EP4719187A1 - Methods and apparatus for adjustment of respiratory therapy - Google Patents

Abstract

Systems and methods provide for monitoring treatment of respiratory disorder(s) by a therapy apparatus (4000) A pressure device may deliver pressurised air to a patient interface that is, in use, connected to a patient. Sensor(s) may monitor characteristic(s) of the pressurised air. Processor(s) may control the pressure device to deliver the air at treatment pressures responsive to adverse events. The delivered treatment pressures may be maintained within a range of treatment pressures between a minimum therapeutic pressure and a maximum therapeutic pressure. Adverse events may be detected based on the monitored characteristic(s)of the air during treatment session(s). Counts of the detected adverse events that occur during pressure delivery within a given range of treatment pressures for the treatment sessions may be evaluated. A recommendation or automated operation may be generated that includes a parameter change for use in a subsequent therapy session in response to the counts of detected adverse events.

Description

METHODS AND APPARATUS FOR ADJUSTMENT OF RESPIRATORY THERAPY 1 CROSS-REFERENCE TO RELATED APPLICATIONS [0001] This application claims priority from of U.S. Provisional Patent Application No. 63/505,739 filed June 2, 2023, the contents of which is incorporated herein by reference in its entirety. 2 BACKGROUND OF THE TECHNOLOGY 2.1 FIELD OF THE TECHNOLOGY [0002] The present technology relates to one or more of the screening, diagnosis, monitoring, treatment, prevention and amelioration of respiratory-related disorders. The present technology also relates to medical devices or apparatus, and their use. 2.2 DESCRIPTION OF THE RELATED ART 2.2.1 Human Respiratory System and its Disorders [0003] The respiratory system of the body facilitates gas exchange. The nose and mouth form the entrance to the airways of a patient. [0004] The airways include a series of branching tubes, which become narrower, shorter and more numerous as they penetrate deeper into the lung. The prime function of the lung is gas exchange, allowing oxygen to move from the inhaled air into the venous blood and carbon dioxide to move in the opposite direction. The trachea divides into right and left main bronchi, which further divide eventually into terminal bronchioles. The bronchi make up the conducting airways, and do not take part in gas exchange. Further divisions of the airways lead to the respiratory bronchioles, and eventually to the alveoli. The alveolated region of the lung is where the gas exchange takes place, and is referred to as the respiratory zone. See “Respiratory Physiology”, by John B. West, Lippincott Williams & Wilkins, 9th edition published 2012. [0005] A range of respiratory disorders exist. Certain disorders may be characterised by particular events, e.g. apneas, hypopneas, and hyperpneas. [0006] Examples of respiratory disorders include Obstructive Sleep Apnea (OSA), Cheyne- Stokes Respiration (CSR), respiratory insufficiency, Obesity Hypoventilation Syndrome (OHS), Chronic Obstructive Pulmonary Disease (COPD), Neuromuscular Disease (NMD) and Chest wall disorders. [0007] Obstructive Sleep Apnea (OSA), a form of Sleep Disordered Breathing (SDB), is characterised by events including occlusion or obstruction of the upper air passage during sleep. It results from a combination of an abnormally small upper airway and the normal loss of muscle tone in the region of the tongue, soft palate and posterior oropharyngeal wall during sleep. The condition causes the affected patient to stop breathing for periods typically of 30 to 120 seconds in duration, sometimes 200 to 300 times per night. It often causes excessive daytime somnolence, and it may cause cardiovascular disease and brain damage. The syndrome is a common disorder, particularly in middle aged overweight males, although a person affected may have no awareness of the problem, e.g. see US Patent No.4,944,310 (Sullivan). [0008] Cheyne-Stokes Respiration (CSR) is another form of sleep disordered breathing. CSR is a disorder of a patient's respiratory controller in which there are rhythmic alternating periods of waxing and waning ventilation known as CSR cycles. CSR is characterised by repetitive de- oxygenation and re-oxygenation of the arterial blood. It is possible that CSR is harmful because of the repetitive hypoxia. In some patients CSR is associated with repetitive arousal from sleep, which causes severe sleep disruption, increased sympathetic activity, and increased afterload, e.g. see US Patent No.6,532,959 (Berthon-Jones). [0009] Respiratory failure is an umbrella term for respiratory disorders in which the lungs are unable to inspire sufficient oxygen or exhale sufficient CO2 to meet the patient’s needs. Respiratory failure may encompass some or all of the following disorders. [0010] A patient with respiratory insufficiency (a form of respiratory failure) may experience abnormal shortness of breath on exercise. [0011] A range of therapies have been used to treat or ameliorate such conditions. Furthermore, otherwise healthy individuals may take advantage of such therapies to prevent respiratory disorders from arising. However, these have a number of shortcomings. 2.2.2 Therapies [0012] Various respiratory therapies, such as Continuous Positive Airway Pressure (CPAP) therapy, Non-invasive ventilation (NIV), Invasive ventilation (IV), and High Flow Therapy (HFT) have been used to treat one or more of the above respiratory disorders. 2.2.2.1 Respiratory pressure therapies [0013] Respiratory pressure therapy is the application of a supply of air to an entrance to the airways at a controlled target pressure that is nominally positive with respect to atmosphere throughout the patient’s breathing cycle (in contrast to negative pressure therapies such as the tank ventilator or cuirass). [0014] Continuous Positive Airway Pressure (CPAP) therapy has been used to treat Obstructive Sleep Apnea (OSA). The mechanism of action is that continuous positive airway pressure acts as a pneumatic splint and may prevent upper airway occlusion, such as by pushing the soft palate and tongue forward and away from the posterior oropharyngeal wall. Treatment of OSA by CPAP therapy may be voluntary, and hence patients may elect not to comply with therapy if they find devices used to provide such therapy one or more of: uncomfortable, difficult to use, expensive and aesthetically unappealing. [0015] Non-invasive ventilation (NIV) provides ventilatory support to a patient through the upper airways to assist the patient breathing and/or maintain adequate oxygen levels in the body by doing some or all of the work of breathing. The ventilatory support is provided via a non- invasive patient interface. NIV has been used to treat CSR and respiratory failure, in forms such as OHS, COPD, NMD and Chest Wall disorders. In some forms, the comfort and effectiveness of these therapies may be improved. [0016] Invasive ventilation (IV) provides ventilatory support to patients that are no longer able to effectively breathe themselves and may be provided using a tracheostomy tube or endotracheal tube. In some forms, the comfort and effectiveness of these therapies may be improved. 2.2.3 Respiratory Therapy Systems [0017] These respiratory therapies may be provided by a respiratory therapy system or device. Such systems and devices may also be used to screen, diagnose, or monitor a condition without treating it. [0018] A respiratory therapy system may comprise a Respiratory Pressure Therapy Device (RPT device), an air circuit, a humidifier, a patient interface, an oxygen source, and data management. 2.2.3.1 Patient Interface [0019] A patient interface may be used to interface respiratory equipment to its wearer, for example by providing a flow of air to an entrance to the airways. The flow of air may be provided via a mask to the nose and/or mouth, a tube to the mouth or a tracheostomy tube to the trachea of a patient. Depending upon the therapy to be applied, the patient interface may form a seal, e.g., with a region of the patient's face, to facilitate the delivery of gas at a pressure at sufficient variance with ambient pressure to effect therapy, e.g., at a positive pressure of about 10 cmH₂O relative to ambient pressure. For other forms of therapy, such as the delivery of oxygen, the patient interface may not include a seal sufficient to facilitate delivery to the airways of a supply of gas at a positive pressure of about 10 cmH₂O. For flow therapies such as nasal HFT, the patient interface is configured to insufflate the nares but specifically to avoid a complete seal. One example of such a patient interface is a nasal cannula. 2.2.3.2 Respiratory Pressure Therapy (RPT) Device [0020] A respiratory pressure therapy (RPT) device may be used individually or as part of a system to deliver one or more of a number of therapies described above, such as by operating the device to generate a flow of air for delivery to an interface to the airways. The flow of air may be pressure-controlled (for respiratory pressure therapies) or flow-controlled (for flow therapies such as high flow therapy (HFT)). Thus, RPT devices may also act as flow therapy devices. Examples of RPT devices include a CPAP device and a ventilator. 2.2.3.3 Air circuit [0021] An air circuit is a conduit or a tube constructed and arranged to allow, in use, a flow of air to travel between two components of a respiratory therapy system such as the RPT device and the patient interface. In some cases, there may be separate limbs of the air circuit for inhalation and exhalation. In other cases, a single limb air circuit is used for both inhalation and exhalation. 2.2.3.4 Humidifier [0022] Delivery of a flow of air without humidification may cause drying of airways. The use of a humidifier with an RPT device and the patient interface produces humidified gas that minimizes drying of the nasal mucosa and increases patient airway comfort. In addition, in cooler climates, warm air applied generally to the face area in and about the patient interface is more comfortable than cold air. 2.2.3.5 Vent technologies [0023] Some forms of treatment systems may include a vent to allow the washout of exhaled carbon dioxide. The vent may allow a flow of gas from an interior space of a patient interface, e.g., the plenum chamber, to an exterior of the patient interface, e.g., to ambient. 