Systems and methods for detecting intentional leak characteristics of a respiratory therapy system

Abstract

A plurality of flow values associated with pressurized air directed to an airway of a user of a respiratory therapy system is received. A plurality of pressure values associated with the pressurized air directed to the airway of the user is received. A first time associated with a first breath of the user and a second time associated with a second breath of the user is identified. The plurality of flow values is filtered based at least in part on the identified first time and the identified second time. The filtering results in a subset of the plurality of flow values. A deliberate leak characteristic curve of the respiratory therapy system is determined using at least two flow values of the subset of the plurality of flow values and corresponding pressure values of the at least two flow values of the subset of the plurality of flow values.

Description

[0001] Cross-references to related applications

[0002] This application claims the benefit and priority of U.S. Provisional Patent Application No. 62 / 986,431, filed March 6, 2020; U.S. Provisional Patent Application No. 62 / 072,768, filed August 31, 2020; and U.S. Provisional Patent Application No. 63 / 108,837, filed November 2, 2020, each of which is incorporated herein by reference in its entirety. Technical Field

[0003] The present invention relates generally to respiratory therapy systems, and more specifically to systems and methods for detecting intentional characteristic curves of respiratory therapy systems. Background Technology

[0004] Currently, leakage in face masks is estimated based on general curves for each type. Face mask types can be divided into three main categories: full-face masks, nose masks, and pillow masks. Due to manufacturing tolerances in face masks, general curves are not always as accurate as expected. This can lead to inaccurate estimates of intentional leakage. Therefore, a more precise curve is needed to customize each face mask for each user. This invention aims to solve these problems and address other needs. Summary of the Invention

[0005] According to some implementations of the present invention, a method for determining an intentional leakage characteristic curve of a respiratory therapy system is disclosed below. Flow data is received. The flow data is associated with multiple flow values ​​of pressurized air directed to the airway of a user of the respiratory therapy system. Pressure data is received. The pressure data is associated with multiple pressure values ​​of the pressurized air. A first Cartesian coordinate is generated. The first Cartesian coordinate has a first X value based at least on a first portion of the multiple flow values ​​and a first Y value based at least on a first portion of the multiple pressure values. A second Cartesian coordinate is generated. The second Cartesian coordinate has a second X value based at least on a second portion of the multiple flow values ​​and a second Y value based at least on a second portion of the multiple pressure values. The intentional leakage characteristic curve of the respiratory therapy system is determined based at least in part on the generated first Cartesian coordinate and the generated second Cartesian coordinate.

[0006] According to some implementations of the present invention, a method is disclosed as follows: A pressurized airflow is supplied to a user's airway during a first time period using a pressure generator of a respiratory therapy system. The pressurized airflow has a first nominal pressure value. A first plurality of flow rates for the first time period are generated using a flow sensor located within the respiratory therapy system. A first plurality of pressure values ​​for the first time period are generated using a pressure sensor located within the respiratory therapy system. A first Cartesian coordinate is determined. The first Cartesian coordinate has a first X value based on at least a first flow rate value among the first plurality of flow rates. The first Cartesian coordinate has a first Y value based on at least a first pressure value among the first plurality of pressure values. The supplied pressurized airflow is adjusted to a second nominal pressure value for a second time period. The second nominal pressure value is different from the first nominal pressure value. A second plurality of flow rates for the second time period are generated using a flow sensor located within the respiratory therapy system. A second plurality of pressure values ​​for the second time period are generated using a pressure sensor located within the respiratory therapy system. A second Cartesian coordinate is generated. The second Cartesian coordinate has a second X value based on at least a second flow rate value among the second plurality of flow rates. The second Cartesian coordinate has a second Y value based on at least a second pressure value among the second plurality of pressure values. The intentional leakage characteristic curve associated with the respiratory therapy system is determined, at least in part, based on the generated first Cartesian coordinates and the generated second Cartesian coordinates.

[0007] According to some implementations of the present invention, a method is disclosed as follows: A pressurized airflow is supplied to a user's airway during a first time period using a pressure generator of a respiratory therapy system. The pressurized airflow has a first nominal pressure value. A first plurality of flow rates are generated during the first time period using a flow sensor located within the respiratory therapy system. A first Cartesian coordinate has a first X value based on at least a first flow rate value among the first plurality of flow rates. The first Cartesian coordinate has a first Y value based at least on the first nominal pressure value. The supplied pressurized airflow is adjusted to a second nominal pressure value during a second time period. The second nominal pressure value differs from the first nominal pressure value. A second plurality of flow rates are generated during the second time period using a flow sensor located within the respiratory therapy system. A second Cartesian coordinate is generated. The second Cartesian coordinate has a second X value based on at least a second flow rate value among the second plurality of flow rates. The second Cartesian coordinate has a second Y value based at least on the second nominal pressure value. An intentional leakage characteristic curve associated with the respiratory therapy system is determined based at least in part on the generated first Cartesian coordinate and the generated second Cartesian coordinate.

[0008] According to some implementations of the present invention, a method for determining an intentional leakage characteristic curve of a respiratory therapy system is disclosed below. A plurality of flow rate values ​​associated with pressurized air directed to the airway of a user of the respiratory therapy system are received. A plurality of pressure values ​​associated with the pressurized air directed to the user's airway are received. Each of the plurality of pressure values ​​corresponds to a corresponding one of the plurality of flow rate values. A first time associated with a first breath of the user and a second time associated with a second breath of the user are identified. The plurality of flow rate values ​​are filtered at least in part based on the identified first time and the identified second time. The filtering produces a subset of the plurality of flow rate values. An intentional leakage characteristic curve of the respiratory therapy system is determined using at least two flow rate values ​​from the subset of the plurality of flow rate values ​​and the corresponding pressure values ​​of the at least two flow rate values ​​from the subset of the plurality of flow rate values.

[0009] According to some implementations of the present invention, a system includes a control system and a memory. The control system includes one or more processors. Machine-readable instructions are stored in the memory. The control system is coupled to the memory. When the machine-executable instructions in the memory are executed by at least one of the one or more processors of the control system, any of the methods disclosed above are implemented.

[0010] According to some implementations of the present invention, a system for determining the intentional leakage characteristic curve of a respiratory therapy system is disclosed below. The system includes a control system configured to implement any of the methods disclosed above.

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

[0012] Given the detailed description of various embodiments and / or implementations with reference to the accompanying drawings, the foregoing and additional aspects and implementations of the present invention will be apparent to those skilled in the art, and a brief description of the drawings is provided below. Attached Figure Description

[0013] The foregoing and other advantages of the present invention will become apparent from reading the following detailed description and referring to the accompanying drawings.

[0014] Figure 1 This is a functional block diagram of a system for determining one or more sleep-related parameters during sleep periods, according to some embodiments of the present invention.

[0015] Figure 2 This is according to some embodiments of the present invention. Figure 1 A perspective view of at least a portion of the system, users, and bed partners;

[0016] Figure 3A The breathing waveforms of an individual during sleep are illustrated according to some implementations of the present invention.

[0017] Figure 3B This describes, according to some implementations of the present invention, the selected polysomnography channels (pulse oxygen saturation, flow rate, chest movement, and abdominal movement) of an individual during a non-REM sleep breathing process under normal conditions for a period of approximately ninety seconds.

[0018] Figure 3C The polysomnography of an individual prior to respiratory therapy according to some implementations of the present invention is illustrated.

[0019] Figure 3D The present invention describes flow data of an individual experiencing a series of complete obstructive sleep apneas according to some implementations of the present invention.

[0020] Figure 4A This describes flow data associated with a user of a respiratory therapy system according to some implementations of the present invention.

[0021] Figure 4B Pressure data associated with a user of a respiratory therapy system according to some implementations of the present invention are described.

[0022] Figure 4C Pressure data associated with a user of a respiratory therapy system with an expiratory decompression module, according to some implementations of the present invention, is described.

[0023] Figure 5A The Cartesian coordinates representing the average device pressure and the average total flow rate expressed in liters per minute are illustrated according to some implementations of the present invention.

[0024] Figure 5B This describes some implementations of the present invention. Figure 5A The fitted characteristic curve is plotted on Cartesian coordinates.

[0025] Figure 6 Intentional leakage characteristic curves of a user-associated respiratory therapy system according to some implementations of the present invention are illustrated.

[0026] Figure 7 A flowchart illustrating a method for detecting intentional leakage characteristic curves of a respiratory therapy system according to some implementations of the present invention is provided.

[0027] Figure 8 A flowchart illustrating a method for determining the intentional leakage characteristic curve of a respiratory therapy system according to some implementations of the present invention is provided.

[0028] Figure 9 The present invention describes flow rate (“Q”) and flow volume (“V”) data during respiration under respiratory therapy according to some implementations of the present invention.

[0029] Figure 10 The present invention describes flow rate and flow volume data during two breaths in respiratory therapy according to some implementations of the present invention.

[0030] Figure 11 The intentional leakage characteristic curves are illustrated by fitting a scatter plot of pressure value (“P”; cmH2O) against flow rate value (“Q”; liters per 10 s) according to some implementations of the present invention.

[0031] Figure 12 The effects of some implementations of the present invention on four types of impedance (Z1, Z2, Z3, and Z4) of the intentional leakage characteristic algorithm are explained.

[0032] While the present invention is susceptible to various modifications and substitutions, specific implementations and embodiments thereof have been illustrated 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 the invention to the specific forms disclosed, but rather, the invention is intended to cover all modifications, equivalents, and substitutions falling within the spirit and scope of the invention as defined by the appended claims. Detailed Implementation

[0033] The invention 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 provided merely for illustrative purposes. Several aspects of the invention are described below with reference to exemplary applications used for illustration.

[0034] Traditional methods for estimating intentional leakage in respiratory therapy systems are based on characterizing mask ventilation flow rate relative to mask pressure. Each mask type (full face mask, nasal mask, or pillow mask) is assigned a generalized ventilation flow rate profile, such as the flow rate through the mask vent for a given pressure. This is referred to as intentional leakage or anticipated leakage. For example, when the mask pressure is 30 cmH2O, the full face mask ventilation flow rate is approximately 70 L / min; the nasal mask flow rate is approximately 60 L / min; and the pillow mask flow rate is approximately 56 L / min.

[0035] These generalized ventilation profiles are typically pre-calculated and programmed into lookup tables, and selected by the system based on user input of the mask type. Therefore, selecting the correct mask setting is necessary, which is not always the case. Furthermore, manufacturing tolerances can lead to inaccurate leak estimates. Therefore, a custom model of the mask ventilation profile calculated based on measured mask pressure and measured flow rate is needed to improve the reporting of unintentional leaks associated with respiratory therapy systems.

[0036] Reference Figure 1 The present invention describes a system 100 according to some implementations of the present invention. 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 may also optionally include a respiratory therapy system 120 and an activity tracker 180.

[0037] The control system 110 includes one or more processors 112 (hereinafter, processor 112). The control system 110 is typically used to control various components of the system 100 and / or analyze data acquired and / or generated by the components of the system 100. The processor 112 may be a general-purpose or special-purpose processor or a microprocessor. Although in Figure 1 The diagram illustrates a processor 112, a control system 110 (or any other control system), or a portion thereof, such as processor 112 (or any other processor or any other control system), which 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 relative to each other. Control system 110 may be coupled to and / or located within, for example, the housing of user device 170, a portion (e.g., housing) of respiratory therapy system 120, and / or the housing of one or more sensors 130. Control system 110 may be centralized (within one such housing) or distributed (within two or more physically distinct such housings). In embodiments including two or more housings containing control system 110, such housings may be located close to and / or far from each other.

