Method and apparatus for ventilation treatment of respiratory disorders

Abstract

An apparatus for treating respiratory disorders in a patient is disclosed. The apparatus includes a pressure generator configured to deliver a flow of air at positive pressure through a patient interface to an airway of the patient, a sensor configured to generate a signal representative of a respiratory flow of the patient, and a controller. And a controller configured to control the pressure generator to deliver a ventilation therapy with a base pressure and pressure support through the patient interface, an apnea being detected from the signal representative of a respiratory flow of the patient, controlling the pressure generator to deliver one or more detected breaths to the patient during the apnea, determining patency of the airway of the patient from a waveform of the respiratory flow signal responsive to one of the one or more detected breaths, an effective duration of the apnea is calculated based on the patency of the airway, and a setpoint of the base pressure for the ventilation therapy is adjusted in response to the apnea based on the effective duration of the apnea.

Description

[0001] Divisional application statement

[0002] This application is a divisional application of a PCT international application filed on November 17, 2017, with international application number PCT / AU2017 / 051265, which entered the Chinese national phase on July 12, 2019, entitled "Method and apparatus for ventilation therapy of respiratory disorders" and with application number 201780083336.0.

[0003] 1. Cross-references to related applications

[0004] This application claims the benefit of Australian Provisional Application No. AU2016904724, filed on 18 November 2016, the entire disclosure of which is incorporated herein by reference.

[0005] 2. Statement regarding federally funded research or development

[0006] not applicable

[0007] 3. Sequence List

[0008] not applicable Background Technology 4.1 Technical Field

[0010] This technology relates to one or more of the detection, diagnosis, treatment, prevention, and improvement of respiratory-related disorders. This technology also relates to medical devices or equipment, and their use.

[0011] 4.2 Description of related technologies

[0012] 4.2.1 The human respiratory system and its disorders

[0013] The human respiratory system facilitates gas exchange. The nose and mouth form the airway entrance for the patient.

[0014] The airways consist of a series of branching tubes that become narrower, shorter, and more numerous as they penetrate deeper into the lungs. The primary function of the lungs is gas exchange, allowing oxygen to enter the venous bloodstream from the air and carbon dioxide to be expelled. The trachea divides into the right main bronchus and the left main bronchus, which eventually branch into the terminal bronchioles. The bronchi form the conduction airways but do not participate in gas exchange. Other branches of the airways lead to the respiratory bronchioles and ultimately to the alveoli. The alveolar region of the lungs is where gas exchange occurs and is called the respiratory zone. See *Respiratory Physiology*, 9th edition, by John B. West, Lippincott Williams & Wilkins, 2011.

[0015] There are a range of breathing disorders. Some disorders can be characterized by specific events, such as respiratory arrest, insufficiency, and hyperventilation.

[0016] Obstructive sleep apnea (OSA), a type of sleep disorder (SDB), is characterized by events involving obstruction or blockage of the upper airway during sleep. It results from a combination of abnormally small upper airway and loss of normal muscle tone in the areas of the tongue, soft palate, and posterior oropharyngeal walls during sleep. The condition causes affected patients to stop breathing, typically for 30 to 120 seconds, sometimes up to 200 to 300 times per night. It frequently leads to excessive daytime sleepiness and can potentially cause cardiovascular disease and brain damage. This syndrome is a common disorder, particularly among middle-aged overweight men, although affected individuals may not be aware of the problem. See U.S. Patent No. 4,944,310 (Sullivan).

[0017] Cheyne-Stokes respiration (CSR) is another form of sleep-disordered breathing. CSR is a disorder of a patient's respiratory control, characterized by rhythmic alternations of increasing and decreasing ventilation, known as CSR cycles. CSR is characterized by repeated deoxygenation and reoxygenation of arterial blood. Due to the repetitive hypoxia, CSR can be harmful. In some patients, CSR is associated with repeated awakenings from sleep, leading to severe sleep disruption, increased sympathetic activity, and increased afterload. See U.S. Patent No. 6,532,959 (Berthon-Jones).

[0018] Respiratory failure is a general term for respiratory disorders in which a patient's metabolic activity is significantly greater than at rest, making it impossible for the patient to adequately ventilate to balance the CO2 in the blood. Respiratory failure includes all of the following conditions.

[0019] Obesity hyperventilation syndrome (OHS) is defined as a combination of severe obesity and waking chronic hypercapnia in the absence of other known causes of hypoventilation. The syndrome includes dyspnea, morning headache, and excessive daytime sleepiness.

[0020] Chronic obstructive pulmonary disease (COPD) encompasses any of a group of lower respiratory tract diseases that share certain common characteristics. These include increased resistance to air movement, prolonged expiratory phase of breathing, and loss of normal lung elasticity. Examples of COPD include emphysema and chronic bronchitis. COPD is caused by chronic smoking (a major risk factor), occupational exposure, air pollution, and genetic factors. Symptoms include shortness of breath during exercise, chronic cough, and sputum production.

[0021] Neuromuscular disease (NMD) is a broad term encompassing many diseases and ailments that impair muscle function directly through intrinsic muscle pathology or indirectly through neuropathology. Some NMD patients are characterized by progressive muscle damage leading to loss of mobility, wheelchair use, dysphagia, respiratory muscle weakness, and ultimately death from respiratory failure. Neuromuscular disorders can be classified as rapidly progressive or slowly progressive: (i) rapidly progressive disorders: characterized by muscle damage that worsens within months and leads to death within years (e.g., amyotrophic lateral sclerosis (ALS) and Duchenne muscular dystrophy (DMD) in adolescents); (ii) variable or slowly progressive disorders: characterized by muscle damage that worsens over many years and only slightly reduces life expectancy (e.g., limb girdle, face-scapular-brachial, and myotonic dystrophy). Symptoms of respiratory failure in NMD include: general weakness, dysphagia, shortness of breath during exercise and at rest, fatigue, somnolence, morning headache, poor concentration, and mood changes.

[0022] Chest wall disorders are a group of chest deformities that result in inefficient coupling between the respiratory muscles and the thoracic cavity. These disorders are typically characterized by restrictive defects and have the potential to cause chronic hypercapnia-induced respiratory failure. Scoliosis and / or kyphosis can lead to severe respiratory failure. Symptoms of respiratory failure include: dyspnea on exertion, peripheral edema, orthopnea, recurrent chest infections, morning headache, fatigue, poor sleep quality, and loss of appetite.

[0023] A range of treatments have been used to treat or improve these conditions. Furthermore, healthy individuals can utilize these treatments to prevent the onset of respiratory disorders. However, all of these have many drawbacks.

[0024] 4.2.2 Treatment

[0025] Continuous positive airway pressure (CPAP) therapy has been used to treat obstructive sleep apnea (OSA). The mechanism of action is that CPAP acts as an air splint and can prevent upper airway obstruction by pushing the soft palate and tongue forward and away from the posterior oropharyngeal wall. Treatment for OSA with CPAP can be voluntary, therefore patients may choose not to adhere to treatment if they find the device used to provide such treatment to be uncomfortable, difficult to use, expensive, or unsightly, among other things.

[0026] Noninvasive ventilation (NIV) provides ventilatory support to patients through the upper airway to help them breathe and / or maintain adequate oxygen levels by doing some or all of their breathing work. Ventilation support is provided through a noninvasive patient interface. NIV has been used to treat chronic respiratory failure (CSR) and its forms, such as orthostatic hypoxia (OHS), chronic respiratory disease (COPD), malignant dysplasia (MD), and chest wall disorders. In some forms, it can improve the comfort and effectiveness of these treatments.

[0027] Patients receiving noninvasive ventilation, especially during sleep and / or sedation, often experience upper airway instability and collapse, such as obstructive sleep apnea (OSA). This instability and collapse can impair the effectiveness of ventilation therapy by reducing or even eliminating the actual pressure reaching the lungs from the ventilator.

[0028] The upper airway can be stabilized by maintaining a positive basal pressure, referred to as EPAP in this text, on top of which ventilatory assistance is superimposed. Insufficient EPAP leads to upper airway collapse, while excessive EPAP can completely stabilize the upper airway but negatively impact comfort, promote mask leakage, or cause cardiovascular complications. Selecting an EPAP titration task—a task that typically maintains upper airway stability across sleep states, postures, sedation levels, and disease progression while avoiding negative side effects—using comprehensive multisomnography (PSG) studies is a significant challenge, even for experienced clinicians. Appropriately titrated EPAP is a balance between extremes and does not necessarily prevent all obstructive events. Although the use of NIV is increasing globally, only a small percentage of patients have received NIV to titrate EPAP using PSG studies. Historically, in more urgent settings, knowledge of the effects of sleep and sedation on the efficacy of noninvasive ventilation has been limited.

[0029] Therefore, there is a great need for NIV treatment that can automatically adjust EPAP (i.e., perform “EPAP auto-titering”) in response to changes in the upper airway symptoms of NIV patients.

[0030] 4.2.3 Treatment System

[0031] These treatments can be provided by treatment systems or devices. Such systems and devices can also be used to diagnose conditions without treating them.

[0032] The treatment system may include a respiratory pressure therapy device (RPT device), an air circuit, a humidifier, a patient interface, and data management.

[0033] 4.2.3.1 Patient Interface

[0034] A patient interface can be used to attach a breathing device to its wearer, for example, by providing an airflow into the airway inlet. The airflow can be provided to the nose and / or mouth via a mask, to the mouth via a tube, or to the patient's trachea via a tracheostomy tube. Depending on the treatment to be applied, the patient interface can form a seal with an area such as the patient's face, thereby facilitating the delivery of gas at a pressure sufficiently different from ambient pressure (e.g., a positive pressure of about 10 cm H2O relative to ambient pressure) to achieve the treatment. For other forms of treatment, such as oxygen delivery, the patient interface may not include a seal sufficient to facilitate the delivery of a gas supply at a positive pressure of about 10 cm H2O to the airway.

[0035] 4.2.3.2 Respiratory Pressure Therapy (RPT) Device

[0036] Air pressure generators are known in a range of applications, such as industrial-scale ventilation systems. However, air pressure generators for medical applications have specific requirements that are not met by more general air pressure generators, such as the reliability, size, and weight requirements of medical devices. Furthermore, even devices designed for medical use may have disadvantages related to one or more of the following: comfort, noise, ease of use, efficiency, size, weight, manufacturability, cost, and reliability.

[0037] One known RPT device for treating sleep-disordered breathing is the S9 Sleep Therapy System manufactured by ResMed. Another example of an RPT device is a ventilator. Ventilators such as the ResMed Stellar™ series of adult and pediatric ventilators can provide invasive and non-invasive non-dependent ventilation support for a range of patients to treat a variety of conditions, such as, but not limited to, NMD, OHS, and COPD.

[0038] ResMed Elisée™ 150 and ResMed VS III™ ventilators support invasive and noninvasive dependent ventilation for adult and pediatric patients to treat a variety of conditions. These ventilators offer volumetric and pressure-based ventilation modes with single- or dual-limb circuits. RPT devices typically include a pressure generator, such as an electric motor-driven blower or a compressed gas reservoir, and are configured to supply airflow to the patient's airway. In some cases, airflow can be supplied to the patient's airway under positive pressure. The RPT device outlet is connected to the patient interface via an air circuit, such as those described above.

[0039] 4.2.3.3 Humidifier

[0040] Delivering unhumidified airflow can lead to airway dryness. Humidifiers using an RPT device and patient interface generate humidified gas, minimizing nasal mucosal dryness and increasing patient airway comfort. Furthermore, in colder climates, warm air applied to the patient interface and the surrounding facial area is generally more comfortable than cold air. A range of artificial humidification devices and systems are known; however, they may not meet the specific requirements of medical humidifiers.

[0041] 5. Technical Overview

[0042] This technology aims to provide medical devices for diagnosing, improving, treating or preventing respiratory disorders, which have one or more of the following: improved comfort, cost, efficacy, ease of use and manufacturability.

[0043] The first aspect of this technology relates to devices for diagnosing, improving, treating, or preventing respiratory disorders.

[0044] Another aspect of this technology relates to methods for diagnosing, improving, treating, or preventing respiratory disorders.

[0045] This technology includes methods and devices for ventilatory therapy of respiratory disorders, which automatically titrate the basal pressure of ventilation to maintain upper airway stability so that applied ventilatory assistance can reach the patient's lungs. Automatic titration increases the basal pressure by an amount generally proportional to the severity of the detected apnea and / or the occurrence of flow restriction episodes, and reduces the basal pressure to a minimum in the absence of such episodes. Specifically, the adjustment takes into account the open / closed state of the airway during apnea, as determined by analyzing the flow response to timed backup breaths delivered during apnea.

[0046] According to one form of the present technology, an apparatus for treating a patient's respiratory distress is provided. The apparatus includes a pressure generator configured to deliver a positive pressure airflow to the patient's airway via a patient interface, a sensor configured to generate a signal representing the patient's respiratory flow, and a controller. The controller is configured to control the pressure generator to deliver ventilation therapy with baseline pressure and pressure support via the patient interface, detect apnea by the signal representing the patient's respiratory flow, control the pressure generator to deliver one or more probe breaths to the patient during apnea, determine airway patency from the waveform of the respiratory flow signal in response to one of the probe breaths, calculate the effective duration of apnea based on airway patency, and adjust the setpoint of the baseline pressure for the ventilation therapy in response to apnea based on the effective duration of apnea.

[0047] According to another form of the present technology, a method for treating respiratory distress in a patient is provided. The method includes controlling a pressure generator to deliver ventilation therapy to a patient via a patient interface, the ventilation therapy having a baseline pressure and pressure support; detecting apnea by a signal representing the patient's respiratory flow in a controller of the pressure generator; controlling the pressure generator to deliver one or more probe breaths to the patient during apnea; determining airway patency of the patient by a waveform of the respiratory flow signal in response to one of the one or more probe breaths; calculating the effective duration of apnea based on airway patency; and adjusting the setpoint of the baseline pressure of the ventilation therapy in response to apnea based on the effective duration of apnea.

[0048] According to another embodiment of the present technology, a respiratory therapy system is provided, comprising: means for delivering ventilation therapy to a patient via a patient interface, the ventilation therapy having a base pressure and pressure support; means for generating a signal representing the patient's respiratory flow; means for detecting apnea by the respiratory flow signal; means for delivering one or more probe breaths to the patient during apnea; means for determining airway patency of the patient by the waveform of the respiratory flow signal in response to one of the one or more probe breaths; means for calculating the effective duration of apnea based on airway patency; and means for adjusting a setpoint of the base pressure of the ventilation therapy in response to apnea based on the effective duration of apnea.

[0049] According to another embodiment of the present technology, a method is provided for determining airway patency during a patient's breathing apnea. The method includes: controlling a pressure generator to deliver one or more probe breaths to the patient during breathing apnea; and determining airway patency by a signal representing the patient's respiratory flow rate during breathing apnea, wherein the determination depends on the shape of a waveform of the respiratory flow signal in response to at least one of the one or more probe breaths.

[0050] According to another aspect of the present technology, an apparatus for treating a patient's respiratory distress is provided. The apparatus includes: a pressure generator configured to deliver a positive pressure airflow to the patient's airway via a patient interface; a sensor configured to generate a signal representing the patient's respiratory flow; and a controller configured to: control the pressure generator to deliver ventilation therapy via the patient interface; control the pressure generator to deliver one or more probe breaths to the patient during apnea; and during apnea, determine airway patency by the waveform of the respiratory flow signal, wherein determining patency depends on the shape of the respiratory flow waveform in response to at least one of the one or more probe breaths.

[0051] According to another embodiment of the present technology, a respiratory therapy system is provided, comprising: means for delivering a positive pressure airflow to a patient's airway via a patient interface; means for generating a signal representing the patient's respiratory flow rate; means for delivering one or more probe breaths to the patient during a period of apnea; and means for determining airway patency from the waveform of the respiratory flow signal during apnea. The determination of patency depends on the shape of the respiratory flow waveform in response to at least one probe breath.

[0052] Of course, the various parts of these aspects can form sub-aspects of this technology. Furthermore, the various sub-aspects and / or aspects can be combined in various ways and also constitute other aspects or sub-aspects of this technology.

[0053] Other features of the technology will become clear from consideration of the information contained in the following detailed description, abstract, drawings, and claims. Attached Figure Description

[0054] This technology is illustrated in the accompanying drawings by way of example rather than limitation, wherein the same reference numerals denote similar elements, including:

[0055] 6.1 Treatment System

[0056] Figure 1 A system is shown for a patient 1000 including a wearable patient interface 3000 that receives a positive-pressure air supply from an RPT device 4000 in the form of a full-face mask. The air from the RPT device is humidified in a humidifier 5000 and delivered to the patient 1000 along an air circuit 4170.

[0057] 6.2 Respiratory System and Facial Anatomy

[0058] Figure 2 A schematic diagram of the human respiratory system is shown, including the nasal cavity and oral cavity, larynx, vocal cords, esophagus, trachea, bronchi, lungs, alveolar sacs, heart, and diaphragm.

[0059] 6.3 Patient Interface

[0060] Figure 3 A patient interface in the form of a nasal mask according to the present technology is shown.

[0061] 6.4 RPT device

[0062] Figure 4A An RPT device of one form according to the present technology is shown.

[0063] Figure 4B This is a schematic diagram of the pneumatic path of one form of RPT device according to this technology. The upstream and downstream directions are indicated.

[0064] Figure 4C This is a schematic diagram of the electrical components of one form of RPT device according to the present technology.

[0065] Figure 4D This is a schematic diagram of an algorithm implemented in an RPT device according to one form of the present technology.

[0066] 6.5 Humidifier

[0067] Figure 5A An isometric view of one form of humidifier according to the present technology is shown.

[0068] Figure 5BAn isometric view of one form of humidifier according to the present technology is shown, illustrating the humidifier reservoir 5110 removed from the humidifier reservoir parking area 5130.

[0069] 6.6 Respiratory waveform

[0070] Figure 6A The waveform of a typical breathing pattern during sleep is shown. The horizontal axis represents time, and the vertical axis represents respiratory flow. Although parameter values ​​can vary, typical breathing can be approximated by the following: tidal volume, Vt, 0.5 L; inspiratory time, Ti, 1.6 s; peak inspiratory flow rate, Qpeak, 0.4 L / s; expiratory time, Te, 2.4 s; peak expiratory flow rate, Qpeak, -0.5 L / s. The total duration of breathing, Ttot, is approximately 4 s. This person typically breathes at a rate of approximately 15 breaths per minute (BPM), with a ventilation (Vent) of approximately 7.5 L / min. In a typical work cycle, the ratio of Ti to Ttot is approximately 40%.

[0071] Figure 6B A scaled-down inspiratory portion of the respiratory flow waveform is shown, illustrating an example of a patient experiencing “classic flatness” inspiratory flow limitation.

[0072] Figure 6C A scaled inspiratory portion of the respiratory flow waveform is shown, illustrating an example of a patient experiencing “chair-shaped” (late flattening) inspiratory flow limitation.

[0073] Figure 6D A scaled inspiratory portion of the respiratory flow waveform is shown, illustrating an example of a patient experiencing “reverse chair” (early flattening) inspiratory flow restriction.

[0074] Figure 6E A scaled inspiratory portion of the respiratory flow waveform is shown, illustrating an example where a patient is experiencing “M-shaped” inspiratory flow restriction.

[0075] Figure 6F A scaled inspiratory portion of the respiratory flow waveform is shown, illustrating an example where a patient is experiencing severe “M-shaped” inspiratory flow limitation.

[0076] 6.7 EPAP Automatic Titration

[0077] Figure 7A This shows what can be used to implement Figure 4D The flowchart shows the algorithm for determining the inhalation flow rate limit.

[0078] Figure 7B This shows what can be used to implement Figure 7A A flowchart of the method for calculating the central part features.

[0079] Figure 7CThis shows what can be used to implement Figure 7A The flowchart shows the steps for calculating the fuzzy truth variable with flow constraints.

[0080] Figure 7D This shows what can be used to implement Figure 7C The flowchart shows one of the steps in the calculation of the fuzzy truth variable with flow constraints.

[0081] Figure 7E This shows what can be used to implement Figure 7A The flowchart shows the steps for calculating the fuzzy truth variable of post-flatness.

[0082] Figure 7F This shows what can be used to implement Figure 4D The flowchart shows the method of the M-shaped detection algorithm.

[0083] Figure 7G This shows what can be used to implement Figure 4D The flowchart shows the method for the sleep apnea detection algorithm.

[0084] Figure 7H and 7I This shows what can be used to implement Figure 4D The flowchart shows the algorithm for determining airway patency.