2.2.4 Screening, Diagnosis, and Monitoring Systems [0024] Polysomnography (PSG) is a conventional system for diagnosis and monitoring of cardio-pulmonary disorders, and typically involves expert clinical staff to apply the system. PSG typically involves the placement of 15 to 20 contact sensors on a patient in order to record various bodily signals such as electroencephalography (EEG), electrocardiography (ECG), electrooculograpy (EOG), electromyography (EMG), etc. PSG for sleep disordered breathing has involved two nights of observation of a patient in a clinic, one night of pure diagnosis and a second night of titration of treatment parameters by a clinician. PSG is therefore expensive and inconvenient. In particular, it is unsuitable for home screening / diagnosis / monitoring of sleep disordered breathing. [0025] Screening and diagnosis generally describe the identification of a condition from its signs and symptoms. Screening typically gives a true / false result indicating whether or not a patient’s SDB is severe enough to warrant further investigation, while diagnosis may result in clinically actionable information. Screening and diagnosis tend to be one-off processes, whereas monitoring the progress of a condition can continue indefinitely. Some screening / diagnosis systems are suitable only for screening / diagnosis, whereas some may also be used for monitoring. [0026] Clinical experts may be able to screen, diagnose, or monitor patients adequately based on visual observation of PSG signals. However, there are circumstances where a clinical expert may not be available, or a clinical expert may not be affordable. Different clinical experts may disagree on a patient’s condition. In addition, a given clinical expert may apply a different standard at different times. 3 BRIEF SUMMARY OF THE TECHNOLOGY [0027] The present technology is directed towards providing medical devices used in the screening, diagnosis, monitoring, amelioration, treatment, or prevention of respiratory disorders having one or more of improved comfort, cost, efficacy, ease of use and manufacturability. [0028] A first aspect of the present technology relates to apparatus used in the screening, diagnosis, monitoring, amelioration, treatment or prevention of a respiratory disorder. [0029] Another aspect of the present technology relates to methods used in the screening, diagnosis, monitoring, amelioration, treatment or prevention of a respiratory disorder. [0030] An aspect of certain forms of the present technology is to provide methods and/or apparatus that improve the compliance of patients with respiratory therapy. [0031] Some implementations of the present technology may include a system for monitoring treatment of a respiratory disorder provided by a therapy apparatus. The system may include a pressure device configured to deliver a flow of pressurised air to a patient interface that is, in use, connected to a patient airway. The system may include one or more sensors to monitor one or more characteristics of the pressurised air. The system may include one or more processors. The one or more processors may be configured to (a) control the pressure device to deliver the flow of pressurised air at treatment pressures responsive to adverse events, wherein the delivered treatment pressures may be maintained within a range of treatment pressures between a minimum therapeutic pressure and a maximum therapeutic pressure. The one or more processors may be configured to (b) detect adverse events, based on the monitored one or more characteristics of the air during each of a plurality of treatment sessions. The one or more processors may be configured to (c) evaluate counts of the detected adverse events that occur during pressure delivery within a given range of treatment pressures for the plurality of treatment sessions. The one or more processors may be configured to (d) generate output that may include a parameter change, for use in a subsequent therapy session, to the minimum therapeutic pressure setting or the maximum therapeutic pressure setting, in response to the counts of detected adverse events, at or exceeding a threshold value. [0032] In some implementations, the output may include any one or more of: a recommendation for the parameter change, and an automatic control operation of the pressure device with the parameter change. The evaluated counts may be apnea and/or hypopnea events. The output may include an increase to the minimum therapeutic pressure, in response to the evaluated counts exceeding a threshold value, wherein the given range of treatment pressures may be between the minimum therapeutic pressure and a critical range of treatment pressures. The one or more processors may be further configured to: control ramping of the delivered pressurised air from an initial ramp pressure to the minimum therapeutic pressure in ramp periods of the plurality of treatment sessions. The one or more processors may be further configured to: generate a recommendation for, or application of, a parameter change, for use in a subsequent therapy session, to a profile of a controlled ramping of the delivered pressure over a ramp period, wherein the parameter change may include: (a) an increase to a rate of pressure increase for the profile and/or (b) an increase to an initial pressure for the profile, or (c) disabling a ramp period, wherein the recommendation may be based on the evaluated counts exceeding a threshold value, wherein the given range of treatment pressures may be between the initial ramp pressure and the minimum therapeutic pressure and occur during the ramp periods. The one or more processors may be further configured to generate a recommendation for a parameter change, for use in a subsequent therapy session, to increase the maximum therapeutic pressure in response to the evaluated counts exceeding a threshold value during a period when the treatment pressure remains at or near the maximum therapeutic pressure. The evaluated counts may be mask off events. The one or more processors may be further configured to generate a recommendation for, or application of, a parameter change, for use in a subsequent therapy session, to reduce the maximum therapeutic pressure in response to the evaluated counts exceeding a threshold value, wherein each of the mask off events occur when pressure rises to a particular pressure. [0033] In some implementations, the one or more processors may be further configured to determine a critical range of therapeutic pressures during which a count of adverse events may be below a threshold value. The one or more processors may be further configured to generate a recommendation for, or application of, a parameter change, for use in a ramp period of a subsequent therapy session, for ramping of pressure to a therapeutic pressure that may be above the minimum therapeutic pressure and may be in the determined critical range of therapeutic pressures. The one or more processors may be further configured to reduce an intended change in pressure to be made in response to a detected adverse event may include any of snore, flow limitation or obstructive apnea, when a currently delivered pressure is in the determined critical range of therapeutic pressures and the intended change in pressure takes the pressure outside of the determined critical range of therapeutic pressures. The evaluated counts may be mask off events, and wherein the one or more processors may be further configured to generate a recommendation for, or to automatically introduce, a parameter change, for use in a subsequent therapy session, the introduced change being related to reduction of the maximum value of the determined critical range of therapeutic pressures. [0034] In some implementations, the one or more processors consist of one or more processors in a controller of a respiratory pressure therapy device. The one or more processors may comprise a processor of a server and a processor in a controller of a respiratory pressure therapy device. The server may be configured to evaluate the counts of the detected adverse events and to generate the output for a parameter change. The server may be configured to detect the adverse events. The controller may be configured to control the pressure device to deliver the flow of pressurised air and to detect the adverse events. The one or more processors may be further configured to apply a generated recommendation for a parameter change for control of the pressure device in response to the generated recommendation. The one or more processors may be further configured to apply the generated recommendation in response to manual input on a user interface. [0035] In some implementations, the automatic control operation of the pressure device with the parameter change may include a set of pressure adjustment factors. The set of pressure adjustment factors may include a set of increase factors. Each increase factor in the set of increase factors may correspond to respective pressure values of a set of pressure values. Increase factors in the set of increase factors that cause fast increases in pressure may correspond to smaller pressure values in the set of pressure values. Increase factors in the set of increase factors that cause slow increases in pressure may correspond to larger pressure values in the set of pressure values. Increase factors in the set of increase factors having larger magnitudes may correspond to respective pressure values in the set of pressure values having smaller magnitudes. The set of pressure adjustment factors may include a set of decrease factors. Each decrease factor in the set of decrease factors may correspond to respective pressure values in the set of pressure values. Decrease factors in the set of decrease factors that cause slow decreases in pressure may correspond to smaller pressure values in the set of pressure values. Decrease factors in the set of decrease factors that cause fast decreases in pressure may correspond to larger pressure values in the set of pressure values. Each decrease factor in the set of decrease factors may be a decay time constant. The one or more processors may be configured to determine the set of pressure adjustment factors based on a biasing function. The one or more processors may be configured to determine the biasing function from a pressure distribution. The one or more processors may be configured to generate the pressure distribution from a set of stored pressure traces. Each pressure trace in the set of stored pressure traces may be measured or determined during respective respiratory therapy period. The set of stored pressure traces may include at least seven pressure traces. The set of stored pressure traces may include between seven and ten pressure traces. To determine the biasing function, the one or more processors may be configured to select the adjustment function from among a plurality of biasing functions based on the pressure distribution. [0036] In some implementations, the one or more processors may be configured to select the biasing function from among the plurality of biasing functions based on a number of local maxima in the pressure distribution. The selected biasing function may be an elastic automatic positive airway pressure function when the pressure distribution has only one local maxima. The selected biasing function may be an orbital automatic positive airway pressure function when the pressure distribution has two local maxima. The selected biasing function may be an optimal pressure range function when the pressure distribution has more than two local maxima or other than one or two local maxima. The automatic control operation of the pressure device with the parameter change may include the set of pressure adjustment factors when at least one of the detected adverse events includes a flow limitation respiratory event. [0037] Some implementations of the present technology may include a method in one or more processors for monitoring treatment of a respiratory disorder provided by a therapy apparatus. The