, and positional therapy.

Images