[0085] Figure 8A It shows that it can be passed Figure 4D The flowchart shows a method for automatically titrating EPAP values ​​using an algorithm to determine treatment parameters.

[0086] Figure 8B This shows what can be used to implement Figure 8A A flowchart of the “shape doctor” step of the EPAP-automatic titration method.

[0087] Figure 8C This shows what can be used to implement Figure 8B The flowchart shows one step of the method.

[0088] Figure 8D This shows what can be used to implement Figure 8B The flowchart shows another step of the method.

[0089] Figure 8E This shows what can be used to implement Figure 8A A flowchart of the "Sleep Apnea Doctor" procedure for the EPAP-automated titration method.

[0090] Figure 8F This shows what can be used to implement Figure 8E The flowchart describes the steps for managing sleep apnea.

[0091] Figure 8GThis shows what can be used to implement Figure 8A The flowchart shows the steps of the EPAP-automatic titration method.

[0092] Figure 9A Includes events that respond to traffic restrictions. Figure 8A A diagram illustrating an embodiment of the behavior of the EPAP automated titration method.

[0093] Figure 9B Includes apnea in response to closure Figure 8A A diagram illustrating an embodiment of the behavior of the EPAP automated titration method.

[0094] Figure 9C Includes instructions demonstrating response to mixed apnea. Figure 7H A diagram illustrating an embodiment of the behavior of the airway patency determination algorithm.

[0095] Figure 9D Include Figure 9C The expansion of each part of the diagram. 7 Detailed Implementation

[0097] Before describing the present technology in further detail, it should be understood that the present technology is not limited to the specific embodiments described herein, and variations are possible. It should also be understood that the terminology used in this disclosure is for the purpose of describing the specific embodiments described herein only and is not intended to be limiting.

[0098] The following description relates to various embodiments that may share one or more common features and / or characteristics. It should be understood that one or more features of any embodiment may be combined with one or more features of another embodiment or other embodiments. Furthermore, any single feature or combination of features in any embodiment may constitute another embodiment.

[0099] 7.1 Treatment

[0100] In one form, the technology includes a method for treating respiratory disorders, comprising supplying positive pressure air to the airway inlet of a patient 1000.

[0101] In some embodiments of this technology, a positive pressure air supply is provided to the patient's nasal passages through one or both nostrils.

[0102] 7.2 Treatment System

[0103] In one form, the technology includes a device or apparatus for treating respiratory disorders. The device or apparatus may include an RPT device 4000 for supplying pressurized air to a patient 1000 via an air circuit 4170 leading to a patient interface 3000.

[0104] 7.3 Patient Interface

[0105] According to one aspect of the present technology, a non-invasive patient interface 3000 includes the following functional aspects: a sealing-forming structure 3100, an inflation chamber 3200, a positioning and stabilizing structure 3300, an air vent 3400, a connection port 3600 for connection to an air circuit 4170, and a forehead support 3700. In some forms, the functional aspects may be provided by one or more physical components. In some forms, a single physical component may provide one or more functional aspects. In use, the sealing-forming structure 3100 is arranged around the inlet of the patient's airway to facilitate the delivery of positively pressurized air into the airway.

[0106] 7.4 RPT device

[0107] According to one aspect of the present technology, an RPT device 4000 includes mechanical and pneumatic components 4100, electrical components 4200, and is configured to execute one or more algorithms 4300. The RPT device may have a housing 4010, which is formed in two parts, an upper portion 4012 and a lower portion 4014. Furthermore, the housing 4010 may include one or more panels 4015. The RPT device 4000 includes a chassis 4016 supporting one or more internal components of the RPT device 4000. The RPT device 4000 may include a handle 4018.

[0108] The pneumatic path of the RPT device 4000 may include one or more air path items, such as an inlet air filter 4112, an inlet silencer 4122, a pressure generator 4140 (e.g., a blower 4142) capable of supplying air under positive pressure, an outlet silencer 4124, and one or more converters 4270, such as a pressure sensor 4272 and a flow sensor 4274.

[0109] One or more air path items may be located within a removable integral structure referred to as pneumatic block 4020. Pneumatic block 4020 may be located within housing 4010. In one form, pneumatic block 4020 is supported by or formed as part of chassis 4016.

[0110] The RPT device 4000 may include a power supply 4210, one or more input devices 4220, a central controller 4230, a treatment device controller 4240, a pressure generator 4140, one or more protection circuits 4250, a memory 4260, a converter 4270, a data communication interface 4280, and one or more output devices 4290. Electrical components 4200 may be mounted on a single printed circuit board assembly (PCBA) 4202. In an alternative form, the RPT device 4000 may include more than one PCBA 4202.

[0111] 7.4.1 Mechanical and pneumatic components of the RPT unit

[0112] The RPT device may include one or more of the following components in an overall unit. In an alternative form, one or more of the following components may be positioned as separate units.

[0113] 7.4.1.1 Air Filter

[0114] An RPT device according to one form of the invention may include one air filter 4110 or multiple air filters 4110.

[0115] In one configuration, the inlet air filter 4112 is located at the beginning of the pneumatic path upstream of the pressure generator 4140.

[0116] In one configuration, an outlet air filter 4114, such as an antibacterial filter, is located between the outlet of the pneumatic block 4020 and the patient interface 3000.

[0117] 7.4.1.2 Muffler

[0118] In one embodiment of this technology, the inlet silencer 4122 is located in the pneumatic path upstream of the pressure generator 4140.

[0119] In one embodiment of this technology, the outlet silencer 4124 is located in the pneumatic path between the pressure generator 4140 and the patient interface 3000.

[0120] 7.4.1.3 Pressure Generator

[0121] In one form of this technology, the pressure generator 4140 for delivering or supplying a positive pressure airflow is a controllable blower 4142. For example, the blower 4142 may include a brushless DC motor 4144 having one or more impellers housed in a volute. The blower is capable of supplying air at a positive pressure of about 4 cmH2O to about 20 cmH2O, or at other pressures up to about 30 cmH2O, for example at a rate up to about 120 liters per minute. The blower may be as described in any of the following patents or patent applications, the contents of which are incorporated herein by reference in their entirety: U.S. Patent No. 7,866,944; U.S. Patent No. 8,638,014; U.S. Patent No. 8,636,479; and PCT Patent Application Publication No. WO2013 / 020167.

[0122] The pressure generator 4140 is controlled by the treatment device controller 4240.

[0123] In other forms, the pressure generator 4140 may be a piston-driven pump, a pressure regulator connected to a high-pressure source (e.g., a compressed air reservoir), or a bellows.

[0124] 7.4.1.4 Converter

[0125] The transducer can be located inside or outside the RPT device. An external transducer can be situated on, for example, an air circuit (e.g., a patient interface) or formed part of it. An external transducer can be in the form of a non-contact sensor, such as a Doppler radar motion sensor that transmits or transfers data to the RPT device.

[0126] In one form of this technology, one or more converters 4270 may be located upstream and / or downstream of pressure generator 4140. One or more converters 4270 may be configured and arranged to measure properties such as flow rate, pressure, or temperature at that point in a pneumatic path.

[0127] In one form of this technology, one or more converters 4270 are located proximal to the patient interface 3000.

[0128] In one configuration, the signal from converter 4270 can be filtered, for example by low-pass filtering, high-pass filtering, or band-pass filtering.

[0129] 7.4.1.4.1 Flow Sensor

[0130] The flow sensor 4274 according to this technology can be based on a differential pressure converter, such as the SDP600 series differential pressure converter from SENSIRION.

[0131] In one form, a signal representing, for example, the total flow rate Qt from the flow sensor 4274 is received by the central controller 4230.

[0132] 7.4.1.4.2 Pressure Sensor

[0133] The pressure sensor 4272 according to this technology is positioned in fluid communication with the pneumatic path. A suitable example of a pressure sensor is the sensor from the HONEYWELL ASDX series. An alternative suitable pressure transducer is the NPA series sensor from GENERALELECTRIC.

[0134] In one configuration, the signal from pressure sensor 4272 is received by central controller 4230.

[0135] 7.4.1.4.3 Motor speed converter

[0136] In one embodiment of this technology, a motor speed converter 4276 is used to determine the rotational speed of the motor 4144 and / or the blower 4142. The motor speed signal from the motor speed converter 4276 can be provided to the treatment device controller 4240. The motor speed converter 4276 can be, for example, a speed sensor, such as a Hall effect sensor.

[0137] 7.4.1.5 Anti-overflow valve

[0138] In one embodiment of this technology, an anti-backflow valve is positioned between the humidifier 5000 and the pneumatic block 4020. The anti-backflow valve is constructed and arranged to reduce the risk of water flowing upstream from the humidifier 5000 to, for example, the motor 4144.

[0139] 7.4.1.6 Air Circuit

[0140] According to one aspect of the technology, the air circuit 4170 is a conduit or tube that is constructed and arranged to allow air to flow between two components (e.g., pneumatic block 4020 and patient interface 3000) during use.

[0141] Specifically, the air circuit 4170 can be fluidly connected to the outlet of the pneumatic block and the patient interface. The air circuit may be referred to as an air delivery tube. In some cases, the circuit may have separate branches for inhalation and exhalation. In other cases, a single branch is used.

[0142] In some forms, the air circuit 4170 may include one or more heating elements configured to heat air in the air circuit, for example, to maintain or raise the temperature of the air. The heating element may be in the form of a heating wire circuit and may include one or more transducers, such as temperature sensors. In one form, the heating wire circuit may be helically wound around an axis of the air circuit 4170. The heating element may be connected to a controller such as a central controller 4230 or a humidifier controller 5250. An example of an air circuit 4170 including a heating wire circuit is described in U.S. Patent Application No. US / 2011 / 0023874, which is incorporated herein by reference in its entirety.

[0143] 7.4.1.7 Oxygen Delivery

[0144] In one form of this technology, supplemental oxygen 4180 is delivered to one or more points in the pneumatic path (such as upstream of pneumatic block 4020), air circuit 4170 and / or patient interface 3000.

[0145] 7.4.2 Electrical components of RPT device

[0146] 7.4.2.1 Power Supply

[0147] The power supply 4210 can be located inside or outside the housing 4010 of the RPT device 4000.

[0148] In one embodiment of this technology, power supply 4210 supplies power only to RPT device 4000. In another embodiment of this technology, power supply 4210 supplies power to both RPT device 4000 and humidifier 5000.

[0149] 7.4.2.2 Input Device

[0150] In one form of this technology, the RPT device 4000 includes one or more input devices 4220 in the form of buttons, switches, or dials to allow personnel to interact with the device. The buttons, switches, or dials can be physical devices or software devices accessible via a touchscreen. In one form, the buttons, switches, or dials can be physically connected to a housing 4010, or in another form, they can communicate wirelessly with a receiver electrically connected to a central controller 4230.

[0151] In one form, the input device 4220 may be configured or arranged to allow a person to select values ​​and / or menu options.

[0152] 7.4.2.3 Central Controller

[0153] In one form of this technology, the central controller 4230 is one or more processors adapted to control the RPT device 4000.

[0154] Suitable processors may include x86 Intel processors, processors based on ARM® Cortex®-M processors from ARM Holdings, such as the STM32 series microcontrollers from ST Microelectronics. In some alternative forms of this technology, 32-bit RISC CPUs such as the STR9 series microcontrollers from ST Microelectronics, or 16-bit RISC CPUs such as the MSP430 series microcontrollers from Texas Instruments, may also be used.

[0155] In one form of this technology, the central controller 4230 is a dedicated electronic circuit.

[0156] In one form, the central controller 4230 is an application-specific integrated circuit (ASIC). In another form, the central controller 4230 includes discrete electronic components.

[0157] The central controller 4230 can be configured to receive input signals from one or more converters 4270 and one or more input devices 4220.

[0158] The central controller 4230 can be configured to provide output signals to one or more of the output device 4290, the treatment device controller 4240, the data communication interface 4280, and the humidifier controller 5250.

[0159] In some forms of this technology, the central controller 4230 is configured to implement one or more methods described herein, such as one or more algorithms 4300 represented as computer programs, said computer programs being stored in a non-transitory computer-readable storage medium such as memory 4260. In some forms of this technology, the central controller 4230 may be integrated with the RPT device 4000. However, in some forms of this technology, some methods may be performed by a remote positioning device. For example, a remote positioning device may determine the control settings of the ventilator or detect respiratory-related events by analyzing stored data from any of the sensors described herein.

[0160] 7.4.2.4 Clock

[0161] RPT device 4000 may include a clock 4232 connected to central controller 4230.

[0162] 7.4.2.5 Treatment device controller

[0163] In one form of this technology, the treatment device controller 4240 is a treatment control module 4330, which forms part of an algorithm 4300 executed by the central controller 4230.

[0164] In one embodiment of this technology, the treatment device controller 4240 is a dedicated motor control integrated circuit. For example, in one embodiment, an MC33035 brushless DC motor controller manufactured by ONSEMI is used.

[0165] 7.4.2.6 Protection Circuit

[0166] One or more protection circuits 4250 according to the present technology may include electrical protection circuits, temperature and / or pressure safety circuits.

[0167] 7.4.2.7 Memory

[0168] According to one embodiment of the present technology, the RPT device 4000 includes a memory 4260, such as non-volatile memory. In some embodiments, the memory 4260 may include battery-powered static RAM. In some embodiments, the memory 4260 may include volatile RAM.

[0169] The memory 4260 may be located on PCBA 4202. The memory 4260 may be in the form of EEPROM or NAND flash memory.

[0170] Alternatively or concurrently, the RPT device 4000 includes a removable memory 4260, such as a memory card made according to the Secure Digital (SD) standard.

[0171] In one form of this technology, memory 4260 serves as a non-transitory computer-readable storage medium storing computer program instructions expressing one or more methods described herein (e.g., one or more algorithms 4300). Memory 4260 can also serve as a volatile or non-volatile storage medium for acquiring, collecting, using, or generating data when one or more methods described herein are executed as instructions by one or more processors.

[0172] 7.4.2.8 Data Communication System

[0173] In one embodiment of this technology, a data communication interface 4280 is provided and connected to a central controller 4230. The data communication interface 4280 can be connected to a remote external communication network 4282 and / or a local external communication network 4284. The remote external communication network 4282 can be connected to a remote external device 4286. The local external communication network 4284 can be connected to a local external device 4288.

[0174] In one embodiment, the data communication interface 4280 is part of the central controller 4230. In another embodiment, the data communication interface 4280 is separate from the central controller 4230 and may include an integrated circuit or a processor.

[0175] In one embodiment, the remote external communication network 4282 is the Internet. The data communication interface 4280 can use wired communication (e.g., via Ethernet or fiber optic) or wireless protocols (e.g., CDMA, GSM, LTE) to connect to the Internet.

[0176] In one form, the local external communication network 4284 utilizes one or more communication standards, such as Bluetooth or consumer infrared protocols.

[0177] In one form, the remote external device 4286 can be one or more computers, such as a cluster of networked computers. In another form, the remote external device 4286 can be a virtual computer rather than a physical computer. In either case, this remote external device 4286 can be accessed by appropriately authorized personnel, such as clinicians.

[0178] The local external device 4288 can be a personal computer, mobile phone, tablet, or remote control device.

[0179] 7.4.2.9 Includes optional display and alarm output devices.

[0180] The output device 4290 according to this technology can take the form of one or more of visual, audio, and tactile units. The visual display can be a liquid crystal display (LCD) or a light-emitting diode (LED) display.

[0181] 7.4.2.9.1 Display Driver

[0182] Display driver 4292 receives characters, symbols, or images to be displayed on monitor 4294 as input and converts them into commands that cause monitor 4294 to display those characters, symbols, or images.

[0183] 7.4.2.9.2 Monitor

[0184] Display 4294 is configured to visually display characters, symbols, or images in response to commands received from display driver 4292. For example, display 4294 may be an eight-segment display, in which case display driver 4292 converts each character or symbol (such as the number "0") into eight logic signals indicating whether the eight corresponding segments will be activated to display a specific character or symbol.

[0185] 7.4.3 RPT device algorithm

[0186] 7.4.3.1 Preprocessing Module

[0187] According to one form of the present technology, a preprocessing module 4310 receives a signal from a converter 4270 (e.g., a flow sensor 4274 or a pressure sensor 4272) as input and performs one or more processing steps to calculate one or more output values ​​that will be used as input to another module (e.g., a treatment engine module 4320).

[0188] In one form of this technology, the output values ​​include the interface or mask pressure Pm, the breathing flow rate Qr, and the leakage flow rate Ql.

[0189] In various forms of this technology, the preprocessing module 4310 includes one or more of the following algorithms: pressure compensation 4312, ventilation flow estimation 4314, leakage flow estimation 4316, and respiratory flow estimation 4318.

[0190] 7.4.3.1.1 Pressure Compensation

[0191] In one embodiment of the invention, the pressure compensation algorithm 4312 receives a signal indicating the pressure in the pneumatic path near the pneumatic block outlet as input. The pressure compensation algorithm 4312 estimates the pressure drop in the air circuit 4170 and provides an estimated pressure Pm in the patient interface 3000 as output.

[0192] 7.4.3.1.2 Ventilation flow rate estimation

[0193] In one form of this technology, the ventilation flow estimation algorithm 4314 receives the estimated pressure Pm in the patient interface 3000 as input and estimates the ventilation flow Qv from the ventilation port 3400 in the patient interface 3000.

[0194] 7.4.3.1.3 Leakage Flow Estimation

[0195] In one form of this technology, the leakage flow estimation algorithm 4316 receives the total flow rate Qt and the ventilation flow rate Qv as inputs and provides an estimated leakage flow rate Ql as output. In another form, the leakage flow estimation algorithm 4316 estimates the leakage flow rate Ql by calculating the average of the difference between the total flow rate and the ventilation flow rate Qv over a period of time long enough to include several respiratory cycles (e.g., about 10 seconds).

[0196] In one form, the leakage flow estimation algorithm 4316 receives the total flow rate Qt, ventilation flow rate Qv, and estimated pressure Pm from the patient interface 3000 as input, and provides the leakage flow rate Ql as output by calculating the leakage conductance and determining the leakage flow rate Ql as a function of the leakage conductance and pressure Pm. The leakage conductance can be calculated as the quotient between the low-pass filtered non-ventilation flow rate (equal to the difference between the total flow rate Qt and the ventilation flow rate Qv) and the square root of the low-pass filtered pressure Pm, where the low-pass filter time constant has a value long enough to encompass several respiratory cycles (e.g., approximately 10 seconds). The leakage flow rate Ql can be estimated as the product of the leakage conductance and the pressure function Pm.

[0197] 7.4.3.1.4 Respiratory Flow Estimation

[0198] In one form of this technology, the respiratory flow estimation algorithm 4318 receives total flow Qt, ventilatory flow Qv, and leakage flow Ql as inputs, and estimates the patient's respiratory flow Qr by subtracting the ventilatory flow Qv and leakage flow Ql from the total flow Qt.

[0199] 7.4.3.2 Healing Engine Module

[0200] In one form of this technology, the treatment engine module 4320 receives one or more of the pressure Pm and the patient's respiratory flow Qr from the patient interface 3000 as inputs and provides one or more treatment parameters as outputs.

[0201] In one form of this technique, the treatment parameter is the treatment pressure Pt.

[0202] In various forms, the treatment engine module 4320 includes one or more of the following algorithms: phase determination 4321, waveform determination 4322, ventilation determination 4323, inspiratory flow restriction detection 4324, apnea detection 4325, inspiratory M-shaped detection 4326, airway patency determination 4327, typical recent ventilation determination 4328, and treatment parameter determination 4329.

[0203] 7.4.3.2.1 Phase Determination

[0204] In one form of this technology, the phase determination algorithm 4321 receives a signal indicating respiratory flow Qr as input and provides the phase Φ of the patient's current respiratory cycle as output.

[0205] In some forms known as discrete phase determination, the phase output Φ is a discrete variable. One implementation of discrete phase determination provides a binary phase output Φ with an inspiratory or expiratory value (e.g., values ​​represented as 0 and 0.5 revolutions, respectively) after the start of spontaneous inhalation and exhalation, respectively. Because the trigger point and the cycle point are the time points when the phase changes from exhalation to inhalation and from inhalation to exhalation, respectively, the "triggered" and "cycled" RPT device 4000 effectively performs discrete phase determination. In one implementation of binary phase determination, the phase output Φ is determined to have a discrete value of 0 (indicating inhalation) when the respiratory flow Qr exceeds the "trigger threshold" (thus triggering the RPT device 4000 to deliver "spontaneous breathing"), and a discrete value of 0.5 revolutions (indicating exhalation) when the respiratory flow Qr is below the "cycle threshold" (thus "spontaneously cycling" the RPT device 4000). In some such implementations, the trigger and cycle thresholds may vary over time during respiration according to corresponding trigger and cycle threshold functions. These functions are described in ResMed Limited’s Patent Cooperation Treaty Patent Application No. PCT / AU2005 / 000895, published under WO 2006 / 000017, the entire contents of which are incorporated herein by reference.