method may include detecting adverse events, based on monitoring of one or more characteristics of pressurized air delivered during each of a plurality of treatment sessions wherein a pressure device may be controlled to deliver a flow of pressurized air to a patient interface at treatment pressures responsive to respiratory events, wherein the delivered treatment pressures may be maintained within a range of treatment pressures between a minimum therapeutic pressure and a maximum therapeutic pressure. The method may include evaluating counts of the detected adverse events that occur during pressure delivery within a given range of treatment pressures for the plurality of treatment sessions. The method may include generating output that may include a parameter change, for use in a subsequent therapy session, to the minimum therapeutic pressure setting or the maximum therapeutic pressure setting, in response to the counts of detected adverse events, at or exceeding a threshold value. [0038] In some implementations, the output may include any one or more of: a recommendation for the parameter change, and an automatic control operation of the pressure device with the parameter change. The evaluated counts may be apnea and/or hypopnea events. The output may include an increase to the minimum therapeutic pressure, in response to the evaluated counts exceeding a threshold value, wherein the given range of treatment pressures may be between the minimum therapeutic pressure and a critical range of treatment pressures. The method may further include, by the one or more processors, controlling ramping of the delivered pressurized air from an initial ramp pressure to the minimum therapeutic pressure in ramp periods of the plurality of treatment sessions. The method may further include generating a recommendation for a parameter change, for use in a subsequent therapy session, to a profile of a controlled ramping of the delivered pressure over a ramp period, wherein the parameter change may include: (a) an increase to a rate of pressure increase for the profile and/or (b) an increase to an initial pressure for the profile, or (d) disabling the ramp period, wherein the recommendation may be based on the evaluated counts exceeding a threshold value, wherein the given range of treatment pressures may be between the initial ramp pressure and the minimum therapeutic pressure and occur during the ramp periods. [0039] In some implementations, the method may include generating a recommendation for a parameter change, for use in a subsequent therapy session, to increase the maximum therapeutic pressure in response to the evaluated counts exceeding a threshold value during a period when the treatment pressure remains at or near the maximum therapeutic pressure. The evaluated counts may be mask off events. The method my further include generating a recommendation for a parameter change, for use in a subsequent therapy session, to reduce the maximum therapeutic pressure in response to the evaluated counts exceeding a threshold value, wherein each of the mask off events occur when pressure rises to a particular pressure. The method may further include determining a critical range of therapeutic pressures during which a count of adverse events may be below a threshold value. The method may further include generating a recommendation for a parameter change, for use in a ramp period of a subsequent therapy session, for ramping of pressure to a therapeutic pressure that may be above the minimum therapeutic pressure and may be in the determined critical range of therapeutic pressures. [0040] In some implementations, the method may further include reducing an intended change in pressure to be made in response to a detected adverse event may include any of snore, flow limitation or obstructive apnea, when a currently delivered pressure is in the determined critical range of therapeutic pressures and the intended change in pressure takes the pressure outside of the determined critical range of therapeutic pressures. The evaluated counts may be mask off events, and the method may further include generating a recommendation for, or automatically introducing, a parameter change, for use in a subsequent therapy session, the introduced change being related to reduction of the maximum value of the determined critical range of therapeutic pressures. The one or more processors may consist of one or more processors in a controller of a respiratory pressure therapy device. The one or more processors may comprise a processor of a server and a processor in a controller of a respiratory pressure therapy device. The server may evaluate the counts of the detected adverse events and generates the output for a parameter change. The server may detect the adverse events. The controller may control the pressure device to deliver the flow of pressurised air and detects the adverse events. The method may further include applying a generated recommendation for a parameter change for control of the pressure device in response to the generated recommendation. The method may further include applying the generated recommendation in response to manual input on a user interface. [0041] In the method, the automatic control operation of the pressure device with the parameter change may include a set of pressure adjustment factors. The set of pressure adjustment factors may include a set of increase factors. Each increase factor in the set of increase factors may correspond to respective pressure values of a set of pressure values. Increase factors in the set of increase factors that cause fast increases in pressure may correspond to smaller pressure values in the set of pressure values. Increase factors in the set of increase factors that cause slow increases in pressure correspond to larger pressure values in the set of pressure values. Increase factors in the set of increase factors having larger magnitudes may correspond to respective pressure values in the set of pressure values having smaller magnitudes. The set of pressure adjustment factors may include a set of decrease factors. Each decrease factor in the set of decrease factors may correspond to respective pressure values in the set of pressure values. Decrease factors in the set of decrease factors that cause slow decreases in pressure may correspond to smaller pressure values in the set of pressure values. Decrease factors in the set of decrease factors that cause fast decreases in pressure may correspond to larger pressure values in the set of pressure values. Each decrease factor in the set of decrease factors may be a decay time constant. [0042] In some implementations, the method may include determining the set of pressure adjustment factors based on a biasing function. The method may include determining the biasing function from a pressure distribution. The method may include generating the pressure distribution from a set of stored pressure traces. Each pressure trace in the set of stored pressure traces may be measured or determined during respective respiratory therapy period. The set of stored pressure traces may include at least seven pressure traces. The set of stored pressure traces may include between seven and ten pressure traces. Determining the biasing function may include selecting the biasing function from among a plurality of biasing functions based on the pressure distribution. Selecting the biasing function from among the plurality of biasing functions may be based on a number of local maxima in the pressure distribution. The selected biasing function may be an elastic automatic positive airway pressure function when the pressure distribution has only one local maxima. The selected biasing function may be an orbital automatic positive airway pressure function when the pressure distribution has two local maxima. The selected biasing function may be an optimal pressure range function when the pressure distribution has more than two local maxima. The automatic control operation of the pressure device with the parameter change may include the set of pressure adjustment factors when at least one of the detected adverse events includes a flow limitation respiratory event. [0043] Some implementations of the present technology may include a computer-readable medium encoded with computer-readable instructions, which when executed by one or more processors implement any of the methods described herein. [0044] Some implementations of the present technology may include a system for monitoring treatment of a respiratory disorder provided by a therapy apparatus. The system may include a pressure device configured to deliver a flow of pressurised air to a patient interface that is, in use, connected to a patient airway. The system may include one or more sensors to monitor one or more characteristics of the pressurised air. The system may include one or more processors. The one or more processors may be configured to: (a) control the pressure device to deliver the flow of pressurised air at treatment pressures responsive to adverse events, wherein the delivered treatment pressures may be maintained within a range of treatment pressures between a minimum therapeutic pressure and a maximum therapeutic pressure. The one or more processors may be configured to: (b) detect adverse events, based on the monitored one or more characteristics of the pressurized air during each of a plurality of treatment sessions. The one or more processors may be configured to: (c) evaluate counts of the detected adverse events that occur during pressure delivery within a given range of treatment pressures for the plurality of treatment sessions. The one or more processors may be configured to: (d) generate an output that may include a parameter change, for use in a subsequent therapy session, in response to the counts of detected adverse events, at or exceeding a threshold value. [0045] Some implementations of the present technology may include a method in one or more processors for monitoring treatment of a respiratory disorder provided by a therapy apparatus. The method may include detecting adverse events, based on monitoring of one or more characteristics of pressurized air delivered during each of a plurality of treatment sessions wherein a pressure device may be controlled to deliver a flow of pressurized air to a patient interface at treatment pressures responsive to respiratory events, wherein the delivered treatment pressures may be maintained within a range of treatment pressures between a minimum therapeutic pressure and a maximum therapeutic pressure. The method may include evaluating counts of the detected adverse events that occur during pressure delivery within a given range of treatment pressures for the plurality of treatment sessions. The method may include generating output that may include a parameter change, for use in a subsequent therapy session, in response to the counts of detected adverse events, at or exceeding a threshold value. [0046] Some implementations of the present technology may include a system for monitoring treatment of a respiratory disorder provided by a therapy apparatus. The system may include a pressure device configured to deliver a flow of pressurised air to a patient interface that is, in