Methods and systems for determining a treatment for a sleep disorder for a patient

Abstract

A method for determining treatment for a patient's sleep disorder. The method includes administering positive airway pressure (PAP) therapy to the patient in a first setting using a respiratory pressure therapy (RPT) device, and acquiring initial sleep parameter signals from one or more sensors associated with the patient. Further administering the PAP therapy to the patient in a second setting using the PAP device, and acquiring subsequent sleep parameter signals from the one or more sensors. Processing the initial sleep parameter signals and the subsequent sleep parameter signals to generate a sleep parameter difference signal. Processing the sleep parameter difference signal to generate a sleep disorder characteristic signal. Determining treatment for the patient's sleep disorder based on the sleep disorder characteristic signal.

Description

TECHNICAL FIELD

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

[0002] Obstructive sleep apnea (OSA) is a disorder characterized by recurrent collapse of the upper airway during sleep resulting in sleep fragmentation and sympathetic activation. Obstructive sleep apnea (OSA) is a common and debilitating disorder. Continuous positive airway pressure (CPAP) is an effective treatment for OSA when properly configured. Other treatments for OSA include mandibular advancement devices, hypoglossal nerve stimulation, pharyngeal surgery, and positional therapy. The present disclosure aims to provide alternative and potentially improved techniques for more effectively configuring CPAP therapy, and determining when non-CPAP therapies can be effective. SUMMARY

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

[0004] In some embodiments, the sleep disorder characteristic signals are indicative of an airway collapse characteristic relating to a characteristic of airway collapse in the patient. The characteristic of the airway collapse can comprise one or more of: an anatomical site at which the airway collapse occurs, a structure of the airway implicated in the airway collapse, a degree of the airway collapse, and a frequency of the airway collapse. The structure of the airway implicated in the airway collapse can comprise one or more pharyngeal structures. In some embodiments, the characteristic of the airway collapse is a complete concentric collapse of the soft palate.

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

[0006] The initial sleep parameter signal and the subsequent sleep parameter signal may each include one or more of the following: electroencephalogram (EEG) signal, electrooculogram (EOG) signal, electromyogram (EMG) signal, electrocardiogram (ECG) signal, nasal airflow signal, oral airflow signal, chest exertion signal, abdominal exertion signal, body movement recording signal, oxygen saturation signal, heart rate signal, pulse rate signal, peripheral arterial tension signal, sleep architecture signal, analyte signal, and audio signal.

[0007] In some embodiments, the sleep phenotype data includes one or more of the following: physiological measurements, anatomical measurements, genetic data, treatment response data, treatment adherence data, neurocognitive function data, and data derived from sleep questionnaires.

[0008] In some embodiments, the identified sleep disorder treatment includes the setting of the RPT device. In other embodiments, the identified sleep disorder treatment includes one or more of the following: mandibular advancement devices, hypoglossal nerve stimulation, pharyngeal surgery, and postural therapy.

[0009] According to another embodiment of this disclosure, a system for determining treatment for a patient's sleep disorder is provided. The system includes: a memory storing machine-readable instructions; and one or more processors configured to execute the machine-readable instructions to: acquire initial sleep parameter signals from one or more sensors associated with the patient, the initial sleep parameter signals indicating that the patient is receiving positive airway pressure ventilation (PAP) treatment in a first setting using a respiratory pressure therapy (RPT) device; acquire subsequent sleep parameter signals from one or more sensors associated with the patient, the subsequent sleep parameter signals indicating that the patient is receiving PAP treatment in a second setting using the RPT device; process the initial sleep parameter signals and the subsequent sleep parameter signals to generate a sleep parameter difference signal; acquire sleep phenotype data associated with the patient; process the sleep parameter difference signal and the sleep phenotype data to generate a sleep disorder feature signal; and determine treatment for the patient's sleep disorder based on the sleep disorder feature signal.

[0010] The foregoing description is not intended to represent every embodiment or aspect of this disclosure. Additional features and benefits of this disclosure will become apparent from the detailed description and accompanying drawings set forth below. Attached Figure Description

[0011] The foregoing and other advantages of this disclosure will become apparent from reading the following detailed description and referring to the accompanying drawings.

[0012] Figure 1A It is a diagram illustrating an overview of the patient's respiratory system;

[0013] Figure 1B This is an example Figure 1A A diagram of the patient's upper airway;

[0014] Figure 2 This is a functional block diagram of a system for determining treatment for a patient's sleep disorder according to some embodiments of the present disclosure.

[0015] Figure 3 This is based on some embodiments of the present disclosure. Figure 2 A perspective view of at least a part of the system, the patient, and the bed partner.

[0016] Figure 4 Methods for determining treatment for a patient's sleep disorder according to some embodiments of the present disclosure are illustrated.

[0017] While this disclosure allows for various modifications and alternatives, specific implementations and embodiments have been shown by way of example in the accompanying drawings and will be described in detail herein. However, it should be understood that this is not intended to limit this disclosure to the specific forms disclosed, but rather, this disclosure is intended to cover all modifications, equivalents, and substitutions falling within the spirit and scope of this disclosure as defined by the appended claims. Detailed Implementation

[0018] refer to Figure 1A This diagram shows an overview of the respiratory system 12 of patient 10, which generally includes the nasal cavity, oral cavity, larynx, vocal cords, esophagus, trachea, bronchi, lungs, alveolar sacs, heart, and diaphragm. More specifically, patient 10 has a larynx 20, which encompasses the region of the respiratory system 12 of patient 10, typically located in the neck region of patient 10. The diaphragm of patient 10 is a sheet of muscle extending across the base of the rib cage of patient 10. The diaphragm typically separates the thoracic cavity 40 (containing the heart, lungs, and ribs) of patient 10 from the abdominal cavity 40 of patient 10. As the diaphragm contracts, the volume of the thoracic cavity 40 increases and air is drawn into the lungs.

[0019] refer to Figure 1B The image shows a view of the upper airway 14 of patient 10, which includes the nasal cavity, nasal bones, lateral nasal cartilage, greater alar cartilage, nostrils (one nostril is shown), upper lip, lower lip, larynx, hard palate, soft palate, oropharynx, tongue, epiglottis, vocal cords, esophagus, and trachea.

[0020] The respiratory system 12 of patient 10 facilitates gas exchange. The nose 50 and mouth 60 of patient 10 form the airway inlets for patient 10. Figure 1A As clearly shown, the airway comprises a series of branching tubes, which become narrower, shorter, and more numerous as they penetrate deeper into the patient's lungs. The primary function of the lungs is gas exchange, allowing oxygen to move from inhaled air into the venous blood and allowing carbon dioxide to move in the opposite direction. The trachea divides into the left and right main bronchioles, which eventually further divide into terminal bronchioles. The bronchi form the conduction airway but do not participate in gas exchange. Further branches of the airway lead to the respiratory bronchioles and ultimately to the alveoli. The alveolar regions of the lungs are where gas exchange occurs and are known as the respiratory zones.

[0021] A range of breathing disorders exist that can affect patients10. Some disorders are characterized by specific events (e.g., apnea, hypoventilation, hyperventilation, or any combination thereof). Examples of sleep-related and / or breathing disorders include periodic limb movement disorder (PLMD), restless legs syndrome (RLS), sleep-disordered breathing (SDB), obstructive sleep apnea (OSA), Cheyne-Stokes respiration (CSR), respiratory failure, obesity-induced hyperventilation syndrome (OHS), chronic obstructive 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 during sleep involving closure or obstruction of the upper airway caused by a combination of an abnormally small upper airway and loss of normal muscle tone in the areas of the tongue, soft palate, and posterior oropharyngeal walls. More specifically, apnea generally refers to the cessation of breathing caused by air obstruction (obstructive sleep apnea) or cessation of respiratory function (often referred to as central apnea). Other types of apnea include hypoventilation, hyperventilation, and hypercapnia. Hypoventilation is typically characterized by slow or shallow breathing caused by a narrowed airway, rather than airway obstruction. Hyperventilation is typically characterized by an increased depth and / or rate of breathing. Hypercapnia is typically characterized by elevated or excessive carbon dioxide in the bloodstream, usually caused by inadequate breathing.

[0023] Cheyne-Stokes respiration (CSR) is another form of sleep-disordered breathing. CSR is a disorder of the patient's respiratory controller, characterized by rhythmic alternations of waxing and waning ventilation known as the CSR cycle. CSR is characterized by repetitive deoxygenation and reoxidation of arterial blood. CSR can be harmful due to repetitive hypoxia. In some patients, CSR is associated with repetitive micro-arousals from sleep, which cause severe sleep disruption, increased sympathetic activity, and increased afterload.