[0206] In some such implementations, looping can be prevented during a "refractory period" (denoted as Timin) following the last triggering moment, and non-spontaneous looping must occur within an interval (denoted as Timax) following the last triggering moment. The values ​​of Timin and Timax are settings of the RPT device 4000 and can be set, for example, by hardcoding or by manual input via the input device 4220 during the configuration of the RPT device 4000.

[0207] In other forms known as continuous phase determination, the phase output Φ is a continuous variable, such as from 0 to 1 revolution, or from 0 to 2π radians. The RPT device 4000 performing continuous phase determination can be triggered and cycled when the continuous phase reaches 0 and 0.5 revolutions, respectively. In one implementation of continuous phase determination, the inhalation time Ti and expiratory time Te are first estimated from the respiratory flow rate Qr. The phase Φ is then determined as half the inhalation time Ti elapsed since the last triggering moment, or 0.5 revolutions plus half the expiratory time Te elapsed since the last cycle moment (whichever is more recent).

[0208] In some implementations suitable for ventilation therapy (described below), the phase determination algorithm 4321 is configured to trigger even when the respiratory flow Qr is not significant (e.g., during apnea). Thus, the RPT device 4000 delivers a “backup breath” in the absence of spontaneous breathing effort from the patient. For this form, referred to as spontaneous / timed (ST) mode, the phase determination algorithm 4321 can use a “backup frequency” Rb. The backup frequency Rb is a setting of the RPT device 4000 and can be set, for example, by hard-coding during the configuration of the RPT device 4000 or manually input via the input device 4220.

[0209] The phase determination algorithm 4321 (discrete or continuous) can implement the ST mode using a backup frequency Rb in a manner called timed backup. Timed backup can be implemented as follows: The phase determination algorithm 4321 attempts to detect the onset of inspiration due to spontaneous breathing effort, for example, by comparing the respiratory flow rate Qr with the aforementioned trigger threshold. If no spontaneous inhalation is detected within an interval after the last trigger moment, the duration of which is equal to the reciprocal or inverse of the backup frequency Rb (the interval called the backup timed threshold, Tbackup), then the phase determination algorithm 4321 sets the phase output Φ to 0, thereby triggering the RPT device 4000 to provide backup breathing. The phase determination algorithm 4321 then attempts to detect the onset of spontaneous exhalation, for example, by comparing the respiratory flow rate Qr with the aforementioned cycle threshold. The cycle threshold for backup breathing can be different from the cycle threshold for spontaneous breathing. Similar to spontaneous breathing, spontaneous cycles during backup breathing can be prevented during the "refractory period" of the duration Timin after the last trigger moment.

[0210] Similar to spontaneous breathing, if the start of spontaneous exhalation is not detected within Timax seconds after the last trigger moment during backup breathing, the phase determination algorithm 4321 sets the phase output Φ to a value of 0.5, thereby causing the RPT device 4000 cycles. The phase determination algorithm 4321 then attempts to detect the start of spontaneous inspiration by comparing the respiratory flow rate Qr with the aforementioned trigger threshold.

[0211] 7.4.3.2.2 Waveform Determination

[0212] In one form of this technology, waveform determination algorithm 4322 provides approximately constant therapeutic pressure throughout the patient's respiratory cycle.

[0213] In other forms of this technology, waveform determination algorithm 4322 controls pressure generator 4140 to provide therapeutic pressure Pt that varies throughout the patient's respiratory cycle according to a waveform template.

[0214] In one form of this technology, waveform determination algorithm 4322 provides a waveform template Π(Φ), which has a value in the range [0,1] over the phase value range provided by phase determination algorithm 4321, for use by waveform determination algorithm 4322.

[0215] In a form suitable for discrete or continuous phase values, the waveform template Π(Φ) is a square wave template with a value of 1 for phase values ​​up to and including 0.5 revolutions and a value of 0 for phase values ​​exceeding 0.5 revolutions. In a form suitable for continuous phase values, the waveform template Π(Φ) includes two smooth curves: a smooth curve rising from 0 to 1 (e.g., raised cosine) for phase values ​​up to 0.5 revolutions, and a smooth curve decaying from 1 to 0 (e.g., exponential) for phase values ​​exceeding 0.5 revolutions.

[0216] In some forms of this technology, the waveform determination algorithm 4322 selects a waveform template Π(Φ) from a waveform template library according to the settings of the RPT device 4000. Each waveform template Π(Φ) in the library may be provided as a lookup table of value Π relative to phase value Φ. In other forms, the waveform determination algorithm 4322 calculates the waveform template Π(Φ) "in operation" using a predetermined function form, which may be parameterized by one or more parameters (e.g., rise time and fall time). The parameters of the function form may be predetermined or dependent on the current state of the patient 1000.

[0217] In some forms of this technique, applicable to discrete two-valued phases of inhalation (Φ=0 rpm) or exhalation (Φ=0.5 rpm), waveform determination algorithm 4322 calculates the "running" waveform template Π as a function of the discrete phase Φ and time t measured since the most recent triggering moment. In such a form, waveform determination algorithm 4322 calculates the waveform template Π(Φ, t) in two parts (inhalation and exhalation) as follows:

[0218]

[0219] Among them, Π i (t) and Π e (t) represents the inhalation and exhalation portions of the waveform template Π(Φ, t).

[0220] In one such form, the air intake portion Π of the waveform template i (t) smoothly rises from 0 to 1 in two consecutive parts:

[0221] • The first half of the parameter known as the "time scale" increases linearly to 2 / 3;

[0222] • The parabola rises to 1 in the latter half of the time scale.

[0223] The "rise time" of this inhalation phase Π i (t) can be defined as Π i The time it takes for (t) to rise to a value of 0.875.

[0224] The exhalation portion of the waveform template Π e (t) is divided into two consecutive parabolic portions that smoothly decrease from 1 to 0, with the inflection point between 25% and 50% of the time scale. This is the expiratory portion Π. e The "descent time" of (t) can be defined as Π e (t) is the time it takes for the value to drop to 0.125.

[0225] 7.4.3.2.3 Ventilation Determination

[0226] In one form of this technology, the ventilation determination algorithm 4323 receives respiratory flow Qr as input and determines a measurement Vent indicating the current patient ventilation.

[0227] In some implementations, ventilation determination algorithm 4323 calculates Vent as “instantaneous ventilation” Vint, which is half the absolute value of the respiratory flow signal Qr.

[0228] In some implementations, the ventilation determination algorithm 4323 filters the instantaneous ventilation Vint using a low-pass filter (e.g., a fourth-order Bessel low-pass filter with a corner frequency of approximately 0.10 Hz) to calculate Vent as “VveryFast”. This is equivalent to a time constant of approximately ten seconds.

[0229] In some implementations, the ventilation determination algorithm 4323 calculates the instantaneous ventilation Vint as “fast ventilation” Vfast by filtering the instantaneous ventilation Vint with a low-pass filter (e.g., a fourth-order Bessel low-pass filter with a corner frequency of about 0.05 Hz). This is equivalent to a time constant of about 20 seconds.

[0230] In some implementations of this technique, ventilation determination algorithm 4323 determines Vent as a measure of alveolar ventilation. Alveolar ventilation is a measure of the amount of air that actually reaches the gas exchange surfaces of the respiratory system within a given time. Because a patient's respiratory system includes significant "anatomical dead space," i.e., the volume where no gas exchange occurs, alveolar ventilation is less than the "total" ventilation value that the above calculations would produce directly on the respiratory flow rate Qr, but it is a more accurate measure of the patient's respiratory performance.

[0231] In this implementation, ventilation determination algorithm 4323 can determine instantaneous alveolar ventilation as zero or half the absolute value of respiratory flow rate Qr. The condition for zero instantaneous alveolar ventilation is:

[0232] • When the respiratory flow rate changes from a non-negative value to a negative value, or

[0233] • When the respiratory flow rate changes from a negative value to a non-negative value, and

[0234] • After the sign of the change in respiratory flow rate, during the time period in which the absolute value of the integral of respiratory flow rate Qr is less than the patient's anatomical dead space volume.

[0235] The patient's anatomical dead space volume can be set by the RPT device 4000, for example, by hard coding or by manual input via the input device 4220 during the configuration of the RPT device 4000.

[0236] In some such implementations, the ventilation determination algorithm 4323 can calculate Vent as “very fast alveolar ventilation” and / or “fast alveolar ventilation” by using the corresponding low-pass filter described above to low-pass filter the transient alveolar ventilation.

[0237] The term "alveolar" is omitted in the following text, but it can be assumed that it exists in some implementations of the treatment engine module 4320. That is, references to "ventilation" and "tidal volume" in the following description can be used to apply alveolar ventilation and alveolar tidal volume as well as "total" ventilation and tidal volume.

[0238] 7.4.3.2.4 Determination of Inspiratory Flow Limit

[0239] In one form of this technology, the treatment engine module 4320 executes one or more algorithms to determine the degree of flow restriction in the inspiratory portion of a respiratory flow waveform (sometimes shortened to "inspiratory waveform" herein), sometimes referred to as partial upper airway obstruction. In one form, the flow restriction determination algorithm 4324 receives a respiratory flow signal Qr as input and provides a measure as output of the degree of flow restriction exhibited by each inspiratory waveform.

[0240] A normal inhalation waveform is circular, close to a sine wave (see...). Figure 6AWith sufficient upper airway muscle tone (or EPAP), the airway essentially functions as a rigid tube, where flow increases in response to increased respiratory effort (or external ventilation assistance). In some cases (e.g., sleep, sedation), the upper airway can be foldable, for example, in response to respiratory effort, or even from pressures below atmospheric pressure in the applied ventilation. This can lead to complete obstruction (apnea), or a phenomenon known as “flow restriction.” The term “flow restriction” refers to the behavior where increased respiratory effort only causes increased airway narrowing, such that the inspiratory flow becomes restricted to a constant value, independent of effort (“Starling resistor behavior”). Therefore, the inspiratory flow curve exhibits a flattened shape (see [link to relevant documentation]). Figure 6B ).

[0241] In fact, upper airway behavior is even more complex and involves multiple flow patterns that indicate upper airway-related inspiratory flow limitations, and even more when external ventilation is present (see [link to relevant documentation]). Figures 6C to 6F Therefore, the flow limit determination algorithm 4324 can respond to one or more of the following types of inspiratory flow limits: "classical flatness" (see...). Figure 6B ), "chair-shaped" (see Figure 6C ) and "reverse chair" (see Figure 6D ("M-shape" (see...)) Figure 6E and 6F (The M-shaped detection algorithm 4326 is used separately for processing.)

[0242] Figure 7A The flowchart of method 7000 is shown, which can be used to calculate a measure of the flow limit of the inspiratory portion of the respiratory flow waveform as part of an inspiratory flow limit determination algorithm 4324 of this technology.

[0243] Method 7000 begins with step 7010, which calculates multiple "central portion features" CF1 to CF8 based on the central portion of the inspiratory waveform, including central slope, central deviation, central concavity, and the range of the waveform having a large initial cross section and a large final cross section (approximately before and after the central portion). See below for reference. Figure 7B Detailed description of step 7010.

[0244] The next step, 7020, uses fuzzy logic to combine the central portion features CF1 to CF8 calculated in step 7010 to calculate multiple flow-limiting fuzzy truth variables FL1 to FL6, which indicate the similarity between the inhalation waveform and the corresponding prototype flow-limiting (partially blocked) inhalation waveform. These prototypes may include: moderately flat, possibly concave; slightly negative slope, indicating high inhalation resistance; very flat; flat or M-shaped with a large initial peak; flat, possibly with a slightly negative slope and a peak in the later central portion; and concave and flat. See below for reference. Figure 7C Detailed description of step 7020.

[0245] Then, in step 7030, method 7000 calculates the fuzzy truth variable fuzzyLateFlatness, which indicates the degree of "chair-shaped" inspiratory waveforms, i.e., a combination of early peak, late linearity, and moderate late slope (e.g., ...). Figure 6C (As shown). See below for reference. Figure 7E Detailed description of step 7030.

[0246] Finally, in step 7040, a "fuzzy OR" operation is used to combine all the calculated flow-limiting fuzzy truth variables FL1 to FL6 (from step 7020) and the fuzzy truth variable fuzzyLateFlatness (from step 7030) to calculate the fuzzy truth variable flowLimitation, which indicates the degree of inspiratory flow restriction. The fuzzy "OR" takes the maximum value of the fuzzy truth variables, so the fuzzy truth variable flowLimitation indicates the degree of similarity to one of the prototype flow-limiting inspiratory waveforms that is most similar to the inspiratory waveform.

[0247] By detecting flow limitation primarily based on features extracted from the central portion of the inspiratory waveform, Method 7000 is more robust to unmodeled leaks than other methods for detecting inspiratory flow limitation. This is because unmodeled leaks tend to cause a (typically but not exclusively) positive offset in the respiratory flow signal Qr, a function of the mask pressure Pm, which typically tends to be constant during the central and later portions of the inspiratory waveform (e.g., once the rise time has elapsed). Therefore, the offset in the respiratory flow signal Qr caused by unmodeled leaks also tends to be constant during the central and later portions of the inspiratory waveform.

[0248] Figure 7B This is a flowchart illustrating method 7100, which can be used to implement step 7010 of method 7000 in one form of the present technology. Method 7100 receives a "low fraction" and a "high fraction" of the duration of the inspiratory waveform as parameters. The "center portion" of the inspiratory waveform used by method 7100 extends from the low fraction multiplied by the duration of the inspiratory waveform to the high fraction multiplied by the duration of the inspiratory waveform. In one implementation of step 7010, the "low fraction" passed to method 7100 is 0.3, and the "high fraction" is 0.85.

[0249] Method 7100 begins with step 7110, which constructs an imaginary "ramp" on a portion of the inhalation waveform before the central portion (i.e., the "pre-central" portion). The ramp extends from the starting point of the inhalation waveform to the starting point of the central portion.

[0250] Then, step 7120 calculates the breathing flow above the ramp by adding the difference between the inspiratory waveform and the ramp on the central anterior part. This quantity is called flowAboveRampToStartOfCentralPart.

[0251] The next step, 7130, calculates the first central portion characteristic (CF1), propnAboveRampToStartOfCentralPart, by dividing flowAboveRampToStartOfCentralPart by the sum of all inspiratory waveform values. Significantly positive values ​​of propnAboveRampToStartOfCentralPart are typically associated with early peaks in the inspiratory flow waveform. Negative values ​​indicate a very gradual increase in flow towards the center portion and are unlikely to be associated with flow limitation or high inspiratory resistance.

[0252] Next is step 7140, where method 7100 calculates a second central portion feature (CF2), centralSlope, on the central portion. CentralSlope represents the slope of a linear approximation of the normalized central portion of the inspiratory waveform, which can be calculated by linear regression of the normalized central portion. The normalization factor is the maximum value of the mean of the complete inspiratory waveform and the minimum normalized flow rate, which is defined as 24 liters / minute in one implementation.

[0253] Then, step 7150 calculates centralFlowsMean, which is the average value of the normalized central portion of the inspiratory waveform. Then, step 7150 multiplies centralFlowsMean by the duration of the central portion to obtain a third central portion feature (CF3), centralPartDurationByFlowsMeanProduct.

[0254] Method 7100 proceeds to step 7160, which calculates the fourth central portion feature (CF4), postCentralActualAbovePredicted, which is a measure of how much flow rate in the portion of the inspiratory waveform following the central portion (post-central portion) exceeds the linear approximation of the central portion. Step 7160 calculates postCentralActualAbovePredicted by adding the difference between the normalized inspiratory waveform value and the linear approximation extending over the post-central portion, and dividing that sum by the duration of the post-central portion and the value of centralFlowsMean. (In one implementation, postCentralActualAbovePredicted is set to zero if centralFlowsMean is not positive.) A negative value for postCentralActualAbovePredicted generally indicates normal breathing because in partial upper airway obstruction, the flow rate is typically greater than or equal to the forward projection of the central portion in time, while in normal breathing, the flow rate following the central portion almost always decreases faster than that of the central portion. In other words, during normal breathing, the flow rate in the central and postcentral portions is generally convex, while during flow-limited breathing, most of the flow rate in the central and postcentral portions is usually not convex.

[0255] The next step, 7170, calculates the fifth central portion characteristic (CF5), centralDeviation, which is a measure of the variation between the actual inspiratory waveform value and the linear approximation of the central portion. In one embodiment of step 7170, centralDeviation is calculated as the square root of the sum of the squares of the differences between the normalized flow rate and the linear approximation of the central portion (calculated by centralFlowsMean and centralSlope). A significantly positive value of centralDeviation indicates significant nonlinearity in the central portion.

[0256] Finally, in step 7180, method 7100 calculates the sixth central portion feature (CF6), centralConcavity, which is a measure of the concavity of the central portion. A positive value of centralConcavity indicates that the central portion is approximately concave upwards. In one embodiment of step 7180, centralConcavity is calculated as a measure of the similarity between a normalized, subtracted-means inhalation waveform and a V-shaped function of the central portion. In one embodiment of step 7180, the similarity measure is calculated as the sum of the products of the normalized, subtracted-means inhalation waveform and the V-shaped function of the central portion, divided by the autocorrelation of the V-shaped function. Other concavity measures can also be used to calculate centralConcavity in step 7180, such as the curvature of the parabolic approximation of the central portion. Another concavity measure is the inner product of the second derivatives of the Gaussian function of the central portion, which has a suitable scaling factor in a suitable range, for example, corresponding to + / -2 standard deviations. Alternatively, this can be achieved by taking the second derivative of the flux at the center of the low-pass filter's central portion, where the low-pass filter has already been performed via a convolution with a Gaussian function, possibly with appropriate windowing. Other low-pass filters can be used, followed by standard numerical methods for calculating the second derivative of the filtered waveform.

[0257] To calculate the seventh and eighth central feature, step 7010 can repeat method 7100 with different definitions of the central feature, i.e., different values ​​for the parameters “low score” and “high score”. In one such implementation, the “low score” of the second iteration of method 7100 is 0.2 and the “high score” is 0.8, both lower than the corresponding scores defined for calculating the central features CF1 to CF6 of the central feature, thus defining the early portion of the inspiratory waveform. The seventh central feature (CF7), called ConcaveAndFlattishCentralSlope, is the centralSlope value returned by step 7140 of the second iteration of method 7100. The ConcaveAndFlattishCentralSlope value is slightly different from the centralSlope (CF2) value calculated for the early portion of the inspiratory waveform.

[0258] The eighth central feature (CF8) (referred to as ConcaveAndFlattishCentralConcavity) is the centralConcavity value returned by step 7180 of the second iteration of method 7100. The ConcaveAndFlattishCentralConcavity value is slightly different from the centralConcavity (CF6) value calculated for the earlier part of the inhalation waveform.

[0259] By basing the calculation of the central features CF1 to CF8 on a normalized quantity, Method 7100 makes itself more robust to offsets in the intake waveform caused by unmodeled leakage.

[0260] Figure 7C This is a flowchart of method 7200, which illustrates step 7020 of method 7000 in one form of the present technology. As described above, step 7020 combines the central portion features CF1 to CF8 calculated in step 7010, for example by using fuzzy logic, to calculate a plurality of flow-limiting fuzzy truth variables FL1 to FL6, which indicate the similarity between the inhalation waveform and the corresponding prototype flow-limiting inhalation waveform.

[0261] Each step 7210 to 7260 independently operates on a subset of the eight central features CF1 to CF8 to compute one of the six flow-limited fuzzy truth variables FL1 to FL6. For this purpose, steps 7210 to 7260 can be performed in parallel or in any convenient order.