use, connected to a patient airway. The system may include one or more sensors to monitor one or more characteristics of the pressurised air. The system may include one or more processors. The one or more processors may be configured to: (a) control the pressure device to deliver the flow of pressurised air at treatment pressures responsive to adverse events, wherein the delivered treatment pressures may be maintained within a range of treatment pressures between a minimum therapeutic pressure and a maximum therapeutic pressure. The one or more processors may be configured to: (b) detect adverse events, based on the monitored one or more characteristics of the air during each of a plurality of treatment sessions. The one or more processors may be configured to: (c) evaluate counts of the detected adverse events that occur during pressure delivery within a given range of treatment pressures for the plurality of treatment sessions. The one or more processors may be configured to: (d) control ramping of the delivered pressurised air from an initial ramp pressure to the minimum therapeutic pressure in ramp periods of the plurality of treatment sessions. The one or more processors may be configured to: (e) generate an output for a parameter change, for use in a subsequent therapy session, to a profile of a controlled ramping of the delivered pressure over a ramp period, wherein the parameter change may include (i) an increase to a rate of pressure increase for the profile and/or (ii) an increase to an initial pressure for the profile, or (iii) disabling a ramp period. The output may be based on the evaluated counts exceeding a threshold value, wherein the given range of treatment pressures may be between the initial ramp pressure and the minimum therapeutic pressure and occur during the ramp periods. [0047] Some implementations of the present technology may include a method in one or more processors for monitoring treatment of a respiratory disorder provided by a therapy apparatus. The method may include detecting adverse events, based on monitoring of one or more characteristics of pressurized air delivered during each of a plurality of treatment sessions wherein a pressure device may be controlled to deliver a flow of pressurized air to a patient interface at treatment pressures responsive to respiratory events, wherein the delivered treatment pressures may be maintained within a range of treatment pressures between a minimum therapeutic pressure and a maximum therapeutic pressure. The method may include evaluating counts of the detected adverse events that occur during pressure delivery within a given range of treatment pressures for the plurality of treatment sessions. The method may include controlling ramping of the delivered pressurized air from an initial ramp pressure to the minimum therapeutic pressure in ramp periods of the plurality of treatment sessions. The method may include generating an output for a parameter change, for use in a subsequent therapy session, to a profile of a controlled ramping of the delivered pressure over a ramp period. The parameter change may include: (a) an increase to a rate of pressure increase for the profile and/or (b) an increase to an initial pressure for the profile, or (d) disabling the ramp period. The output may be based on the evaluated counts exceeding a threshold value, wherein the given range of treatment pressures may be between the initial ramp pressure and the minimum therapeutic pressure and occur during the ramp periods. [0048] Some implementations of the present technology may include a system for monitoring treatment of a respiratory disorder provided by a therapy apparatus. The system may include a pressure device configured to deliver a flow of pressurised air to a patient interface that is, in use, connected to a patient airway. The system may include one or more sensors to monitor one or more characteristics of the pressurised air. The system may include one or more processors. The one or more processors may be configured to: (a) control the pressure device to deliver the flow of pressurised air at treatment pressures responsive to adverse events, wherein the delivered treatment pressures may be maintained within a range of treatment pressures between a minimum therapeutic pressure and a maximum therapeutic pressure. The one or more processors may be configured to (b) detect adverse events, based on the monitored one or more characteristics of the air during each of a plurality of treatment sessions. The one or more processors may be configured to (c) evaluate counts of the detected adverse events that occur during pressure delivery within a given range of treatment pressures for the plurality of treatment sessions. The one or more processors may be configured to (d) control ramping of the delivered pressurised air from an initial ramp pressure to the minimum therapeutic pressure in ramp periods of the plurality of treatment sessions. The one or more processors may be configured to (e) generate an output for a parameter change, for use in a ramp period of a subsequent therapy session, for ramping of pressure to a therapeutic pressure that may be above the minimum therapeutic pressure and may be in a determined critical range of therapeutic pressures. [0049] Some implementations of the present technology may include a method in one or more processors for monitoring treatment of a respiratory disorder provided by a therapy apparatus. The method may include detecting adverse events, based on monitoring of one or more characteristics of pressurized air delivered during each of a plurality of treatment sessions wherein a pressure device may be controlled to deliver a flow of pressurized air to a patient interface at treatment pressures responsive to respiratory events, wherein the delivered treatment pressures may be maintained within a range of treatment pressures between a minimum therapeutic pressure and a maximum therapeutic pressure. The method may include evaluating counts of the detected adverse events that occur during pressure delivery within a given range of treatment pressures for the plurality of treatment sessions. The method may include controlling ramping of the delivered pressurized air from an initial ramp pressure to the minimum therapeutic pressure in ramp periods of the plurality of treatment sessions. The method may include generating an output for a parameter change, for use in a ramp period of a subsequent therapy session, for ramping of pressure to a therapeutic pressure that may be above the minimum therapeutic pressure and may be in a determined critical range of therapeutic pressures. [0050] Some implementations of the present technology may include a system for providing therapy for a respiratory disorder using a therapy apparatus. The system may include a pressure device configured to deliver a flow of pressurised air to a patient interface that is, in use, connected to a patient airway. The system may include one or more sensors to monitor one or more characteristics of the pressurised air. The system may include one or more processors. The one or more processors may be configured to control the pressure device to deliver the flow of pressurised air at treatment pressures responsive to adverse events for a plurality of therapy periods. The one or more processors may be configured to evaluate delivered treatment pressures from the plurality of treatment sessions to determine one or more concentrations of treatment pressure. The one or more processors may be configured to, based on the determination of one or more concentrations of treatment pressure, activate one or more biasing functions to bias adjustments to treatment pressure with respect to one or more critical treatment pressures of the plurality of therapy periods. The one or more processors may be configured to control the pressure device to deliver the flow of pressurised air at treatment pressures in a subsequent therapy period. The one or more processors may be configured to adjust changes to the treatment pressures in the subsequent therapy period according to the one or more biasing functions. [0051] In some implementations, to evaluate the delivered treatment pressures, the one or more processors may be configured to compute a distribution from the delivered treatment pressures. To evaluate the delivered treatment pressures, the one or more processors may be configured to detect one or more local maxima in the distribution from the delivered treatment pressures. A biasing function of the one or more biasing functions may bias adjustments to treatment pressure with respect to a treatment pressure attributable to a single local maxima in the distribution. A biasing function of the one or more biasing functions may bias adjustments to treatment pressure with respect to treatment pressures attributable to a dual local maxima in the distribution. A biasing function of the one or more biasing functions may bias adjustments to treatment pressure with respect to treatment pressures attributable to an upper and lower bound of delivered pressures. To adjust changes to the treatment pressures in the subsequent therapy period according to the one or more biasing functions, a biasing function may decrease a determined pressure increase amount, wherein the determined pressure increase amount may be a prescribed pressure increase for a detected event may include any one of an event of flow limitation, an event of snoring and a hypopnea. To adjust changes to the treatment pressures in the subsequent therapy period according to the one or more biasing functions, a biasing function may change a time constant of a pressure decay, wherein the pressure decay may be provided in a recent absence of a detection of any one of an event of flow limitation, an event of snoring and a hypopnea. To bias adjustments to treatment pressure with respect to the one or more treatment pressures of the plurality of therapy periods, the one more biasing functions may be configured to slow pressure changes away from the one or more critical treatment pressures. To bias adjustments to treatment pressure with respect to the one or more treatment pressures of the plurality of therapy periods, the one more biasing functions may be configured to accelerate pressure changes toward the one or more critical treatment pressures. The one or more critical treatment pressures may include a treatment pressure attributable to a local maxima of a distribution determined from delivered treatment pressures from one or more treatment sessions of the plurality of treatment sessions. The one or more critical treatment pressures may include two treatment pressures that are each attributable to a local maxima of a distribution determined from delivered treatment pressures from one or more treatment sessions of the plurality of treatment sessions. The one or more critical treatment pressures may include two treatment pressures attributable to upper and lower pressure bounds from delivered treatment pressures from one or more treatment sessions of the plurality of treatment sessions, wherein a distribution determined from delivered treatment pressures from the one or more treatment sessions of the plurality of treatment sessions lacks a local