[0024] Respiratory failure is a broad term encompassing respiratory disorders in which the lungs are unable to inhale enough oxygen or exhale enough CO2 to meet the patient's needs. Respiratory failure can cover some or all of the following disorders. Patients with respiratory insufficiency (a form of respiratory failure) may experience unusual shortness of breath during exercise.

[0025] Obesity hyperventilation syndrome (OHS) is defined as a combination of severe obesity and chronic hypercapnia at wakefulness in the absence of other known causes of 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 share certain common characteristics, such as increased resistance to air movement, prolonged expiratory phase of breathing, and loss of normal lung elasticity. Examples of COPD include emphysema and chronic bronchitis. COPD is caused by chronic smoking (a major risk factor), occupational exposure, air pollution, and genetic factors. Symptoms include exertional dyspnea, chronic cough, and sputum production.

[0027] Neuromuscular diseases (NMDs) encompass a wide range of conditions and ailments that impair muscle function directly through intrinsic muscle pathology or indirectly through neuropathology. Some patients with NMD are characterized by progressive muscle damage that leads to loss of mobility, wheelchair use, dysphagia, respiratory muscle weakness, and ultimately death from respiratory failure. Neuromuscular disorders can be classified as rapidly progressive or slowly progressive: (i) rapidly progressive disorders: characterized by muscle damage that worsens over months and leads to death within years (e.g., amyotrophic lateral sclerosis (ALS) and Duchenne muscular dystrophy (DMD) in adolescents); (ii) variable or slowly progressive disorders: characterized by muscle damage that worsens over years and only slightly shortens life expectancy (e.g., limb-girdle, facioscapulohumeral, and ankylosing spondylitis). Symptoms of respiratory failure in NMD include: progressive general weakness, dysphagia, shortness of breath during and at rest, fatigue, somnolence, morning headache, difficulty concentrating, and mood swings.

[0028] Chest wall disorders are a group of chest wall deformities that result in inefficient connection between the respiratory muscles and the thoracic cavity. These disorders are typically characterized by restrictive defects and have the potential to cause chronic hypercapnia-related respiratory failure. Scoliosis and / or kyphosis can cause severe respiratory failure. Symptoms of respiratory failure include: dyspnea during exercise, peripheral edema, orthopnea, recurrent chest infections, morning headache, fatigue, poor sleep quality, and loss of appetite.

[0029] These other sleep-related disorders are characterized by specific events that occur when an individual is sleeping (e.g., snoring, sleep apnea, insomnia, restless legs, sleep disturbances, apnea, increased heart rate, difficulty breathing, asthma attacks, seizures, convulsions, or any combination thereof). While these other sleep-related disorders may have symptoms similar to insomnia, distinguishing them from insomnia helps in tailoring an effective treatment plan that differentiates features that may require different treatments. For example, fatigue is often a characteristic of insomnia, while excessive daytime sleepiness is a characteristic feature of other disorders (e.g., PLMD) and reflects a physiological tendency to fall asleep unconsciously.

[0030] The Apnea-Insufficiency 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 insufficiency events experienced by the patient during a sleep session by the total number of hours of sleep in the session. An event can be, for example, an apnea lasting at least 10 seconds. An AHI less than 5 is considered normal. An AHI greater than or equal to 5 but less than 15 is considered an indicator of mild sleep apnea. An AHI greater than or equal to 15 but less than 40 is considered an indicator of moderate sleep apnea. An AHI greater than or equal to 40 is considered an indicator of severe sleep apnea. In children, an AHI greater than 1 is considered abnormal. When the AHI is normal, or when the AHI is normal or mild, sleep apnea can be considered “controlled.” The AHI can also be used in conjunction with oxygen desaturation levels to indicate the severity of obstructive sleep apnea.

[0031] This disclosure relates to methods and systems for determining treatments for one or more of the sleep-related and / or breathing disorders discussed above. According to one embodiment, this disclosure provides a method and system for determining treatments for OSA. As discussed below, the determined treatment may involve treatment using a specifically configured continuous positive airway pressure (CPAP) or another modality, such as treatment with a mandibular advancement device, hypoglossal nerve stimulation, pharyngeal surgery, or postural therapy.

[0032] refer to Figure 2 The diagram illustrates a system 100 according to some embodiments of the present disclosure. 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 embodiments, system 100 also includes a respiratory therapy system 120 and an activity tracker 180.

[0033] Control system 110 includes one or more processors 112 (hereinafter referred to as processor 112). Control system 110 is generally used to control (e.g., actuate) various components of system 100 and / or analyze data acquired and / or generated by the components of system 100. Processor 112 may be a general-purpose or special-purpose processor or a microprocessor. Control system 110 (or any other control system) or a portion thereof, such as processor 112 (or any other processor or a portion thereof), may be used to perform one or more steps of any of the methods described herein and / or claimed. Control system 110 may include any suitable number of processors (e.g., one processor, two processors, five processors, ten processors, etc.), which may be located in a single housing or remotely to each other. Control system 110 may be coupled to, for example, the housing of user equipment 170, a portion of respiratory therapy system 120 (e.g., a housing), and / or the housing of one or more sensors in sensor 130 and / or located within the foregoing. Control system 110 may be centralized (within one such housing) or distributed (within two or more physically different such housings). In such embodiments, which include two or more housings containing the control system 110, such housings may be positioned close to and / or far from each other.

[0034] Memory device 114 stores machine-readable instructions executable by processor 112 of control system 110. Memory device 114 can be any suitable computer-readable storage device or medium, such as, for example, random or serial access memory devices, hard disk drives, solid-state drives, flash memory devices, etc. Although one memory device 114 is shown in Figure 1, system 100 may include any suitable number of memory devices 114 (e.g., one memory device, two memory devices, five memory devices, ten memory devices, etc.). Memory device 114 may be coupled to the housing of respiratory therapy device 122 of respiratory therapy system 120, the housing of user device 170, the housing of one or more sensors in sensor 130, or any combination thereof and / or located within the foregoing. As with control system 110, memory device 114 can be centralized (within one such housing) or distributed (within two or more physically different such housings).

[0035] In some embodiments, memory device 114 stores a user profile associated with the patient. The user profile may include patient-related sleep phenotype data, such as patient-related demographic information, patient-related biometric information, patient-related medical information, patient-related physiological measurements, patient-related anatomical measurements, patient-related genetic data, patient-related treatment response data, patient-related treatment adherence data, patient-related neurocognitive function data, data from sleep questionnaires, self-reported patient feedback, patient-related sleep parameters (e.g., sleep-related parameters recorded from one or more early sleep sessions), or any combination thereof. Anatomical measurements may include measurements such as the patient's pharyngeal width. Patient physiological measurements may include measurements such as the patient's polysomnography (PSG) data. Demographic information may include, for example, information indicating the patient's age, sex, ethnicity, geographic location, relationship status, family history of insomnia or sleep apnea, employment status, education status, socioeconomic status, or any combination thereof. Medical information may include, for example, information indicating one or more medical conditions associated with the patient, the patient's medication use, or both. Medical information data may also include Multisleep Latency Test (MSLT) results or scores and / or Pittsburgh Sleep Quality Index (PSQI) scores or values. Self-reported user feedback may include information indicating self-reported patient sleep scores (e.g., poor, average, excellent), user-reported patient stress levels, user-reported patient fatigue levels, user-reported patient health status, recent life events experienced by the user, or any combination thereof.

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

[0037] As noted above, in some embodiments, system 100 includes a respiratory therapy system 120 (also referred to as a respiratory therapy system). The respiratory therapy system 120 may 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 air circuit), a display device 128, a humidifier canister 129, or any combination thereof. In some embodiments, one or more of the control system 110, memory device 114, display device 128, sensor 130, and humidifier canister 129 are part of the respiratory therapy device 122. Respiratory pressure therapy refers to the application of supplying air to the inlet of a patient's airway at a controlled target pressure nominally positive relative to atmosphere (e.g., as opposed to negative pressure therapy such as a canister ventilator or thoracic shield) throughout the user's respiratory cycle. The respiratory therapy system 120 is typically used to treat individuals suffering from one or more sleep-related breathing disorders (e.g., obstructive sleep apnea, central sleep apnea, or mixed sleep apnea).