[0038] Memory 114 stores machine-readable instructions executable by the processor 112 of the control system 110. Memory device 114 can be any suitable computer-readable storage device or medium, such as random or serial access storage devices, hard disk drives, solid-state drives, flash memory devices, etc. Although Figure 1A memory device 114 is shown, but system 100 may include any suitable number of memory devices 114 (e.g., one memory device, two memory devices, five memory devices, ten memory devices, etc.). The memory device 114 may be coupled to and / or located within the housing of the respiratory therapy device 122 of the respiratory therapy system 120, within the housing of the user device 170, within the housing of one or more sensors 130, or any combination thereof. Similar to control system 110, the memory device 114 may be centralized (within one such housing) or distributed (within two or more physically different such housings).

[0039] In some implementations, memory device 114 stores a user profile associated with the user. The user profile may include, for example, user-associated demographic information, user-associated biostatistics, user-associated medical information, self-reported user feedback, user-associated sleep parameters (e.g., sleep-related parameters recorded from one or more earlier sleep periods), or any combination thereof. Demographic information may include, for example, information indicating the user's age, gender, ethnicity, geographic location, relationship status, family history of insomnia or sleep apnea, employment status, user identity, education level, socioeconomic status, or any combination thereof. Medical information may include, for example, information indicating one or more medical conditions associated with the user, medication use, or both. Medical information data may further 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 subjective sleep scores (e.g., poor, average, excellent), user's self-reported subjective stress levels, user's self-reported subjective fatigue levels, user's self-reported subjective health status, recent life events experienced by the user, or any combination thereof.

[0040] Electronic interface 119 is configured to receive data (e.g., 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 implementations, electronic interface 119 is coupled to or integrated into user device 170. In other implementations, electronic interface 119 is coupled to or integrated with control system 110 and / or memory device 114 (e.g., within a housing).

[0041] As described above, in some embodiments, system 100 may optionally include a respiratory therapy system 120 (also referred to as a respiratory therapy system). The respiratory therapy system 120 may include a respiratory pressure therapy device 122 (also referred to herein as a respiratory therapy device), 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 implementations, a control system 110, a memory device 114, a display device 128, one or more sensors 130, and a humidifier canister 129 are part of the respiratory therapy device 122. Respiratory pressure therapy refers to supplying air to the user's airway inlet at a controlled target pressure that is nominally positive relative to the atmosphere throughout the user's respiratory cycle (e.g., the opposite of negative pressure therapy with a canister ventilator or tubing ventilator). 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).

[0042] The respiratory therapy device 122 is typically used to generate pressurized air to be delivered to a user (e.g., using one or more motors driving one or more compressors). In some implementations, the respiratory therapy device 122 generates a continuous, constant air pressure that is delivered to the user. In other implementations, the respiratory therapy device 122 generates two or more predetermined pressures (e.g., a first predetermined air pressure and a second predetermined air pressure). In still other implementations, the respiratory therapy device 122 is configured to generate a variety of different air pressures within a predetermined range. For example, the respiratory therapy device 122 may deliver at least about 6 cm H2O, at least about 10 cm H2O, at least about 20 cm H2O, between about 6 cm H2O and about 10 cm H2O, between about 7 cm H2O and about 12 cm H2O, etc. The respiratory therapy device 122 may also deliver pressurized air at predetermined flow rates, for example, between about -20 L / min and about 150 L / min, while maintaining positive pressure (relative to ambient pressure).

[0043] User interface 124 engages with a portion of the user's face and delivers pressurized air from respiratory therapy device 122 to the user's airway to help prevent airway narrowing and / or collapse during sleep. This also increases the user's oxygen intake during sleep. Typically, user interface 124 engages with the user's face such that pressurized air is delivered to the user's airway via the user's mouth, the user's nose, or both the user's mouth and nose. Respiratory therapy device 122, user interface 124, and conduit 126 together form an air passage fluidly connected to the user's airway. Pressurized air also increases the user's oxygen intake during sleep.

[0044] Depending on the treatment to be applied, the user interface 124 may form a seal with, for example, an area or portion of the user's face, thereby facilitating the delivery of gas at a pressure sufficiently different from ambient pressure (e.g., a positive pressure of approximately 10 cm H2O relative to ambient pressure) to achieve the treatment. For other forms of treatment, such as oxygen delivery, the user interface may not include a seal sufficient to deliver gas at a positive pressure of approximately 10 cm H2O to the airway.

[0045] like Figure 2As shown, in some implementations, user interface 124 is a mask (e.g., a full-face mask) that covers the user's nose and mouth. Alternatively, user interface 124 may be a nasal mask that supplies air to the user's nose or a pillow cover that supplies air directly to the user's nostrils. User interface 124 may include multiple straps (e.g., including hook and loop fasteners) for forming a headband that positions and / or stabilizes the interface on a part of the user (e.g., the face) and conformal cushioning pads (e.g., silicone, plastic, foam, etc.) to help provide an airtight seal between user interface 124 and the user. User interface 124 may also include one or more vents for allowing carbon dioxide and other gases exhaled by user 210 to escape. In other implementations, user interface 124 includes a mouthpiece (e.g., a night-protective mouthpiece molded to conform to the user's teeth, a jaw repositioning device, etc.).

[0046] 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 implementations, there may be separate branches for the inspiratory and expiratory conduits. In other implementations, a single-branch air conduit is used for both inspiratory and expiratory functions.

[0047] One or more of the respiratory therapy device 122, user interface 124, conduit 126, display device 128, and humidifier 129 may include one or more sensors (e.g., pressure sensor, flow sensor, or any other sensor 130 described more generally 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.

[0048] Display device 128 is typically used to display images including still images, video images, or both, and / or information about the respiratory therapy device 122. For example, display device 128 may provide information about the status of the respiratory therapy device 122 (e.g., whether the respiratory therapy device 122 is on / off, the pressure of the air delivered by the respiratory therapy device 122, the temperature of the air delivered by the respiratory therapy device 122, etc.) and / or other information (e.g., sleep score and / or treatment score, also known as myAir). TMScores, as described in WO2016 / 061629, are incorporated herein by reference in their entirety; current date / time; personal information of user 210; etc.). In some implementations, display device 128 acts as a human-machine interface (HMI) including a graphical user interface (GUI) configured to display images as input. Display device 128 may be an LED display, OLED display, LCD display, etc. 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.

[0049] The humidifier canister 129 is connected to or integrated into the respiratory therapy device 122 and includes a water reservoir for humidifying pressurized air delivered from the respiratory therapy device 122. The respiratory therapy device 122 may include a heater to heat the water in the humidifier canister 129 to humidify the pressurized air supplied to the user. Additionally, in some implementations, the conduit 126 may include a heating element (e.g., coupled to and / or embedded in the conduit 126) that heats the pressurized air delivered to the user. The humidifier canister 129 may be fluidly coupled to a water vapor inlet of an air passage and deliver water vapor into the air passage via the water vapor inlet, or it may be formed in a straight line with the air passage as part of the air passage itself.

[0050] In some implementations, system 100 may be used to deliver at least a portion of a substance from container 180 to a user's air path, at least in part based on physiological data, sleep-related parameters, other data or information, or any combination thereof. Typically, modifying the delivery of a portion of the substance into the air path may include: (i) initiating the delivery of the substance into the air path, (ii) ending the delivery of a portion of the substance into the air path, (iii) modifying the amount of substance delivered into the air path, (iv) modifying the temporal characteristics of the delivery of a portion of the substance into the air path, (v) modifying the quantitative characteristics of the delivery of a portion of the substance into the air path, (vi) modifying any parameters associated with the delivery of the substance into the air path, or (vii) combinations of (i) to (vi).

[0051] Altering the temporal characteristics of the delivery of a portion of a substance into the air channel can include changing the delivery rate, starting and / or ending at different times, lasting for different periods, changing the temporal distribution or characteristics of the delivery, and changing the quantity distribution independently of the time distribution. Independent temporal and quantity variations ensure that, in addition to changing the frequency of substance release, the amount of substance released each time can be altered. In this way, various combinations of release frequency and release quantity can be achieved (e.g., higher frequency but lower release quantity, higher frequency and higher release quantity, lower frequency and higher release quantity, lower frequency and lower release quantity, etc.). Other variations in the delivery of a portion of the substance into the air channel can also be utilized.

[0052] The respiratory therapy system 120 can be used as, for example, a ventilator or a positive airway pressure (PAP) system, such as a continuous positive airway pressure (CPAP) system, an 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 pressure (e.g., determined by a sleep physician) to the user. An APAP system automatically changes the pressure delivered to the user based on, for example, breathing data relevant to 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 lower than the first predetermined pressure (e.g., expiratory positive airway pressure or EPAP).

[0053] Reference Figure 2 The system 100 is shown according to some implementation methods. Figure 1 As part of the respiratory therapy system 120, the user 210 and bed partner 220 are located in the bed 230 and lie on the mattress 232. A user interface 124 (also referred to herein as a mask, such as a full-face mask, nasal mask, pillowcase, etc.) can be worn by the user 210 during sleep. The user interface 124 is fluidly connected and / or connected to the respiratory therapy device 122 via a conduit 126. The respiratory therapy device 122, in turn, delivers pressurized air to the user 210 via the conduit 126 and the user interface 124 to increase the air pressure in the user 210's throat, thereby helping to prevent airway closure and / or narrowing during sleep. The respiratory therapy device 122 can be positioned such as... Figure 2 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.

[0054] See again Figure 1The system 100 includes one or more sensors 130, such as 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 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 humidity 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 received and stored in a memory device 114 or one or more other memory devices.

[0055] Although one or more sensors 130 are shown and described as including 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, EEG sensor 158, capacitance sensor 160, force sensor 162, strain gauge sensor 164, electromyography (EMG) sensor 166, oxygen sensor 168, analyte sensor 174, humidity sensor 176, and lidar sensor 178, more generally, one or more sensors 130 may include any combination and any number of each of the sensors described and / or shown herein.

[0056] As described herein, system 100 can typically be used to generate information with the user (e.g., during sleep periods) Figure 2 The physiological data associated with the user of the respiratory therapy system 120 shown can be analyzed to generate one or more sleep-related parameters, which may include any parameters, measurements, etc., related to the user during sleep periods. One or more sleep-related parameters that can be determined for the user 210 during sleep periods 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 pattern, stage, pressure setting of respiratory therapy device 122, heart rate, heart rate variability, user 210's movement, temperature, EEG activity, EMG activity, awakening, 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 the one or more sensors 130 to determine the interaction between the user 210 and the sleeper during sleep periods. Figure 2 The sleep-wake signals and one or more sleep-related parameters are associated. The sleep-wake signals may indicate one or more sleep states, including wakefulness, relaxed wakefulness, micro-wakefulness, or different sleep stages, such as, for example, rapid eye movement (REM) stage, first non-REM stage (commonly referred to as "N1"), second non-REM stage (commonly referred to as "N2"), third non-REM stage (commonly referred to as "N3"), or any combination thereof. Methods for determining sleep states and / or sleep stages based on physiological data generated by one or more sensors (e.g., one or more sensors 130) are described in, for example, WO 2014 / 047310, US 2014 / 0088373, WO 2017 / 132726, WO 2019 / 122413, and WO 2019 / 122414, each of which is incorporated herein by reference in its entirety.