maxima or lacks a single or double local maxima. [0052] Some implementations of the present technology may include a method for providing therapy for a respiratory disorder using a therapy apparatus that may include a pressure device configured to deliver a flow of pressurized air to a patient interface that is, in use, connected to a patient airway. The method may include controlling, with one or more processors, the pressure device to deliver the flow of pressurised air at treatment pressures responsive to adverse events for a plurality of therapy periods. The method may include evaluating, with the one or more processors, delivered treatment pressures from the plurality of treatment sessions to determine one or more concentrations of treatment pressure. The method may include, based on the determination of one or more concentrations of treatment pressure, activating in a subsequent therapy period, with the one or more processors, one or more biasing functions to bias adjustments to treatment pressure with respect to one or more critical treatment pressures of the plurality of therapy periods. The method may include controlling, with the one or more processors, the pressure device to deliver the flow of pressurised air at treatment pressures in the subsequent therapy period. The method may include adjusting, with the one or more processors, changes to the treatment pressures in the subsequent therapy period according to the one or more biasing functions. [0053] In some implementations, the evaluating the delivered treatment pressures may include computing a distribution from the delivered treatment pressures. Evaluating the delivered treatment pressures may include detecting one or more local maxima in the distribution from the delivered treatment pressures. A biasing function of the one or more biasing functions may bias adjustments to treatment pressure with respect to a treatment pressure attributable to a single local maxima in the distribution. A biasing function of the one or more biasing functions may bias adjustments to treatment pressure with respect to treatment pressures attributable to a dual local maxima in the distribution. A biasing function of the one or more biasing functions may bias adjustments to treatment pressure with respect to treatment pressures attributable to an upper and lower bound of delivered pressures. Adjusting changes to the treatment pressures in the subsequent therapy period according to the one or more biasing functions may include decreasing a determined pressure increase amount, wherein the determined pressure increase amount may be a prescribed pressure increase for a detected event may include any one of an event of flow limitation, an event of snoring and a hypopnea. Adjusting changes to the treatment pressures in the subsequent therapy period according to the one or more biasing functions may include changing a time constant of a pressure decay, wherein the pressure decay may be provided in a recent absence of a detection of any one of an event of flow limitation, an event of snoring and a hypopnea. Biasing adjustments to treatment pressure with respect to the one or more treatment pressures of the plurality of therapy periods may include slowing pressure changes away from the one or more critical treatment pressures. Biasing adjustments to treatment pressure with respect to the one or more treatment pressures of the plurality of therapy periods may include accelerating pressure changes toward the one or more critical treatment pressures. The one or more critical treatment pressures may include a treatment pressure that may be attributable to a local maxima of a distribution determined from delivered treatment pressures from one or more treatment sessions of the plurality of treatment sessions. The one or more critical treatment pressures may include two treatment pressures that may be each attributable to a local maxima of a distribution determined from delivered treatment pressures from one or more treatment sessions of the plurality of treatment sessions. The one or more critical treatment pressures may include two treatment pressures that may be attributable to upper and lower pressure bounds from delivered treatment pressures from one or more treatment sessions of the plurality of treatment sessions, wherein a distribution determined from delivered treatment pressures from the one or more treatment sessions of the plurality of treatment sessions lacks a local maxima, or lacks both of a single local maxima or a dual local maxima. [0054] Some implementations of the present technology may include a computer-readable medium encoded with computer-readable instructions, which when executed by one or more processors implement any of the methods described herein. [0055] The methods, systems, devices and apparatus described may be implemented so as to improve the functionality of a processor, such as a processor of a specific purpose computer, respiratory monitor and/or a respiratory therapy apparatus. Moreover, the described methods, systems, devices and apparatus can provide improvements in the technological field of automated management, monitoring and/or treatment of respiratory conditions, including, for example, sleep disordered breathing. [0056] Of course, portions of the aspects may form sub-aspects of the present technology. Also, various ones of the sub-aspects and/or aspects may be combined in various manners and also constitute additional aspects or sub-aspects of the present technology. [0057] Other features of the technology will be apparent from consideration of the information contained in the following detailed description, abstract, drawings and claims. 4 BRIEF DESCRIPTION OF THE DRAWINGS [0058] The present technology is illustrated by way of example, and not by way of limitation, in the figures of the accompanying drawings, in which like reference numerals refer to similar elements including: 4.1 RESPIRATORY THERAPY SYSTEMS [0059] Fig. 1A shows a system including a patient 1000 wearing a patient interface 3000, in the form of nasal pillows, receiving a supply of air at positive pressure from an RPT device 4000. Air from the RPT device 4000 is humidified in a humidifier 5000, and passes along an air circuit 4170 to the patient 1000. A bed partner 1100 is also shown. The patient is sleeping in a supine sleeping position. [0060] Fig. 1B shows a system including a patient 1000 wearing a patient interface 3000, in the form of a nasal mask, receiving a supply of air at positive pressure from an RPT device 4000. Air from the RPT device is humidified in a humidifier 5000, and passes along an air circuit 4170 to the patient 1000. [0061] Fig. 1C shows a system including a patient 1000 wearing a patient interface 3000, in the form of a full-face mask, receiving a supply of air at positive pressure from an RPT device 4000. Air from the RPT device is humidified in a humidifier 5000, and passes along an air circuit 4170 to the patient 1000. The patient is sleeping in a side sleeping position. 4.2 RESPIRATORY SYSTEM AND FACIAL ANATOMY [0062] Fig.2A shows an overview of a human respiratory system including the nasal and oral cavities, the larynx, vocal folds, oesophagus, trachea, bronchus, lung, alveolar sacs, heart and diaphragm. [0063] Fig.2B shows a view of a human upper airway including the nasal cavity, nasal bone, lateral nasal cartilage, greater alar cartilage, nostril, lip superior, lip inferior, larynx, hard palate, soft palate, oropharynx, tongue, epiglottis, vocal folds, oesophagus and trachea. 4.3 PATIENT INTERFACE [0064] Fig. 3 shows an example patient interface in the form of a nasal mask in accordance with one form of the present technology. 4.4 RPT DEVICE [0065] Fig.4A shows an RPT device in accordance with one form of the present technology. [0066] Fig.4B is a schematic diagram of the pneumatic path of an RPT device in accordance with one form of the present technology. The directions of upstream and downstream are indicated with reference to the blower and the patient interface. The blower is defined to be upstream of the patient interface and the patient interface is defined to be downstream of the blower, regardless of the actual flow direction at any particular moment. Items which are located within the pneumatic path between the blower and the patient interface are downstream of the blower and upstream of the patient interface. [0067] Fig. 4C is a schematic diagram of the electrical components of an RPT device in accordance with one form of the present technology. [0068] Fig. 4C-1 is a schematic diagram illustrating the interconnection of various electrical components of the RPT device. [0069] Fig. 4D is a schematic diagram of the processing implemented in an RPT device in accordance with one form of the present technology. [0070] Fig.4E is a flow chart illustrating a method carried out by the therapy engine module of Fig.4D in accordance with one form of the present technology. 4.5 HUMIDIFIER [0071] Fig. 5A shows an isometric view of a humidifier in accordance with one form of the present technology. [0072] Fig. 5B shows an isometric view of a humidifier in accordance with one form of the present technology, showing a humidifier reservoir 5110 removed from the humidifier reservoir dock 5130. 4.6 BREATHING WAVEFORMS [0073] Fig.6 shows a model typical breath waveform of a person while sleeping. 4.7 SCREENING, DIAGNOSIS AND MONITORING SYSTEMS [0074] Fig.7 is a schematic diagram of the components of a screening / diagnosis / monitoring device that may be used to implement a Respiratory polygraphy (RPG) headbox in an RPG screening / diagnosis / monitoring system concentrator in accordance with one form of the present technology. 4.8 THERAPEUTIC PRESSURE WAVEFORMS [0075] Fig.8A shows a waveform of therapeutic pressure applied to a sleeping patient who is undergoing PAP therapy during a sleeping session. [0076] Fig.8B shows a waveform of therapeutic pressure applied to a sleeping patient who is having trouble tolerating their PAP therapy patient interface during a sleeping session. [0077] Fig. 8C shows several waveforms of therapeutic pressure from subsequent sleep sessions of a patient whose PAP therapeutic pressure settings may be adjusted to make the therapy more effective. 