[0038] The respiratory therapy device 122 is typically used to generate pressurized air to be delivered to a patient (e.g., using one or more motors driving one or more compressors). In some embodiments, the respiratory therapy device 122 generates a continuous, constant air pressure to be delivered to the user. In other embodiments, 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 other embodiments, 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 may deliver at least about 6 cmH2O, at least about 10 cmH2O, at least about 20 cmH2O, between about 6 cmH2O and about 10 cmH2O, between about 7 cmH2O and about 12 cmH2O, etc. The respiratory therapy device 122 may also deliver pressurized air at a predetermined flow rate between, for example, about -20 L / min and about 150 L / min, while maintaining positive pressure (relative to ambient pressure).

[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 delivered by the respiratory therapy device 122 to the patient 10, the length of ramp time during which the pressure level increases from the initial pressure to the target pressure, and the level of humidification applied to the air before it is supplied to the patient's airway.

[0040] User interface 124 engages with a portion of the patient's face and delivers pressurized air from respiratory therapy device 122 to the patient's airway to help prevent airway narrowing and / or collapse during sleep. This also increases the patient's oxygen intake during sleep. Typically, user interface 124 engages with the patient's face such that 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. Respiratory therapy device 122, user interface 124, and conduit 126 together form an air passage fluidly connected to the patient's airway. Pressurized air also increases the patient's oxygen intake during sleep.

[0041] Depending on the therapy to be applied, the user interface 124 may form a seal, for example, with an area or portion of the patient's face, to facilitate the delivery of gas to achieve the therapy at a pressure that varies sufficiently with ambient pressure (e.g., at a positive pressure of about 10 cmH2O relative to ambient pressure). For other forms of therapy, such as oxygen delivery, the user interface may not include a seal sufficient to facilitate the delivery of a gas supply to the airway at a positive pressure of about 10 cmH2O.

[0042] like Figure 3 As shown, in some embodiments, the user interface 124 is a mask (e.g., a full-face mask) that covers the patient's nose and mouth. Alternatively, the user interface 124 is a nasal mask that supplies air to the patient's nose or a nasal pillow mask that delivers air directly to the user's nostrils. Therefore, for the purposes of this disclosure, the term "user interface" 124 is used interchangeably with the terms "CPAP mask" or "PAP mask". The user interface 124 may include: a plurality of straps (e.g., including hook-and-loop fasteners) forming a headband, for example, to help position and / or stabilize the interface on a part of the patient (e.g., the face), and a conformal cushioning pad (e.g., silicone, plastic, foam, etc.) to help provide an airtight seal between the user interface 124 and the patient. The user interface 124 may also include one or more vents to allow carbon dioxide and other gases exhaled by the patient 210 to escape. In other embodiments, the user interface 124 includes a mouthpiece (e.g., a night-use protective mouthpiece molded to conform to the patient's teeth, a mandibular repositioning device, etc.).

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

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

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

[0046] The humidifier canister 129 is coupled to or integrated into the respiratory therapy device 122 and includes a water reservoir for humidifying pressurized air supplied from the respiratory therapy device 122. The respiratory therapy device 122 may include a heater for heating the water in the humidifier canister 129 to humidify the pressurized air supplied to the user. Additionally, in some embodiments, the conduit 126 may also include a heating element (e.g., coupled to and / or embedded in the conduit 126) for heating the pressurized air supplied to the user. The humidifier canister 129 may be fluidly coupled to a water vapor inlet of an air passage and supply water vapor into the air passage via the water vapor inlet, or it may be linearly integrated into the air passage itself.

[0047] In some implementations, system 100 can be used to deliver a substance from a receiver to a patient's airway based at least in part on physiological data, sleep-related parameters, other data or information, or any combination thereof. Typically, modifying the delivery of a substance into the airway can include: (i) initiating delivery of a substance into the airway, (ii) terminating delivery of a substance into the airway, (iii) modifying the amount of substance delivered into the airway, (iv) modifying the timing characteristics of delivery of a substance into the airway, (v) modifying the quantitative characteristics of delivery of a substance into the airway, (vi) modifying any parameters associated with delivery of a substance into the airway, or (vii) combinations of (i) to (vi).

[0048] Modifying the temporal characteristics of substance delivery in the air pathway can include changing the rate of substance delivery, starting and / or ending at different times, lasting for different periods, altering the temporal distribution or characteristics of delivery, and changing the quantity distribution independently of the time distribution. Independent time and quantity variations ensure that, in addition to changing the frequency of substance release, the amount of substance released each time can be changed. In this way, a variety of different combinations of release frequency and release quantity can be achieved (e.g., higher frequency but lower release quantity, higher frequency and higher quantity, lower frequency and higher quantity, lower frequency and lower quantity, etc.). Other modifications to this portion of the substance delivery in 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 automated positive airway pressure (APAP) system, a bilevel or variable positive airway pressure (BPAP or VPAP) system, or any combination thereof. A CPAP system delivers a predetermined air pressure (e.g., determined by a sleep physician) to the user. An APAP system automatically changes the air pressure delivered to the user based on, for example, breathing data associated with the user. A BPAP or VPAP system is configured to deliver a first predetermined pressure (e.g., inspiratory positive airway pressure or IPAP) and a second predetermined pressure below the first predetermined pressure (e.g., expiratory positive airway pressure or EPAP).

[0050] refer to Figure 3 System 100 according to some implementations is illustrated. Figure 2 As part of the respiratory therapy system 120, the patient 210 and bed partner 220 are located on the bed 230 and lying on the mattress 232.

[0051] User interface 124 (also referred to herein as a mask or CPAP mask, e.g., a full-face mask, nasal mask, nasal pillow mask, etc.) can be worn by patient 210 during a sleep session. The sleep session can take place in a hospital setting or... Figure 3The environment shown was performed using home sleep apnea testing (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 incorporated herein by reference in its entirety). The NightOwl system includes: a sensor placed on the patient's fingertip (which performs the function of sensor 130); and a cloud-based analytics platform. The sensor acquires accelerometer data and reflectivity-based photoplethysmography (PPG) data. The analytics platform derives motion recordings from the accelerometer and derives blood oxygen saturation, peripheral arterial tension measurement (PAT), and pulse rate, among other characteristics, from the sensor.

[0052] User interface 124 is fluidly connected and / or connected to respiratory therapy device 122 via conduit 126. Respiratory therapy device 122, in turn, delivers pressurized air to user 210 via conduit 126 and user interface 124 to increase air pressure in user 210's throat, helping to prevent airway closure and / or narrowing during sleep. Respiratory therapy device 122 can be positioned such as... Figure 3 The bedside table 240 shown is directly adjacent to the bed 230, or more generally, is positioned on any surface or structure that is typically adjacent to the bed 230 and / or the user 210.

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

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

[0055] As described herein, system 100 can typically be used for purposes such as generating information with the patient during a sleep session (e.g., Figure 2 The respiratory therapy system 120 shown contains physiological data associated with the user. Technicians can also remotely modify one or more settings of the respiratory therapy system 120 during a sleep session and generate updated physiological data associated with the patient. As described below, this disclosure utilizes signal processing techniques to generate a sleep parameter difference signal from the physiological data and the updated physiological data. The generated sleep parameter difference signal and patient-related sleep phenotypic data are algorithmically combined to generate a sleep disorder feature signal, which can be used to determine a treatment modality for the patient.

[0056] Physiological data can be analyzed to generate one or more sleep-related parameters, which may include any parameters, measurements, etc., relevant to the user during a sleep session. One or more sleep-related parameters that can be determined for the user 210 during a sleep session include, for example, apnea-hypopnea index (AHI) score, sleep score, flow signal, respiratory signal, respiratory rate, inspiratory amplitude, expiratory amplitude, inspiratory-expiratory ratio, number of events per hour, event type, stage, pressure setting of the respiratory therapy device 122, heart rate, heart rate variability, user 210's movement, temperature, EEG activity, EMG activity, microarousing, snoring, choking, coughing, whistling, wheezing, or any combination thereof.