[0058] In some implementations, the sleep-wake signals described herein can be timestamped to indicate the time a user enters the bed, the time a user leaves the bed, the time a user attempts to fall asleep, etc. The sleep-wake signals can be measured by one or more sensors 130 during a sleep period at a predetermined sampling rate, such as one sample per second, one sample every 30 seconds, one sample per minute, etc. In some implementations, the sleep-wake signals can also indicate respiratory signals, respiratory rate, inspiratory amplitude, expiratory amplitude, inspiratory-expiratory ratio, number of events per hour, event pattern, pressure setting of breathing device 122, or any combination thereof. Events can include snoring, sleep apnea, central sleep apnea, obstructive sleep apnea, mixed sleep apnea, hypoventilation, mask leakage (e.g., from user interface 124), restless legs, sleep disturbance, apnea, increased heart rate, dyspnea, asthma attack, seizure, epilepsy, or any combination thereof. One or more sleep-related parameters that can be determined for a user during a sleep period based on sleep-wake signals include, for example, total time in bed, total sleep time, sleep onset wait time, 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.

[0059] Physiological and / or audio data generated by one or more sensors 130 can also be used to determine respiratory signals associated with the user during sleep periods. Respiratory signals typically represent the user's breathing during sleep periods. Respiratory signals can indicate and / or be analyzed to determine (e.g., using control system 110) one or more sleep-related parameters, such as respiratory rate, respiratory rate variability, inspiratory amplitude, expiratory amplitude, inspiratory-expiratory ratio, occurrence of one or more events, number of events per hour, event pattern, 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, epilepsy, elevated blood pressure, or any combination thereof. Many of the described sleep-related parameters are physiological parameters, although some 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, sleep periods can be defined in several ways. For example, a sleep period can be defined by an initial start time and an end time. In some implementations, a sleep period is the duration of a user's sleep; that is, a sleep period has a start time and an end time, and the user does not wake up until the end time during the sleep period. In other words, any period during which the user is awake is not included in a sleep period. According to this first definition of a sleep period, if a user wakes up and falls asleep multiple times during the same night, each sleep interval separated by the wake-up intervals is a sleep period.

[0061] Alternatively, in some implementations, the sleep period has a start time and an end time, and during the sleep period, the user can remain awake as long as the continuous duration of wakefulness is less than a wakefulness duration threshold, without the sleep period ending. The wakefulness duration threshold can be defined as a percentage of the sleep period. The wakefulness duration threshold can be, for example, approximately 20 percent of the sleep period, approximately 15 percent of the sleep period duration, approximately 10 percent of the sleep period duration, 5 percent of the sleep period duration, approximately 2 percent of the sleep period duration, or any other threshold percentage. In some implementations, the wakefulness duration threshold is defined as a fixed amount of time, such as approximately one hour, approximately thirty minutes, approximately fifteen minutes, approximately ten minutes, approximately five minutes, approximately two minutes, or any other amount of time.

[0062] In some implementations, a sleep period is defined as the entire time between the time a user first goes to bed at night and the time the user last leaves bed the following morning. In other words, a sleep period can be defined as the time period that begins at a first time (e.g., 10:00 PM) on a first date (e.g., Monday, January 6, 2020), which can be referred to as the current night, when the user first enters bed intending to go to sleep (e.g., if the user does not intend to watch TV or use their smartphone before going to sleep), and ends at a second time (e.g., 7:00 AM) on a second date (e.g., Tuesday, January 7, 2020), which can be referred to as the following morning, when the user first leaves bed with the intention of not returning to sleep the following morning.

[0063] In some implementations, the user can manually define the start and / or end of a sleep period. For example, the user can select (e.g., by clicking or tapping) on ​​the user device 170 ( Figure 1 One or more user-selectable elements are displayed on the monitor 172 to manually initiate or terminate sleep periods.

[0064] Typically, a sleep period includes any point in time after the user 210 has already lay down or sat in 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 period can therefore include time periods (i) when the user 210 is using the CPAP system, but before the user 210 attempts to fall asleep (e.g., when the user 210 is lying in bed 230 reading a book); (ii) when the user 210 begins to try to fall asleep but is still awake; (iii) when the user 210 is in light sleep (also known as stages 1 and 2 of non-rapid eye movement (NREM) sleep); (iv) when the user 210 is in deep sleep (also known as slow-wave sleep, SWS, or stage 3 of NREM sleep); (v) when the user 210 is in rapid eye movement (REM) sleep; (vi) when the user 210 periodically wakes up between light sleep, deep sleep, or REM sleep; or (vii) when the user 210 wakes up without falling back asleep.

[0065] A sleep period is typically defined as ending once user 210 removes user interface 124, shuts off respiratory therapy device 122, and leaves bed 230. In some implementations, a sleep period may include additional time periods, or may be limited to only some of the aforementioned time periods. For example, a sleep period may be defined as a time period that begins when respiratory therapy device 122 begins supplying pressurized air to the airway or user 210, ends when respiratory therapy device 122 stops supplying pressurized air to the airway of user 210, and includes some or all of the time points between when user 210 is asleep or awake.

[0066] Pressure sensor 132 outputs pressure data that can be stored in memory device 114 and / or analyzed by processor 112 of control system 110. In some implementations, 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 implementations, pressure sensor 132 can be coupled to or integrated into respiratory therapy device 122. Pressure sensor 132 can 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] The flow sensor 134 outputs flow data that can be stored in memory device 114 and / or analyzed by processor 112 of control system 110. An example of a flow sensor (e.g., flow sensor 134) is described in International Publication No. WO 2012 / 012835, which is incorporated herein by reference in its entirety. In some implementations, flow sensor 134 is used to determine the airflow rate from respiratory therapy device 122, the airflow rate through conduit 126, the airflow rate through user interface 124, or any combination thereof. In such implementations, 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., a Hall effect flow meter), turbine flow meter, orifice flow meter, ultrasonic flow meter, hot wire sensor, eddy current sensor, membrane sensor, or any combination thereof. In some implementations, 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 implementations, flow data can be analyzed to determine a user’s cardiogenic oscillations. In one example, the pressure sensor 132 can be used to determine a user’s blood pressure.

[0068] Temperature sensor 136 outputs temperature data that can be stored in memory device 114 and / or analyzed by processor 112 of control system 110. In some implementations, temperature sensor 136 generates instructions for user 210 ( Figure 2 Temperature data may include the user's core body temperature, skin temperature, temperature of air flowing from the respiratory therapy device 122 and / or through the conduit 126, temperature in the user interface 124, ambient temperature, or any combination thereof. 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 that can 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 user 210 during sleep, and / or movement of any component of 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 implementations, motion sensor 138 alternatively or additionally generates one or more signals representing the user's body movements, from which signals representing the user's sleep state can be obtained; for example, through the user's breathing movements. In some implementations, motion data from motion sensor 138 may be combined with additional data from another sensor 130 to determine the user's sleep state.

[0070] The output of microphone 140 can 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 user 210) during sleep periods. The audio data from microphone 140 can also be used to identify (e.g., using control system 110) events experienced by the user during sleep periods, as described further in detail herein. Microphone 140 can be coupled to or integrated into respiratory therapy device 122, user interface 124, catheter 126, or user device 170. In some implementations, system 100 includes multiple microphones (e.g., two or more microphones and / or a microphone array with beamforming) such that sound data generated by each of the multiple microphones can be used to distinguish sound data generated by another of the multiple microphones.

[0071] The speaker 142 outputs to the user of the system 100 (e.g., Figure 2 The 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 implementations, the speaker 142 can be used to transmit audio data generated by the microphone 140 to the user. 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 implementations, microphone 140 and speaker 142 can be combined into acoustic sensor 141 (e.g., a sonar sensor), as described, for example, in WO 2018 / 050913 and WO2020 / 104465, which are incorporated herein by reference in their entirety. In this implementation, 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 user 210 or bed partner 220. Figure 2 Based at least in part on data from microphone 140 and / or speaker 142, control system 110 can determine user 210 ( Figure 2 The location of the sonar sensor and / or one or more of the sleep-related parameters described herein, such as respiratory signal, respiratory rate, inspiratory amplitude, expiratory amplitude, inspiratory-expiratory ratio, number of events per hour, event pattern, sleep state, sleep stage, pressure setting of the respiratory therapy device 122, or any combination thereof. In this document, a sonar sensor can be understood to involve active acoustic sensing, such as by generating and / or transmitting ultrasonic and / or low-frequency ultrasonic sensing signals through the air (e.g., in a frequency range of, for example, about 17-23 kHz, 18-22 kHz, or 17-18 kHz). Such a system can be considered in relation to WO2018 / 050913 and WO2020 / 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 with 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 emitted from RF transmitter 148, and this data can be analyzed by control system 110 to determine the user 210 (…). Figure 2 The location of the sensor and / or one or more of the 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 device 170, or any combination thereof. While RF receiver 146 and RF transmitter 148 are in... Figure 1 While shown as separate and distinct components, in some implementations, the RF receiver 146 and RF transmitter 148 are combined as part of the RF sensor 147 (e.g., a radar sensor). In some such implementations, the RF sensor 147 includes control circuitry. The specific format of the RF communication can be 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 satellite including 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 of the sleep-related parameters described herein, such as 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 pattern, sleep state, sleep stage, or any combination thereof. Furthermore, image data from camera 150 can be used, for example, to identify the user's location and determine chest movement of user 210. Figure 2 ), determine the airflow from user 210's mouth and / or nose, and determine the time when user 210 enters bed 230. Figure 2 The camera 150 also determines when the user 210 leaves the bed 230. In some implementations, the camera 150 includes a wide-angle lens or a fisheye lens.

[0077] Infrared (IR) sensor 152 outputs infrared image data that can be reproduced as one or more infrared images (e.g., still images, video images, or both) that can be stored in memory device 114. The infrared data from IR sensor 152 can be used to determine one or more sleep-related parameters during a sleep period, including the user 210's temperature and / or the user 210's movement. IR sensor 152 can also be used in conjunction with camera 150 when measuring the presence, location, and / or movement of user 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 user 210 ( Figure 2 The associated physiological data can be used to determine one or more sleep-related parameters, such as 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 user 210, embedded in clothing and / or fabric worn by the user 210, embedded in and / or coupled to the user interface 124 and / or its associated helmet (e.g., straps, etc.).

[0079] ECG sensor 156 outputs physiological data associated with the electrical activity of the heart of user 210. In some implementations, ECG sensor 156 includes one or more electrodes located above or around a portion of user 210 during sleep periods. Physiological data from ECG sensor 156 can be used, for example, to determine one or more of the sleep-related parameters described herein.

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

[0081] The capacitive sensor 160, force sensor 162, and strain gauge sensor 164 output data that can be stored in memory device 114 and used by control system 110 to determine one or more of the sleep-related parameters described herein. EMG sensor 166 outputs physiological data related to electrical activity generated by one or more muscles. Oxygen sensor 168 outputs oxygen data indicating the oxygen concentration of a gas (e.g., in catheter 126 or at user interface 124). Oxygen sensor 168 can 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 also include a ground-skin response (GSR) sensor, a blood flow sensor, a respiration sensor, a pulse sensor, a blood pressure sensor, a blood oxygen sensor, or any combination thereof.