4.9 AUTOMATIC PRESSURE ADJUSTMENTS [0078] Figs.9A and 9B depict examples of an automatic pressure adjustment system. [0079] Figs.10A and 10B depict examples of parameterization of expiratory positive airway pressure (EPAP) and decay curves, which may be implemented as a biasing function to bias pressure changes toward some pressure settings in an automatic pressure adjustment system. [0080] Fig.11 depicts graphs illustrating how pressure ranges may be biased. [0081] Figs. 12A, 12B, and 12C illustrate respective pressure treatment algorithm operation based on determined critical pressure(s) or a finding of an absence of any critical pressure. [0082] Figs.13A, 13B, and 13C depict examples of increase behaviors with example increase directed biasing functions for the three pressure adjustment algorithms of Figs. 12A, 12B, and 12C, respectively. [0083] Figs.14A, 14B, and 14C depict examples of decrease behaviors with example decrease directed biasing functions for the three pressure adjustment algorithms of Figs. 12A, 12B, and 12C, respectively. [0084] Fig. 15A depicts an example pressure distribution graph and an example pressure density graph for a uni-modal pressure pattern. [0085] Fig. 15B depicts an example pressure distribution graph and an example pressure density graph for a bi-modal pressure pattern. [0086] Fig. 16 depicts an example learning module process, which may be implemented in the automatic pressure adjustment system of Figs. 9A and 9B. The learning module process may determine an algorithm, such one of the algorithms shown by Figs. 12A to 12C, to apply to a particular patient based on therapy pressures provided during respiratory therapy period(s). [0087] Fig.17 depicts an example control loop for controlling the operation of the automatic pressure adjustment system of Figs.9A and 9B. 5 DETAILED DESCRIPTION OF EXAMPLES OF THE TECHNOLOGY [0088] Before the present technology is described in further detail, it is to be understood that the technology is not limited to the particular examples described herein, which may vary. It is also to be understood that the terminology used in this disclosure is for the purpose of describing only the particular examples discussed herein, and is not intended to be limiting. [0089] The following description is provided in relation to various examples which may share one or more common characteristics and/or features. It is to be understood that one or more features of any one example may be combinable with one or more features of another example or other examples. In addition, any single feature or combination of features in any of the examples may constitute a further example. 5.1 THERAPY [0090] In one form, the present technology comprises a method for treating a respiratory disorder comprising applying positive pressure to the entrance of the airways of a patient 1000. [0091] In certain examples of the present technology, a supply of air at positive pressure is provided to the nasal passages of the patient via one or both nares. [0092] In certain examples of the present technology, mouth breathing is limited, restricted or prevented. 5.2 RESPIRATORY THERAPY SYSTEMS [0093] In one form, the present technology comprises a respiratory therapy system for treating a respiratory disorder. The respiratory therapy system may comprise an RPT device 4000 for supplying a flow of air to the patient 1000 via an air circuit 4170 and a patient interface 3000 or 3800. 5.3 PATIENT INTERFACE [0094] A non-invasive patient interface 3000, such as that shown in Fig.3, in accordance with one aspect of the present technology comprises the following functional aspects: a seal-forming structure 3100, a plenum chamber 3200, a positioning and stabilising structure 3300, a vent 3400, one form of connection port 3600 for connection to air circuit 4170, and a forehead support 3700. In some forms a functional aspect may be provided by one or more physical components. In some forms, one physical component may provide one or more functional aspects. In use the seal- forming structure 3100 is arranged to surround an entrance to the airways of the patient so as to maintain positive pressure at the entrance(s) to the airways of the patient 1000. The sealed patient interface 3000 is therefore suitable for delivery of positive pressure therapy. 5.4 RPT DEVICE [0095] An RPT device 4000 in accordance with one aspect of the present technology comprises mechanical, pneumatic, and/or electrical components and is configured to execute one or more algorithms 4300, such as any of the methods, in whole or in part, described herein. The RPT device 4000 may be configured to generate a flow of air for delivery to a patient’s airways, such as to treat one or more of the respiratory conditions described elsewhere in the present document. [0096] In one form, the RPT device 4000 is constructed and arranged to be capable of delivering a flow of air in a range of -20 L/min to +150 L/min while maintaining a positive pressure of at least 4 cmH2O, or at least 10cmH2O, or at least 20 cmH2O. [0097] The RPT device may have an external housing 4010, formed in two parts, an upper portion 4012 and a lower portion 4014. Furthermore, the external housing 4010 may include one or more panel(s) 4015. The RPT device 4000 comprises a chassis 4016 that supports one or more internal components of the RPT device 4000. The RPT device 4000 may include a handle 4018. [0098] The pneumatic path of the RPT device 4000 may comprise one or more air path items, e.g., an inlet air filter 4112, an inlet muffler 4122, a pressure generator 4140 capable of supplying air at positive pressure (e.g., a blower 4142), an outlet muffler 4124 and one or more transducers 4270, such as pressure sensors 4272 and flow rate sensors 4274. [0099] One or more of the air path items may be located within a removable unitary structure which will be referred to as a pneumatic block 4020. The pneumatic block 4020 may be located within the external housing 4010. In one form a pneumatic block 4020 is supported by, or formed as part of the chassis 4016. [0100] As shown in Fig.4C, the RPT device 4000 may have an electrical power supply 4210, one or more input devices 4220, a central controller 4230, a therapy device controller 4240, a pressure generator 4140, one or more protection circuits 4250, memory 4260, transducers 4270, 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. 5.4.1 RPT device mechanical & pneumatic components [0101] An RPT device may comprise one or more of the following components in an integral unit. In an alternative form, one or more of the following components may be located as respective separate units. 5.4.1.1 Air filter(s) [0102] An RPT device in accordance with one form of the present technology may include an air filter 4110, or a plurality of air filters 4110. [0103] In one form illustrated in Fig.4B, an inlet air filter 4112 is located at the beginning of the pneumatic path upstream of a pressure generator 4140. [0104] In one form illustrated in Fig.4B, an outlet air filter 4114, for example an antibacterial filter, is located between an outlet of the pneumatic block 4020 and a patient interface 3000 or 3800. 5.4.1.2 Muffler(s) [0105] An RPT device in accordance with one form of the present technology may include a muffler 4120, or a plurality of mufflers 4120. [0106] In one form of the present technology (see e.g., Fig. 4B), an inlet muffler 4122 is located in the pneumatic path upstream of a pressure generator 4140. [0107] In one form of the present technology, an outlet muffler 4124 is located in the pneumatic path between the pressure generator 4140 and a patient interface 3000 or 3800. 5.4.1.3 Pressure generator [0108] In one form of the present technology, a pressure generator 4140 for producing a flow, or a supply, of air at positive pressure is a controllable blower 4142. For example, the blower 4142 may include a brushless DC motor 4144 with one or more impellers. The impellers may be located in a volute. The blower may be capable of delivering a supply of air, for example at a rate of up to about 120 litres/minute, at a positive pressure in a range from about 4 cmH2O to about 20 cmH2O, or in other forms up to about 30 cmH2O when delivering respiratory pressure therapy. The blower may be as described in any one 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. WO 2013/020167. [0109] The pressure generator 4140 may be under the control of the therapy device controller 4240. [0110] In other forms, a pressure generator 4140 may be a piston-driven pump, a pressure regulator connected to a high pressure source (e.g. compressed air reservoir), or a bellows. 5.4.1.4 Transducer(s) [0111] Transducers may be internal of the RPT device, or external of the RPT device. External transducers may be located for example on or form part of the air circuit, e.g., the patient interface. External transducers may be in the form of non-contact sensors such as a Doppler radar movement sensor that transmit or transfer data to the RPT device. [0112] In one form of the present technology (see e.g., Fig.4B), one or more transducers 4270 are located upstream and/or downstream of the pressure generator 4140. The one or more transducers 4270 may be constructed and arranged to generate signals representing properties of the flow of air such as a flow rate, a pressure or a temperature at that point in the pneumatic path. [0113] In one form of the present technology, one or more transducers 4270 may be located proximate to the patient interface 3000 or 3800. [0114] In one form, a signal from a transducer 4270 may be filtered, such as by low-pass, high-pass or band-pass filtering. 5.4.1.4.1 Flow rate sensor [0115] A flow rate sensor 4274 in accordance with the present technology may be based on a differential pressure transducer, for example, an SDP600 Series differential pressure transducer from SENSIRION. [0116] In one form, a signal generated by the flow rate sensor 4274 and representing a flow rate is received by the central controller 4230. 5.4.1.4.2 Pressure sensor [0117] A pressure sensor 4272 in accordance with the present technology is located in fluid communication with the pneumatic path. An example of a suitable pressure sensor is a transducer from the HONEYWELL ASDX series. An alternative suitable pressure sensor is a transducer from the NPA Series from GENERAL ELECTRIC. [0118] In one form, a signal generated by the pressure sensor 4272 and representing a pressure is received by the central controller 4230. 5.4.1.4.3 Motor speed transducer [0119] In one form of the present technology a motor speed transducer 4276 is used to determine a rotational velocity of the motor 4144 and/or the blower 4142. A motor speed signal from the motor speed transducer 4276 may be provided to the therapy device controller 4240. The motor speed transducer 4276 may, for example, be a speed sensor, such as a Hall effect sensor. 5.4.1.5 Anti-spill back valve [0120] As shown in Fig.4B, one form of the present technology, an anti-spill back valve 4160 is located between the humidifier 5000 and the pneumatic block 4020. The anti-spill back valve is constructed and arranged to reduce the risk that water will flow upstream from the humidifier 5000, for example to the motor 4144. 5.4.2 RPT device electrical components 5.4.2.1 Power supply [0121] A power supply 4210 may be located internal or external of the external housing 4010 of the RPT device 4000. [0122] In one form of the present technology, power supply 4210 provides electrical power to the RPT device 4000 only. In another form of the present technology, power supply 4210 provides electrical power to both RPT device 4000 and humidifier 5000. [0123] As illustrated in Fig.4C-1, the power supply 4210 may provide electrical power to the input device 4220, the central controller 4230, the output device 4290, and the pressure generator 4140. The power supply 4210 may also provide electric energy to other components of the RPT device 4000 (or the humidifier 5000, as described above). 