[0057] One or more sensors 130 can be used to generate, for example, physiological data, audio data, or both. The control system 110 can use the physiological data generated by one or more of the sensors 130 to determine the interaction between the user 210 and the user during a sleep session. Figure 2This refers to sleep-wake signals and one or more sleep-related parameters. Sleep-wake signals can indicate one or more sleep states, including wakefulness, relaxed wakefulness, micro-awakening, or different sleep stages (or sleep architectures), such as, for example, the rapid eye movement (REM) stage, the first non-REM stage (commonly referred to as "N1"), the second non-REM stage (commonly referred to as "N2"), the third non-REM stage (commonly referred to as "N3"), or any combination thereof. For example, methods for determining sleep states and / or sleep stages based on physiological data generated by one or more sensors (such as one or more sensors 130) are described in WO 2014 / 047310, US 2014 / 0088373, WO 2017 / 132726, WO 2019 / 122413, and WO 2019 / 122414, each of which is incorporated herein by reference in its entirety.

[0058] In some embodiments, the sleep-wake signals described herein can be timestamped to indicate the time a patient goes to bed, the time a patient gets out of bed, the time a patient attempts to fall asleep, etc. The sleep-wake signals can be measured by one or more sensors 130 during a sleep session at a predetermined sampling rate (such as, for example, one sample per second, one sample every 30 seconds, one sample per minute, etc.). In some embodiments, the sleep-wake signals can also indicate respiratory signals, respiratory rate, inspiratory amplitude, expiratory amplitude, inspiratory-expiratory ratio, number of events per hour, event type, pressure setting of the respiratory therapy device 122, or any combination thereof during a sleep session. Events can include snoring, sleep apnea, central sleep apnea, obstructive sleep apnea, mixed sleep apnea, hypopnea, mask leakage (e.g., from user interface 124), restless legs, sleep disturbance, apnea, increased heart rate, dyspnea, asthma attack, seizure, convulsion, or any combination thereof. One or more sleep-related parameters can be determined for a patient during a sleep session based on sleep-wake signals, including, for example, total bed rest time, total sleep time, sleep onset latency, wakefulness parameters after sleep onset, sleep efficiency, segmentation index, or any combination thereof. As described further in detail herein, physiological data and / or sleep-related parameters can be analyzed to determine one or more sleep-related scores and treatment modalities.

[0059] Physiological and / or audio data generated by one or more sensors 130 can also be used to determine respiratory signals associated with the patient during a sleep session. Respiratory signals typically indicate the patient's respiration / breathing during a sleep session. Respiratory signals can indicate and / or be analyzed to determine (e.g., using control system 110) one or more sleep-related parameters, such as, for example, respiratory rate, respiratory rate variability, inspiratory amplitude, expiratory amplitude, inspiratory-expiratory ratio, occurrence of one or more events, number of events per hour, event type, sleep state, sleep stage, apnea-hypopnea index (AHI), pressure setting of respiratory therapy device 122, or any combination thereof. One or more events may include snoring, apnea, central apnea, obstructive apnea, mixed apnea, hypopnea, mask leakage (e.g., from user interface 124), coughing, restless legs, sleep disturbance, choking, increased heart rate, dyspnea, asthma attack, seizure, convulsion, increased blood pressure, or any combination thereof. Many of the described sleep-related parameters are physiological parameters, although some of these sleep-related parameters may be considered non-physiological parameters. Other types of physiological and / or non-physiological parameters can also be determined based on data from one or more sensors 130 or based on other types of data.

[0060] As used herein, a sleep session can be defined in several 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 the duration of a patient's sleep, i.e., a sleep session has a start time and an end time, and during a sleep session, the patient does not wake up until the end time. That is, any period of time during which the patient is awake is not included in a sleep session. According to this first definition of a sleep session, if a patient wakes up and falls asleep multiple times in the same night, each of the sleep intervals separated by wakefulness intervals is a sleep session.

[0061] Alternatively, in some embodiments, the sleep session has a start time and an end time, and during the sleep session, the patient may wake up without ending the sleep session as long as the continuous duration of wakefulness is below a wakefulness duration threshold. The wakefulness duration threshold can be defined as a percentage of the sleep session. The wakefulness duration threshold can be, for example, approximately 20% of the sleep session, approximately 15% of the sleep session duration, approximately 10% of the sleep session duration, approximately 5% of the sleep session duration, approximately 2% of the sleep session duration, etc., or any other threshold percentage. In some embodiments, the wakefulness 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 earliest time a patient goes to bed at night and the latest time a user gets out of bed the following morning. In other words, a sleep session can be defined as a period of time that begins at the first time of the first date of the night (e.g., Monday, January 6, 2020) when the patient first goes to bed wanting to fall asleep (e.g., if the user does not intend to watch TV or use a smartphone before falling asleep), and ends at the second time of the second date of the following morning (e.g., Tuesday, January 7, 2020) when the patient first gets out of bed and does not want to return to sleep the following morning.

[0063] In some implementations, the patient can manually define the start and / or terminate the sleep session. For example, the patient can select (e.g., by clicking or tapping) on ​​the user device 170 ( Figure 3 One or more user-selectable elements are displayed on the monitor 172 to manually initiate or terminate a sleep session.

[0064] Typically, a sleep session includes any point in time after the patient 210 has already lay down or sat on bed 230 (or another area or object where they intend to sleep) and has turned on the breathing therapy device 122 and put on the user interface 124. A sleep session can therefore include the following time periods: (i) when the patient 210 is using the CPAP system but before attempting to fall asleep (e.g., when the patient 210 is lying on bed 230 reading); (ii) when the patient 210 begins to attempt to fall asleep but remains awake; (iii) when the patient 210 is in light sleep (also known as stages 1 and 2 of non-rapid eye movement (NREM) sleep); (iv) when the patient 210 is in deep sleep (also known 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 periodically wakes up between light sleep, deep sleep, or REM sleep; or (vii) when the patient 210 wakes up and does not fall back asleep.

[0065] A sleep session is typically 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, a sleep session may include additional time periods, or may be limited to only some of the time periods disclosed above. For example, a sleep session may be defined as a time period that begins when the respiratory therapy device 122 begins supplying pressurized air to the airway or the patient 210, ends when the respiratory therapy device 122 stops supplying pressurized air to the patient 210's airway, and includes some or all of the time points between when the patient 210 falls asleep or wakes up.

[0066] Pressure sensor 132 outputs pressure data, which may be stored in memory device 114 and / or analyzed by processor 112 of control system 110. In some embodiments, pressure sensor 132 is an air pressure sensor (e.g., an atmospheric pressure sensor) that generates sensor data indicating the breathing (e.g., inhalation and / or exhalation) and / or ambient pressure of the user of respiratory therapy system 120. In such embodiments, pressure sensor 132 may be coupled to or integrated into respiratory therapy device 122. Pressure sensor 132 may be, for example, a capacitive sensor, an electromagnetic sensor, a piezoelectric sensor, a strain gauge sensor, an optical sensor, a potential sensor, or any combination thereof.

[0067] Flow sensor 134 outputs flow data, which may be stored in memory device 114 and / or analyzed by processor 112 of control system 110. Examples of flow sensors (such as flow sensor 134, for example) are described in International Publication No. WO 2012 / 012835, which is incorporated herein by reference in its entirety. In some embodiments, flow sensor 134 is used to determine the airflow from respiratory therapy device 122, the airflow through conduit 126, the airflow through user interface 124, or any combination thereof. In such embodiments, flow sensor 134 may be coupled to or integrated into respiratory therapy device 122, user interface 124, or conduit 126. Flow sensor 134 may be a mass flow sensor, such as a rotary flow meter (e.g., Hall effect flow meter), turbine flow meter, orifice plate flow meter, ultrasonic flow meter, hot wire sensor, eddy current sensor, membrane sensor, or any combination thereof. In some embodiments, the flow sensor 134 is configured to measure ventilation flow (e.g., intentional “leakage”), unintentional leakage (e.g., mouth leak and / or mask leak), patient flow (e.g., air entering and / or leaving the lungs), or any combination thereof. In some embodiments, flow data can be analyzed to determine the patient’s cardiogenic oscillations. In one example, the pressure sensor 132 can be used to determine the patient’s blood pressure.

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

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

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

[0071] The speaker 142 output can be controlled by a user of system 100 (e.g., Figure 3The speaker 142 can be used as, for example, an alarm clock or to play alarms or messages to the user 210 (e.g., in response to an event). In some embodiments, the speaker 142 can be used to convey audio data generated by the microphone 140 to the patient. The speaker 142 can be coupled to or integrated into the respiratory therapy device 122, user interface 124, catheter 126, or user device 170.