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

[0083] The humidity sensor 176 outputs data that can be stored in the storage device 114 and used by the control system 110. The humidity sensor 176 can be used to detect humidity in various areas surrounding the user (e.g., inside the conduit 126 or user interface 124, near the user 210's face, near the connection between the conduit 126 and user interface 124, near the connection between the conduit 126 and the respiratory therapy device 122, etc.). Therefore, in some implementations, the humidity sensor 176 can be coupled to or integrated into the user interface 124 or the conduit 126 to monitor the humidity of pressurized air from the respiratory therapy device 122. In other implementations, the humidity sensor 176 is placed near any area where the humidity level needs to be monitored. The humidity sensor 176 can also be used to monitor the humidity of the surrounding environment around the user 210, such as the air in a bedroom.

[0084] The optical detection and ranging (LiDAR) 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 (e.g., a living space). LiDAR typically utilizes pulsed lasers for time-of-flight measurements. LiDAR is also known as 3D laser scanning. In an example using this sensor, a fixed or mobile device (such as a smartphone) with 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. 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 to 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 off solid surfaces (e.g., transmissive materials) to allow for the classification of different types of obstacles.

[0085] In some implementations, one or more sensors 130 may also include a skin conductance response (GSR) sensor, a blood flow sensor, a respiration sensor, a pulse sensor, a blood pressure sensor, a pulse oximeter sensor, a sonar sensor, a radar sensor, a blood glucose sensor, a color sensor, a pH sensor, an air quality sensor, a tilt sensor, a rain sensor, a soil moisture sensor, a water flow sensor, an alcohol sensor, or any combination thereof.

[0086] Although Figure 1While shown separately, any combination of one or more sensors 130 may be integrated into and / 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, external 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; pressure sensor 130 and / or flow sensor 132 may be integrated into and / or coupled to respiratory therapy device 122. In some implementations, 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 typically positioned adjacent to or in contact with user 210 during sleep periods (e.g., positioned on or in contact with a portion of user 210, worn by user 210, coupled to or positioned on a bedside table, coupled to a mattress, coupled to a ceiling, etc.).

[0087] Data from one or more sensors 130 can be analyzed to determine one or more sleep-related parameters, which may include respiratory signals, respiratory rate, respiratory pattern, inspiratory amplitude, expiratory amplitude, inspiratory-expiratory ratio, occurrence of one or more events, number of events per hour, event pattern, sleep state, apnea-hypopnea index (AHI), or any combination thereof. One or more events may include snoring, apnea, central apnea, obstructive apnea, mixed apnea, hypopnea, mask leakage, coughing, restless legs, sleep disturbance, apnea, increased heart rate, dyspnea, asthma attack, seizure, epilepsy, elevated blood pressure, or any combination thereof. Many of these sleep-related parameters are physiological parameters, although some may be considered non-physiological parameters. Other types of physiological and non-physiological parameters may also be determined based on data from one or more sensors 130 or based on other types of data.

[0088] User device 170 ( Figure 1The system includes a display device 172. The user device 170 may be, for example, a mobile device such as a smartphone, tablet, game console, smartwatch, laptop, etc. Alternatively, the 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 implementations, the user device is a wearable device (e.g., a smartwatch). The display device 172 is typically used to display images including still images, video images, or both. In some implementations, the display device 172 acts as a human-machine interface (HMI) including a graphical user interface (GUI) configured to display images and provide input. The display device 172 may be an LED display, OLED display, 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 the user device 170. In some implementations, the system 100 may use and / or include one or more user devices.

[0089] In some implementations, system 100 also includes an activity tracker 180. The activity tracker 180 is typically used to help generate physiological data associated with the user. The activity tracker 180 may include one or more sensors 130 described herein, such as 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 can 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, average respiratory rate, resting respiratory rate, maximum respiratory rate, respiratory rate variability, heart rate, average heart rate, resting heart rate, maximum heart rate, heart rate variability, calories burned, blood oxygen saturation, electrodermal activity (also known as skin conductance or skin response), or any combination thereof. In some implementations, the activity tracker 180 is coupled (e.g., electronically or physically) to the user device 170.

[0090] In some implementations, the activity tracker 180 is a wearable device that can be worn by the user, such as a smartwatch, wristband, ring, or patch. For example, see reference... Figure 2The activity tracker 180 is worn on the wrist of the user 210. The activity tracker 180 may also be coupled to or integrated into clothing or garments worn by the user. Alternatively, the activity tracker 180 may also be coupled to an external device 170 or integrated into the user device (e.g., within the same housing). More generally, the activity tracker 180 may be communicatively coupled to, or physically integrated therein (e.g., within a housing) the control system 110, memory 114, breathing system 120, and / or user device 170.

[0091] Although the control system 110 and the memory device 114 are in Figure 1 While described and shown as separate and distinct components of system 100, in some implementations, control system 110 and / or memory device 114 are integrated into user device 170 and / or respiratory therapy device 122. Alternatively, in some implementations, control system 110 or a portion thereof (e.g., processor 112) may 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).

[0092] Although system 100 is shown as including all the components described above, a system according to an implementation of the invention may include more or fewer components. For example, a first alternative system includes a control system 110, a storage 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 storage 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 storage 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 part or multiple parts of the components shown and described herein and / or in combination with one or more other components.

[0093] Figure 3A The diagram illustrates the respiratory waveform of a person during sleep according to some implementations of the present invention. The horizontal axis represents time, and the vertical axis represents respiratory flow. Although parameter values ​​can vary, the example respiratory cycle can have the following approximations: tidal volume V t 0.5L, inhalation time T i 1.6s, peak inspiratory flow rate Q 峰 0.4 L / s, expiratory time T e 2.4s, peak expiratory flow Q 峰 -0.5 L / s. Total duration of respiratory cycle T 总It takes approximately four (4) seconds. Individuals typically breathe at a rate of about 15 breaths per minute (BPM), with a minute ventilation of about 7.5 L / min. For the example respiratory cycle, T i With T 总 The ratio is approximately 40%.

[0094] Figure 3B This illustrates an individual's selected polysomnography channel (pulse oximetry, flow rate, chest movement, and abdominal movement) during non-REM sleep apnea, typically exceeding approximately ninety seconds, with approximately 34 breaths, treated with automated PAP therapy, and an interface pressure of approximately 11 cmH2O. The top channel displays pulse oximetry saturation (oxygen saturation or SpO2), scaled vertically from 90% to 99% saturation. The individual maintained approximately 95% saturation throughout the illustrated period. The second channel displays quantitative respiratory flow, scaled vertically from -1 to +1 LPS, with inspiration positive. Chest and abdominal movements are shown in the third and fourth channels.

[0095] Figure 3C This illustration shows a polysomnography of an individual prior to treatment, based on some implementations of the invention. There are 11 signal channels from top to bottom, with a horizontal time span of six minutes. The first two channels are electroencephalograms (EEGs) from different scalp locations. Periodic spikes in the second EEG indicate cortical arousal and associated activity. The third channel downwards is a submental electromyography (EMG). Increased spikes near the arousal time indicate genioglossus muscle recruitment. The fourth and fifth channels are electrooculograms (EOGs). The sixth channel is an electrocardiogram (ECG). The seventh channel shows pulse oxygen saturation (SpO2) repeatedly desaturating from approximately 90% to below 70%. The eighth channel is the respiratory airflow using a nasal cannula connected to a differential pressure sensor. Repetitive apneas of 25 to 35 seconds alternate with bursts of resuming breathing of 10 to 15 seconds, consistent with EEG arousal and increased EMG activity. The ninth channel represents chest movements, and the tenth channel represents abdominal movements. The abdomen exhibits progressively stronger movements along the length of the apnea leading to arousal. Both become uncomfortable during awakening due to the full-body movement during hyperventilation. Therefore, the apnea is obstructive, and the condition is severe. The lowest channel is posture, and in this example, it does not show any change.

[0096] Figure 3D The following describes flow data of an individual experiencing a series of complete obstructive apneas according to some implementations of the present invention. The recording duration was approximately 160 seconds. The flow rate ranged from approximately +1 L / s to approximately -1.5 L / s. Each apnea lasted approximately 10-15 seconds.

[0097] In some implementations, the system of the present invention includes a flow sensor (e.g., Figure 1Flow sensor 134) and pressure sensor (e.g., Figure 1 (Pressure sensor 132). The flow sensor is configured to generate flow data during the treatment period. For example, Figure 4A This describes some implementations of a respiratory therapy system according to the present invention (e.g., Figure 1 Users of the respiratory therapy system 120 (e.g., Figure 2 This is part of the traffic data associated with user 210. Figure 4A As shown, multiple flow values ​​measured over approximately seven complete respiratory cycles (401-407) are plotted as a continuous curve 410.

[0098] In some implementations, the pressure sensor is configured to generate pressure data over the treatment period. For example, Figure 4B This describes stress data associated with users of a CPAP system according to some implementations of the present invention. Figure 4B The pressure data shown is in comparison with Figure 4A Produced within the same treatment period. For example... Figure 4B As shown, multiple pressure values ​​measured over approximately seven full respiratory cycles (401-407) are plotted as a continuous curve 420. Because a CPAP system was used, Figure 4B The continuous pressure curve exhibits an approximate sine curve pattern with relatively small amplitude because the CPAP system attempts to maintain a constant predetermined air pressure throughout seven full breathing cycles.

[0099] See Figure 4C According to some implementations of the present invention, pressure data associated with a user of a respiratory therapy system having an expiratory decompression (EPR) module is described. Figure 4C The pressure data shown is in comparison with Figure 4A Produced within the same treatment period. For example... Figure 4C As shown, multiple pressure values ​​measured over approximately seven full respiratory cycles (401-407) are plotted as a continuous curve 430. Figure 4C The continuous curve is different from Figure 4B The continuous curve, because of the EPR module (used for...) Figure 4C The pressure data in the data may have different settings for the EPR level, which is associated with the difference between the pressure level during inhalation and the reduced pressure level during exhalation.

[0100] Receive multiple traffic values ​​over a time period (e.g., Figure 4A ) and multiple corresponding pressure values ​​over a time period (e.g., Figure 4B or Figure 4CAs input, the intentional leakage characteristic algorithm of this invention can output an intentional leakage characteristic curve of the respiratory therapy system associated with the user. As discussed herein, in some implementations, the path is formed by a respiratory therapy device (e.g., respiratory therapy device 122), a mask (e.g., user interface 124), and a conduit (e.g., conduit 126). The conduit generates a first impedance Z1 (humidifier container, tube, and mask impedance, such as...). Figure 12 As shown in the diagram, this in turn causes a pressure drop ΔP as a function of the total flow rate Qt. The user interface (e.g., face mask) pressure Pm is less than the device pressure Pd, which is less than the pressure drop ΔP through the conduit.