5.4.2.2 Input devices [0124] In one form of the present technology, an RPT device 4000 includes one or more input devices 4220 in the form of buttons, switches or dials to allow a person to interact with the device. The buttons, switches or dials may be physical devices, or software devices accessible via a touch screen. The buttons, switches or dials may, in one form, be physically connected to the external housing 4010, or may, in another form, be in wireless communication with a receiver that is in electrical connection to the central controller 4230. [0125] In one form, the input device 4220 may be constructed and arranged to allow a person to select a value and/or a menu option. 5.4.2.3 Central controller [0126] In one form of the present technology, the central controller 4230 is one or a plurality of processors suitable to control an RPT device 4000. The central controller 4230 is show in Figs. 4C and 4C-1. [0127] Suitable processors may include an x86 INTEL processor, a processor based on ARM® Cortex®-M processor from ARM Holdings such as an STM32 series microcontroller from ST MICROELECTRONIC. In certain alternative forms of the present technology, a 32-bit RISC CPU, such as an STR9 series microcontroller from ST MICROELECTRONICS or a 16-bit RISC CPU such as a processor from the MSP430 family of microcontrollers, manufactured by TEXAS INSTRUMENTS may also be suitable. [0128] In one form of the present technology, the central controller 4230 is a dedicated electronic circuit. [0129] In one form, the central controller 4230 is an application-specific integrated circuit. In another form, the central controller 4230 comprises discrete electronic components. [0130] The central controller 4230 may be configured to receive input signal(s) from one or more transducers 4270, one or more input devices 4220, and/or the humidifier 5000. [0131] The central controller 4230 may be configured to provide output signal(s) to one or more of an output device 4290, a pressure generator 4140, a therapy device controller 4240, a data communication interface 4280, and/or the humidifier 5000. [0132] In some forms of the present technology, the central controller 4230 is configured to implement the one or more methodologies described herein, such as the one or more algorithms 4300 which may be implemented with processor-control instructions, expressed as computer programs stored in a non-transitory computer readable storage medium, such as memory 4260. In some forms of the present technology, the central controller 4230 may be integrated with an RPT device 4000. However, in some forms of the present technology, some methodologies may be performed by a remotely located device. For example, the remotely located device may determine control settings for a ventilator or detect respiratory related events by analysis of stored data such as from any of the sensors described herein. 5.4.2.4 Clock [0133] The RPT device 4000 may include a clock 4232 that is connected to the central controller 4230. 5.4.2.5 Therapy device controller [0134] In one form of the present technology, therapy device controller 4240 is a therapy control module 4330 that forms part of the algorithms 4300 executed by the central controller 4230. [0135] In one form of the present technology, therapy device controller 4240 is a dedicated motor control integrated circuit. For example, in one form a MC33035 brushless DC motor controller, manufactured by ONSEMI is used. 5.4.2.6 Protection circuits [0136] The one or more protection circuits 4250 in accordance with the present technology may comprise an electrical protection circuit, a temperature and/or pressure safety circuit. 5.4.2.7 Memory [0137] In accordance with one form of the present technology the RPT device 4000 includes memory 4260, e.g., non-volatile memory. In some forms, memory 4260 may include battery powered static RAM. In some forms, memory 4260 may include volatile RAM. [0138] Memory 4260 may be located on the PCBA 4202. Memory 4260 may be in the form of EEPROM, or NAND flash. [0139] Additionally, or alternatively, RPT device 4000 includes a removable form of memory 4260, for example a memory card made in accordance with the Secure Digital (SD) standard. [0140] In one form of the present technology, the memory 4260 acts as a non-transitory computer readable storage medium on which is stored computer program instructions expressing the one or more methodologies described herein, such as the one or more algorithms 4300. 5.4.2.8 Data communication systems [0141] In one form of the present technology, a data communication interface 4280 is provided, and is connected to the central controller 4230 (see e.g., Fig.4C). Data communication interface 4280 may be connectable to a remote external communication network 4282 and/or a local external communication network 4284. The remote external communication network 4282 may be connectable to a remote external device 4286. The local external communication network 4284 may be connectable to a local external device 4288. [0142] In one form, data communication interface 4280 is part of the central controller 4230. In another form, data communication interface 4280 is separate from the central controller 4230, and may comprise an integrated circuit or a processor. [0143] In one form, remote external communication network 4282 is the Internet. The data communication interface 4280 may use wired communication (e.g. via Ethernet, or optical fibre) or a wireless protocol (e.g. CDMA, GSM, LTE) to connect to the Internet. [0144] In one form, local external communication network 4284 utilises one or more communication standards, such as Bluetooth, or a consumer infrared protocol. [0145] In one form, remote external device 4286 is one or more computers, such as one or more servers, for example a cluster of networked computers. In one form, remote external device 4286 may be virtual computers, rather than physical computers. In either case, such a remote external device 4286 may be accessible to an appropriately authorised person such as a clinician. [0146] The local external device 4288 may be a personal computer, mobile phone, tablet or remote control. 5.4.2.9 Output devices including optional display, alarms [0147] An output device 4290 in accordance with the present technology may take the form of one or more of a visual, audio and haptic unit. A visual display may be a Liquid Crystal Display (LCD) or Light Emitting Diode (LED) display. 5.4.2.9.1 Display driver [0148] A display driver 4292 receives as an input the characters, symbols, or images intended for display on the display 4294, and converts them to commands that cause the display 4294 to display those characters, symbols, or images. 5.4.2.9.2 Display [0149] A display 4294 is configured to visually display characters, symbols, or images in response to commands received from the display driver 4292. For example, the display 4294 may be an eight-segment display, in which case the display driver 4292 converts each character or symbol, such as the figure “0”, to eight logical signals indicating whether the eight respective segments are to be activated to display a particular character or symbol. 5.4.3 RPT device algorithms [0150] As mentioned above, in some forms of the present technology, the central controller 4230 may be configured to implement one or more processes according to algorithms 4300 that are expressed as computer programs stored in a non-transitory computer readable storage medium, such as memory 4260. The algorithms 4300 are generally grouped into groups referred to as modules. [0151] In other forms of the present technology, some portion or all of the processes of algorithms 4300 may be implemented by a controller of an external device such as the local external device 4288 or the remote external device 4286. In such forms, data representing the input signals and / or intermediate algorithm outputs necessary for the portion of the processes of algorithms 4300 to be executed at the external device may be communicated to the external device via the local external communication network 4284 or the remote external communication network 4282. In such forms, the portion of the processes of algorithms 4300 to be executed at the external device may be expressed as computer programs, such as with processor control instructions to be executed by one or more processor(s), stored in a non-transitory computer readable storage medium accessible to the controller of the external device. Such programs configure the controller of the external device to execute the portion of the processes of algorithms 4300. [0152] In such forms, the therapy parameters generated by the external device via the therapy engine module 4320 (if such forms part of the portion of the algorithms 4300 executed by the external device) may be communicated to the central controller 4230 to be passed to the therapy control module 4330. 5.4.3.1 Pre-processing module [0153] A pre-processing module 4310 in accordance with one form of the present technology receives as an input a signal from a transducer 4270, for example a flow rate sensor 4274 or pressure sensor 4272, and performs one or more process steps to calculate one or more output values that will be used as an input to another module, for example a therapy engine module 4320. [0154] In one form of the present technology, the output values include the interface pressure Pm, the vent flow rate Qv, the respiratory flow rate Qr, and the leak flow rate Ql. [0155] In various forms of the present technology, the pre-processing module 4310 comprises one or more of the following algorithms: interface pressure estimation 4312, vent flow rate estimation 4314, leak flow rate estimation 4316, and respiratory flow rate estimation 4318. 5.4.3.1.1 Interface pressure estimation [0156] In one form of the present technology, an interface pressure estimation algorithm 4312 receives as inputs a signal from the pressure sensor 4272 indicative of the pressure in the pneumatic path proximal to an outlet of the pneumatic block (the device pressure Pd) and a signal from the flow rate sensor 4274 representative of the flow rate of the airflow leaving the RPT device 4000 (the device flow rate Qd). The device flow rate Qd, absent any supplementary gas 4180, may be used as the total flow rate Qt. The interface pressure algorithm 4312 estimates the pressure drop ΔP through the air circuit 4170. The dependence of the pressure drop ΔP on the total flow rate Qt may be modelled for the particular air circuit 4170 by a pressure drop characteristic ΔP(Q). The interface pressure estimation algorithm, 4312 then provides as an output an estimated pressure, Pm, in the patient interface 3000 or 3800. The pressure, Pm, in the patient interface 3000 or 3800 may be estimated as the device pressure Pd minus the air circuit pressure drop ΔP. 5.4.3.1.2 Vent flow rate estimation [0157] In one form of the present technology, a vent flow rate estimation algorithm 4314 receives as an input an estimated pressure, Pm, in the patient interface 3000 or 3800 from the interface pressure estimation algorithm 4312 and estimates a vent flow rate of air, Qv, from a vent 3400 in a patient interface 3000 or 3800. The dependence of the vent flow rate Qv on the interface pressure Pm for the particular vent 3400 in use may be modelled by a vent characteristic Qv(Pm). 