[0262] Step 7210 calculates the flow-limiting fuzzy truth variable FL1 (named flattishMaybeConcave) as a fuzzy AND of three fuzzy truth variables: centralSlopeFlattishMaybeConcave, centralDeviationFlattishMaybeConcave, and centralConcavityFlattishMaybeConcave, obtained from the central portion features centralSlope (CF2), centralDeviation (CF5), and centralConcavity (CF6), respectively. The three fuzzy truth variables resulting in flattishMaybeConcave represent that the central portion of the inhalation waveform is moderately flat (approximately horizontal and reasonably linear) and slightly concave upwards. In one implementation, step 7210 calculates centralSlopeFlattishMaybeConcave, centralDeviationFlattishMaybeConcave, and centralConcavityFlattishMaybeConcave as follows:

[0263] centralSlopeFlattishMaybeConcave = Fuzzy Member(

[0264] centralSlope, -0.2, false, -0.1, true, 0.1, true, 0.2, false) centralDeviationFlattishMaybeConcave=fuzzy member(

[0265] centralDeviation (0.05, True; 0.1, False)

[0266] centralConcavityFlattishMaybeConcave=fuzzy member(

[0267] centralConcavity, -0.2, false, 0.0, true)

[0268] Step 7220 calculates the flow-limiting fuzzy truth variable FL2 (called mildNegSlopeHighInspResistance) from the six central features CF1 to CF6. The flow-limiting fuzzy truth variable mildNegSlopeHighInspResistance indicates that the intake waveform has a slightly negative slope in the central part, which indicates high intake resistance.

[0269] Figure 7D This is a flowchart illustrating method 7300, which can be used to implement method 7200 in one form of the present technology, with steps 7220.

[0270] Method 7300 begins at step 7310, which checks whether the central part feature centralPartDurationByFlowsMeanProduct (CF3) is greater than 0, or whether the central part feature centralSlope (CF2) is between [-0.6, -0.1]. If not (“N”), method 7300 proceeds to step 7320, which sets mildNegSlopeHighInspResistance to false, because the central slope is not slightly negative or the central part is too small to be useful.

[0271] Otherwise ("Y"), Method 7300 begins to calculate four fuzzy truth variables, which will be combined using a fuzzy AND operation to produce mildNegSlopeHighInspResistance.

[0272] The calculation begins at step 7330, which computes the intermediate fuzzy truth variable pairIsHigh from the central part feature propnAboveRampToStartOfCentralPart (CF1), such that pairIsHigh becomes true when the central part becomes "sharper". In one implementation, step 7330 computes pairIsHigh as follows:

[0273] pairIsHigh = fuzzy_member(propnAboveRampToStartOfCentralPart, 0.08, false, 0.12, true)

[0274] Then, step 7340 calculates the slope-related deviation threshold using pairIsHigh and centralSlope. In one implementation, step 7340 calculates slopeDependentDeviationThreshold as follows:

[0275] slopeDependentDeviationThreshold=pairIsHigh*0.08 + (1–pairIsHigh)*(0.04 + (centralSlope + 0.5) * 0.1)

[0276] In the next step 7350, method 7300 uses the central part feature centralDeviation (CF5) to subtract the slope-related deviation threshold just calculated, to calculate the first of the four fuzzy truth variables that cause mildNegSlopeHighInspResistance, namely centralDeviationLow. The value of centralDeviationLow becomes false as this difference increases. In one implementation, step 7350 calculates centralDeviationLow as follows:

[0277] centralDeviationLow = fuzzy member(centralDeviation-slopeDependentDeviationThreshold, 0.0, true, 0.01, false)

[0278] Then, step 7360 calculates the second of the four fuzzy truth variables that cause `mildNegSlopeHighInspResistance` from the central feature `centralConcavity` (CF6), namely `centralConcavityModPositive`, such that `centralConcavityModPositive` is true when the concavity of the central part is moderately positive. In one implementation, step 7360 calculates `centralConcavityModPositive` as follows:

[0279] centralConcavityModPositive = Fuzzy Member(

[0280] centralConcavity, -0.05, False, 0.0, True, 0.3, True, 0.5, False)

[0281] The next step 7370 calculates the third of the four fuzzy truth variables that cause `mildNegSlopeHighInspResistance` from the central feature `postCentralActualAbovePredicted` (CF4), namely `postCentralActualAbovePredMildNegSlope`, such that `postCentralActualAbovePredMildNegSlope` becomes true as `postCentralActualAbovePredicted` increases. In one implementation, step 7370 calculates `postCentralActualAbovePredMildNegSlope` as follows:

[0282] postCentralActualAbovePredMildNegSlope=fuzzy member(

[0283] (postCentralActualAbovePredicted, -0.4, false, -0.3, true)

[0284] The next step is step 7375, in which method 7300 calculates the exponential decay constant, exponentialDecayConstant, from the central part features centralSlope (CF2) and centralPartDurationByFlowsMeanProduct (CF3). In one implementation, step 7375 calculates exponentialDecayConstant as follows:

[0285] exponentialDecayConstant=

[0286] -centralSlope / centralPartDurationByFlowsMeanProduct

[0287] The next step, 7380, calculates the fourth of the four fuzzy truth variables that cause `mildNegSlopeHighInspResistance`, namely `expDecayConstMildNegSlope`, from `exponentialDecayConst`. In one implementation, step 7380 calculates `expDecayConstMildNegSlope` as follows:

[0288] expDecayConstMildNegSlope=fuzzy member(

[0289] (exponentialDecayConstant, -0.4, false, -0.2, true, 0.2, true, 0.4, false)

[0290] The final step of method 7300, 7390, calculates the fuzzy AND of mildNegSlopeHighInspResistance as four fuzzy truth variables: centralDeviationLow, mildNegSlopeHighInspResistance, postCentralActualAbovePredMildNegSlope, and expDecayConstMildNegSlope.

[0291] Step 7230 calculates the flow-limiting fuzzy truth variable FL3 (i.e., veryFlat) as a fuzzy AND of three fuzzy truth variables: centralSlopeVeryFlat, centralConcavityVeryFlat, and propnAboveRampToStartOfCentralPartVeryFlat, obtained from the central part features centralSlope (CF2), centralConcavity (CF6), and propnAboveRampToStartOfCentralPart (CF1), respectively. The three fuzzy truth variables resulting in veryFlat represent that the inhalation waveform is approximately horizontal without upward bulge in the central part and without upward concavity in the front central part, respectively. In one implementation, step 7230 calculates centralSlopeVeryFlat, centralConcavityVeryFlat, and propnAboveRampToStartOfCentralPartVeryFlat as follows:

[0292] centralSlopeVeryFlat = Fuzzy Member(

[0293] centralSlope, -0.1, false, -0.05, true, 0.05, true, 0.1, false)

[0294] centralConcavityVeryFlat = Fuzzy Member(

[0295] centralConcavity, -0.1, false, 0.0, true)

[0296] propnAboveRampToStartOfCentralPartVeryFlat=fuzzy member(

[0297] propnAboveRampToStartOfCentralPart, -0.05, false, 0.0, true)

[0298] Step 7240 calculates the flow-limiting fuzzy truth variable FL4 (i.e., flatAndLargeInitialPeakMShapes) as a fuzzy AND of three fuzzy truth variables: centralSlopeFlatAndLargeInitialPeakMShapes, centralConcavityFlatAndLargeInitialPeakMShapes, and propnAboveRampToStartOfCentralPartFlatAndLargeInitialPeakMShapes, obtained from the central part features centralSlope (CF2), centralConcavity (CF6), and propnAboveRampToStartOfCentralPart(CF1), respectively. The three fuzzy truth variables that cause flatAndLargeInitialPeakMShapes represent that the inhalation waveform is approximately horizontal or M-shaped in the central part and has a large initial peak. In one implementation, step 7240 calculates centralSlopeFlatAndLargeInitialPeakMShapes, centralConcavityFlatAndLargeInitialPeakMShapes, and propnAboveRampToStartOfCentralPartFlatAndLargeInitialPeakMShapes as follows:

[0299] centralSlopeFlatAndLargeInitialPeakMShapes=fuzzy members(

[0300] centralSlope, -0.3, false, -0.2, true, 0.2, true, 0.3, false)

[0301] centralConcavityFlatAndLargeInitialPeakMShapes=fuzzy members(

[0302] centralConcavity, 0.1 (false), 0.3 (true)

[0303] propnAboveRampToStartOfCentralPartFlatAndLargeInitialPeakMShapes = FuzzyMember(propnAboveRampToStartOfCentralPart, 0.15, false, 0.3, true)

[0304] Step 7250 uses the following fuzzy logic operations on the three intermediate fuzzy truth variables to calculate the flow limit fuzzy truth variable FL5, namely flatMNegSlopePostCentralHigh:

[0305] flatMNegSlopePostCentralHigh=

[0306] centralDeviationFlatMNegSlopePostCentralHigh AND

[0307] (flatNegSlope OR flatMoreNegSlopeHighPostCentral)

[0308] The fuzzy truth variable `centralDeviationFlatMNegSlopePostCentralHigh` is obtained from the central portion feature `centralDeviation` (CF5) and indicates that the central portion of the inhalation waveform is reasonably linear. In one implementation, step 7250 calculates `centralDeviationFlatMNegSlopePostCentralHigh` as follows:

[0309] centralDeviationFlatMNegSlopePostCentralHigh=fuzzy member(

[0310] centralDeviation (0.1, True, 0.2, False)

[0311] Step 7250 calculates the fuzzy truth variable `flatNegSlope` as a fuzzy AND of two fuzzy truth variables: `centralSlopeFlatNegSlope` and `postCentralActualAbovePredFlatNegSlope`, obtained from the central portion features `centralSlope` (CF2) and `postCentralActualAbovePredFlatNegSlope` (CF4), respectively. The two fuzzy truth variables causing `flatNegSlope` represent the inhalation waveform having a slightly negative slope in the central portion and concave upwards in the post-central portion, respectively. In one implementation, step 7250 calculates `centralSlopeFlatNegSlope` and `postCentralActualAbovePredFlatNegSlope` as follows:

[0312] centralSlopeFlatNegSlope = Fuzzy Member(

[0313] centralSlope, -0.45, false, -0.3, true, 0.1, true, 0.2, false)

[0314] postCentralActualAbovePredFlatNegSlope=fuzzy member(

[0315] (postCentralActualAbovePredicted, 0.0, false, 0.2, true)

[0316] Step 7250 calculates the fuzzy truth variable `flatMoreNegSlopeHighPostCentral` as a fuzzy AND of two fuzzy truth variables: `centralSlopeFlatMoreNegSlopeHighPostCentral` and `postCentralActualAbovePredictedFlatMoreNegSlopeHighPostCentral`, obtained from the central portion features `centralSlope` (CF2) and `postCentralActualAbovePredFlatMoreNegSlopeHighPostCentral` (CF4), respectively. The two fuzzy truth variables leading to `flatMoreNegSlopeHighPostCentral` represent that the inhalation waveform has a slightly larger slope in the central portion than `flatNegSlope`, and a peak in the later central portion. In one implementation, step 7250 calculates `centralSlopeFlatMoreNegSlopeHighPostCentral` and `postCentralActualAbovePredFlatMoreNegSlopeHighPostCentral` as follows:

[0317] centralSlopeFlatMoreNegSlopeHighPostCentral=fuzzy member(

[0318] centralSlope, -0.55, false, -0.45, true, 0.1, true, 0.2, false)

[0319] postCentralActualAbovePredFlatMoreNegSlopeHighPostCentral=

[0320] Fuzzy member(postCentralActualAbovePredicted, 0.2, false, 0.3, true)

[0321] The fuzzy truth variable flatMNegSlopePostCentralHigh (FL5) calculated by step 7250 indicates that the inhalation waveform is reasonably linear in the central portion and has a slight negative slope in the central portion and is concave upward in the post-central portion, or has a larger negative slope in the central portion and a peak in the post-central portion.

[0322] Step 7260 calculates the flow-limiting fuzzy truth variable FL6 (i.e., concaveAndFlattish) as a fuzzy AND of two fuzzy truth variables: centralSlopeConcaveAndFlattishFuzzy and centralConcavityConcaveAndFlattishFuzzy, obtained from the central portion features centralSlopeConcaveAndFlattish (CF7) and centralConcavityConcaveAndFlattish (CF8), respectively. The two fuzzy truth variables that result in concaveAndFlattish represent that the central portion of the inhalation waveform is approximately horizontal and concave upwards. The flow-limiting fuzzy truth variable concaveAndFlattish (FL6) differs slightly from the first flow-limiting fuzzy truth variable flattishMaybeConcave (FL1) because it depends on the central portion features CF7 and CF8, which are calculated by comparing the central portions defined earlier by CF2 and CF6, which result in flattishMaybeConcave. In other words, concaveAndFlattish indicates that horizontality and concavity appeared slightly earlier than flattishMaybeConcave.

[0323] In one implementation, step 7260 calculates centralSlopeConcaveAndFlattishFuzzy and centralConcavityConcaveAndFlattishFuzzy as follows:

[0324] centralSlopeConcaveAndFlattishFuzzy = Fuzzy member(

[0325] centralSlopeConcaveAndFlattish, -0.4, false, -0.2, true, 0.2, true, 0.4, false)

[0326] centralConcavityConcaveAndFlattishFuzzy = Fuzzy member(

[0327] centralConcavityConcaveAndFlattish, 0.3, false, 0.4, true)

[0328] Figure 7E This is a flowchart of method 7400, which can be used to implement step 7030 of method 7000 in one form of the present technology. As described above, step 7030 calculates the fuzzy truth variable fuzzyLateFlatness, which indicates the degree of "chair-shaped" inspiratory waveform.

[0329] Method 7400 begins at step 7410, which checks whether the inspiratory time Ti is greater than an inspiratory time threshold and whether the typical recent ventilation Vtyp (returned by the typical recent ventilation determination algorithm 4328, as described below) is greater than zero. In one implementation, the inspiratory time threshold is 0.5. If not (“N”), step 7440 sets fuzzyLateFlatness to “fuzzy false”, and method 7400 ends. Otherwise (“Y”), step 7420 calculates the start and end positions of the “late portion” of the inspiratory waveform. The purpose of step 7420 is to define the late portion in order to exclude any early peaks in the inspiratory waveform. In one implementation, the late portion is defined using the rise and fall time parameters of the pressure waveform template determined by waveform determination algorithm 4322. In such an implementation, the start position of the late portion is 1.25 times the rise time, and the end position is one-quarter of the fall time returned from the end of the inspiratory waveform. Other implementations of step 7420 defining the “late portion” can be expected. For example, step 7420 can test a portion later than the “late part” defined above, in order to reasonably approximate the line with a gradient close to zero, and if found, search backward to find the approximation with a knot that rapidly descends from the initial peak, using, for example, a criterion for locating the knot that is approximately equal to a large positive smooth second derivative at the knot, and then considering that the late part begins roughly at that knot. This method is computationally more intensive than the previously described method.

[0330] Then, method 7400 checks whether the duration of the late portion, as defined in step 7420, is higher than the minimum fraction of the inspiratory time Ti. In one implementation, the minimum fraction is 0.25. If not (“N”), the late portion is considered too short for reliable analysis, and method 7400 proceeds to step 7440, which sets fuzzyLateFlatness to “fuzzy false”, and method 7400 terminates. Otherwise (“Y”), the next step 7450 calculates a normalized factor normFactorTypVent from typical recent ventilation Vtyp, such that normFactorTypVent generally increases with increasing Vtyp. In one implementation, suitable when Vtyp is alveolar ventilation, step 7450 calculates normFactorTypVent as Vtyp / 4.5.

[0331] Then, step 7455 calculates a linear approximation of the inspiratory waveform on the later portion. Step 7455 can use linear regression to calculate the linear approximation, characterized by the average flow rate (flowMean), slope (flowSlope), and root mean square prediction error (rootMeanSqFlowPredErr). Then, step 7455 calculates a normalized version of these three parameters by dividing each parameter by a normalization factor (normFactorTypVent):

[0332] lateFlowMean=flowMean / normFactorTypVent

[0333] lateSlope=flowSlope / normFactorTypVent

[0334] lateFlatness=rootMeanSqFlowPredErr / normFactorTypVent

[0335] The term "flatness" in the text refers to the degree to which the later part resembles a straight line, without considering its gradient, and the value of lateFlatness is zero when the later part is actually a straight line.

[0336] In the next step 7460, method 7400 calculates the amount of flowAboveLateBeforeLate, which indicates how much the inspiratory waveform exceeds the back projection of the linear approximation of the later portion of the inspiratory waveform calculated in step 7455, i.e., the magnitude of the early peak in the inspiratory waveform. To calculate flowAboveLateBeforeLate, the earliest position before the respiratory flow Qr exceeds the later portion of the back projection of the linear approximation of the later portion is found. The difference between the respiratory flow Qr and the linear approximation is then averaged between this position and the beginning position of the later portion. The average is then normalized by the normalization factor normFactorTypVent to obtain flowAboveLateBeforeLate. Then, in step 7460, the fuzzy truth variable flowAboveLateBeforeLateAdequate is calculated from the value of flowAboveLateBeforeLate, such that flowAboveLateBeforeLateAdequate is fuzzy false, until flowAboveLateBeforeLate exceeds a threshold that typically increases with the average flow of the late-phase lateFlowMean (calculated in step 7455). The effect of the correlation between flowAboveLateBeforeLateAdequate and lateFlowMean is that a larger early peak is required for fuzzyLateFlatness to be fuzzy true as the average flow of the late-phase increases.

[0337] In one implementation, step 7460 calculates flowAboveLateBeforeLateAdequate as follows:

[0338] flowAboveLateBeforeLateAdequate = Fuzzy Member(

[0339] (flowAboveLateBeforeLate, LOWER_THRESH, false, UPPER_THRESH, true)

[0340] In one implementation, LOWER_THRESH and UPPER_THRESH are constant thresholds equal to 0.5 * lateFlowMean and lateFlowMean, respectively. In one implementation, the thresholds have minimum values ​​of 2.25 and 4.5 liters per minute, respectively, regardless of the value of lateFlowMean.

[0341] Method 7400 proceeds to step 7465, where it calculates the fuzzy truth variable `lateFlatnessFuzzyComp`, which changes from true to false as `lateFlatness` increases; that is, the later part becomes less linear. In one implementation, step 7465 calculates `lateFlatnessFuzzyComp` as follows:

[0342] lateFlatnessFuzzyComp = Fuzzy member(

[0343] (lateFlatness, LOWER_FLATNESS_THRESH, True, UPPER_FLATNESS_THRESH, False)

[0344] In one implementation, LOWER_FLATNESS_THRESH and UPPER_FLATNESS_THRESH are constant thresholds equal to 0.6 and 0.9, respectively.

[0345] The next step, 7470, calculates the fuzzy truth variable `lateSlopeFuzzyComp`, which is true only when `lateSlope` is "medium," i.e., within a specific range. That is, large positive or negative values ​​of `lateSlope` may cause `lateSlopeFuzzyComp` to be false. In one implementation, step 7470 calculates `lateSlopeFuzzyComp` as follows:

[0346] lateSlopeFuzzyComp = Fuzzy member(

[0347] lateSlope,LOWER_SLOPE_THRESH, False,UPPER_SLOPE_THRESH, True,6.0, True,9.0,False)

[0348] Where LOWER_SLOPE_THRESH and UPPER_SLOPE_THRESH are thresholds. In one implementation, the thresholds typically become more negative as lateFlowMean increases, meaning that a larger negative slope is allowed within the definition of "medium" as the average flow rate increases in the later part. In one implementation, step 7470 calculates LOWER_SLOPE_THRESH and UPPER_SLOPE_THRESH as follows:

[0349] LOWER_SLOPE_THRESH=-9.0–3*slopeThresholdExtension

[0350] UPPER_SLOPE_THRESH=-6.0–3*slopeThresholdExtension

[0351] Where slopeThresholdExtension is a real number that decreases from 1 to 0 as lateFlowMean increases. In one implementation, step 7470 calculates slopeThresholdExtension as follows:

[0352] slopeThresholdExtension=fuzzy member(

[0353] lateFlowMean, 0.6, 1.0,0.9, 0.0)

[0354] The final step 7475 of method 7400 calculates fuzzyLateFlatness as a fuzzy AND of the fuzzy truth variables flowAboveLateBeforeLateAdequate, lateFlatnessComp, and lateSlopeFuzzyComp calculated in steps 7460, 7465, and 7470, respectively.

[0355] 7.4.3.2.5 M-shape detection

[0356] In one form of this technology, the treatment engine 4320 module executes one or more algorithms to detect an "M-shape" in the inspiratory waveform. In one form, the M-shape detection algorithm 4326 receives the respiratory flow signal Qr as input and provides a measurement indicating the degree to which each inspiratory waveform exhibits an M-shape as output.

[0357] An M-shaped inspiratory waveform with tidal volume or other respiratory ventilation values ​​not significantly larger than typical recent values ​​indicates flow limitation. This inspiratory waveform has a relatively rapid rise and fall and a flow angle or "notch" approximately at the center, the angle being due to flow limitation (see [link to relevant documentation]). Figure 6E and 6F At higher climax volumes or respiratory ventilation values, this waveform is typically behavioral, i.e., micro-awakening or sighing during sleep, and does not indicate flow limitation.