[0101] Pm=Pd-ΔP (1)

[0102] Where ΔP is the pressure drop, a characteristic of the conduit. The pressure drop ΔP is a function of the total flow rate Qt:

[0103] ΔP=Z1·Qt (2)

[0104] In some implementations, the vent of the mask generates a second impedance Z2 (vent impedance, such as...). Figure 12 (As shown). The mask interface pressure Pm is directly related to the ventilation flow rate Qv through the ventilation resistance characteristic Z2:

[0105] Pm=Z2·Qv (3)

[0106] Similarly, the ventilation flow rate Qv is directly related to the mask interface pressure Pm through the ventilation admittance characteristic Y2:

[0107]

[0108] Qv=Y2·Pm (5)

[0109] Combining equation (1) and equation (5), the ventilation flow rate Qv can be determined as:

[0110] Qv=Y2·(Pd-ΔP) (6)

[0111] Unintentional leakage, which is unknown and unpredictable, generates a third impedance Z3 (leakage impedance, such as...). Figure 12 (As shown). Additionally, in some implementations, a fourth impedance (airway resistance and lung compliance, such as...) Figure 12 The parameters described herein include (i) patient airway resistance Z4, (ii) patient lung compliance Clung, and / or (iii) variable pressure source Plung, each representing a characteristic of the user's breathing circuit. Therefore, the total flow rate Qt is equal to the sum of the tidal flow rate Qv, the leakage flow rate Qleak, and the respiratory flow rate Qr:

[0112] Qt=Qv+Qleak+Qr (7)

[0113] In some implementations, the respiratory flow rate Qr is averaged as zero over multiple respiratory cycles (e.g., respiratory 360 cycles) because the average respiratory flow rate entering or leaving the lungs must be zero. The tilde (~) is used to represent the average over multiple respiratory cycles:

[0114]

[0115] Therefore, by averaging the flow rate over multiple breathing cycles (e.g., respiration), the average leakage flow rate can be approximated as:

[0116]

[0117] The averaging process can be implemented using a low-pass filter with a sufficiently long time constant to encompass multiple breathing cycles. The time constant can be any suitable duration, such as five seconds, ten seconds, thirty seconds, one minute, etc. However, other time intervals can also be envisioned.

[0118] Combining equations (1), (2), and (3), the average device pressure It can be written as:

[0119]

[0120] In the absence of any leakage flow (e.g., In the case of ), average total flow equal to average ventilation flow rate Then equation (10) can be written to reflect the average total flow. and the average device pressure characterizing the air circuit of respiratory therapy The relationship between them:

[0121]

[0122]

[0123]

[0124] The relationship between the intentional leakage characteristic curves of the system is determined by the ventilation resistance characteristic Z2 and the duct pressure drop resistance characteristic Z1. When leakage is present, When there is no leakage,

[0125] Now for reference Figure 5A This shows the average total flow. (in liters per minute) Average device pressure Scatter plot 500a (in cmH2O units). Figure 5AThis illustrates the Cartesian coordinates used to plot the average device pressure and average total flow rate (expressed in liters per minute (“LPM”, equivalent to 60 times L / s)). Each Cartesian coordinate includes an X and Y value. As shown, the five Cartesian coordinates 510, 512, 514, 516, and 518 are plotted in scatter plot 500a during treatment. Each Cartesian coordinate can also be represented as...

[0126] For example, the first Cartesian coordinate 512 has a first X value of approximately 20 liters per minute and a first Y value of approximately 6 cmH2O. Thus, the first Cartesian coordinate 512 can be represented as (20, 6). The first X value can be estimated and / or calculated at least based on a first plurality of flow values ​​generated during a first time period. In some implementations, the first X value is the average flow value of the first plurality of flow values ​​generated during the first time period.

[0127] In some implementations, the first time period is a predetermined time interval, such as five seconds, ten seconds, 30 seconds, one minute, two minutes, etc. In some implementations, the first time period includes one or more complete breathing cycles, such as one breathing cycle, two breathing cycles, five breathing cycles, ten breathing cycles, etc. Therefore, in some implementations, the first X value is such as seven breathing cycles (… Figure 4A The average flow rate of the first plurality of flow rates generated on one or more respiratory cycles.

[0128] Similarly, the first Y value can be estimated and / or calculated at least based on a first plurality of pressure values ​​generated during a first time period. Each of the first plurality of pressure values ​​corresponds to a corresponding one of the first plurality of flow values. For example, in some implementations, each of the first plurality of flow values ​​has a corresponding timestamp (e.g., Figure 4A Using the corresponding timestamp, a specific pressure value can be identified from the first plurality of pressure values ​​(e.g., Figure 4B or Figure 4C Therefore, in some implementations, the first Y value is in one or more respiratory cycles, such as seven respiratory cycles (e.g., Figure 4B or Figure 4C The average pressure value of the first plurality of pressure values ​​generated on the ).

[0129] The second Cartesian coordinate 516 has a second X value of approximately 28 liters per minute and a second Y value of approximately 10 cmH2O. Thus, the second Cartesian coordinate 516 can be represented as (28, 10). The second Cartesian coordinate 516 is estimated and / or calculated in the same or similar manner as the first Cartesian coordinate 512, using the same first X and first Y values.

[0130] Reference Figure 5BBased at least in part on the first Cartesian coordinates 512 and the second Cartesian coordinates 516, the intentional leakage characteristic curve 550 can be fitted in curve 500b. For example, in some implementations, the intentional leakage characteristic curve 550 can be approximated using a polynomial equation such as a quadratic equation:

[0131]

[0132] The parameters of the intentional leakage characteristic curve, in this quadratic equation, are two non-zero constants (or coefficients), k1 and k2, which characterize the series connection of the ventilation resistance characteristic Z1 and the air loop pressure drop resistance characteristic Z2. In some implementations, the polynomial equation defines the intentional leakage of the system (e.g., the ventilation flow rate of the system) by providing a corresponding intentional leakage flow rate for a given pressure.

[0133] If at least two Cartesian coordinates are known, the nonzero constants k1 and k2 can be solved. For example, using the first Cartesian coordinate 512(20, 6) and the second Cartesian coordinate 516(28, 10), equation (8) can be solved and rewritten as an approximation:

[0134]

[0135] In other words, for a conic section passing through the first Cartesian coordinate 512 and the second Cartesian coordinate 516, the nonzero constant k1 is (Approximately 0.00714), while the non-zero constant k2 is (Approximately 0.157). Therefore, in some implementations, the intentional leakage characteristic curve 550 can be estimated and / or defined as equation (15).

[0136] In some implementations, the nonzero constant depends on (i) the unit used for the pressure value, (ii) the unit used for the flow rate value, (iii) the unit used for ventilation in the respiratory therapy system, (iv) the unit used for the mask in the respiratory therapy system, (v) the unit used for the humidifier container in the respiratory therapy system, (vi) the unit used for the tubing in the respiratory therapy system, (vii) a scaling factor, or (viii) any combination thereof. The nonzero constant can also vary with different types of masks, masks from different manufacturers, and / or different batches of masks. For example, for a specific vent in a respiratory therapy system, the nonzero constants k1 and k2, measured in cmH2O and L / min respectively, could be approximately and approximately

[0137] In some implementations, the polynomial equation can have more than two non-zero constants, such as three, four, or five. For example, the polynomial equation can be expressed as:

[0138]

[0139] In some implementations, polynomial equations can involve powers of three, four, five, etc. For example, a polynomial equation can be expressed as:

[0140]

[0141] In some implementations, unintentional leaks are more likely to occur in systems with high pressure values. To obtain a better fit to the intentional leak characteristic curve, only Cartesian coordinates with lower values ​​are used to solve the polynomial equation. For example, in some implementations, Cartesian coordinates where the Y value does not exceed a predetermined pressure threshold are used. The predetermined pressure threshold can be associated with a low probability of unintentional leaks, such as less than about 10 cmH2O, less than about 9 cmH2O, less than about 8 cmH2O, less than about 7 cmH2O, less than about 6 cmH2O, less than about 5 cmH2O, less than about 4 cmH2O, less than about 3 cmH2O, or less than about 2 cmH2O.

[0142] As an example, some implementations of the present invention... Figure 7 A flowchart of a method (700) for detecting intentional leakage characteristic curves of a respiratory therapy system is shown. Method 700 can be implemented using any system disclosed herein. At step 702 of method 700, a pressure generator of the respiratory therapy device generates a pressurized airflow, which is then supplied to the user's airway during a first time period (e.g., a first one or more sleep cycles). The pressurized airflow has a first nominal pressure value. In some implementations, the first nominal pressure value is a pressure that the pressure generator attempts to maintain during operation of the pressure generator by modifying one or more operating parameters to at least account for the user's breathing. In some such implementations, one or more operating parameters include the rotational speed per minute of the pressure generator's motor, the power supplied to the motor, or a combination thereof.

[0143] As described in this article, in order to obtain a better fit to the intentional leakage characteristic curve, the first nominal pressure value can be a value not exceeding a predetermined pressure threshold. For example, the first nominal pressure value can be between about 1 cmH2O and about 8 cmH2O, such as 1.5 cmH2O, 2 cmH2O, 2.5 cmH2O, 3 cmH2O, 3.5 cmH2O, 4 cmH2O, 4.5 cmH2O, 5 cmH2O, 5.5 cmH2O, 6 cmH2O, 6.5 cmH2O, 7 cmH2O, and 7.5 cmH2O.

[0144] When the pressure generator attempts to maintain a first nominal pressure value, in step 704 of method 700, a first plurality of flow values ​​for a first time period are generated using, for example, a flow sensor 134. Furthermore, in step 706 of method 700, a first plurality of pressure values ​​for the first time period are generated using, for example, a pressure sensor 132. In step 708 of method 700, as described herein, a first Cartesian coordinate is determined based on at least one of the first plurality of flow values ​​and at least one of the first plurality of pressure values. For example, in some implementations, the first Cartesian coordinate is determined at least partially based on the first flow value and the first pressure value. In some implementations, the first Cartesian coordinate is determined at least partially based on the average of the first plurality of flow values ​​and the average of the first plurality of pressure values. Alternatively, in some implementations, instead of using the first plurality of pressure values ​​to estimate and / or calculate the first Y value, the first nominal pressure value is used as the first Y value.

[0145] Once the first Cartesian coordinates are determined, in step 710 of method 700, the pressure generator adjusts the pressurized airflow to a second nominal pressure value during a second time period (e.g., a second or more breathing cycles). In some implementations, the supplied pressurized airflow is increased to a second nominal pressure value greater than the first nominal pressure value. For example, the second nominal pressure value may be between approximately 0.1 cmH2O and approximately 1 cmH2O greater than the first nominal pressure value, such as 0.1 cmH2O, 0.2 cmH2O, 0.25 cmH2O, 0.5 cmH2O, 0.75 cmH2O, or 1 cmH2O greater than the first nominal pressure value. Alternatively or additionally, the second nominal pressure value may be approximately 0.1% to approximately 5% greater than the first nominal pressure value, such as 0.1%, 0.15%, 0.2%, 0.25%, 0.3%, 0.35%, 0.4%, 0.45%, or 0.5% greater than the first nominal pressure value.

[0146] In some implementations, the variation between the first and second nominal pressure values ​​can be any suitable value, provided that both are low enough to cause negligible unintentional leakage by the user. In some implementations, the variation between the first and second nominal pressure values ​​is small (e.g., between about 0.05 cmH2O and about 0.1 cmH2O). However, in some such implementations, a variation that is too small (e.g., <0.05 cmH2O) may lead to inaccuracies in the calculation of the resulting parameters (e.g., estimation of non-zero constants in the intentional leakage characteristic curve).

[0147] When the pressure generator attempts to maintain a second nominal pressure value, in step 712 of method 700, a second plurality of flow rates for a second time period are generated. Furthermore, in step 714 of method 700, a second plurality of pressure values ​​for a second time period are generated. In step 716 of method 700, as described herein, a second Cartesian coordinate is determined based on at least one of the second plurality of flow rates and at least one of the second plurality of pressure values. Alternatively, in some implementations, instead of using the second plurality of pressure values ​​to estimate and / or calculate the second Y value, the second nominal pressure value is used as the second Y value.