[0072] Microphone 140 and speaker 142 can be used as separate devices. In some embodiments, microphone 140 and speaker 142 can be combined into acoustic sensor 141 (e.g., a SONAR sensor), as described in, for example, WO 2018 / 050913 and WO2020 / 104465, each of which is incorporated herein by reference in its entirety. In such embodiments, speaker 142 generates or emits sound waves at predetermined intervals, and microphone 140 detects reflections of the emitted sound waves from speaker 142. The sound waves generated or emitted by speaker 142 have frequencies inaudible to the human ear (e.g., below 20 Hz or above about 18 kHz) so as not to disturb the sleep of patient 210 or bed partner 220. Figure 3 Based at least in part on data from microphone 140 and / or speaker 142, control system 110 can determine patient 210 ( Figure 3 The location of the sonar sensor and / or one or more of the sleep-related parameters described herein, such as, for example, respiratory signal, respiratory rate, inspiratory amplitude, expiratory amplitude, inspiratory-expiratory ratio, number of events per hour, event type, sleep state, sleep stage, pressure setting of the respiratory therapy device 122, or any combination thereof. In this context, the sonar sensor can be understood to involve active acoustic sensing, such as by generating and / or emitting ultrasonic and / or low-frequency ultrasonic sensing signals (e.g., in the frequency range of, for example, about 17-23 kHz, 18-22 kHz, or 17-18 kHz) via air. Such systems can be considered relative to WO 2018 / 050913 and WO 2020 / 104465 mentioned above, each of which is incorporated herein by reference in its entirety.

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

[0074] RF transmitter 148 generates and / or transmits radio waves having a predetermined frequency and / or predetermined amplitude (e.g., in the high-frequency band, in the low-frequency band, long-wave signal, short-wave signal, etc.). RF receiver 146 detects the reflection of the radio waves transmitted from RF transmitter 148, and this data can be analyzed by control system 110 to determine the patient 210 ( Figure 2 The location of the sensor and / or one or more sleep-related parameters described herein. An RF receiver (RF receiver 146 and RF transmitter 148 or another RF pair) may also be used for wireless communication between the control system 110, the respiratory therapy device 122, one or more sensors 130, the user equipment 170, or any combination thereof. Although RF receiver 146 and RF transmitter 148 are shown as separate and distinct elements in FIG. 1, in some embodiments, RF receiver 146 and RF transmitter 148 are combined as part of RF sensor 147 (e.g., a RADAR sensor). In some such embodiments, RF sensor 147 includes control circuitry. Specific formats for RF communication may include Wi-Fi, Bluetooth, etc.

[0075] In some implementations, RF sensor 147 is part of a mesh system. An example of a mesh system is a Wi-Fi mesh system, which may include mesh nodes, mesh routers, and mesh gateways, each of which may be mobile / movable or fixed. In such an implementation, the Wi-Fi mesh system includes Wi-Fi routers and / or Wi-Fi controllers, and one or more satellites (e.g., access points), each of which includes the same or similar RF sensor as RF sensor 147. The Wi-Fi routers and satellites communicate continuously with each other using Wi-Fi signals. The Wi-Fi mesh system can be used to generate motion data based on changes in the Wi-Fi signals between the routers and satellites (e.g., differences in received signal strength), caused by a moving object or person partially blocking the signal. The motion data may indicate movement, breathing, heart rate, gait, falls, behavior, etc., or any combination thereof.

[0076] Camera 150 outputs image data that can be reproduced as one or more images (e.g., still images, video images, thermal images, or any combination thereof) that can be stored in memory device 114. Image data from camera 150 can be used by control system 110 to determine one or more sleep-related parameters described herein, such as, for example, one or more events (e.g., periodic limb movements or restless legs syndrome), respiratory signals, respiratory rate, inspiratory amplitude, expiratory amplitude, inspiratory-expiratory ratio, number of events per hour, event type, sleep state, sleep stage, or any combination thereof. Furthermore, image data from camera 150 can be used, for example, to identify the patient's location to determine the patient 210 ( Figure 3 Chest movements of patient 210 are used to determine airflow through the mouth and / or nose of patient 210, in order to determine when patient 210 is on the bed 230. Figure 3 The time for determining when patient 210 gets out of bed 230. In some embodiments, camera 150 includes a wide-angle lens or a fisheye lens.

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

[0078] PPG sensor 154 output and patient 210 ( Figure 2 The associated physiological data can be used to determine one or more sleep-related parameters, such as, for example, heart rate, heart rate variability, cardiac cycle, respiratory rate, inspiratory amplitude, expiratory amplitude, inspiratory-expiratory ratio, estimated blood pressure parameters, or any combination thereof. The PPG sensor 154 can be worn by the patient 210, embedded in clothing and / or fabric worn by the patient 210, embedded in and / or coupled to the user interface 124 and / or its associated headband (e.g., a strip, etc.).

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

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

[0081] Capacitive sensor 160, force sensor 162, and strain gauge sensor 164 output data, which may be stored in memory device 114 and used by control system 110 to determine one or more sleep-related parameters among those described herein. EMG sensor 166 outputs physiological data associated with electrical activity generated by one or more muscles. Oxygen sensor 168 outputs oxygen data indicating the oxygen concentration of a gas (e.g., in conduit 126 or at user interface 124). Oxygen sensor 168 may be, for example, an ultrasonic oxygen sensor, an electro-oxygen sensor, a chemical oxygen sensor, an optical oxygen sensor, a pulse oximeter (e.g., an SpO2 sensor), or any combination thereof. In some embodiments, one or more sensors 130 may also include a ground-skin response (GSR) sensor, a blood flow sensor, a respiration sensor, a pulse sensor, a blood pressure sensor, a blood oxygenation sensor, or any combination thereof.

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

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

[0084] A LiDAR (Light Detection and Ranging) sensor 178 can be used for depth sensing. This type of optical sensor (e.g., a laser sensor) can be used to detect objects and construct a three-dimensional (3D) map of the surrounding environment, such as a living space. LiDAR typically utilizes pulsed lasers for time-of-flight measurements. LiDAR is also known as 3D laser scanning. In instances using such sensors, fixed or mobile devices (such as smartphones) with a LiDAR sensor 166 can measure and map an area extending 5 meters or more from the sensor. For example, LiDAR data can be fused with point cloud data estimated by an electromagnetic RADAR sensor. The LiDAR sensor 178 can also use artificial intelligence (AI) to automatically geofence the RADAR system by detecting and classifying features in the space that may cause problems for the RADAR system, such as glass windows (which may be highly reflective of RADAR). For example, LiDAR can also be used to provide an estimate of a person's height, and how that height changes when the person sits down or falls. LiDAR can be used to form a 3D mesh representation of the environment. In further applications, LiDAR can reflect radio waves away from solid surfaces (e.g., semi-transparent materials), allowing for the classification of different types of obstacles.

[0085] Although shown separately in Figure 1, any combination of one or more sensors 130 may be integrated into or coupled to any one or more components of system 100, including respiratory therapy device 122, user interface 124, catheter 126, humidifier 129, control system 110, user device 170, activity tracker 180, or any combination thereof. For example, microphone 140 and speaker 142 may be integrated into and / or coupled to user device 170; and pressure sensor 130 and / or flow sensor 132 may be integrated into and / or coupled to respiratory therapy device 122. In some embodiments, at least one of the one or more sensors 130 is not coupled to respiratory therapy device 122, control system 110, or user device 170, and is positioned substantially adjacent to patient 210 during a sleep session (e.g., positioned on or in contact with a portion of user 210, worn by patient 210, coupled to or positioned on a bedside table, coupled to a mattress, coupled to a ceiling, etc.).

[0086] User equipment 170 ( Figure 2The system 100 includes a display device 172. User device 170 may be, for example, a mobile device such as a smartphone, tablet, game console, smartwatch, laptop, etc. Alternatively, user device 170 may be an external sensing system, a television (e.g., a smart TV), or another smart home device (e.g., a smart speaker such as Google Home, Amazon Echo, Alexa, etc.). In some embodiments, the user device is a wearable device (e.g., a smartwatch). Display device 172 is typically used to display images including still images, video images, or both. In some embodiments, display device 172 acts as a human-machine interface (HMI) including a graphical user interface (GUI) configured to display images and an input interface. Display device 172 may be an LED display, OLED display, LCD display, etc. Input interface may be, for example, a touchscreen or touch-sensitive substrate, a mouse, keyboard, or any sensor system configured to sense input made by a human user interacting with user device 170. In some embodiments, system 100 may use and / or include one or more user devices.