[0358] To detect the M-shaped waveform, the M-shaped detection algorithm 4326 determines the similarity between the inhalation waveform and the approximate M-shaped waveform.

[0359] Figure 7F This is a flowchart illustrating a method 7500 that can be used to implement the M-shaped detection algorithm 4326 in one form of this technology.

[0360] Since the notch may not be at the center of the inhalation waveform, method 7500 attempts to find the location of the notch and then performs a linear time warp on the waveform so that the notch is located at the center of the waveform. To find the notch, the first step 7510 performs a modified convolution on the normalized inhalation waveform f(t) (where normalization is divided by the mean), where the V-shaped kernel V(t) has a length of Ti / 2 and a center of zero, where Ti is the inhalation time.

[0361]

[0362] The modified convolution is based on separate convolutions of the left and right halves of the kernel V(t). The left half convolution is calculated as follows:

[0363]

[0364] And the right half convolution is

[0365]

[0366] The corrected convolution I(τ) is computed as the left and right half convolutions I. L (τ) and I R The combination of (τ) ensures that if either the left or right half of the convolution is zero, the result is zero, regardless of other quantities; if both are 1, the result is 1. Therefore, the combination of the left and right half convolutions is, in a sense, analogous to a logical "and" function, and is thus given the name "V-anded convolution". In one implementation, this combination is the corrected geometric mean of the left and right half convolutions:

[0367] (1)

[0368] The above constraint provides the condition that the inspiratory waveform to the left of the notch typically increases to the left, while the inspiratory waveform to the right typically increases to the right. This is more specific than simply summing the integrals of the left and right sides. In the implementation given in equation (1), the integral of the product of the time-shifted normalized inspiratory waveform and each half of V must be strictly positive, otherwise the V-anded convolution is zero. This prevents various malfunctions, such as the inspiratory portion flowing to the left of the V center not actually increasing to the left, but the integral of the right half of the V waveform being too large to exceed the actual reduction of the left half.

[0369] V-anded convolution is performed at the center position of kernel V(t) in the range of Ti / 4 to 3Ti / 4, thus obtaining a result of half the center of the inspiratory waveform.

[0370] Step 7520: Locate the position of the peak of the corrected convolution I(τ). If the height of this peak is greater than the threshold, the notch is considered to exist at the center position t of the kernel V(t). notchAt that location, the peak value is located. In one implementation, the threshold is set to 0.15.

[0371] If at position t via step 7520 (“Y”) notch If a notch is found at the location, then in step 7530, the inhalation waveform f(t) undergoes time distortion or "symmetry," causing half of the waveform to lie at t. notch Half of it is on the left and half on the right. This operation gives a time-distorted or "symmetric" version of the inspiratory waveform f(t) G(t):

[0372]

[0373] If no notch (“N”) is found in step 7520, then step 7535 sets G(t) to the intake waveform f(t) because some waveforms that do not have a detectable notch may still have M-shaped flow limitations.

[0374] To detect the M-shaped flow limitation in the symmetrical waveform G(t), the first and third sinusoidal harmonic functions of the half-width Ti are first defined as...

[0375]

[0376] and

[0377]

[0378] These two harmonic functions are orthogonal on [0, Ti]. For t in [0, Ti], F3(t) is approximately similar to an M-shaped inhalation waveform, and F1(t) is approximately similar to a normal inhalation waveform. Therefore, the degree to which the symmetrical waveform G(t) is similar to F3(t) is an indicator of the degree to which the waveform is similar to M. Step 7540 calculates this degree. In one implementation, step 7540 calculates the power in the first harmonic of the symmetrical waveform G(t) as the square of the inner product of the first harmonic function F1 and the symmetrical waveform G(t), and calculates the power in the third harmonic of the symmetrical waveform G(t) as the square of the inner product of the third harmonic function F3 and the symmetrical waveform G(t). Both inner products are calculated on the inhalation interval [0, Ti]. Then, step 7540 calculates the degree to which the symmetrical waveform G(t) is similar to F3(t) as the ratio M3Ratio of the power in the third harmonic of the symmetrical waveform G(t) to the sum of the power in the first and third harmonics of the symmetrical waveform G(t):

[0379]

[0380] When M3Ratio is large, the intake waveform is typically similar to M. However, if the waveform is highly asymmetrical, M3Ratio can also be large, with the average flow rate in the first or second half of the waveform being much higher than in the other half. To rule out this possibility, step 7540 also calculates Symm, a measure of the symmetry of the intake waveform f(t) with respect to the notch location. In one implementation, step 7540 calculates the third harmonic components of the first and second halves of the symmetrical waveform G(t):

[0381]

[0382]

[0383] Then, step 7540 calculates the metric Symm as the ratio of the smaller of these components to the sum of their absolute values:

[0384]

[0385] Then, step 7550 tests whether the metric Symm is less than a low threshold, which is set to 0.3 in one implementation. If so (“Y”), the inspiratory waveform is considered not to be a symmetrical M-shape, and in step 7560, the amount of M3SymmetryRatio (which is a measure of the degree to which the inspiratory waveform is a symmetrical M-shape) is set to zero. Otherwise (“N”), in step 7570, M3SymmetryRatio is set to equal to M3Ratio.

[0386] The final step 7580 calculates the variable RxProportion from M3SymmetryRatio, so that it typically increases from 0 to 1 as M3SymmetryRatio increases. In one implementation, step 7580 calculates RxProportion as follows:

[0387] RxProportion = Fuzzy Deweighting (

[0388] M3SymmetryRatio, LOWER_M3SYMMETRYRATIO_THRESH, 0.0, UPPER_M3SYMMETRYRATIO_THRESH, 1.0)

[0389] LOWER_M3SYMMETRYRATIO_THRESH and UPPER_M3SYMMETRYRATIO_THRESH are constants, equal to 0.17 and 0.3 respectively in one implementation. The variable RxProportion is an indication of the M-shape of the inspiratory portion of the respiratory flow waveform in the range [0,1].

[0390] 7.4.3.2.6 Apnea Detection

[0391] In one form of this technology, the treatment engine module 4320 executes the apnea detection algorithm 4325 to detect apnea.

[0392] In one form, the apnea detection algorithm 4325 receives a respiratory flow signal Qr as input and provides a series of events indicating the start and end of a detected apnea as output.

[0393] Figure 7G This is a flowchart illustrating a method 7600 for detecting apnea that can be used to implement the apnea detection algorithm 4325 in one form of the present technology.

[0394] Method 7600 typically seeks low ventilation associated with the expected normal ventilation Vnorm. In some implementations, the expected normal ventilation Vnorm can be the typical recent ventilation Vtyp returned by the typical recent ventilation determination algorithm 4328 described below.

[0395] Ventilation is measured at two different timescales, one corresponding to a breath and the other to the duration of one of several breaths, and low ventilation associated with the expected normal ventilation (Vnorm) at either timescale indicates that apnea is in progress. Method 7600 includes a hysteresis, where if apnea has been previously determined to be in progress, but neither of the two criteria indicating apnea is true, then the other criterion derived from ventilation relative to the expected normal ventilation (Vnorm) must be true for apnea to have ended. The hysteresis of Method 7600 makes it more robust to transient increases in respiratory flow than previous methods for detecting apnea.

[0396] Method 7600 also returns the effective duration of apnea for therapeutic purposes. The effective duration of apnea is defaulted to the total duration of its closed airway segments, i.e., the sum of the times elapsed between the start and termination of each closed airway segment of the apnea. However, the effective duration of apnea can be obtained by subtracting one or both of two separate deweighting factors from this default "closure duration" value: one derived from ventilation relative to the expected normal ventilation Vnorm, and the other from the estimated leakage flow Ql during the apnea. The effect of this deweighting is that in cases where leakage is significant during apnea and / or ventilation is close to the expected normal ventilation Vnorm, the effective duration of apnea is shorter than its closure duration, resulting in less increase in EPAP at the opening.

[0397] Method 7600 begins at step 7610, which calculates the short-duration ventilation Vshort as the average of the instantaneous ventilation Vint returned by ventilation determination algorithm 4323 over the most recent “short interval.” In one implementation, the short interval is 2 seconds. Step 7610 also calculates the “very fast” relative ventilation error veryFastRelVentError as the relative difference between the “very fast ventilation” VveryFast obtained by ventilation determination algorithm 4323 and the expected normal ventilation Vnorm. Step 7610 calculates the very fast relative ventilation error veryFastRelVentError as follows:

[0398] veryFastRelVentErr=(VveryFast–Vnorm) / Vnorm

[0399] Then, method 7600 continues in step 7615 to check whether the short-term ventilation Vshort is less than or equal to a low fraction of the expected normal ventilation Vnorm. In one implementation, the low fraction is set to 0.2. If yes (“Y”), apnea is detected. If not (“N”), the next step 7620 checks whether veryFastRelVentError is less than or equal to an apnea threshold, which in one implementation is equal to -0.95. The check in step 7620 is equivalent to determining whether very rapid ventilation VveryFast is less than a small fraction of the expected normal ventilation Vnorm, in this case the small fraction is 0.05. If yes (“Y”), apnea is detected.

[0400] Method 7600 maintains the current state of apnea as a Boolean indication value inApnea (which is initialized to false at the start of treatment). If the apnea initiation criteria (tested in steps 7615 and 7620) (“Y”) are met, then in step 7630, method 7600 checks whether apnea is in progress by checking if inApnea is true. If not (“N”), the next step 7635 issues an apnea initiation event and sets inApnea to true to indicate that the patient has just entered an apnea state. Then, step 7645 sets the effective duration of apnea to zero. The effective duration of apnea is stored in the variable effectiveApneaDuration, which is maintained as long as the apnea state persists.

[0401] The next step, 7670, sets a variable called intVentErrAboveThresh to zero. This variable holds the cumulative sum of the amount by which the very fast relative ventilation error (veryFastRelVentError) calculated in step 7610 exceeds the apnea threshold, which is used to confirm that the apnea has indeed ended if neither of the two apnea initiation criteria tested in 7615 and 7620 has been met.

[0402] If the examination in step 7630 reveals that patient 1000 is experiencing apnea (“Y”), then method 7600 proceeds directly to step 7670 to set the cumulative sum intVentErrAboveThresh to zero, as described above. Method 7600 then proceeds to step 7680, as described below.

[0403] If neither of the two apnea initiation criteria is met ("N" in steps 7615 and 7620), method 7600 checks whether an apnea is in progress by checking if inApnea is true in step 7640. If not ("N"), method 7600 terminates in step 7655. Otherwise ("Y"), the current apnea may have ended. However, as described above, method 7600 confirms this by proceeding to step 7650, which updates the cumulative sum intVentErrAboveThresh by adding the current difference between veryFastRelVentError and the apnea threshold, provided the difference is greater than zero.

[0404] Then, step 7660 checks the apnea termination criterion, i.e., whether the cumulative sum intVentErrAboveThresh is greater than a threshold. In one implementation, this threshold is set to 0.15. If the apnea termination criterion (“Y”) is met, the apnea ends, and therefore step 7665 ends the apnea state by issuing an apnea termination event and setting inApnea to false. Then method 7600 ends.

[0405] If the apnea termination criterion is not met, i.e. the cumulative sum intVentErrAboveThresh is not greater than the threshold ("N" in step 7660), then the apnea is still in progress, and method 7600 proceeds to step 7680 as described below.

[0406] Step 7680 calculates the deweighting factor aponaTimeWeighting from veryFastRelVentError, such that when VeryFastRelVentError increases above the apnea threshold, aponaTimeWeighting decreases from 1 to 0. In one implementation, step 7680 calculates aponaTimeWeighting as follows:

[0407] apneaTimeWeighting = Fuzzy Deweighting (

[0408] veryFastRelVentError, APNEA_THRESHOLD, 1.0,APNEA_THRESHOLD_PLUS_A_BIT, 0.0)

[0409] Where APNEA_THRESHOLD is the apnea threshold from step 7620, and APNEA_THRESHOLD_PLUS_A_BIT is a threshold set slightly higher than APNEA_THRESHOLD, which in one implementation is equal to APNEA_THRESHOLD + 0.05. The deweighting factor aponaTimeWeighting discounts the contribution of the current moment to the final effective duration of apnea.

[0410] Step 7685 then calculates a second deweighting factor, leakDeweighting, based on the leakage flow estimate Q1 from leakage flow estimation algorithm 4316. In one implementation, step 7685 calculates LeakDeweighting such that LeakDeweighting decreases from 1 to 0 as the estimated leakage flow increases.

[0411] LeakDeweighting = Fuzzy deweighting (

[0412] Ql,LOWER_LEAK_THRESH, 1.0,UPPER_LEAK_THRESH, 0.0)

[0413] LOWER_LEAK_THRESH and UPPER_LEAK_THRESH are constants, which in one implementation are equal to 48 liters / minute and 60 liters / minute, respectively.

[0414] Finally, in step 7690, method 7600 updates the effective apnea duration by considering two deweighting factors, apneaTimeWeighting and LeakDeweighting, and airway patency determined by airway patency determination algorithm 4327 (described below).

[0415] In one implementation, if the airway is currently determined to be closed, step 7690 updates the effective duration of apnea by adding the current value of effective ApneaDuration to the product of two deweighting factors, apopeTimeWeighting and LeakDeweighting. Otherwise, step 7690 does not update the effective duration of apnea.

[0416] The final value of the effective ApneaDuration returned by the apnea detection algorithm 4325 for a completed apnea will be a time unit equal to the duration of the apnea detection interval. The apnea detection interval is the reciprocal of the frequency at which the treatment engine module 4320 executes the apnea detection algorithm 4325.

[0417] 7.4.3.2.7 Typical recent ventilation determination

[0418] In one form of this technology, the central controller 4230 takes the current ventilation measurement value Vent as input and executes one or more typical recent ventilation determination algorithms 4328 to determine the Vtyp value indicating typical recent ventilation for patient 1000.

[0419] A typical recent ventilation Vtyp is a value around which the distribution of current ventilation measurements (Vent) at multiple times tends to cluster over some predetermined time scale; that is, it is a measure of the central tendency of recent ventilation measurements in recent history. In one implementation of the typical recent ventilation determination algorithm 4328, the recent history is on the order of minutes, but in any case should be longer than the time scale of the Cheyne-Stokes fading and amplification periods. The typical recent ventilation determination algorithm 4328 can use any of a variety of well-known measures of central tendency to determine the typical recent ventilation Vtyp from the current ventilation measurement (Vent). One such measure is the output of a low-pass filter to the current ventilation measurement (Vent) with a time constant of one hundred seconds.

[0420] 7.4.3.2.8 Confirmation of airway patency

[0421] In one form of this technology, the central controller 4230 executes an airway patency determination algorithm 4327 to determine airway patency. In some implementations, the airway patency determination algorithm 4327 returns "closed" or "open," or equivalent Boolean values, such as "true" for closed and "false" for open.

[0422] As described above, in the absence of autonomous triggering, the RPT device 4000 delivers backup breaths at a backup rate Rb. The backup breaths delivered during the apnea state detected by the apnea detection algorithm 4325 are referred to as "probe breaths".

[0423] Probe breathing can have the same amplitude (pressure support value A) as normal standby breathing, but it can have its own timing parameters, namely special values ​​for Timin and Timax, referred to as Timin_PB and Timax_PB. The values ​​of Timin_PB and Timax_PB can be obtained from other settings of the RPT device 4000. In one implementation, Timin_PB and Timax_PB are defined as follows:

[0424] Timin_PB=Max (0.1, Min (1.2, 0.4 * Tbackup)(2)

[0425] Timax_PB=Min (Max (Timax, Timin_PB), 0.4 * Tbackup)(3)

[0426] The main reason for correcting Timin for probe breathing is to prevent probe breathing from being prematurely cycled, which would make the probe breathing duration too short to provide reliable information relative to the upper airway status.

[0427] In some implementations, the airway patency determination algorithm 4327 is not invoked when the pressure support A (see below) is above a threshold (e.g., 10 cmH2O). For pressure support A above this threshold, any apnea is considered a closed airway apnea.

[0428] The airway patency determination algorithm 4327 analyzes the respiratory flow Qr in response to the probe breath transmitted when the RPT device 4000 is in a breathing apnea state to determine airway patency during breathing apnea. Airway opening / closing determination is based on analysis of the shape of the inspiratory and expiratory portions of the respiratory flow signal Qr in response to the probe breath. Generally, a "decreasing" portion indicates an open airway, while a "stable" portion indicates a closed airway.

[0429] Figure 7H and 7IThis document includes a flowchart illustrating a method 7700 that can be used to implement an airway patency determination algorithm 4327 in one form of the present technology. Method 7700 takes as input an inspiratory portion (inspiratory waveform) and an expiratory portion (expiratory waveform) of a respiratory flow waveform in response to a detected breath. Method 7700 begins at step 7710, which "trimmes" the inspiratory and expiratory waveforms. The purpose of trimming is to eliminate the "initial shock" to the flow, as gas compression and compliance elements in the airway occupy a certain volume. In one implementation, step 7710 trims the inspiratory waveform to the "late portion" as defined above with respect to the "chair-shaped" detection step 7030 of method 7000. In such an implementation, the starting position of the late portion is 1.25 times the rise time, and the ending position of the late portion is one-quarter of the fall time from the end of the inspiratory waveform. The expiratory portion can be trimmed in a similar manner, exchanging the rise and fall times.

[0430] Next is step 7720, where a linear regression is performed on the trimmed inspiratory and expiratory waveforms. The linear regression finds the “best” (in the least-squares sense) linear approximation of the function within an interval, returning the following parameters: gradient, y-intercept (initial value), mean, and correlation coefficient (which represents the “goodness of fit” of the linear approximation). In the next step 7730, method 7700 calculates the average value Qmean of the absolute values ​​of the respiratory flow Qr on the trimmed inspiratory and expiratory waveforms. Then, step 7730 divides this value Qmean by the typical recent ventilation Vtyp calculated by the typical recent ventilation determination algorithm 4328 to obtain a value Sz indicating the magnitude of the response to the probed breath in relation to the current ventilation level. Then, step 7740 determines upper and lower thresholds to be compared with the respiratory magnitude indication value Sz. In some form of step 7740, the lower threshold is a function of the current pressure support A (see below), which is the amplitude of the transmitted probed breath. In one such form, the lower threshold is the A value of the clipping within the range of [4 cmH2O, 8 cmH2O] divided by 20 cmH2O. Therefore, in such an implementation, the lower threshold will be in the range of 0.2 to 0.4. In one implementation of step 7740, the upper threshold is set to 0.75.

[0431] Then, step 7750 compares the respiratory size indicator value Sz calculated in step 7730 with a lower threshold. If the respiratory size indicator value Sz is lower than the lower threshold (“Y”), method 7700 terminates in step 7770 with a return value of “closed” because the respiratory flow rate is too small for the airway to be considered open. Otherwise (“N”), method 7700 proceeds to step 7760, which determines whether the respiratory size indicator value Sz is higher than the upper threshold. If yes (“Y”), method 7700 terminates in step 7770 with a return value of “open” because the respiratory flow rate is too large for the airway to be considered closed. If no (“N”), method 7700 proceeds to step 7790, which calculates the Sz_rel value of the respiratory size indicator value Sz relative to the lower and upper thresholds as follows:

[0432] Sz_rel=(Sz–lower_threshold) / (upper_threshold–lower_threshold)

[0433] The relative respiratory size indicator value Sz_rel ranges from 0 (when the respiratory size indicator value Sz equals the lower threshold) to 1 (when the respiratory size indicator value Sz equals the upper threshold).

[0434] Then, method 7700 from Figure 7I Step 7810 continues to finally determine airway patency based on the relative respiratory size indicator value Sz_rel and the linear approximation of the trimmed inspiratory and expiratory waveforms found in step 7720 (which may be performed after steps 7730 to 7790 in some implementations).

[0435] In step 7810, method 7700 calculates the “final value” of the linear approximation of the trimmed inhalation waveform. This final value can be calculated by adding the y-intercept (initial value) to the product of the gradient and duration of the trimmed inhalation portion. Then, step 7810 calculates the “inhalation flatness ratio” as the ratio of the final value to the initial value of the linear approximation of the trimmed inhalation waveform.

[0436] In the next step 7820, method 7700 calculates the “final value” of the linear approximation of the trimmed expiratory waveform. This final value can be calculated by adding the y-intercept (initial value) to the product of the gradient and duration of the trimmed expiratory portion. Then, step 7820 calculates the “expiratory flatness ratio” as the ratio of the final value to the initial value of the linear approximation of the trimmed expiratory waveform.