[0148] Similarly, in some implementations, a third, fourth, fifth, or more Cartesian coordinates can be determined when the pressure generator regulates the flow rate of pressurized air. Given two or more Cartesian coordinates, in step 718 of method 700, the methods disclosed herein can be used to determine the intentional leakage characteristic curve associated with the respiratory therapy system.

[0149] Now for reference Figure 6 Curve 600 shows the intentional leakage characteristic curve 650 of the respiratory therapy system associated with the user. The intentional leakage characteristic curve 650 will average the device pressure. Each value is related to the average total flow rate at the pressure value of the device. Related. An offset to the right of the intentional leakage characteristic curve 650, for example, at Cartesian coordinates 622, indicates unintentional leakage, which manifests as an increased flow rate for a given device pressure.

[0150] During respiratory therapy, at mean device pressure Without any changes and without any unintentional leaks, point The curve shifts around 650 according to the intentional leakage characteristic curve. However, unintentional leakage will cause this point to shift. Move to the right of the intentional leakage characteristic curve 650 for a period of time. If the unintentional leakage is resolved, then that point... Returning to the intentional leakage characteristic curve 650, unintentional leakage can therefore be shown on graph 600 as a shift to the right of the intentional leakage characteristic curve 650.

[0151] For example, at the point where the user experiences an unintentional leak (as shown by Cartesian coordinates 622), Greater than the average device pressure Average ventilation at value like Figure 6 As shown, at a given average device pressure Below, in reality and mean airflow The difference is the flow offset d, which is approximately 9 liters per minute. On the other hand, there is an intentional leftward shift of the characteristic curve 650, for example... Less than the average ventilation airflow point It can indicate airway obstruction (e.g., airway obstruction caused by the pillow due to the movement of the head relative to the pillow on which the user sleeps).

[0152] As discussed here, respiratory therapy systems typically include components such as respiratory therapy devices, catheters, and user interfaces. Various forms of user interfaces can be used with a given respiratory therapy device, such as a nasal pillow, nasal fork, nasal mask, nasal and mouth (oronasal) mask, or full-face mask. Furthermore, catheters of different lengths and diameters can be used. To provide improved control over the therapy delivered to the user interface, estimating therapeutic parameters such as pressure, ventilatory flow rate, and involuntary flow rate within the user interface can be advantageous. In systems using therapeutic parameter estimation, knowing the type of component used by the user can improve the accuracy of the therapeutic parameter estimation and thus enhance the efficacy of the therapy.

[0153] To obtain information about component types, some respiratory therapy devices include menu systems that allow users to input and / or select the type of system component, including the user interface used (e.g., brand, manufacturer, type, model, serial number, mask series, size, etc.). Once the user inputs and / or selects the component type, the respiratory therapy device can select the appropriate operating parameters of the flow generator that best coordinates with the selected component, and can more accurately monitor treatment parameters during treatment. However, in some cases, the user may not have selected the component type correctly, or may not have selected the component type correctly at all, resulting in the respiratory therapy device being erroneous or unaware of the type of component being used.

[0154] Accordingly, in some implementations, the intentional leakage described herein can be used to identify the user interface. For example, if the pipe pressure drop characteristic ΔP is known (e.g., because the type of pipes constituting the pipe is known, or through previous calibration operations), the parameters of the intentional leakage characteristic curve effectively characterize the vent, which in turn indicates the type of user interface.

[0155] In some implementations, when used with a known pipeline, the user interface can be identified by comparing the computed parameters k1 and k2 with a data structure such as an array or database containing pairs (k1, k2) associated with a known user interface type. The type of the user interface associated with the stored pair (k1, k2) that best matches the computed parameters k1 and k2 can be taken as the type of the user interface.

[0156] Alternatively, the average device pressure can be used before fitting the quadratic equation to the resulting intentional leakage characteristic curve of the mask. Subtract the pressure drop from each value The obtained parameters k1 and k2 can then be compared with the data structure of (k1, k2) pairs associated with the known user interface type to identify the user interface or access data of the operation of the respiratory therapy device associated with the use of a specific user interface.

[0157] Therefore, detected parameters can be compared with expected parameters over a period of time, collecting longitudinal and cross-sectional data. In some implementations, the system can determine production variations by analyzing a batch of masks and use this information for production quality improvements.

[0158] In some implementations, the system can examine changes over time and understand whether the mask seal itself has degraded over time (because the system can determine how long a particular mask has been used based on RFID tags and / or user input), and what conditions caused the unintentional leak (e.g., whether it depends on location, whether it was changed according to recommendations to tighten or loosen the headband, whether the seal follows the expected degradation cycle (assuming regular cleaning), whether it shows accelerated wear, etc.).

[0159] In some implementations, the user interface used can be determined by: user input, optical detection of the user interface, detection of the user interface via RFID, detection of the user interface using electronic devices (e.g., user interface detection components), detection of the conduit via a connector with a heating tube having electronic devices (e.g., electronic connections to the control system of the respiratory therapy device, electronic chips, etc.), or any combination thereof. Therefore, an initial profile describing the correct functioning of this type of new mask can be selected. The initial profile can be specific to the respiratory therapy device, operating mode, operating parameters, other settings such as EPR or bi-level, user interface (e.g., brand, manufacturer, form, model, serial number, mask series, size, etc.), or any combination thereof.

[0160] In some implementations, the system can also select, for example, a model of the expected behavior of this type of partially or completely worn mask as an initial curve over time, from a lookup table or from a cloud system. These expected models can describe different levels of airflow obstruction, duct obstruction (e.g., for masks with airflow through a hose around the head), and different levels of seal wear, headband stretching, etc. Therefore, by selecting an appropriate initial model, the system can detect intentional and unintentional leaks during sleep periods and / or through multiple sleep periods.

[0161] In some implementations, the system is capable of detecting low intentional leaks (e.g., below-expected intentional leaks), early indications of vent blockages (e.g., excessive moisture, aging, etc.), and / or an unsuitable sleeping position of the user (e.g., blocked vents). In some such implementations, prompts may be sent to the user (i) to warn them of these problems, (ii) to recommend replacing the mask and / or vent, or (iii) both.

[0162] In some implementations, the system can detect pillows blocking the call port. In some such implementations, prompts can be sent to the user suggesting changes to bedding, mask configuration, mask type, or any combination thereof.

[0163] In addition, in some implementations, unintentional leaks may occur around the humidifier container / seal. This can manifest as a high-pitched sound (audible and potentially annoying to people with higher hearing frequencies, such as the user's bed partner), as well as another source of "intentional leakage" unrelated to airflow. To avoid confusion with unintentional leaks from the seal, the system can also process the sound to check for characteristics of such a humidifier container leak. The system recommends that the user reposition the container. By isolating this leak source, a more precise identification of the leak type (and recommended solutions) can be provided to the user and / or healthcare professionals.

[0164] A more robust and / or accurate leak reporting model will result in more users remaining who might otherwise discontinue use due to inconsistent and / or inaccurate leak reports. In some implementations, the invention may provide systems and methods for determining whether a mouth leak is occurring, allowing for the recommendation of a full-face mask to a user before they withdraw from treatment due to a nasal mask and the resulting mouth leak problem.

[0165] Typically, users designated to use the respiratory system 120 tend to experience higher quality sleep and less fatigue throughout the day following use, compared to those who do not use the respiratory system 120 (especially when the user has sleep apnea or other sleep-related conditions). However, many users do not adhere to the prescribed usage due to discomfort or inconvenience with the user interface 124, or due to other side effects (e.g., dry mouth, dry lips, dry throat, discomfort, etc.). If users fail to perceive any benefits they are experiencing (e.g., less fatigue during the day), they are more likely to fail to use the respiratory system 120 as prescribed (or discontinue use altogether).

[0166] However, side effects and / or lack of improvement in sleep quality may be due to mouth leakage rather than a lack of therapeutic efficacy. Therefore, it is beneficial to identify the user's mouth leakage status and communicate this status to the user to help them achieve higher quality sleep, so that the user does not discontinue or reduce their use of the respiratory system 120 due to a perceived lack of benefit.

[0167] In some implementations, it is not necessary to select a mask type setting on the respiratory therapy device. This invention provides an automated system for detecting mask type by estimating an intentional leakage characteristic curve and comparing the estimated intentional leakage characteristic curve with existing data for a specific mask type (e.g., from a lookup table, from other users of the same mask type, from previous data associated with the same user, or any combination thereof).

[0168] According to some implementations of the invention, alternative or additional methods (e.g., Figure 8 Method 800) can be used to determine respiratory therapy systems (e.g., Figure 1 Intentional leakage characteristic curves of the respiratory therapy system 120. These methods improve the impedance determination of masks (e.g., user interface 124) and / or tubes (e.g., catheter 126), which in some implementations can be characterized by intentional leakage characteristic curves. The benefits of improved impedance determination include: (i) improved leakage estimation accuracy, (ii) addressing the underreporting of mask leaks, (iii) providing impedance characteristic inputs to aid in mask identification, (iv) providing diffuser loss characteristic inputs to aid in estimating and / or determining mouth leaks, and (v) utilizing air loop signals and / or impedance.

[0169] In some implementations, the method disclosed herein utilizes a CPAP airflow path model. In some implementations, the method disclosed herein may be at least partially based on breathing synchronization, which reduces and / or eliminates confounding factors from user breathing when determining the intentional leakage characteristic profile. In some such implementations, the impedance determination method disclosed herein eliminates confounding breathing signals, leading to more accurate and / or precise impedance determination.

[0170] See Figure 8 According to some implementations of the present invention, a method 800 for determining an intentional leakage characteristic curve of a respiratory therapy system is disclosed. In some implementations, one or more steps of method 800 may be similar to one or more steps of method 700, or a part of one or more steps of method 700. In some other implementations, one or more steps of method 800 may include one or more steps of method 700.

[0171] As disclosed herein, in some implementations, continuous-time filtering (or sampling) is replaced by breath-by-breath sampling at the start of inhalation and / or exhalation. Some advantages of this impedance determination and / or measurement method include: (i) the respiratory signal (independent of its frequency content) is accurately removed by default; (ii) intentional leakage components are accurately preserved; (iii) impedance flow and pressure signals are downsampled, reducing computational complexity; (iv) the respiratory signal signature can be used to: (a) support leakage thresholding, breath-by-breath calculation; (b) address promiscuous leakage problems (e.g., any unintentional leakage, such as mouth leakage); (v) utilize existing respiratory (e.g., CPAP) system parameters; and (vi) the breath-by-breath method is additionally advantageous for dual horizontal flow filtering.

[0172] In step 810, the user (e.g., the user who is directed to the respiratory therapy system (e.g., respiratory therapy system 120) receives the information. Figure 2 Multiple flow rates associated with pressurized air directed into the user's airway are received in step 812. In some implementations, each of the multiple pressure values ​​corresponds to a specific one of the multiple flow rates.

[0173] In step 820, a first time associated with the user's first breath and a second time associated with the user's second breath are identified. In some implementations, the first and second times can be identified by analyzing multiple corresponding flow volumes of at least a portion of multiple flow values. In some implementations, the multiple corresponding flow volume values ​​are determined by time integration of at least a portion of the multiple flow values. Figure 9 This illustrates an example method for identifying the first timeframe associated with a user's first breath. The second timeframe can be identified in the same or similar manner.