[0087] In some embodiments, system 100 also includes an activity tracker 180. The activity tracker 180 is typically used to help generate phenotypic data (including physiological data) associated with the patient. The activity tracker 180 may include one or more of the sensors 130 described herein, such as, for example, motion sensors 138 (e.g., one or more accelerometers and / or gyroscopes), PPG sensors 154, and / or ECG sensors 156. Physiological data from the activity tracker 180 may be used to determine, for example, steps, distance traveled, number of steps climbed, duration of physical activity, type of physical activity, intensity of physical activity, time spent standing, respiratory rate, mean respiratory rate, resting respiratory rate, maximum respiratory rate, respiratory rate variability, heart rate, mean heart rate, resting heart rate, maximum heart rate, heart rate variability, calories burned, blood oxygen saturation, electrodermal activity (also referred to as skin conductance or skin response), or any combination thereof. In some embodiments, the activity tracker 180 is coupled (e.g., electronically or physically) to user equipment 170.

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

[0089] Although the control system 110 and memory device 114 are described and shown in FIG. 1 as separate and distinct components of system 100, in some embodiments, the control system 110 and / or memory device 114 are integrated into user equipment 170 and / or respiratory therapy device 122. Alternatively, in some embodiments, the control system 110 or a portion thereof (e.g., processor 112) may reside in the cloud (e.g., integrated into a server, integrated into an Internet of Things (IoT) device, connected to the cloud, subjected to edge cloud processing, etc.) or 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 the components described above, systems according to embodiments of this disclosure may include more or fewer components. For example, a first alternative system includes a control system 110, a memory device 114, and at least one of one or more sensors 130, but does not include the respiratory therapy system 120. As another example, a second alternative system includes a control system 110, a memory device 114, at least one of one or more sensors 130, and a user device 170. As yet another example, a third alternative system includes a control system 110, a memory device 114, a respiratory therapy system 120, at least one of one or more sensors 130, and a user device 170. Therefore, various systems can be formed using any one or more portions of the components shown and described herein and / or in combination with one or more other components.

[0091] refer to Figure 4 According to some embodiments of this disclosure, a method 400 for determining treatment for a sleep disorder in a patient 210 is disclosed. This method is particularly useful in characterizing pharyngeal collapse associated with OSA (such as in terms of the anatomical location of the collapse, the structure of the airway involved in the collapse, the degree of collapse, and the frequency of collapse). These characterizations can be used to quantify the severity of OSA and determine treatment.

[0092] For example, those skilled in the art will understand that some treatment alternatives to CPAP (such as mandibular advancement devices (MAD), hypoglossal nerve stimulation (HGNS), pharyngeal surgery, and postural therapy) appear to be more effective for tongue and epiglottic collapse compared to oropharyngeal lateral wall (OPLW) collapse. Studies have also indicated that complete concentric collapse (CCC) of the palatopharyngeal airway is an exclusion category for HGNS. The pharyngeal collapse characterization provided in this disclosure is useful in both customizing PAP therapies (such as, but not limited to, CPAP therapies) and selecting alternative treatment modalities. In the cases where CPAP is mentioned in this application, it is intended as a non-limiting example of PAP therapy unless otherwise stated.

[0093] This disclosure also provides a useful non-surgical alternative to the routine method of determining the site of pharyngeal collapse via drug-induced sleep endoscopic surgery (DISE). Due to its high cost and invasiveness, DISE is rarely performed in the initial assessment of OSA, and treatment decisions are rarely made with knowledge of the obstruction site. Knowing the obstruction site can be valuable in considering treatment modalities at an early stage.

[0094] At step 402, CPAP is administered to patient 210 using the respiratory therapy device 122 in a first setting. In the illustrated embodiment, this setting is the pressure of the pressurized air delivered by the respiratory therapy device 122 to the airway of patient 210.

[0095] At step 404, the control system 110 operates one or more sensors in the sensor 130 to collect corresponding sensor data and outputs 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 indicates the sleep parameters sensed when the respiratory therapy device is operating in a first pressure setting. In the illustrated embodiment, the initial sleep parameter signal includes one or more of the following: electroencephalogram (EEG) signal, electrooculogram (EOG) signal, electromyogram (EMG) signal, electrocardiogram (ECG) signal, nasal airflow signal, oral airflow signal, chest exertion signal, abdominal exertion signal, body movement recording signal, oxygen saturation signal, heart rate signal, pulse rate signal, peripheral arterial tension signal, sleep architecture signal, analyte signal, and audio signal.

[0096] At step 406, after a predetermined time interval, CPAP is administered to patient 210 using the respiratory therapy device 122 in a second setting. A technician conducting sleep research can remotely change the respiratory therapy device 122 to the second setting via remote access to the user interface 124 or user device 170 of the respiratory therapy device 122. Alternatively, the respiratory therapy device can automatically change to the second setting in response to an automated sleep research module stored in memory device 114 and executed by processor 112. In the illustrated embodiment, the second setting is a different pressure of pressurized air delivered by the respiratory therapy device 122 to the airway of patient 210.

[0097] At step 408, the control system 110 operates one or more sensors in the sensor 130 to collect corresponding sensor data and outputs 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 a subsequent sleep parameter signal. The subsequent sleep parameter signal indicates the sleep parameters sensed when the respiratory therapy device is operating in the second pressure setting. Typically, the subsequent sleep parameter signal contains the same sleep parameters present in the initial sleep parameter signal. Similar to the initial sleep parameter signal, in this embodiment, the subsequent sleep parameter signal includes one or more of the following: electroencephalogram (EEG) signal, electrooculogram (EOG) signal, electromyogram (EMG) signal, electrocardiogram (ECG) signal, nasal airflow signal, oral airflow signal, chest exertion signal, abdominal exertion signal, body movement recording signal, oxygen saturation signal, heart rate signal, pulse rate signal, peripheral arterial tension signal, sleep architecture signal, analyte signal, and 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 initial and subsequent sleep parameter signals to generate a sleep parameter difference signal and / or determine changes or trends over a time range (e.g., two weeks). In an illustrated embodiment, the programming executed by the processor 112 for generating the sleep parameter difference signal includes a signal processing routine that highlights differences and changes between the initial and subsequent sleep parameter signals. These differences and changes indicate the effect of varying air pressure on the airway of the patient 210.

[0099] The sleep parameters determined may include one or more apnea-hypopnea indices (AHIs) for the user. Before the user begins therapy, he or she will have an initial AHI. During therapy, the AHI is expected to decrease, but a residual AHI may still exist. AHI determination can be performed before therapy to obtain the initial AHI. As mentioned, it can be performed during therapy delivery to obtain the residual AHI. Additionally or alternatively, AHI determination can be performed during a test time window when the PAP device (such as a CPAP device) is turned off to obtain the therapy interruption AHI. This shutdown time can be a controlled shutdown time during which no PAP therapy is delivered. The test window can be initiated based on the detection that the user is not wearing a CPAP mask, or it can be initiated by the controller to temporarily interrupt therapy.

[0100] Therefore, sleep parameter signals may include signals that can be analyzed to determine AHI, such as, but not limited to, oxygen level signals or electrocardiogram (ECG) signals. Other examples exist. For example, sleep parameter signals may additionally or alternatively include respiratory signals that can be measured to determine the presence of apnea (e.g., accelerometer signals), where a apnea of ​​sufficient length can indicate an apnea event, and thus analysis of the respiratory signals can determine AHI. These examples are not intended to be limiting. Other examples of measurable signals have been mentioned in this disclosure, and those skilled in the art can determine whether they can be used to determine AHI. Two or more types of signals may be included to more accurately determine AHI and reduce false positive determinations of apnea events.

[0101] These signals can be obtained by sensors included in a device worn by the user. For example, sensors can be included in wearable devices such as smartwatches or smart rings. These can be activity trackers 180 throughout system 100. Alternatively, sensors can be attached via a cover or harness to provide a user interface 124 to the PAP therapy system 120. In some PAP therapy devices, the controller can be configured to measure the user's residual AHI, i.e., the user's AHI while therapy is being delivered. This can also provide sleep parameters directly. In embodiments using one or more wearable devices (such as smartwatches or rings), the control system 110 can be configured to receive physiological and / or phenotypic data, such as BMI, weight, height, heart rate, etc., from the memory of the one or more wearable devices.