[0437] The next step is step 7830, in which method 7700 calculates inspiratory and expiratory thresholds to be compared with the inspiratory and expiratory flatness ratios, respectively. If either ratio is below its corresponding threshold, the airway is determined to be open; otherwise, the airway is determined to be closed.

[0438] In one implementation of step 7830, a threshold can be calculated to increase linearly as the relative respiratory size indicator value Sz_rel increases from 0 to 1. For example,

[0439] inspiratory_threshold=Sz_rel(4)

[0440] expiratory_threshold=Sz_rel(5)

[0441] The effect of this is that for a probe breath whose size indicator value Sz is sufficiently large to approach an upper threshold, exceeding this upper threshold, the airway is determined to be open in step 7780, the inspiratory and expiratory thresholds will be close to 1, and therefore the inspiratory and expiratory flatness ratio is more likely to be below the threshold, and the airway will be determined to be open. Conversely, for a probe breath whose size indicator value Sz is sufficiently small to approach a lower threshold, below this lower threshold, the airway is determined to be closed in step 7770, the inspiratory and expiratory thresholds will be close to 0, and therefore the inspiratory and expiratory flatness ratio is more likely to be above the threshold, and the airway is determined to be closed.

[0442] In some implementations, the latter determination can be limited by adjusting the inspiratory and expiratory thresholds based on the "goodness of fit" of the linear approximation of the inspiratory and expiratory waveforms. When the linear approximation is a "good fit," as quantified by a correlation coefficient, the minimum threshold is proportionally greater than zero, making an "open" determination more likely even for relatively small breaths. In one such implementation, the "goodness of fit" parameter of the inspiratory waveform is calculated as...

[0443] insp_goodness=InterpOnInterval(Insp_corrcoefft, -0.95, 0.5, -0.8,0.0)

[0444] When the inspiratory correlation coefficient is below -0.95 (indicating a good fit), it equals 0.5; when the inspiratory correlation coefficient is above -0.8, it equals 0; and when the inspiratory correlation coefficient is above -0.95 but below -0.8, it linearly decreases from 0.5 to 0.

[0445] In this implementation, the "goodness of fit" parameter of the expiratory waveform is calculated as follows:

[0446] exp_goodness=InterpOnInterval(Exp_corrcoefft, 0.8, 0, 0.95, 0.5)

[0447] When the expiratory correlation coefficient is higher than 0.95 (indicating a good fit), it is equal to 0.5; when the expiratory correlation coefficient is lower than 0.8, it is equal to 0; and when the expiratory correlation coefficient is higher than 0.8 but lower than 0.95, it increases linearly from 0 to 0.5.

[0448] In this implementation, equations (4) and (5) for the inhalation and exhalation thresholds are modified as follows:

[0449] inspiratory_threshold=insp_goodness + (1-insp_goodness) * Sz_rel

[0450] expiratory_threshold=exp_goodness + (1-exp_goodness) * Sz_rel

[0451] In this implementation, when the "goodness of fit" is poor, the inspiratory and expiratory thresholds increase linearly from 0 to 1 as Sz_rel increases from 0 to 1; however, when it is good, the thresholds increase linearly from 0.5 to 1 as Sz_rel increases from 0 to 1. Therefore, for any given Sz_rel value, the better the linear fit, the higher the threshold, increasing the chance that the airway is found to be open for reasonable "linear" breathing.

[0452] Returning to method 7700, in step 7840, the inspiratory flatness ratio is compared to the inspiratory threshold. If the inspiratory flatness ratio is lower than the inspiratory threshold (“Y”), then in step 7870, the airway is determined to be open and method 7700 ends. Otherwise (“N”), in step 7850, the expiratory flatness ratio is compared to the expiratory threshold. If the expiratory flatness ratio is lower than the expiratory threshold (“Y”), then in step 7870, the airway is determined to be open and method 7700 ends. Otherwise (“N”), in step 7860, the airway is determined to be closed and method 7700 ends.

[0453] 7.4.3.2.9 Determination of Treatment Parameters

[0454] In some forms of this technology, the central controller 4230 executes one or more treatment parameter determination algorithms 4329 to determine one or more treatment parameters using values ​​returned by one or more other algorithms in the treatment engine module 4320.

[0455] In one form of this technology, the treatment parameter is the instantaneous treatment pressure Pt. In one implementation of this form, the treatment parameter determination algorithm 4329 determines the treatment pressure Pt as follows.

[0456] (6)

[0457] Where A is the quantity of "pressure support", Π(Φ, t) is the waveform template value at the current phase value Φ and time τ (ranging from 0 to 1), and P0 is the base pressure.

[0458] By using equation (6) to determine the treatment pressure Pt and applying it as a set point to the controller 4230 of the RPT device 4000, the treatment parameter determination algorithm 4329 causes the treatment pressure Pt to oscillate synchronously with the spontaneous breathing effort of the patient 1000. That is, based on the typical waveform template Π(Φ) described above, the treatment parameter determination algorithm 4329 increases the treatment pressure Pt at the beginning of inspiration or during inspiration and decreases the treatment pressure Pt at the beginning of expiration or during expiration. The (non-negative) pressure support A is the amplitude of the oscillation.

[0459] If the waveform determination algorithm 4322 provides a waveform template Π(Φ) as a lookup table, the treatment parameter determination algorithm 4329 applies equation (6) by locating the closest lookup table entry to the current phase value Φ returned by the phase determination algorithm 4321, or by interpolating between two entries that span the current phase value Φ.

[0460] The values ​​of pressure support A and baseline pressure P0 can be determined by the treatment parameter determination algorithm 4329 according to the selected respiratory pressure treatment mode.

[0461] 7.4.3.3 Treatment Control Module

[0462] According to one aspect of the present technology, the treatment control module 4330 receives treatment parameters as input from the treatment parameter determination algorithm 4329 of the treatment engine module 4320, and controls the pressure generator 4140 to transmit airflow according to the treatment parameters.

[0463] In one form of this technology, the treatment parameter is the treatment pressure Pt, and the treatment control module 4330 controls the pressure generator 4140 to deliver airflow, and the mask pressure Pm at the patient interface 3000 is equal to the treatment pressure Pt.

[0464] 7.4.3.4 Fault Condition Detection

[0465] In one form of this technology, the central controller 4230 performs one or more methods for detecting fault conditions. The fault conditions detected by the one or more methods may include at least one of the following:

[0466] - Power failure (no power or insufficient power)

[0467] - Converter fault detection

[0468] - Component not detected

[0469] - Operating parameters outside the recommended range (e.g., pressure, flow rate, temperature, PaO2)

[0470] - Test alarm faults that generate detectable alarm signals.

[0471] When a fault condition is detected, the corresponding algorithm indicates the presence of a fault through one or more of the following:

[0472] - Issue audible, visual, and / or dynamic (e.g., vibration) alarms.

[0473] - Send messages to external devices

[0474] -Record events

[0475] 7.5 Humidifier

[0476] In one form of this technology, a humidifier 5000 is provided (e.g., such as...). Figure 5A As shown), it changes the absolute humidity of the air or gas used to deliver the patient relative to ambient air. Typically, the humidifier 5000 is used to increase the absolute humidity of the airflow and increase the temperature of the airflow (relative to ambient air) before it is delivered to the patient's airway.

[0477] The humidifier 5000 may include a humidifier reservoir 5110, a humidifier inlet 5002 for receiving an airflow, and a humidifier outlet 5004 for delivering a humidified airflow. In some forms, such as Figure 5A and Figure 5B As shown, the inlet and outlet of the humidifier reservoir 5110 can be a humidifier inlet 5002 and a humidifier outlet 5004, respectively. The humidifier 5000 may also include a humidifier base 5006, which may be adapted to receive the humidifier reservoir 5110 and includes a heating element 5240.

[0478] 7.6 Respiratory Pressure Therapy Mode

[0479] Depending on the values ​​of parameters A and P0 in the treatment pressure equation (6) used by the treatment parameter determination algorithm 4329 in one form of this technology, various respiratory pressure treatment modes can be implemented by the RPT device 4000.

[0480] 7.6.1 CPAP Therapy

[0481] In some implementations, the pressure support A is also zero, so the treatment pressure Pt is equal to the baseline pressure P0 throughout the respiratory cycle. These implementations are typically categorized under the heading of CPAP therapy. In such implementations, it is not necessary to determine the phase Φ or waveform template Π(Φ) for the treatment engine module 4320.

[0482] 7.6.2 Ventilation therapy

[0483] In other implementations, the value of pressure support A in equation (6) can be positive. This implementation is referred to as ventilation therapy. In some forms of ventilation therapy (referred to as fixed pressure support ventilation therapy), pressure support A is fixed at a predetermined value, for example, 10 cmH2O. The predetermined value of pressure support A is the setting of the RPT device 4000 and can be set, for example, by hard coding during the configuration of the RPT device 4000 or manually input via the input device 4220.

[0484] The pressure support value A can be limited to a range defined as [Amin, Amax]. The pressure support limits Amin and Amax are settings of the RPT device 4000, for example, set during the configuration of the RPT device 4000 by hard coding or manually entered via the input device 4220. The minimum pressure support Amin of 3 cmH2O is approximately 50% of the pressure support required to perform all respiratory work of a typical patient in a steady state. The maximum pressure support Amax of 12 cmH2O is approximately twice the pressure support required to perform all respiratory work of a typical patient, thus sufficient to support the patient's breathing if they cease making any effort, but below this value would be uncomfortable or dangerous.

[0485] 7.6.2.1 Automated Titration of EPAP

[0486] In ventilation therapy, the baseline pressure P0 is sometimes referred to as EPAP. EPAP can be set by a process called titration or by establishing a constant value. This constant EPAP can be set, for example, during the configuration of the RPT device 4000 by hard coding or manually entered via the input device 4220. This alternative is sometimes referred to as fixed EPAP ventilation therapy. Titration of the constant EPAP for a given patient can be performed by a clinician using a PSG study conducted during the titration.

[0487] Alternatively, the treatment parameter determination algorithm 4329 can repeatedly calculate the EPAP during ventilation therapy. In such an implementation, the treatment parameter determination algorithm 4329 repeatedly calculates the EPAP as a function of an index or measurement of upper airway instability returned by the corresponding algorithm in the treatment engine module 4320, such as one or more of inspiratory flow limitation and apnea. Because the repeated calculation of EPAP is similar to a clinician manually adjusting EPAP during EPAP titration, this process is sometimes referred to as automatic EPAP titration, and the overall treatment is referred to as automatic titration EPAP ventilation therapy, or automatic EPAP ventilation therapy.

[0488] Airway patency is particularly important in automated EPAP ventilation. When the pressure support A is greater than a certain threshold (e.g., 10 cmH2O), if the apnea detection algorithm 4325 detects apnea, it can be inferred that the airway is closed, because otherwise the pressure support A would be large enough to generate a certain type of respiratory flow and apnea would not be detected.

[0489] However, when the pressure support A is less than the threshold, if the apnea detection algorithm 4325 detects apnea, the RPT device 4000 cannot immediately infer that the airway is closed. Therefore, when the RPT device 4000 is in an apnea state, the airway patency determination algorithm 4327 repeatedly determines the measure of airway patency. As the apnea state persists, the apnea detection algorithm 4325 accumulates the duration for which the upper airway is confirmed to be closed. This accumulated duration is defined as the effective duration of the apnea.

[0490] At the end of the apnea, the RPT device 4000 determines the EPAP increment (prescription) based on the effective duration of the apnea. The longer the effective duration of the apnea, the larger the increment.

[0491] Figure 8A This is a flowchart illustrating an automated titration method 8000 suitable for use in conjunction with non-invasive ventilation therapy. Method 8000 can be repeated as part of a treatment parameter determination algorithm 4329.

[0492] Method 8000 automatically titrates EPAP by maintaining and updating a “desired” EPAP value, which is a target value or set point for repeatedly adjusting the actual EPAP. The desired EPAP is updated based on the degree of inspiratory flow restriction determined by inspiratory flow restriction determination algorithm 4324, the M-shape determined by M-shape detection algorithm 4326, recent apnea detected by apnea detection algorithm 4325, and the current EPAP.

[0493] Method 8000 automatically titrates EPAP within the range of [EPAPmin, EPAPmax]. The lower and upper limits of EPAP, EPAPmin and EPAPmax, are settings of the RPT device 4000, for example, set by hard coding during the configuration of the RPT device 4000 or manually entered via the input device 4220.

[0494] The pressure support limits Amax and Amin, EPAP limits EPAPmin and EPAPmax, are interdependent with Plimit, where Plimit is the total maximum pressure that the RPT unit 4000 can deliver. These interdependencies can be expressed as follows:

[0495] EPAPMin+AMax≤Plimit

[0496] EPAPMax+Amin≤Plimit

[0497] During automated EPAP ventilation, pressure support A can be reduced (not less than Amin) to allow EPAP to increase and stabilize the upper airway. In other words, maintaining a stable upper airway with appropriate EPAP should be prioritized first, followed by ventilation therapy.

[0498] Method 8000 begins at step 8010, which calculates an increase in EPAP, ShapeRxIncrease, based on the abnormal inspiratory waveform shape. Specifically, this is determined by the inspiratory flow limit determination algorithm 4324 and the degree of M-shape determination by the M-shape detection algorithm 4326. Step 8010 is referred to as the "shape doctor" because its output, ShapeRxIncrease, is a "prescription" based on the shape of the inspiratory portion of the respiratory flow waveform. See below for reference. Figure 8B Step 8010 is described in more detail.

[0499] The next step, 8020, calculates an increase in EPAP, ApneaRxIncrease, based on one or more recent apneas detected by the apnea detection algorithm 4325. Step 8020 is referred to as the "apnea doctor" because its output, ApneaRxIncrease, is a "prescription" based on the severity of one or more recent apneas. See below for reference. Figure 8E Step 8020 is described in more detail.

[0500] The next step is step 8030, where the EPAP automated titration method 8000 uses the current EPAP value and the increases in ShapeRxIncrease and ApneaRxIncrease initiated by the Shape Doctor (step 8010) and Apnea Doctor (step 8020) to update the desired EPAP. See below for reference. Figure 8GStep 8030 is described in more detail.

[0501] The final step 8040 converts the current EPAP to the desired EPAP at a conversion rate of 1 cmH2O per second (for increasing the current EPAP) or -1 cmH2O per second (for decreasing the current EPAP). In one implementation, the current EPAP does not increase during inhalation (i.e., when the phase is between 0 and 0.5), but may decrease during inhalation.

[0502] 7.6.2.1.1 Shape Doctor

[0503] Figure 8B This is a flowchart illustrating method 8100, which can be used to implement step 8010 (“Shape Doctor”) of method 8000.

[0504] Method 8100 begins at step 8105, which checks whether the expected normal ventilation Vnorm is less than a threshold, which in one implementation is set to 1 liter / minute. As mentioned above, the expected normal ventilation Vnorm can be the typical recent ventilation Vtyp returned by the typical recent ventilation determination algorithm 4328. If so (“Y”), step 8110 sets ShapeRxIncrease to zero, because such a low value indicates some type of error condition, and it would be unwise to continue calculating the EPAP increase.

[0505] Otherwise (“N”), step 8115 calculates the current inspiratory and expiratory tidal volumes Vi and Ve from the respiratory flow signal Qr. Step 8120 averages the two tidal volumes Vi and Ve to obtain the average tidal volume Vt. The next step 8125 calculates “ventilation” (in liters per minute) by multiplying the average tidal volume Vt by 60 seconds and dividing by the duration Ttot of the current breath. Then, step 8130 divides the ventilation by the expected normal ventilation Vnorm to obtain a unitless value for relative ventilation. If relative ventilation is significantly less than 1, the current breath is smaller than the expected normal ventilation Vnorm, indicating significant inadequate ventilation. Step 8135 formalizes this relationship by calculating the variable significantBreathHypoventilation, which decreases from 1 to 0 as relative ventilation increases beyond the inadequate ventilation threshold. In one implementation, step 8135 calculates significant Breath Hypotitilation as follows:

[0506] significantBreathHypoventilation = Fuzzy deweighting (

[0507] relativeVentilation, LOWER_HYPOVENT_THRESH, 1.0, UPPER_HYPOVENT_THRESH, 0.0) where LOWER_HYPOVENT_THRESH and UPPER_HYPOVENT_THRESH are constant thresholds, which are equal to 0.2 and 0.8 respectively in one implementation.

[0508] Conversely, if relative ventilation is significantly greater than 1, the current breathing is larger than the expected normal ventilation (Vnorm), indicating significant hyperventilation.

[0509] In the next step 8140, method 8100 calculates the variable recentBreathFlowLimitedHypovent from the current and most recent values ​​of significantBreathHypoventilation. The general effect of step 8140 is that recentBreathFlowLimitedHypovent is higher when there is a recent, persistent, significant inadequacy of ventilation, combined with flow limitation or an M-shaped inspiratory waveform across multiple breaths rather than just a single breath instance. (See below for reference.) Figure 8C Step 8140 is described in more detail.

[0510] Method 8000 continues from step 8145, which calculates the fuzzy truth variable mShapeRxProportion, which reflects the (real-valued) M-shape indicator value variable RxProportion returned by the M-shape detection algorithm 4326. In one implementation, step 8145 takes the maximum value of RxProportion and 0; in other words, 0 is set as the base value of RxProportion. Then, step 8150 calculates the fuzzy truth variable flowLimitedValue as a "fuzzy OR" of the fuzzy truth variable mShapeRxProportion (representing the degree of M-shape) and flowLimitation (representing the degree of flow restriction).

[0511] The next step, 8155, calculates the flow limit threshold, flowLimitedThreshold, to be compared with flowLimitedValue. The flow limit threshold, flowLimitedThreshold, is calculated from the current value of EPAP such that it increases as the current value of EPAP increases. In this way, the degree of anomalousness in the inspiratory waveform shape required for an increase in EPAP increases with the increase in EPAP itself. In one implementation, step 8155 calculates flowLimitedThreshold as follows:

[0512] flowLimitedThreshold = Fuzzy unweighting (

[0513] EPAP, LOWER_EPAP_THRESH, 0.0, MIDDLE_EPAP_THRESH, 0.3, UPPER_EPAP_THRESH, 0.8)

[0514] LOWER_EPAP_THRESH, MIDDLE_EPAP_THRESH and UPPER_EPAP_THRESH are constant thresholds, which in one implementation are equal to 14 cmH2O, 18 cmH2O and 20 cmH2O, respectively.

[0515] The ShapeRxIncrease at the EPAP prescription is set proportionally to the amount by which the flowLimitedValue exceeds the flowLimitedThreshold (if any). This increase can be set as a function of one or more multipliers, such as dynamically calculated multipliers. Therefore, method 8100 may first calculate one or more of the following three multipliers with respect to a proportionality constant, and these can be collectively considered as multipliers. For example, step 8160 calculates the base pressure-related multiplier flowLimitedRxMultiplier, such that flowLimitedRxMultiplier, and therefore the final prescription ShapeRxIncrease, typically decreases with the current increase in EPAP. In one implementation, flowLimitedRxMultiplier is calculated as follows:

[0516] flowLimitedRxMultiplier = Fuzzy Deweighting (

[0517] EPAP,LOWER_MULT1_THRESH, 1.0, MIDDLE_MULT1_THRESH, 0.7,MIDDLE2_MULT1_THRESH, 0.4, UPPER_MULT1_THRESH, 0.0,)

[0518] LOWER_MULT1_THRESH, MIDDLE_MULT1_THRESH, MIDDLE2_MULT1_THRESH and UPPER_MULT1_THRESH are constant thresholds, which in one implementation are equal to 10 cmH2O, 15 cmH2O, 19 cmH2O and 20 cmH2O respectively.

[0519] Step 8165 calculates the leakage-related multiplier leakRxMultiplier, such that leakRxMultiplier, and therefore prescription ShapeRxIncrease, generally decreases as the leakage flow estimate Q1 (from leakage flow estimation algorithm 4316) increases. In one implementation, leakRxMultiplier is calculated as follows:

[0520] leakRxMultiplier = Fuzzy Deweighting (

[0521] Ql,LOWER_LEAK_THRESH, 1.0,UPPER_LEAK_THRESH, 0.0)

[0522] LOWER_LEAK_THRESH and UPPER_LEAK_THRESH are constant thresholds, set to 30 liters / minute and 60 liters / minute respectively in one implementation.