[0174] In step 830, multiple flow values ​​are filtered at least in part based on the identified first time and the identified second time. A subset of the multiple flow values ​​is then filtered to produce the desired result. In some implementations, the subset of multiple flow values ​​may include 1% or less of the received multiple flow values. Alternatively or additionally, in some implementations, the subset of multiple flow values ​​may include (i) one, two, three, four, or five flow values ​​from the first breath and (ii) one, two, three, four, or five flow values ​​from the second breath. For example, the subset of multiple flow values ​​may include (i) one, two, three, four, or five flow values ​​approximately at the start of inhalation of the first breath and (ii) one, two, three, four, or five flow values ​​approximately at the start of inhalation of the second breath.

[0175] In some implementations, multiple flow rate values ​​are filtered using a low-pass filter to produce a subset of flow rate values ​​that are purely DC (e.g., excluding and / or unaffected by the user's breathing). The DC values ​​can be averages. The low-pass filter removes higher frequencies and leaves lower frequencies, thus being an average. For example, in some implementations, the low-pass filter includes first analyzing flow rate values ​​to identify a first time associated with the user's first breath and a second time associated with the user's second breath (step 820). However, any suitable filtering can be employed in the disclosed method to identify the first time associated with the user's first breath and the second time associated with the user's second breath.

[0176] In some implementations, filtering multiple flow values ​​(step 830) includes, or additionally or alternatively includes, filtering out (i) flow values ​​affected by the user’s breathing, (ii) flow values ​​indicating leakage from the user’s mouth, (iii) flow values ​​associated with irregular breathing (e.g., for inhalation, exhalation, or the entire breath, the duration is too long or too short), or (iv) any combination thereof.

[0177] In step 840, an intentional leakage characteristic curve of the respiratory therapy system is determined using at least two of a subset of multiple flow rates and their corresponding pressure values. In some implementations, step 840 includes one or more steps 842, 844, and 846. In step 842, a first Cartesian coordinate with a first X value and a first Y value is generated. The first X value is a first flow rate value corresponding to a first time (identified in step 820). The first Y value is a first pressure value corresponding to the first time. In step 844, a second Cartesian coordinate with a second X value and a second Y value is generated. The second X value is a second flow rate value corresponding to a second time (identified in step 820). The second Y value is a second pressure value corresponding to the second time. In step 846, the intentional leakage characteristic curve is determined at least in part based on the generated first Cartesian coordinate and the generated second Cartesian coordinate. For example, in some implementations, a reference may be used at step 840. Figures 5A-5B and / or Figure 6 The systems and methods shown and described.

[0178] In some implementations, the following equation is used to calculate the intentional leakage characteristic curve:

[0179]

[0180] Where Z is the intentional leakage characteristic curve (e.g., indicating impedance), P is the pressure value among multiple pressure values, Q is the flow rate value among multiple flow rate values, k1 is a first constant, and k2 is a second constant. In some such implementations, the first constant k1 can be associated with turbulence; the second constant k2 can be associated with laminar flow. Equation (12) is advantageous because the impedance can be calculated using a linear equation.

[0181] In some implementations, method 800 further includes outputs of diffuser loss (step 850) and unintentional leakage (step 860). The diffuser loss output (step 850) may include features such as mask recognition (e.g., identifying a user interface associated with the respiratory therapy system), automatic mask setting (e.g., adjusting one or more settings associated with the user interface), and / or mouth leak detection (e.g., determining a mouth leak associated with a user of the respiratory therapy system). The unintentional leakage output (step 860) may include features such as mouth leak detection and / or user leak correction.

[0182] Reference Figure 9 The diagram illustrates flow rate data (“Q”) and flow volume data (“V”) during the first breath in respiratory therapy according to some implementations of the present invention. Curve 900 illustrates the first breath 980 of a user in the respiratory therapy system. In some implementations, each breath of the user includes an inspiratory portion and an expiratory portion. As shown, the first breath 980 includes an inspiratory portion 982 and an expiratory portion 984. The upper segment 902 of curve 900 shows the flow rate data 910 (dashed line) associated with the user during the first breath 980. The lower segment 904 of curve 900 shows the corresponding flow volume data 950 (dashed line). In some implementations, the corresponding flow volume data 950 can be determined by time integration of the flow rate data 910.

[0183] Flow volume data can be used to identify a first time associated with a user's first breath and a second time associated with a user's second breath (e.g., step 820 of method 800), which can then be used to determine an intentional leakage characteristic curve (e.g., step 840 of method 800). In some implementations, the first time is within the inspiratory portion of the first breath, and the second time is within the inspiratory portion of the second breath.

[0184] In some implementations, the first time is when the user's first breath begins, and the second time is when the user's second breath begins. Alternatively or additionally, in some implementations, the first time is approximately the beginning and / or commencement of the inhalation portion of the first breath, and the second time is approximately the beginning of the inhalation portion of the second breath. Figure 9In the example shown, the first moment can correspond to flow rate 912 of flow rate data 910 and flow volume 952 of flow volume data 950. Flow volume 952 is the minimum flow volume value during the first breath 980.

[0185] In some such implementations, the minimum flow volume value of the first breath 980 (e.g., flow volume 952) corresponds to the start of the first breath 980 and / or the start of the inspiratory portion 982 of the first breath 980. Therefore, the first time can be identified by analyzing the flow volume data 950 to find the minimum flow volume value. The minimum flow volume value of the first breath 980 (e.g., flow volume 952) also corresponds to the flow rate 912, which is the flow rate at the start of inspiration (Q = "zero" flow rate at...). Figure 9 (Displayed as a horizontal line 906 in the middle).

[0186] In some implementations, the first time is within the exhalation portion of the first breath, and the second time is within the exhalation portion of the second breath. Alternatively or additionally, in some implementations, the first time is approximately at the beginning of the exhalation portion of the first breath, and the second time is approximately at the beginning of the exhalation portion of the second breath. Figure 9 In the example shown, the first moment can correspond to flow rate 914 in flow rate data 910 and flow volume 954 in flow volume data 950. Flow volume 954 is the maximum flow volume value during the first breath 980.

[0187] In some such implementations, the maximum flow volume value of the first breath 980 (e.g., flow volume 954) corresponds to the start of the exhalation portion 984 of the first breath 980. Therefore, the first moment can be identified by analyzing the flow volume data 950 to find the maximum flow volume value. The maximum flow volume value of the first breath 980 (e.g., flow volume 954) also corresponds to a flow rate 914, which is a "zero" flow rate (i.e., the flow rate at a point in the user's respiratory cycle between inhalation and exhalation).

[0188] Identifying the time when flow is zero can be advantageous because it eliminates conflicting breathing signals (i.e., when flow is not zero). However, in some cases, when the user is experiencing unintentional leakage (e.g., mouth leakage), the flow may not return to zero at the end of the breath. Figure 9 In this example, at the end of the first breath 980, the flow rate 916 did not return to "zero". Instead, in the absence of unintentional leakage by the user, the flow data 930 shows that the final flow volume returned to "zero".

[0189] To determine the presence and / or amount of unintentional leakage during the first breath 980, flow volume data can be analyzed by comparing the ending flow volume 956 associated with the end of the first breath 980 and the starting flow volume 952 associated with the beginning of the first breath 980. Based at least in part on this comparison, it can be determined whether the user is experiencing an unintentional leak during the first breath 980 (e.g., a mouth leak indicating air leakage from the user's mouth). An ending flow volume 956 greater than the starting flow volume 952 indicates that the user is experiencing an unintentional leak during the first breath 980. In response to determining that the user is experiencing an unintentional leak during the first breath 980, the first breath 980 can be excluded when determining the intentional leak characteristic curve of the respiratory therapy system. Figure 8 Step 840).

[0190] Additionally or alternatively, in some implementations, the amount of unintentional leakage during the first breath 980 can be determined by analyzing flow volume data. Figure 9 In this example, the area difference 972 between the flow volume data 950 and the reference data 970 represents the amount of unintentional leakage during the first breath 980. For the inhalation portion 982, the reference data 970 is aligned with the flow volume data 950, so the user may not experience unintentional leakage during the inhalation portion 982. For the exhalation portion 984, the reference data 970 is plotted as a straight line between the maximum flow volume 954 and the final flow volume 956. The area difference 972 can be calculated by subtracting the first time integral of the flow volume value of the flow volume data 950 from the second time integral of the flow volume value in the reference data 970.

[0191] As an implementation method 800 ( Figure 8 Another example of one or more steps, Figure 10 Flow rate and flow volume data during two breaths are illustrated under respiratory therapy according to some implementations of the present invention. The upper part of curve 1000 shows the relationship between flow rate and time. The lower part of curve 1000 shows the corresponding relationship between flow volume and time. Flow volume data 1050 can be calculated by integrating the flow rate data 1010 over time. Flow rate data 1010 and flow volume data 1050 are plotted on two breaths. The first breath includes a first inspiratory portion 1082 and a first expiratory portion 1084. The second breath includes a second inspiratory portion 1086 and a second expiratory portion 1088.

[0192] exist Figure 10In this example, flow rate 1012 and flow volume 1052 correspond to a first time; flow rate 1016 and flow volume 1056 correspond to a second time. Therefore, the first time is the time when the user begins a first breath, and the second time is the time when the user begins a second breath. Additionally or optionally, the first time is approximately the start of the inspiratory portion 1082 of the first breath, and the second time is approximately the start of the inspiratory portion 1086 of the second breath. Further additionally or optionally, the first time corresponds to the minimum flow volume value of the first breath, and the second time corresponds to the minimum flow volume value of the second breath.

[0193] refer to Figure 11 The diagram illustrates an intentional leakage characteristic curve fitted to scatter plot 1100 (pressure versus flow rate), which can implement one or more steps of the methods disclosed herein, such as step 840 of method 800. The X-axis represents the flow rate value (“Q”, liters / 10 seconds), and the Y-axis represents the pressure value (“P”, cmH2O). For each breath, Cartesian coordinates with a first X-value and a first Y-value are plotted as points in scatter plot 1100. The first X-value of the first Cartesian coordinate is the first flow rate value corresponding to a first time (identified at step 820 of method 800). The first Y-value of the first Cartesian coordinate is the first pressure value corresponding to a first time (identified at step 820 of method 800). Subsequent Cartesian coordinates are plotted in the same or similar manner as the first Cartesian coordinates. The intentional leakage characteristic curve 1020 is then fitted to scatter plot 1100.

[0194] In some implementations, certain Cartesian coordinates of the scatter point 1100 are excluded during the fitting of the intentional leakage characteristic curve 1020. For example, Cartesian coordinates corresponding to breathing that the user experiences during an unintentional leak can be excluded, as further disclosed above.

[0195] In some implementations, patient impedance can be determined once the PAP air loop impedance is known. Circuit modeling shows that patient impedance determination methods can be implemented. In some implementations, patient impedance determination methods can determine whether mouth breathing can be distinguished from nasal breathing from a full-face mask. In some such implementations, additional information associated with the leakage stage can improve the distinction between mouth and nasal breathing.

[0196] One or more elements, aspects, or steps or any part thereof from any one of claims 1-127 may be combined with one or more elements, aspects, or steps or any part thereof from any other claims 1-127 or a combination thereof to form one or more additional embodiments and / or claims of the present invention.

[0197] While the invention has been described with reference to one or more specific embodiments or implementations, those skilled in the art will recognize that many changes can be made thereto without departing from the spirit and scope of the invention. Each of these implementations and their obvious variations is considered to fall within the spirit and scope of the invention. Additional embodiments according to aspects of the invention are also contemplated, which may combine any number of features from any of the embodiments described herein.