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

[0103] The determined sleep parameters 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 performs programming to access sleep phenotypic data related to the patient 210 stored in a memory device. In an exemplary embodiment, the sleep phenotypic data includes one or more of the following: physiological measurements, anatomical measurements, genetic data, treatment response data, treatment adherence data, neurocognitive function data, and data derived from a sleep questionnaire. The control system 110 may also access data related to whether the patient is taking any medication that may cause changes in phenotypic data, such as BMI. This data may also include dosage data related to the medication. For example, the user may be taking medications such as glucagon-like peptide-1 (GLP-1) agonists or sodium-glucose cotransporter 2 (SGLT-2) inhibitors. The control system 110 may also access phenotypic data such as, but not limited to, weight, body measurements, and BMI. Such data may be data included in a sleep questionnaire or may be provided by the patient 210 through an electronic interface, such as an online portal of a patient management platform, or a human-machine interface (HMI) provided via a mobile application, or an HMI provided on a 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 following: sleep parameter signals, sleep parameter difference signals, and sleep phenotype data to generate sleep disorder characteristic signals and / or therapy adjustment signals. The therapy adjustment signals and (if applicable) the current therapy mode (e.g., CPAP) will be used to determine the treatment for the sleep disorder. For example, regarding the processing of data including sleep phenotype data to determine the sleep adjustment signals, the sleep phenotype data can be used to determine measurements of upper airway collapse and can be used to generate signals relating to whether and / or how the therapy should be adjusted to better suit the patient, taking into account the individual's likelihood of upper airway collapse. For example, for an individual whose sleep phenotype data is associated with low upper airway collapse and who is currently undergoing PAP treatment, the adjustment may include adjusting the PAP treatment settings, or the adjustment may include utilizing another intervention, such as using neural stimulation to stimulate respiratory drive, for example, in the presence of a higher-than-expected degree of apnea, taking into account upper airway collapse.

[0106] In embodiments where the sleep parameter signals include signals indicating apnea-hypopnea (AHI), a sleep disorder characteristic signal can be generated based on the determined AHI. The sleep disorder characteristic signal can be associated with an increase or decrease in apnea severity. An increase in the sleep parameter difference signal can cause a change in the sleep disorder characteristic signal. The sleep disorder characteristic signal can be an apnea severity level, and an increase in the sleep disorder characteristic signal indicates an increase in apnea severity or above a previously determined severity, while a decrease indicates a decrease in apnea severity or below a previously determined severity. The determined increase or decrease can be used to generate a therapy adjustment signal. For example, if the measured AHI during a therapy interruption or the measured residual AHI decreases over time, the sleep disorder characteristic signal can indicate a decreased apnea severity. This can be fed back to the control system to generate a therapy adjustment signal, thereby adjusting the PAP therapy pressure to reduce the pressure. The adjustment amount can 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 can be a signal obtained based on a comparison between a measured AHI and one or more thresholds. For example, if the measured AHI includes residual AHI and / or AHI measured when the user interrupts therapy (“therapy interruption AHI”), the threshold can be set by the amount of change in the residual AHI or the therapy interruption AHI that triggers a change in therapy pressure. If the measured residual AHI or the therapy interruption AHI is at or above the threshold, the therapy pressure is increased. One or more such thresholds may exist, each associated with a therapy pressure adjustment amount. The therapy pressure adjustment amount can be an absolute amount of pressure or a proportional amount relative to a predetermined therapy pressure. If the adjusted therapy pressure is still within the recommended PAP pressure range, this can indicate that the recommended therapy is still PAP. Therefore, in this case, sleep disorder treatment includes the application of PAP at the adjusted therapy pressure.

[0108] In embodiments contemplated in this disclosure, therapy adjustments can be of different types. For example, a therapy adjustment can be an adjustment to one or more settings of the PAP therapy as a supplement to or alternative to a change in therapy pressure. For instance, if the measured AHI decreases, but the decrease is insufficient to trigger a change in therapy pressure, the EPR (expiratory pressure release) setting can be adjusted to increase expiratory release. In another instance, if the measured AHI is between two threshold levels (corresponding to two therapy pressure levels), the adjusted therapy pressure setting can be set to a higher pressure level, but the EPR setting needs to be adjusted to increase expiratory release.

[0109] Treatment adjustments can involve switching from one type of PAP therapy to a different type. For example, if the sleep parameter difference signal shows statistically significant changes during the observation period but does not show a trend, the treatment adjustment signal could involve a change in the treatment modality to a therapy that delivers variable pressure, rather than a therapy that delivers predetermined pressure, such as CPAP.

[0110] Sleep parameter signals may include signals indicating leakage pressure during therapy, and in this case, sleep parameters include leakage pressure. Leakage pressure can be monitored over time to determine differential parameters of leakage pressure, such as changes, rates of change, or trends, which can be used to establish a leakage pressure profile. Leakage pressure can be compared to a threshold to determine if it is high enough to require therapy adjustment, such as reinstalling the therapy device, for example, using a different PAP mask or strip type or size. Leakage volume and / or leakage pressure profiles can be used to determine the recommended PAP mask or strip type. Preferably, signals measured within an observation window are used to determine the leakage pressure parameters to ensure that the calculated leakage pressure represents the patient's sleep parameters, thereby minimizing the effects of momentary movements (e.g., the user pushing the CPAP mask away from his or her nose) or other forms of "noise." In this case, the therapy adjustment signal relates to the recommended reinstallation, such as changes in mask type or size. In a non-limiting example, if the leakage pressure is at or above a threshold within the measurement window, and it is determined from the sleep parameter difference signal (i.e., the leakage parameter difference signal) that there has been no statistically significant change in the leakage pressure, a therapy adjustment including a reinstallation recommendation signal is generated. The reinstallation recommendation can be obtained by comparing the leakage pressure to one or more thresholds, each threshold associated with a reinstallation value. The generation of the reinstallation recommendation signal may also take into account the patient's current AHI sleep parameters. Treatment of the sleep disorder then involves applying PAP with the reinstallation recommendation.

[0111] Treatment adjustments can be determined based on one, two, or more sleep parameters. In another non-limiting example, both AHI and leakage pressure can be used. For instance, the AHI during a treatment interruption can be compared to a predetermined threshold, and the leakage pressure can be compared to a threshold associated with a minimum reinstallation value (corresponding to the minimum size of the mask). The predetermined threshold can be selected based on clinical data related to low to moderate apnea. If the AHI is below the predetermined threshold and the leakage parameter is at or above the threshold corresponding to the minimum reinstallation value, then sleep disorder treatment can be a treatment modality adjustment, such as a change from CPAP to alternative therapies (e.g., intraoral devices, mandibular devices, neurostimulation, postural therapy devices, etc.). Therefore, in this case, sleep disorder treatment includes alternative therapies.

[0112] Initial sleep disorder therapy (if the patient has not yet undergone any sleep therapy) or sleep disorder therapy determined based on therapy adjustments (if the patient is currently receiving therapy) can be determined based on one or more sleep therapy parameters and phenotypic data. For example, for a patient taking medications expected to cause weight loss (such as GLP-1 agonists), expected phenotypic data (including patient weight) will decrease over time. The expected weight change curve can vary depending on the specific medication being taken. For example, the expected weight change curve could be a roughly linear weight loss curve, in contrast to patients experiencing weight loss based on exercise or diet protocols. This data can be used to determine when to initiate testing to measure sleep parameters, such as AHI during therapy interruptions, leak pressure, or flow data. This data can also be used to determine the frequency of obtaining sleep parameters and / or the duration of the observation period or window for acquiring sleep parameter signals.

[0113] Given a predicted phenotypic or physiological change curve (such as a predicted weight loss curve), it is also possible to determine whether the behavioral patterns of the measured sleep parameters correspond statistically to the phenotypic change curve. For example, changes in sleep parameters associated with certain types or known physiological causes of apnea can be expected to change in a manner related to weight loss. Therefore, by determining whether predicted changes corresponding to a specific apnea characteristic are observed, the characteristics of a patient's sleep disorder can be assessed.

[0114] In an exemplary embodiment, the sleep disorder characteristic signal indicates airway collapse characteristics related to the patient's airway collapse, such as the anatomical location of the airway collapse, the structure of the airway involved in the airway collapse, the degree of airway collapse, and the frequency of airway collapse. The structure of the airway involved in the airway collapse includes one or more pharyngeal structures. A characteristic of airway collapse is complete concentric collapse of the soft palate.