[0523] Then, step 8170 calculates the ventilation-related multiplier flowLimRxPropRelVent based on the current respiratory ventilation relative to the expected normal ventilation Vnorm (denoted as relativeVentilation, calculated in step 8130), the amount of flow restriction or M-shape (denoted as flowLimitedValue, calculated in step 8150), and / or the amount of significant respiratory inadequacy due to recent persistent flow restriction (denoted as recentBreathFlowLimitedHypovent, calculated in step 8140). See below for reference. Figure 8D Step 8170 is described in more detail.

[0524] The final step, 8175, calculates the Shape Doctor's prescription, ShapeRxIncrease, by first checking if flowLimitedValue exceeds flowLimitedThreshold. If not, ShapeRxIncrease is set to zero. Otherwise, step 8175 calculates ShapeRxIncrease as follows:

[0525] ShapeRxIncrease=(flowLimitedValue-flowLimitedThreshold) *

[0526] flowLimitedRxMultiplier * leakRxMultiplier*

[0527] flowLimRxPropRelVent *EPAP_GAIN

[0528] EPAP_GAIN is a constant. In one implementation, EPAP_GAIN is set to 0.2 cmH2O.

[0529] Figure 8C This is a flowchart of method 8200, illustrating step 8140 of method 8100 for implementing a method for calculating a variable named recentBreathFlowLimitedHypovent. As described above, the general effect of step 8140 is that recentBreathFlowLimitedHypovent is higher when there is a recent persistent flow limitation or significant M-shaped inadequacy of ventilation for multiple breaths rather than just a single breath instance.

[0530] Method 8200 begins at step 8210, which calculates the variable OHV by multiplying the maximum of the M-shape indicator value RxProportion and the flow limitation indicator value flowLimitation by the significantBreathHypotitilation calculated in step 8135. OHV represents obstructive (i.e., flow-limiting) inadequate ventilation, where the value of OHV represents the current breath. Step 8210 stores the OHV value of the current position in a recirculation buffer representing a small number of recent breaths. In one implementation, the recirculation buffer contains eight entries. Step 8220 then checks whether the recirculation buffer is full. If not (“N”), method 8200 sets recentBreathFlowLimitedHypovent to zero in step 8230 because there is insufficient stored information to indicate recent persistent flow limitation or significant M-shape inadequate ventilation. Otherwise (“Y”), step 8240 applies adjacency weighting to each entry in the recirculation buffer. The adjacency weight is a function of entries in the circular buffer and their predecessors, such that the adjacency-weighted entry is highest when two entries are approximately equal and close to 1. In one implementation, step 8240 calculates the adjacency-weighted OHV as follows:

[0531] The adjacency-weighted OHV = OHV + (1–OHV) * Min (OHV, OHVPrev)

[0532] Here, OHVPrev is the predecessor of the current entry OHV in the circular buffer. In other implementations, other functions of OHV and OHVPrev can be used in step 8240, such as arithmetic or geometric mean.

[0533] The next step 8250 of method 8200 sums the squared values ​​of the adjacent weighted OHVs for the circular buffer. In one implementation, each squared value is weighted with the highest weight for the current entry and decreases toward zero as the entry becomes less recent. In one implementation, the weights (acting inversely through the circular buffer) are {1, 0.95, 0.9, 0.8, 0.7, 0.55, 0.4, 0.25}.

[0534] Finally, step 8260 calculates recentBreathFlowLimitedHypovent as the square root of the sum of (weighted) squared values ​​divided by the sum of weights used in step 8250 (if used, otherwise divided by the number of squared values). In other words, recentBreathFlowLimitedHypovent is the root mean square of the nearest adjacent weighted values ​​of the inadequate ventilation indication value with obvious flow limitation (possibly with the largest weight given to the most recent value).

[0535] Figure 8D This is a flowchart of method 8300, which can be used to implement step 8170 of method 8100. As described above, step 8170 calculates the ventilation-related multiplier flowLimRxPropRelVent with respect to the shape doctor's final prescription based on an indicator value of the relative magnitude of the current breath, relativeVentilation, the amount of flow restriction or M-shape (denoted as flowLimitedValue), and an indicator value of the amount of recent persistent flow restriction-induced hypoventilation, recentBreathFlowLimitedHypovent.

[0536] The general effect of step 8170 is that flowLimRxPropRelVent typically has a neutral value of 1, but tends to decrease below 1 as relative ventilation exceeds the flow restriction threshold that increases with the amount of flow restriction or M-shape. That is, shape doctor's prescriptions tend to be discounted if relative hyperventilation occurs. However, as the severity of flow restriction or M-shape increases, the amount of relative hyperventilation required to discount a shape doctor's prescription increases. If relative ventilation is significantly less than 1, flowLimRxPropRelVent increases, possibly above 1, thus amplifying a shape doctor's prescription, generally proportional to the indicator value of recent persistent flow restriction or significant respiratory hypoventilation in M-shapes (recentBreathFlowLimitedHypovent).

[0537] Method 8300 begins at step 8310, which checks whether the value of relativeVentilation (calculated in step 8130) is greater than or equal to 1, i.e., respiratory ventilation is greater than or equal to the expected normal ventilation Vnorm. If yes (“Y”), method 8300 proceeds to step 8320, where the variable severeFlowLimitation is calculated, typically increasing from 0 to 1 as the amount of flow restriction or M-shape (represented by the variable flowLimitedValue) increases. In one implementation, step 8320 calculates severeFlowLimitation as follows:

[0538] severeFlowLimitation = Fuzzy Deweighting (

[0539] flowLimitedValue, LOWER_FL_THRESH, 0.0, UPPER_FL_THRESH, 1.0 )

[0540] LOWER_FL_THRESH and UPPER_FL_THRESH are constant thresholds, which are equal to 0.7 and 0.9 respectively in one implementation.

[0541] The next step, 8330, calculates the lower and upper relative ventilation thresholds, lowerRelVentThreshold and upperRelVentThreshold, with respect to relative ventilation based on the value of severeFlowLimitation calculated in step 8320. The lower and upper relative ventilation thresholds, lowerRelVentThreshold and upperRelVentThreshold, are at least 1.0 and typically increase proportionally to severeFlowLimitation. In one implementation, step 8330 calculates the lower and upper relative ventilation thresholds, lowerRelVentThreshold and upperRelVentThreshold, from severeFlowLimitation as follows:

[0542] lowerRelVentThreshold=1 + 0.7*severeFlowLimitation

[0543] upperRelVentThreshold=1.5 + 0.7*severeFlowLimitation

[0544] Finally, step 8340 calculates flowLimRxPropRelVent so that, typically relative to the relative ventilation lowerRelVentThreshold and upperRelVentThreshold calculated in step 8330, they decrease from 1 to 0 as relative ventilation increases. In one implementation, step 8340 calculates flowLimRxPropRelVent as follows:

[0545] flowLimRxPropRelVent = Fuzzy Deweighting (

[0546] relativeVentilation, lowerRelVentThreshold, 1.0,upperRelVentThreshold, 0.0)

[0547] Returning to step 8310, if step 8310 finds that relativeVentilation is less than 1 (“N”), then step 8350 checks whether the current breathing is significant hypoventilation by examining whether the variable significantBreathHypoventilation, calculated in step 8135, is greater than 0. (In the above implementation, significantBreathHypoventilation is greater than zero only if relativeVentilation is less than UPPER_HYPOVENT_THRESH). If not (“N”), then step 8370 sets flowLimRxPropRelVent to a neutral value of 1.0. Otherwise (“Y”), step 8360 calculates flowLimRxPropRelVent to increase proportionally to the indicator value recentBreathFlowLimitedHypovent, which is typically associated with recent persistent flow restriction or significant M-shaped hypoventilation. In one implementation, step 8360 calculates flowLimRxPropRelVent as follows:

[0548] flowLimRxPropRelVent=0.5 + 2*recentBreathFlowLimitedHypovent

[0549] Then method 8300 ends.

[0550] 7.6.2.1.2 Sleep Apnea Doctor

[0551] Figure 8E This is a flowchart illustrating a method 8400 that can be used to implement step 8020 (“sleep apnea doctor”) of method 8000.

[0552] As described above, the purpose of the sleep apnea physician is to calculate the EPAP increase ApneaRxIncrease based on the sleep apnea detected by the sleep apnea detection algorithm 4325.

[0553] In one implementation, the apnea detection algorithm 4325, once finished, places the apneas it detected (each feature being the start time, end time, and effective duration) into a single list of pending apneas.

[0554] Therefore, method 8400 begins at step 8410 by sorting the list of apneas to be treated in ascending order of their start times. The next step, 8420, removes any duplicates (i.e., apneas with the same start and end times) from the sorted list of apneas to be treated. The next step is step 8430, which removes any apneas that have already been treated by an apnea physician from the sorted list.

[0555] In the next step 8440, method 8400 resolves the (potentially overlapping) apneas in the sorted list into non-overlapping apneas. Then, step 8450 processes the completed apneas in the sorted non-overlapping list in ascending order of start time. See below for reference. Figure 8F The treatment of apnea in step 8450 is described in more detail. The treatment of apnea in the list in step 8450 results in an increase in EPAP due to apnea, referredEPAP. Then, step 8460 calculates the increase in EPAP due to apnea, ApneaRxIncrease, based on referredEPAP and the current value of EPAP. In one implementation, step 8460 calculates ApneaRxIncrease as follows:

[0556] ApneaRxIncrease=prescribedEPAP–EPAP–ShapeRxIncrease(7)

[0557] In one implementation, step 8460 clips the following ApneaRxIncrease to zero, such that ApneaRxIncrease cannot be negative. In an alternative implementation, step 8460 calculates ApneaRxIncrease as follows:

[0558] ApneaRxIncrease=prescribedEPAP–EPAP(8)

[0559] If step 8460 uses equation (7) to calculate ApneaRxIncrease, then when step 8030 calculates the desired EPAP as described below, the effect is to increase the desired EPAP by the larger of ApneaRxIncrease and ShapeRxIncrease. If step 8460 uses equation (8) to calculate ApneaRxIncrease, then when step 8030 calculates the desired EPAP, the effect is to increase the desired EPAP by the sum of ApneaRxIncrease and ShapeRxIncrease.

[0560] Step 8460 can also use the desired EPAP instead of the current EPAP in equations (7) and (8).

[0561] Then method 8400 ends.

[0562] Figure 8F This is a flowchart illustrating a method 8500 for treating apnea that can be used to implement step 8450 of method 8400. The output of method 8500 is an increase in EPAP (single Apnea Increase) at the onset of apnea, which increases broadly with the effective duration of apnea.

[0563] One implementation of step 8450 of method 8400 executes method 8500 once for each apnea in the list. In this implementation, before the first iteration of method 8500 to process the first apnea in the list, step 8450 sets the EPAP (output of step 8450) caused by the apnea to zero. After each iteration of method 8500, step 8450 increments the prescribedEPAP by the value of singleApneaIncrease returned by that iteration.

[0564] Method 8500 begins at step 8510, which determines whether the effective duration of the apnea is greater than or equal to a duration threshold, which in one implementation is equal to 9 seconds. If not (“N”), method 8500 ends at step 8590, which increments the EPAP at the start of the apnea by setting singleApneaIncrease to 0. Otherwise (“Y”), method 8500 proceeds to step 8520, which calculates a variable called HighApneaRolloffPressure, which method 8500 increments the current EPAP to if the apnea is indefinite within the effective duration. In one implementation, step 8520 calculates HighApneaRolloffPressure as the maximum of EPAPmax + 2 cmH2O and the minimum value of HighApneaRolloffPressure (a constant set to 12 cmH2O in one implementation). Therefore, HighApneaRolloffPressure may be greater than the value of EPAPmax.

[0565] The next step, 8530, calculates the rate constant of EPAP close to High Apnea Rolloff Pressure so that it decreases as High Apnea Rolloff Pressure increases. In one embodiment of step 8530, the rate constant k (in s) -1 (in units) Calculate as follows:

[0566] k=(1.333 / 60)*(10 / HighApneaRolloffPressure)

[0567] The next step is step 8540, in which method 8500 calculates the variable EPApincreaseWeightingFactor, so that it is typically increased gradually from 0 to 1 as the effective duration of apnea increases. In one implementation, step 8540 calculates EPApincreaseWeightingFactor using a rate constant k, as follows:

[0568] EPAPIncreaseWeightingFactor=1–exp(-k*effectiveDuration)

[0569] Method 8500 ends at step 8550, which calculates singleApneaIncrease as the product of the differences between EPAPIncreaseWeightingFactor and HighApneaRolloffPressure and the current value of prescribedEPAP:

[0570] singleApneaIncrease=EPAPIncreaseWeightingFactor *(HighApneaRolloffPressure-prescribedEPAP)

[0571] Figure 8G This is a flowchart illustrating a method 8600 that can be used to implement step 8030 of method 8000. As described above, step 8030 updates the desired EPAP using the current EPAP, and the EPAP is increased by ShapeRxIncrease and ApneaRxIncrease respectively opened by the shape doctor (step 8010) and the sleep apnea doctor (step 8020).

[0572] Method 8600 begins at step 8610, which checks whether ShapeRxIncrease or ApneaRxIncrease is greater than zero. If yes (“Y”), then step 8620 increments the desired EPAP by the sum of ShapeRxIncrease and ApneaRxIncrease. Then, the next step 8630 clips the incremented desired EPAP to the range [EPAPmin, EPAPmax]. That is, step 8630 sets the desired EPAP to the minimum of the increment from step 8620 and EPAPmax, and the maximum of the increment from step 8620 and EPAPmin.

[0573] If step 8610 finds that neither the shape doctor nor the sleep apnea doctor has initiated a positive increase in EPAP (“N”), then method 8600 exponentially decays the desired EPAP to EPAPmin. First, the decay factor decayFactor is calculated to scale the exponential decay. This calculation is performed in one of two branches, depending on whether the current value of EPAP exceeds EPAPmin by 4 cmH2O or is lower (checked in step 8640). If yes (“Y”), then step 8650 sets the decayFactor to the difference between the current EPAP and EPAPmin. If not (“N”), then step 8660 calculates the decayFactor to increase more slowly as the difference between the current EPAP and EPAPmin increases. In one implementation of step 8660, the value of decayFactor is calculated as follows, where the pressure unit is cmH2O:

[0574] decayFactor=4+0.5 * ((current EPAP–EPAPmin)-4)

[0575] Finally, in step 8670 after step 8660 or step 8650, method 8600 reduces the desired EPAP by an amount proportional to the value of the decayFactor calculated in step 8650 or step 8660:

[0576] desired EPAP=desired EPAP–decayFactor*(1-exp(-timeDiff / timeConstant))

[0577] Where timeDiff is the time elapsed (in seconds) since the last update of the expected EPAP, and timeConstant is the decay time constant (in seconds). In one implementation, timeConstant is 20 minutes * 60.

[0578] Figure 9A Includes events that respond to traffic restrictions. Figure 8AFigure 9000 is a diagram illustrating an embodiment of the behavior of the EPAP automated titration method 8000. Figure 9000 includes a trace 9010 of a therapeutic pressure Pt with fixed pressure support (as indicated by arrow 9015). The therapeutic pressure trace 9010 shows an increase 9025 of EPAP in response to a first episode 9020 of flow restriction. Following the increase 9025 of EPAP, a second episode 9030 of flow restriction occurs. The second episode 9030 results in a second increase 9035 of EPAP. The second increase 9035 of EPAP successfully resolves the flow restriction, leading to an episode of normal breathing 9040, which results in a gradual decrease 9045 of EPAP. Dashed line 9050 represents the upper limit of EPAP, EPAPmax. Dashed line 9060 represents the total upper limit of therapeutic pressure Pt, Plimit. Dashed line 9070 represents the lower limit of EPAP, EPAPmin.

[0579] Figure 9B Includes apnea in response to closure Figure 8A Figure 9100 illustrates an embodiment of the behavior of the EPAP automated titration method 8000. Figure 9100 includes an upper trace 9105 of the therapeutic pressure Pt and a lower trace 9110 of the respiratory flow Qr. A pause 9115 is detected, during which three probe breaths 9120 are delivered, each lasting for a duration Ti. Analysis of the respiratory flow Qr by the airway patency determination algorithm 4327 determines that the pause 9115 is closed. At the end of the pause 9125, EPAP is increased at a fixed rate of 1 cmH2O / sec during the continuous inspiratory portion 9130 until it reaches the desired EPAP 9135.

[0580] Figure 9C Includes instructions demonstrating response to mixed apnea. Figure 7HFigure 9200 illustrates an embodiment of the behavior of the airway patency determination algorithm 4327. Figure 9200 includes a trace 9205 for the treatment pressure Pt, a trace 9210 for the respiratory flow Qr, a trace 9215 indicating the output of the airway patency determination algorithm 4327, and a trace 9220 indicating the output of the apnea detection algorithm 4325. At time 9225, the respiratory flow drops sharply, and the apnea detection trace 9220 rises shortly after 9230, indicating that an apnea state has been detected. The treatment pressure trace 9205 shows the delivery of a series of probe breaths, the first probe breath being 9208. The analysis of the respiratory flow Qr by the airway patency determination algorithm 4327 determines that the apnea was initially closed, therefore the airway patency trace 9215 rises at point 9235 to a value indicating a closed airway. Then the flow trace 9210 begins to show a clear response to probe breaths, such as probe breath 9243, at point 9240. The airway patency determination algorithm 4327 analyzes the respiratory flow rate Qr to determine that apnea is open, therefore the airway patency trace 9215 drops to the value indicating an open airway at point 9245. Then, the flow rate trace 9210 begins to show no significant response to probed breathing at point 9250, for example, probed breathing at 9253. The airway patency determination algorithm 4327 analyzes the respiratory flow rate Qr to determine that apnea is closed, therefore the airway patency trace 9215 returns to the value indicating a closed airway at point 9255.

[0581] Figure 9D Include Figure 9C The various portions of Figure 9200 are extensions. Trace 9300 is an extension of the therapeutic pressure trace 9205 during breath detection 9208, and trace 9310 is an extension of the respiratory flow trace 9210 during breath detection 9208. The respiratory size indication value Sz of breath detection 9208 (calculated using respiratory flow trace 9310 via step 7730) is lower than the lower threshold calculated via step 7740, therefore method 7700 determines in step 7770 that the airway is closed.

[0582] Trace 9320 is an extension of the therapeutic pressure trace 9205 during probing breath 9243, and trace 9330 is an extension of the respiratory flow trace 9210 during probing breath 9243. Arrow 9321 indicates the range of the trimmed inspiratory waveform returned from step 7710. The straight-line approximation 9322 of the trimmed inspiratory waveform has a y-intercept 9324 and a final value 9326. The inspiratory flatness ratio calculated in step 7810 as the ratio of 9326 to 9324 is found to be below the inspiratory threshold in step 7840, therefore method 7700 determines in step 7870 that the airway is open.

[0583] Trajectory 9340 is an extension of the treatment pressure trace 9205 during probed respiration 9253, and trace 9350 is an extension of the respiratory flow trace 9210 during probed respiration 9253. Arrow 9331 indicates the range of the trimmed inspiratory waveform returned from step 7710. The straight-line approximation 9332 of the trimmed inspiratory waveform has a y-intercept 9334 and a final value 9336. The inspiratory flatness ratio, calculated in step 7810 as a ratio of 9336 to 9334, is found to be higher than the inspiratory threshold in step 7840, and the expiratory flatness ratio is also found to be higher than the expiratory threshold in step 7850; therefore, method 7700 determines in step 7860 that the airway is closed.

[0584] 7.7 Glossary

[0585] For the purposes of this technical disclosure, one or more of the following definitions may be applied in certain forms of this technology. Alternative definitions may be applied in other forms of this technology.

[0586] 7.7.1 General Rules

[0587] Air: In some forms of this technology, air may be considered to mean atmospheric air, and in other forms of this technology, air may be considered to mean some other combination of breathable gases, such as oxygen-rich atmospheric air.

[0588] Environment: In some forms of this technology, the term environment may have the following meanings: (i) outside the treatment system or the patient, and (ii) directly surrounding the treatment system or the patient.

[0589] For example, the ambient humidity relative to the humidifier can be the humidity of the air directly surrounding the humidifier, such as the humidity inside the patient's sleeping room. This ambient humidity can differ from the humidity outside the patient's sleeping room.

[0590] In another instance, environmental stress can be stress that is directly around the body or outside the body.

[0591] In some forms, ambient (e.g., acoustic) noise can be considered as the background noise level in the patient's room, excluding noise generated by, for example, the RPT device or from the mask or patient interface. Ambient noise can be generated by sound sources outside the room.

[0592] Respiratory pressure therapy (RPT): Applying air supply to the airway inlet at a therapeutic pressure that is typically positive relative to the atmosphere.