Claims

1. A method for determining the intentional leakage characteristic curve of a respiratory therapy system, comprising: Receive multiple flow rates associated with pressurized air directed to the airway of the user in the respiratory therapy system; Receive multiple pressure values ​​associated with the pressurized air being directed to the user's airway, each of the multiple pressure values ​​corresponding to a corresponding one of the multiple flow rate values; Identify a first time associated with the user's first breath and a second time associated with the user's second breath; The plurality of traffic values ​​are filtered at least in part based on the identified first time and the identified second time, the filtering producing a subset of the plurality of traffic values; as well as The intentional leakage characteristic curve of the respiratory therapy system is determined using at least two flow values ​​from a subset of the plurality of flow values ​​and the corresponding pressure values ​​of the at least two flow values ​​from the subset of the plurality of flow values.

2. The method of claim 1, wherein determining the intentional leakage characteristic curve of the respiratory therapy system comprises: Generate a first Cartesian coordinate with a first X value and a first Y value, wherein the first X value is a first flow rate value corresponding to the first time and the first Y value is a first pressure value corresponding to the first time; Generate a second Cartesian coordinate with a second X value and a second Y value, wherein the second X value is a second flow rate value corresponding to the second time and the second Y value is a second pressure value corresponding to the second time; as well as The intentional leakage characteristic curve is determined at least in part based on the generated first Cartesian coordinates and the generated second Cartesian coordinates.

3. The method of claim 1 or 2, wherein the intentional leakage characteristic curve is calculated using the following equation: Where Z is the intentional leakage characteristic curve, P is the pressure value among the plurality of pressure values, Q is the flow value among the plurality of flow values, k1 is the first constant, and k2 is the second constant.

4. The method of claim 1, further comprising: For at least a portion of the multiple flow values, determine multiple corresponding flow volume values.

5. The method of claim 4, wherein the first time associated with the user's first breath and the second time associated with the user's second breath are identified based at least in part on a plurality of corresponding flow volume values ​​determined by analysis.

6. The method of claim 4, wherein the plurality of corresponding flow volume values ​​are determined by time integration of at least a portion of the plurality of flow values.

7. The method of claim 1, wherein each breath of the user comprises an inhalation portion and an exhalation portion.

8. The method of claim 7, wherein the first time is within the inhalation portion of the first breath, and the second time is within the inhalation portion of the second breath.

9. The method of claim 7, wherein the first time is the beginning of the inhalation portion of the first breath, and the second time is the beginning of the inhalation portion of the second breath.

10. The method of claim 3, wherein the first time corresponds to the minimum flow volume value of the first respiration, and the second time corresponds to the minimum flow volume value of the second respiration.

11. The method of claim 7, wherein the first time is within the expiratory portion of the first breath, and the second time is within the expiratory portion of the second breath.

12. The method of claim 11, wherein the first time is the beginning of the exhalation portion of the first breath, and the second time is the beginning of the exhalation portion of the second breath.

13. The method of claim 1, wherein the first flow value corresponding to the first time is equal to the initial flow value at the start of the first breath; and wherein the second flow value corresponding to the second time is equal to the initial flow value at the start of the second breath.

14. The method of claim 13, wherein the initial flow rate value at the start of the first breath is the same as the initial flow rate value at the start of the second breath.

15. The method of claim 4, wherein the first time corresponds to the maximum flow volume value of the first respiration, and the second time corresponds to the maximum flow volume value of the second respiration.

16. The method of claim 4, further comprising: The end flow volume value associated with the end of the first breath is compared with the start flow volume value associated with the start of the first breath. as well as Based at least in part on the comparison, it is determined that the user is experiencing mouth leakage during the first breath, the mouth leakage indicating that air is leaking from the user's mouth.

17. The method of claim 16, wherein the initial flow volume value is the minimum flow volume value of the first breath.

18. The method of claim 16, wherein the end flow volume value being greater than the start flow volume value indicates that the user experienced the mouth leak during the first breath.

19. The method of claim 16, further comprising: In response to determining that the user is experiencing mouth leakage during the first breath, the first breath is excluded when determining the intentional leakage characteristic curve of the respiratory therapy system.

20. The method of claim 4, further comprising determining the amount of mouth leakage during the first respiration.

21. The method of claim 20, wherein determining the amount of mouth leakage during the first respiration comprises: Take the first time integral over a subset of the flow volume values ​​of the first respiration; as well as Subtract the first time integral from the second time integral of the straight line between the maximum flow volume of the first breath and the final flow volume.

22. The method of claim 21, wherein a subset of the flow volume values ​​of the first breath is associated with the exhalation portion of the first breath.

23. The method of claim 22, wherein the initiation of the exhalation portion of the first breath is associated with the maximum flow volume value of the first breath.

24. The method of claim 1, wherein filtering the plurality of flow values ​​includes filtering out (i) flow values ​​affected by the user's breathing, (ii) flow values ​​indicating mouth leakage of the user, (iii) flow values ​​associated with irregular breathing, or (iv) any combination thereof.

25. The method of claim 1, further comprising determining diffuser loss based at least in part on the determined intentional leakage characteristic curve.

26. The method of claim 25, further comprising, at least in part, based on the determined diffuser loss, (i) identifying a user interface associated with the respiratory therapy system, (ii) adjusting one or more settings associated with the user interface, (iii) determining mouth leakage associated with a user of the respiratory therapy system, or (iv) any combination thereof.

27. The method of claim 1, wherein the subset of the plurality of traffic values ​​comprises 1% or less of the plurality of traffic values.

28. The method of claim 1, wherein the subset of the plurality of flow values ​​includes (i) one, two, three, four or five flow values ​​of the first breath and (ii) one, two, three, four or five flow values ​​of the second breath.

29. A method for determining the intentional leakage characteristic curve of a respiratory therapy system, comprising: Receive multiple flow rates associated with pressurized air directed to the airway of a user in the respiratory therapy system, wherein each of the multiple flow rates is associated with a first time in each of the user’s multiple breaths; Receive multiple pressure values ​​associated with the pressurized air being directed to the user's airway, each of the multiple pressure values ​​corresponding to a corresponding flow rate value among the multiple flow rate values; The intentional leakage characteristic curve of the respiratory therapy system is determined using at least two flow values ​​from a subset of the plurality of flow values ​​and the corresponding pressure values ​​of the at least two flow values ​​from the subset of the plurality of flow values.

30. The method of claim 29, wherein determining the intentional leakage characteristic curve of the respiratory therapy system comprises: Generate a first Cartesian coordinate with a first X value and a first Y value, wherein the first X value is a first flow rate value corresponding to a first time associated with a first respiration, and the first Y value is a first pressure value corresponding to the first time associated with the first respiration; Generate a second Cartesian coordinate system having a second X value and a second Y value, wherein the second X value is a second flow rate value corresponding to the first time associated with the second respiration, and the second Y value is a second pressure value corresponding to the first time associated with the second respiration; and The intentional leakage characteristic curve is determined at least in part based on the generated first Cartesian coordinates and the generated second Cartesian coordinates.

31. The method of claim 29 or 30, wherein the intentional leakage characteristic curve is calculated using the following equation: Where Z is the intentional leakage characteristic curve, P is the pressure value among the plurality of pressure values, Q is the flow value among the plurality of flow values, k1 is the first constant, and k2 is the second constant.

32. The method of claim 29, further comprising: For at least a portion of the multiple flow values, determine multiple corresponding flow volume values.

33. The method of claim 32, wherein the first time associated with the user's first breath and the first time associated with the user's second breath are identified based at least in part on a plurality of corresponding flow volume values ​​determined by analysis.

34. The method of claim 32, wherein the plurality of corresponding flow volume values ​​are determined by time integration of at least a portion of the plurality of flow values.

35. The method of claim 29, wherein each breath of the user comprises an inhalation portion and an exhalation portion.

36. The method of claim 35, wherein the first time occurs within the inspiratory portion of each breath.

37. The method of claim 35, wherein the first time is the beginning of the inhalation portion of each breath.

38. The method of claim 31, wherein the first time corresponds to the minimum flow volume value in each breath.

39. The method of claim 35, wherein the first time occurs within the exhalation portion of each breath.

40. The method of claim 39, wherein the first time is the beginning of the exhalation portion of each breath.

41. The method of claim 29, wherein the first flow rate value corresponding to the first time is equal to the initial flow rate value at the start of each breath.

42. The method of claim 41, wherein the initial flow rate value at the start of the first breath and the initial flow rate value at the start of the second breath are the same.

43. The method of claim 32, wherein the first time corresponds to the maximum flow volume value in each breath.

44. The method of claim 32, further comprising: The end flow volume value associated with the end of the first breath is compared with the start flow volume value associated with the start of the first breath. as well as Based at least in part on the comparison, it is determined that the user is experiencing mouth leakage during the first breath, the mouth leakage indicating that air is leaking from the user's mouth.

45. The method of claim 44, wherein the initial flow volume value is the minimum flow volume value of the first breath.

46. ​​The method of claim 44, wherein the end flow volume value being greater than the start flow volume value indicates that the user experienced the mouth leak during the first breath.

47. The method of claim 44, further comprising: In response to determining that the user is experiencing mouth leakage during the first breath, the first breath is excluded when determining the intentional leakage characteristic curve of the respiratory therapy system.

48. The method of claim 32, further comprising determining the amount of mouth leakage during the first breath.

49. The method of claim 48, wherein determining the amount of mouth leakage during the first respiration comprises: Take the first time integral over a subset of the flow volume values ​​of the first respiration; as well as Subtract the first time integral from the second time integral of the straight line between the maximum flow volume of the first breath and the final flow volume.

50. The method of claim 49, wherein a subset of the flow volume values ​​of the first breath is associated with the exhalation portion of the first breath.

51. The method of claim 50, wherein the initiation of the exhalation portion of the first breath is associated with the maximum flow volume value of the first breath.

52. The method of claim 29, wherein filtering the plurality of flow values ​​includes filtering out (i) flow values ​​affected by the user's breathing, (ii) flow values ​​indicating mouth leakage of the user, (iii) flow values ​​associated with irregular breathing, or (iv) any combination thereof.

53. The method of claim 29, further comprising determining diffuser loss based at least in part on the determined intentional leakage characteristic curve.

54. The method of claim 53, further comprising, at least in part, based on the determined diffuser loss, (i) identifying a user interface associated with the respiratory therapy system, (ii) adjusting one or more settings associated with the user interface, (iii) determining mouth leakage associated with a user of the respiratory therapy system, or (iv) any combination thereof.

55. The method of claim 29, wherein the subset of the plurality of traffic values ​​comprises 1% or less of the plurality of traffic values.

56. The method of claim 29, wherein the subset of the plurality of flow values ​​includes (i) one, two, three, four or five flow values ​​of the first breath and (ii) one, two, three, four or five flow values ​​of the second breath.

57. A system comprising: A control system, which includes one or more processors; as well as A memory containing machine-readable instructions stored thereon; The control system is coupled to the memory, and the method as described in any one of the one or more processors of the control system is implemented when the machine-executable instructions in the memory are executed by at least one processor of the control system.

58. A system for determining an intentional leakage characteristic curve of a respiratory therapy system, the system comprising a control system configured to implement the method as described in any one of claims 1 to 56.

59. A computer program product comprising instructions that, when executed by a computer, cause the computer to perform the method as described in any one of claims 1 to 56.

60. The computer program product of claim 59, wherein the computer program product is a non-transient computer-readable medium.

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