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

[0116] When a patient is undergoing another treatment or regimen aimed at inducing phenotypic (e.g., weight, BMI) or physiological (e.g., cardiovascular health-related indicators such as blood oxygen levels) changes, at least a subset of the signals and parameters mentioned above can be used to determine the efficacy of the treatment or regimen. In cases where a patient is taking GLP-1 medication, sleep parameter difference signals and / or sleep parameters associated with weight loss can be monitored during the observation period. For example, weight loss can be expected to lead to an increase in leakage pressure over time. As another example, weight loss may also affect the severity of sleep apnea, potentially influencing indicators such as treatment interruption periods and / or residual therapy. Data related to one or more relevant sleep parameters, such as observed parameter values ​​and the rate of change of parameter values ​​(whether absolute or relative to initially observed values), can be used as feedback data to support the treatment or regimen. The initiation, duration, and frequency of data monitoring can depend on data related to the treatment or regimen. Feedback data can be used to determine the efficacy and effectiveness of weight loss treatment.

[0117] Neural network methods can be used to determine sleep disorder treatments based on sleep parameter signals. Neural networks can be implemented using different architectures, such as, but not limited to, feedforward neural networks. Hybrid models can be utilized, such as, but not limited to, including a portion with a recurrent architecture within a feedforward architecture, or vice versa. For example, the architecture may include one or more recurrent portions to compute the temporal pattern in the sleep parameter signal or sleep parameter difference signal. Layers of the neural network may include sleep parameter signals at the input layer. Sleep parameters are determined by sleep parameter signals at the sleep parameter layer, where each sleep parameter is a layer node. Embodiments utilizing difference parameters may include difference layers for calculating quantities such as differences, rates of change, etc. The outputs at the difference layers and / or the outputs at the sleep parameter layers can be used to determine one or more sleep disorder characteristics (e.g., severity level, apnea type, etc.). The determined sleep disorder treatment may include a treatment modality and / or treatment intensity.

[0118] Additional data, including current therapy type, current therapy setting, current phenotype, and / or medication data, can also be provided as input nodes so that the data can be used in calculations at one or more higher levels. For example, weight and medication data, along with calculated difference parameters, can be used to determine sleep disorder characteristics at a sleep disorder characteristic layer.

[0119] This disclosure is described with reference to the accompanying drawings, in which the same reference numerals are used throughout the drawings to denote similar or equivalent elements. The drawings are not drawn to scale and are for illustrative purposes only. Several aspects of this disclosure are described herein with reference to exemplary applications.

[0120] One or more elements or aspects or steps, or any part thereof, from any one or more embodiments of any embodiment in the present invention may be combined with one or more elements or aspects or steps, or any part thereof, from any one or more embodiments of any other embodiment or combinations thereof, to form one or more additional implementations and / or embodiments of this disclosure.

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

[0122] It should be understood that if any prior art is mentioned in this document, such reference does not constitute an acknowledgment that the prior art is common knowledge in the field in any country.

[0123] In the following claims and the foregoing description of the invention, unless the context requires otherwise due to explicit language or necessary meaning, the word “comprise” or variations thereof such as “comprises” or “comprising” are used in the sense of inclusion, that is, to indicate the presence of the said feature, but do not exclude the presence or addition of additional features in various embodiments of the invention.

Claims

1. A method for determining a treatment for a patient's sleep disorder, the method comprising: In the first setting, positive airway pressure (PAP) therapy is administered to the patient using a respiratory pressure therapy (RPT) device. Initial sleep parameter signals are acquired from one or more sensors associated with the patient; The positive airway pressure (PAP) treatment is administered to the patient using the PAP device in a second setting; Acquire subsequent sleep parameter signals from the one or more sensors; The initial sleep parameter signal and the 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 feature signal; as well as Treatment for the patient's sleep disorder is determined based on the sleep disorder characteristic signals.

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

3. The method of claim 2, wherein the sleep disorder feature signal is determined by processing the sleep parameter difference signal and the sleep phenotype data.

4. The method of claim 2, wherein the sleep disorder treatment is determined by processing the sleep disorder characteristic signals and the sleep phenotype data and / or the drug data.

5. The method according to any one of claims 1 to 4, wherein the sleep disorder feature signal indicates airway collapse features related to the characteristics of the patient's airway collapse.

6. The method of claim 5, wherein the characteristics of the airway collapse include one or more of the following: the anatomical location where the airway collapse occurs, the structure of the airway involved in the airway collapse, the degree of the airway collapse, and the frequency of the airway collapse.

7. The method of claim 6, wherein the structure of the airway involved in the airway collapse includes one or more pharyngeal structures.

8. The method of claim 7, wherein the characteristic of the airway collapse is a completely concentric collapse of the soft palate.

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

10. The method according to any one of claims 1 to 9, wherein the initial sleep parameter signal and the subsequent sleep parameter signal each comprise one or more of the following: electroencephalogram (EEG) signal, electrooculogram (EOG) signal, electromyogram (EMG) signal, electrocardiogram (ECG) signal, nasal airflow signal, oral airflow signal, chest exertion signal, abdominal exertion signal, body movement recording signal, oxygen saturation signal, heart rate signal, pulse rate signal, peripheral arterial tension signal, sleep architecture signal, analyte signal, and audio signal.

11. The method according to claim 2 or any one of claims 3 to 10 that is subordinate to claim 2, wherein the sleep phenotype data includes one or more of the following: physiological measurements, anatomical measurements, genetic data, treatment response data, treatment compliance data, neurocognitive function data, and data derived from sleep questionnaires.

12. The method according to any one of claims 1 to 11, wherein the determined sleep disorder treatment includes setting the RPT device.

13. The method according to any one of claims 1 to 12, wherein the determined treatment for sleep disorder includes one or more of the following: mandibular advancement device, hypoglossal nerve stimulation, pharyngeal surgery, and postural therapy.

14. A system for determining a treatment for a patient's sleep disorder, the system comprising: Memory, the memory storing machine-readable instructions; and One or more processors, the one or more processors being configured to execute the machine-readable instructions to: Initial sleep parameter signals are acquired from one or more sensors associated with the patient, the initial sleep parameter signals indicating that the patient is receiving positive airway pressure ventilation (PAP) treatment in a first setting using a respiratory pressure therapy (RPT) device; Subsequent sleep parameter signals are acquired from one or more sensors associated with the patient, the subsequent sleep parameter signals indicating that the patient is receiving PAP treatment performed in a second setting using the RPT device; The initial sleep parameter signal and the 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 feature signal; as well as Treatment for the patient's sleep disorder is determined based on the sleep disorder characteristic signals.

15. The system of claim 14, wherein the one or more processors are configured to execute the machine-readable instructions to obtain sleep phenotype data and / or medication data related to the patient.

16. The system of claim 15, wherein the sleep disorder feature signal is determined by processing the sleep parameter difference signal and the sleep phenotype data.

17. The system of claim 15, wherein the treatment of the sleep disorder is determined by processing the sleep disorder characteristic signals and the sleep phenotype data and / or the drug data.

18. The system according to any one of claims 14 to 17, wherein the sleep disorder feature signal indicates airway collapse features related to the characteristics of the patient's airway collapse.

19. The system of claim 18, wherein the characteristics of the airway collapse include one or more of the following: the anatomical location where the airway collapse occurs, the structure of the airway involved in the airway collapse, the degree of the airway collapse, and the frequency of the airway collapse.

20. The system of claim 19, wherein the structure of the airway involved in the airway collapse includes one or more pharyngeal structures.

21. The system of claim 20, wherein the characteristic of the airway collapse is a completely concentric collapse of the soft palate.

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

23. The system according to any one of claims 14 to 22, wherein the initial sleep parameter signal and the subsequent sleep parameter signal each comprise one or more of the following: electroencephalogram (EEG) signal, electrooculogram (EOG) signal, electromyogram (EMG) signal, electrocardiogram (ECG) signal, nasal airflow signal, oral airflow signal, chest exertion signal, abdominal exertion signal, body movement recording signal, oxygen saturation signal, heart rate signal, pulse rate signal, peripheral arterial tension signal, sleep architecture signal, analyte signal, and audio signal.

24. The system according to claim 15 or any one of claims 16 to 23 that are dependent on claim 15, wherein the sleep phenotype data includes one or more of the following: physiological measurements, anatomical measurements, genetic data, treatment response data, treatment compliance data, neurocognitive function data, and data derived from sleep questionnaires.

25. The system according to any one of claims 14 to 24, wherein the determined sleep disorder treatment includes the setting of the RPT device.

26. The system according to any one of claims 14 to 25, wherein the determined treatment for sleep disorder includes one or more of the following: mandibular advancement device, hypoglossal nerve stimulation, pharyngeal surgery, and postural therapy.

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