[0593] Continuous positive airway pressure (CPAP) therapy: In this therapy, the treatment pressure is approximately constant throughout the patient's respiratory cycle. In some forms, the pressure at the airway inlet will be slightly higher during expiration and slightly lower during inspiration. In other forms, the pressure will vary between different respiratory cycles, for example, increasing in response to an indication of partial upper airway obstruction and decreasing in response to the absence of such an indication.

[0594] Patient: A person, whether or not they have a respiratory disorder.

[0595] Automated positive airway pressure (APAP) therapy: CPAP therapy in which the treatment pressure is automatically adjustable between a minimum and a maximum, for example, varying with each breath, depending on the presence of an indication of an SBD event.

[0596] 7.7.2 Multiple Aspects of Respiration and Circulation

[0597] Apnea: According to some definitions, apnea is considered to occur when the respiratory flow rate is below a predetermined threshold for a sustained period of time (e.g., 10 seconds). Obstructive apnea is considered to occur when airway obstruction prevents airflow despite patient effort. Open apnea is considered to occur when apnea is detected due to reduced or absent breathing activity, even though the airway is open (patent). Mixed apnea occurs when reduced or absent breathing effort is consistent with airway obstruction.

[0598] Respiratory rate: The rate at which a patient breathes spontaneously, usually measured as expiratory volume per minute.

[0599] Duty cycle or inspiratory fraction: the ratio of inspiratory time Ti to total respiratory time Ttot.

[0600] Effort (breathing): The work done by a person attempting to breathe voluntarily is considered breathing effort.

[0601] The expiratory portion of the respiratory cycle: the time period from the start of expiratory flow to the start of inspiratory flow.

[0602] Flow restriction: Flow restriction is considered a state of respiratory function in which an increase in the patient's effort does not result in a corresponding increase in flow. Flow restriction occurring during the inspiratory portion of the respiratory cycle is considered inspiratory flow restriction. Flow restriction occurring during the expiratory portion of the respiratory cycle is considered expiratory flow restriction.

[0603] Types of flow-limiting inhalation waveforms:

[0604] (i) (classical) flat: after rising, there is a relatively flat section, and then a falling section.

[0605] (ii) M-shape: has two local peaks, one in the early part and one in the later part, and a relatively flat part between the two peaks.

[0606] (iii) Chair-shaped: has a single local peak, with the peak located in the early part, followed by a relatively flat part.

[0607] (iv) Inverted chair shape: with a relatively flat portion followed by a single local peak, the peak of which is located in the later portion.

[0608] Insufficient breathing: Reduced flow, but not stopped. In one form, insufficient breathing is considered to have occurred when the flow rate drops below a threshold level for a period of time. Central insufficiency can be considered to have occurred when insufficient breathing is detected due to reduced respiratory effort.

[0609] Hyperventilation: The flow rate increases to a level higher than normal.

[0610] Inadequate ventilation: This refers to a condition where the amount of gas exchange occurring within a certain time frame is less than the patient's current requirements.

[0611] Hyperventilation: Hyperventilation is defined as the rate of gas exchange that occurs within a certain time frame when it exceeds the patient’s current requirements.

[0612] The inspiratory portion of the respiratory cycle: The time period from the start of inspiratory flow to the start of expiratory flow is considered the inspiratory portion of the respiratory cycle.

[0613] Open (airway): The degree to which the airway is open or the extent to which the airway is open. A patented airway is open. Airway openness can be quantified, for example, a value of one (1) indicates patentness, and a value of zero (0) indicates closure (obstruction).

[0614] Positive end-expiratory pressure (PEEP): The pressure in the lungs at the end of expiration is greater than atmospheric pressure.

[0615] Peak flow (Qpeak): The maximum flow rate during the inspiratory portion of the respiratory flow waveform.

[0616] Respiratory flow, air flow, patient air flow, respiratory air flow (Qr): These synonyms can be understood as referring to the estimated respiratory airflow from the RPT device, rather than the "actual respiratory flow" or "actual respiratory airflow," which is the actual respiratory flow experienced by the patient, usually expressed in liters per minute.

[0617] Tidal volume (Vt): The amount of air inhaled or exhaled during each breath during normal breathing without additional effort. This amount can be more specifically defined as inspiratory tidal volume (Vi) or expiratory tidal volume (Ve).

[0618] Inspiratory time (Ti): The duration of the inspiratory portion of the respiratory flow waveform.

[0619] Expiratory time (Te): The duration of the expiratory portion of the respiratory flow waveform.

[0620] Total (breathing) time (Ttot): The total duration between the start of the inspiratory portion of a breathing flow waveform and the start of the inspiratory portion of a subsequent breathing flow waveform.

[0621] Recent typical ventilation: Ventilation values ​​that tend to be concentrated in their surroundings within a predetermined time range, i.e., a measure of the concentration trend of recent ventilation values.

[0622] Upper airway obstruction (UAO): This includes partial and complete upper airway obstruction. This may be related to a state of flow restriction, where the flow rate increases only slightly or may even decrease as the pressure differential in the upper airway increases (Starling resistor behavior).

[0623] Ventilation (air ventilator): A measurement of the total amount of gas being exchanged by a patient's respiratory system. Ventilation measurements may include one or both of the inspiratory and expiratory flow rates per unit time. When expressed in volume per minute, this amount is often referred to as "minute ventilation." Minute ventilation is sometimes simply a quantity and can be understood as the amount per minute.

[0624] 7.7.3 RPT Device Parameters

[0625] Flow rate: The instantaneous volume (or mass) of air delivered per unit time. While flow rate and ventilation have the same volume or mass per unit time, flow rate is measured over a much shorter period. In some cases, the reference to flow rate will be to a scalar quantity, i.e., a quantity with only magnitude. In other cases, the reference to flow rate will be to a vector quantity, i.e., a quantity with both magnitude and direction. When indicating a signed quantity, flow rate can nominally be positive for the inspiratory portion of a patient's respiratory cycle, and therefore negative for the expiratory portion. Flow rate can be given by the symbol Q. 'Flowrate' is sometimes simply abbreviated as 'flow'. Total flow rate Qt is the airflow leaving the RPT device. Tidal flow rate Qv is the airflow leaving the vent to allow the clearance of exhaled gas. Leakage flow rate Q1 is the flow rate leaking from the patient interface system. Respiratory flow rate Qr is the airflow received within the patient's respiratory system.

[0626] Leakage: The word leakage is considered to be undesirable airflow. In one embodiment, leakage may occur due to an incomplete seal between the mask and the patient's face. In another embodiment, leakage may occur in a bend in the conduit leading to the surrounding environment.

[0627] Pressure: Force per unit area. Pressure can be measured in a range of units, including cmH2O (cm⁻² water), gf / cm², and hectopascals (hPa). 1 cmH2O equals 1 g⁻¹ / cm² and is approximately 0.98 hPa. In this specification, unless otherwise stated, pressure is given in cmH2O. Pressure at the patient interface is denoted by the symbol Pm, while the treatment pressure representing the target value obtained from the mask pressure Pm at the current moment is denoted by the symbol Pt.

[0628] 7.7.4 Ventilator Terminology

[0629] Backup rate: A parameter of the ventilator that establishes the minimum respiratory rate (usually measured in breaths per minute) that the ventilator will deliver to the patient without being triggered by spontaneous breathing effort.

[0630] Cycle: The end of the inspiratory phase of a respiratory cycle. When a ventilator delivers breaths to a spontaneously breathing patient, the ventilator is considered to be in a cycle and stops delivering breaths at the end of the inspiratory portion of the respiratory cycle.

[0631] Expiratory Positive Airway Pressure (EPAP): Baseline pressure, the pressure that varies during breathing and is added to this baseline pressure to produce the required mask pressure that the ventilator attempts to achieve at a given time.

[0632] End-expiratory pressure (EEP): The required mask pressure that the ventilator will attempt to achieve at the end of the expiratory phase of breathing. If the pressure waveform template Π(Φ) is zero at the end of expiration, i.e., Π(Φ) = 0 when Φ = 1, then EEP is equal to EPAP.

[0633] Inspiratory Positive Airway Pressure (IPAP): The maximum desired mask pressure that the ventilator will attempt to achieve during the inspiratory phase of breathing.

[0634] Pressure support: This refers to the amount of pressure increase during inspiratory breathing relative to expiratory breathing, typically expressed as the pressure difference between the maximum inspiratory pressure and the baseline pressure (e.g., PS = IPAP - EPAP). In some cases, pressure support means the difference the ventilator is designed to achieve, rather than the difference it actually achieves.

[0635] Servo ventilator: A ventilator that measures patient ventilation with target ventilation, which can adjust the level of pressure support to allow the patient to achieve the target ventilation.

[0636] Spontaneous / Timed (S / T): This refers to the pattern in which a ventilator or other device attempts to detect the onset of spontaneous breathing in a patient. However, if the device fails to detect breathing within a predetermined time period, it will automatically initiate respiratory delivery.

[0637] Oscillation: A term equivalent to pressure support.

[0638] Trigger: When a ventilator delivers air to a patient who is breathing spontaneously, it is assumed to be triggered by the patient's effort at the beginning of the inspiratory portion of the respiratory cycle.

[0639] Ventilator: A mechanical device that provides pressure support to a patient to perform part or all of the work of breathing.

[0640] 7.7.5 Analysis of the Respiratory System

[0641] The diaphragm is a muscular membrane that extends through the bottom of the chest cavity. It separates the thoracic cavity, which contains the heart, lungs, and ribs, from the abdominal cavity. When the diaphragm contracts, the volume of the thoracic cavity increases, allowing air to enter the lungs.

[0642] The larynx: The larynx or larynx contains the vocal cords and connects the lower part of the pharynx (hypopharynx) to the trachea.

[0643] Lungs: The human respiratory organ. The conduction area of ​​the lungs includes the trachea, bronchi, bronchioles, and terminal bronchioles. The respiratory area includes the respiratory bronchioles, alveolar ducts, and alveoli.

[0644] Nasal cavity: The nasal cavity (or nasal socket) is the space in the middle of the face above and behind the nose, filled with a large amount of air. The nasal cavity is divided into two parts by a vertical fin called the nasal septum. On both sides of the nasal cavity are three horizontally outward-growing nasal conchas (odd numbers are called nasal turbinates). The anterior part of the nasal cavity is the nose, while the posterior part merges with the nasopharynx through the posterior nasal aperture.

[0645] Pharynx: The pharynx is located directly below the nasal cavity (lower part), above the esophagus and larynx. The pharynx is usually divided into three parts: the nasopharynx (upper pharynx) (the nasal part of the pharynx), the oropharynx (middle pharynx) (the oral part of the pharynx), and the laryngopharynx (lower pharynx).

[0646] 7.7.6 Mathematical Terminology

[0647] Fuzzy logic is used in many places in this disclosure. The following is used to represent fuzzy membership functions, whose output is a "fuzzy truth variable" in the range [0,1], where 0 represents "fuzzy false" and 1 represents "fuzzy true":

[0648] Fuzzy members (ActualQuantity, ReferenceQuantity1, FuzzyTruthValueAtReferenceQuantity1, ReferenceQuantity2, FuzzyTruthValueAtReferenceQuantity2, ... ,ReferenceQuantityN,FuzzyTruthValueAtReferenceQuantityN )

[0649] Fuzzy membership function is defined as

[0650]

[0651] in

[0652] ,

[0653] fj is a fuzzy truth variable, x and x j It is a real number.

[0654] The function "fuzzy deweighting" is defined in the same way as "fuzzy member", except that the value f is different. k It is interpreted as a real number rather than a fuzzy truth variable, and the output is also a real number.

[0655] The "fuzzy OR" of a fuzzy truth variable represents the maximum value of those values; the "fuzzy AND" of a fuzzy truth variable represents the minimum value of those values. These operations on two or more fuzzy truth variables are indicated by the names fuzzy Or and fuzzy And. It should be understood that other typical definitions of these fuzzy operations will serve a similar purpose in this technique.

[0656] Exponential decay towards zero has a decay period that begins at time t=T, and the value of the decay amount V is given by the following formula:

[0657]

[0658] Exponential decay is sometimes parameterized by a rate constant k rather than a time constant τ. If k = 1 / τ, the rate constant k gives the same decay function as the time constant τ.

[0659] The inner product of two functions f and g on a certain interval I is defined as

[0660]

[0661] 7.8 Other Remarks

[0662] This patent document contains a portion of copyrighted material. The copyright holder does not object to the reproduction of the patent document or patent disclosure by any person in the form it appears in the patent office documents or records, but otherwise reserves all copyright rights.

[0663] Unless explicitly stated in the context and a numerical range is provided, it should be understood that every intermediate value between the upper and lower limits of the range, up to one-tenth of the lower limit unit, and any other value or intermediate value within the range are broadly included within this technique. The upper and lower limits of these intermediate ranges may be included independently within the intermediate range and within the scope of this technique, but are subject to any explicitly excluded boundaries within the range. Where the range includes one or both of the extreme values, this technique also includes ranges that exclude any one or both of those included extreme values.

[0664] Furthermore, where one or more values ​​described herein are implemented as part of this technique, it should be understood that such values ​​may be approximate unless otherwise stated, and such values ​​may be used to the extent permitted or required by the practical implementation of the technique for any appropriate valid digits.

[0665] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this technology pertains. Although any methods and materials similar to or equivalent to those described herein may be used in the practice or testing of this technology, a limited number of representative methods and materials are described herein.

[0666] When a particular material is identified for use in constructing a component, a readily available alternative material with similar properties is used as its substitute. Furthermore, unless otherwise stated, any and all components described herein are to be understood as being capable of being manufactured and therefore can be manufactured together or separately.

[0667] It must be noted that, unless the context clearly specifies otherwise, the singular forms “a,” “an,” and “the” used herein and in the appended claims include their plural equivalents.

[0668] All publications mentioned herein are incorporated herein in their entirety by reference to disclose and describe the methods and / or materials that are the subject of those publications. The publications discussed herein are provided only for those published prior to the filing date of this application. Nothing herein should be construed as an admission that the present technology is not entitled to priority of these publications due to prior invention. Furthermore, the publication dates provided may differ from the actual publication dates and may require separate verification.

[0669] The terms “comprises” and “comprising” should be understood as referring to each element, component, or step in a non-exclusive manner, indicating the marked element, component, or step that may be present or utilized, or in combination with other unmarked elements, components, or steps.

[0670] The headings used in the detailed description are for the convenience of the reader only and should not be used to limit the subject matter found in this disclosure or throughout the claims. The headings should not be used to interpret the scope of the claims or to limit the claims.

[0671] Although the present technology has been described herein with reference to specific embodiments, it should be understood that these embodiments are merely illustrative of the principles and applications of the present technology. In some cases, terms and symbols may imply specific details not required for the practice of the present technology. For example, although the terms "first" and "second" may be used, they are not intended to indicate any order unless otherwise specified, but rather to distinguish different elements. Furthermore, although process steps in a method may be described or illustrated in a certain order, this order is not necessary. Those skilled in the art will recognize that this order can be modified, and / or aspects of the order may be performed simultaneously or even concurrently.

[0672] Therefore, it should be understood that numerous modifications can be made to the exemplary embodiments described herein, and that other arrangements can be designed without departing from the spirit and scope of the present technology. Various versions of such arrangements can be considered with respect to the embodiments in the following numbered paragraphs.

[0673] 7.9 List of Figure Labels

[0674] Patient 1000

[0675] Patient Interface 3000

[0676] Non-invasive patient interface 3000

[0677] Sealing Formation Structure 3100

[0678] 3200 air chamber

[0679] Structure 3300

[0680] Vent 3400

[0681] Connection port 3600

[0682] Forehead support 3700

[0683] RPT device 4000

[0684] Casing 4010

[0685] Upper part 4012

[0686] Part 4014

[0687] Panel 4015

[0688] Chassis 4016

[0689] 4018 Handle

[0690] Pneumatic block 4020

[0691] Pneumatic components 4100

[0692] Air filter 4110

[0693] Inlet air filter 4112

[0694] 4114 Outlet air filter

[0695] Inlet silencer 4122

[0696] Export silencer 4124

[0697] Pressure generator 4140

[0698] Controllable blower 4142

[0699] Electric motor 4144

[0700] Air circuit 4170

[0701] Supplementing oxygen 4180

[0702] Electrical components 4200

[0703] Printed circuit board assembly 4202

[0704] Power Supply 4210

[0705] Input device 4220

[0706] Central controller 4230

[0707] Clock 4232

[0708] Treatment device controller 4240

[0709] Protection circuit 4250

[0710] Memory 4260

[0711] Converter 4270

[0712] Pressure sensor 4272

[0713] Flow sensor 4274

[0714] 4276 Motor Speed ​​Converter

[0715] Data communication interface 4280

[0716] Remote external communication network 4282

[0717] Local external communication network 4284

[0718] Remote external device 4286

[0719] Local external device 4288

[0720] Output device 4290

[0721] Display driver 4292

[0722] Monitor 4294

[0723] Algorithm 4300

[0724] Preprocessing module 4310

[0725] Pressure compensation algorithm 4312

[0726] Ventilation flow rate estimated at 4314

[0727] Leakage flow estimation algorithm 4316

[0728] Respiratory flow estimation algorithm 4318

[0729] Healing Engine Module 4320

[0730] Phase determination algorithm 4321

[0731] Waveform Determination Algorithm 4322

[0732] Ventilation determination algorithm 4323

[0733] Inspiratory flow rate limitation determination algorithm 4324

[0734] Sleep apnea detection algorithm 4325

[0735] M-shape detection algorithm 4326

[0736] Algorithm 4327 for determining airway patency

[0737] Typical recent ventilation confirmed 4328

[0738] Treatment parameter determination algorithm 4329

[0739] Treatment control module 4330

[0740] Humidifier 5000

[0741] Humidifier inlet 5002

[0742] Humidifier outlet 5004

[0743] Humidifier base 5006

[0744] Humidifier storage unit 5110

[0745] Humidifier storage area 5130

[0746] Heating element 5240

[0747] Humidifier controller 5250

[0748] Method 7000

[0749] Step 7010

[0750] Step 7020

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[0753] Method 7100

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[0891] Figure 9000

[0892] Treatment of stress traces 9010

[0893] Arrow 9015

[0894] First seizure 9020

[0895] Increase 9025

[0896] Second attack 9030

[0897] The second increase was 9035

[0898] 9040 attacks

[0899] Gradually reduce 9045

[0900] Dashed line 9050

[0901] Dashed line 9060

[0902] Dashed line 9070

[0903] Figure 9100

[0904] Upper trace 9105

[0905] Trajectory 9110

[0906] Sleep apnea 9115

[0907] Breathing 9120

[0908] Termination of 9125

[0909] Inhalation section 9130

[0910] EPAP9135

[0911] Figure 9200

[0912] Treatment of stress traces 9205

[0913] Detecting Respiratory 9208

[0914] Flow trace 9210

[0915] Airway patency trace 9215

[0916] Sleep apnea detection trace 9220

[0917] Time 9225

[0918] Point 9235

[0919] Point 9240

[0920] Detecting respiration 9243

[0921] Point 9245

[0922] Point 9250

[0923] Detecting respiration 9253

[0924] Point 9255

[0925] Trajectory 9300

[0926] Respiratory flow trace 9310

[0927] Trajectory 9320

[0928] Arrow 9321

[0929] The straight line is approximately 9322.

[0930] y-intercept 9324

[0931] Final value: 9326

[0932] Trajectory 9330

[0933] Arrow 9331

[0934] The straight line approximates 9332

[0935] y-intercept 9334

[0936] Final value 9336

[0937] Trajectory 9340

[0938] Trajectory 9350

Claims

1. An automated device for titrating positive expiratory airway pressure for treating patients, comprising: A pressure generator configured to deliver a positive pressure airflow to the patient's airway via a patient interface; A sensor configured to generate a signal representing the patient's respiratory flow; as well as The controller is configured as follows: Control the pressure generator to perform ventilation therapy through the patient interface; The airway condition of the patient is repeatedly measured while the patient is in a state of apnea in order to accumulate the duration of the apnea. as well as The increment of positive expiratory airway pressure is determined by the cumulative duration at the end of the apnea state.

2. An automated device for titrating positive expiratory airway pressure for treating patients, comprising: Device for delivering a positive pressure airflow into the airway of a patient via a patient interface to provide ventilation therapy; Device for generating a signal representing the patient's respiratory flow; A device for repeatedly determining measurements of the airway condition when the patient is in a state of apnea in order to accumulate the duration of the apnea. as well as A device for determining the increment of positive expiratory airway pressure from the cumulative duration at the end of the apnea state.

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