5.4.3.1.3 Leak flow rate estimation [0158] In one form of the present technology, a leak flow rate estimation algorithm 4316 receives as an input a total flow rate, Qt, and a vent flow rate Qv, and provides as an output an estimate of the leak flow rate Ql. In one form, the leak flow rate estimation algorithm estimates the leak flow rate Ql by calculating an average of the difference between total flow rate Qt and vent flow rate Qv over a period sufficiently long to include several breathing cycles, e.g. about 10 seconds. [0159] In one form, the leak flow rate estimation algorithm 4316 receives as an input a total flow rate Qt, a vent flow rate Qv, and an estimated pressure, Pm, in the patient interface 3000 or 3800, and provides as an output a leak flow rate Ql, by calculating a leak conductance, and determining a leak flow rate Ql to be a function of leak conductance and pressure, Pm. Leak conductance is calculated as the quotient of low pass filtered non-vent flow rate equal to the difference between total flow rate Qt and vent flow rate Qv, and low pass filtered square root of pressure Pm, where the low pass filter time constant has a value sufficiently long to include several breathing cycles, e.g. about 10 seconds. The leak flow rate Ql may be estimated as the product of leak conductance and a function of pressure, Pm. 5.4.3.1.4 Respiratory flow rate estimation [0160] In one form of the present technology, a respiratory flow rate estimation algorithm 4318 receives as an input a total flow rate, Qt, a vent flow rate, Qv, and a leak flow rate, Ql, and estimates a respiratory flow rate of air, Qr, to the patient, by subtracting the vent flow rate Qv and the leak flow rate Ql from the total flow rate Qt. 5.4.3.2 Therapy Engine Module [0161] In one form of the present technology, a therapy engine module 4320 receives as inputs one or more of a pressure, Pm, in a patient interface 3000 or 3800, and a respiratory flow rate of air to a patient, Qr, and provides as an output one or more therapy parameters. [0162] In one form of the present technology, a therapy parameter is a treatment pressure Pt. [0163] In one form of the present technology, therapy parameters are one or more of an amplitude of a pressure variation, a base pressure, and a target ventilation. [0164] In various forms, the therapy engine module 4320 comprises one or more of the following algorithms: phase determination 4321, waveform determination 4322, ventilation determination 4323, inspiratory flow limitation determination 4324, apnea / hypopnea determination 4325, snore determination 4326, airway patency determination 4327, target ventilation determination 4328, and therapy parameter determination 4329. 5.4.3.2.1 Phase determination [0165] In one form of the present technology, the RPT device 4000 does not determine phase. [0166] In one form of the present technology, a phase determination algorithm 4321 receives as an input a signal indicative of respiratory flow rate, Qr, and provides as an output a phase φ of a current breathing cycle of a patient 1000. [0167] In some forms, known as discrete phase determination, the phase output φ is a discrete variable. One implementation of discrete phase determination provides a bi-valued phase output φ with values of either inhalation or exhalation, for example represented as values of 0 and 0.5 revolutions respectively, upon detecting the start of spontaneous inhalation and exhalation respectively. RPT devices 4000 that “trigger” and “cycle” effectively perform discrete phase determination, since the trigger and cycle points are the instants at which the phase changes from exhalation to inhalation and from inhalation to exhalation, respectively. In one implementation of bi-valued phase determination, the phase output φ is determined to have a discrete value of 0 (thereby “triggering” the RPT device 4000) when the respiratory flow rate Qr has a value that exceeds a positive threshold, and a discrete value of 0.5 revolutions (thereby “cycling” the RPT device 4000) when a respiratory flow rate Qr has a value that is more negative than a negative threshold. The inhalation time Ti and the exhalation time Te may be estimated as typical values over many respiratory cycles of the time spent with phase φ equal to 0 (indicating inspiration) and 0.5 (indicating expiration) respectively. [0168] Another implementation of discrete phase determination provides a tri-valued phase output φ with a value of one of inhalation, mid-inspiratory pause, and exhalation. [0169] In other forms, known as continuous phase determination, the phase output φ is a continuous variable, for example varying from 0 to 1 revolutions, or 0 to 2Π radians. RPT devices 4000 that perform continuous phase determination may trigger and cycle when the continuous phase reaches 0 and 0.5 revolutions, respectively. In one implementation of continuous phase determination, a continuous value of phase ^ ^is determined using a fuzzy logic analysis of the respiratory flow rate Qr. A continuous value of phase determined in this implementation is often referred to as “fuzzy phase”. In one implementation of a fuzzy phase determination algorithm 4321, the following rules are applied to the respiratory flow rate Qr: 1. If Qr is zero and increasing fast then φ is 0 revolutions. 2. If Qr is large positive and steady then φ is 0.25 revolutions. 3. If Qr is zero and falling fast, then φ is 0.5 revolutions. 4. If Qr is large negative and steady then φ is 0.75 revolutions. 5. If Qr is zero and steady and the 5-second low-pass filtered absolute value of Qr is large then φ is 0.9 revolutions. 6. If Qr is positive and the phase is expiratory, then φ is 0 revolutions. 7. If Qr is negative and the phase is inspiratory, then φ is 0.5 revolutions. 8. If the 5-second low-pass filtered absolute value of Qr is large, φ is increasing at a steady rate equal to the patient’s breathing rate, low-pass filtered with a time constant of 20 seconds. [0170] The output of each rule may be represented as a vector whose phase is the result of the rule and whose magnitude is the fuzzy extent to which the rule is true. The fuzzy extent to which the respiratory flow rate is “large”, “steady”, etc. is determined with suitable membership functions. The results of the rules, represented as vectors, are then combined by some function such as taking the centroid. In such a combination, the rules may be equally weighted, or differently weighted. [0171] In another implementation of continuous phase determination, the phase φ is first discretely estimated from the respiratory flow rate Qr as described above, as are the inhalation time Ti and the exhalation time Te. The continuous phase φ at any instant may be determined as the half the proportion of the inhalation time Ti that has elapsed since the previous trigger instant, or 0.5 revolutions plus half the proportion of the exhalation time Te that has elapsed since the previous cycle instant (whichever instant was more recent). 5.4.3.2.2 Waveform determination [0172] In one form of the present technology, the therapy parameter determination algorithm 4329 provides an approximately constant treatment pressure throughout a respiratory cycle of a patient. [0173] In other forms of the present technology, the therapy control module 4330 controls the pressure generator 4140 to provide a treatment pressure Pt that varies as a function of phase φ of a respiratory cycle of a patient according to a waveform template Π(φ). [0174] In one form of the present technology, a waveform determination algorithm 4322 provides a waveform template Π(φ) with values in the range [0, 1] on the domain of phase values φ provided by the phase determination algorithm 4321 to be used by the therapy parameter determination algorithm 4329. [0175] In one form, suitable for either discrete or continuously-valued phase, the waveform template Π(φ) is a square-wave template, having a value of 1 for values of phase up to and including 0.5 revolutions, and a value of 0 for values of phase above 0.5 revolutions. In one form, suitable for continuously-valued phase, the waveform template Π(φ) comprises two smoothly curved portions, namely a smoothly curved (e.g. raised cosine) rise from 0 to 1 for values of phase up to 0.5 revolutions, and a smoothly curved (e.g. exponential) decay from 1 to 0 for values of phase above 0.5 revolutions. In one form, suitable for continuously-valued phase, the waveform template Π(φ) is based on a square wave, but with a smooth rise from 0 to 1 for values of phase up to a “rise time” that is less than 0.5 revolutions, and a smooth fall from 1 to 0 for values of phase within a “fall time” after 0.5 revolutions, with a “fall time” that is less than 0.5 revolutions. [0176] In some forms of the present technology, the waveform determination algorithm 4322 selects a waveform template Π(φ) from a library of waveform templates, dependent on a setting of the RPT device. Each waveform template Π(φ) in the library may be provided as a lookup table of values Pt against phase values φ. In other forms, the waveform determination algorithm 4322 computes a waveform template Π(φ) “on the fly” using a predetermined functional form, possibly parametrised by one or more parameters (e.g. time constant of an exponentially curved portion). The parameters of the functional form may be predetermined or dependent on a current state of the patient 1000. [0177] In some forms of the present technology, suitable for discrete bi-valued phase of either inhalation (φ = 0 revolutions) or exhalation (φ = 0.5 revolutions), the waveform determination algorithm 4322 computes a waveform template Π “on the fly” as a function of both discrete phase φ and time t measured since the most recent trigger instant. In one such form, the waveform determination algorithm 4322 computes the waveform template Π(φ, t) in two portions (inspiratory and expiratory) as follows: ^Π ( ^Φ, t ) ^{= ^ ^ Π ^} i ⁽ t ^{) , Φ = 0} ^ _{− , Φ = 0.5} and Πe(t) are inspiratory and expiratory portions of the waveform template Π(φ, t). In one such form, the inspiratory portion Πi(t) of the waveform template is a smooth rise from 0 to 1 parametrised by a rise time, and the expiratory portion Πe(t) of the waveform template is a smooth fall from 1 to 0 parametrised by a fall time. 5.4.3.2.3 Ventilation determination [0179] In one form of the present technology, a ventilation determination algorithm 4323 receives an input a respiratory flow rate Qr, and determines a measure indicative of current patient ventilation, Vent. [0180] In some implementations, the ventilation determination algorithm 4323 determines a measure of ventilation Vent that is an estimate of actual patient ventilation. One such implementation is to take half the absolute value of respiratory flow rate, Qr, optionally filtered by low-pass filter such as a second order Bessel low-pass filter with a corner frequency of 0.11 Hz. [0181] In other implementations, the ventilation determination algorithm 4323 determines a measure of ventilation Vent that is broadly proportional to actual patient ventilation. One such implementation estimates peak respiratory flow rate Qpeak over the inspiratory portion of the cycle. This and many other procedures involving sampling the respiratory flow rate Qr produce measures which are broadly proportional to ventilation, provided the flow rate waveform shape does not vary very much (here, the shape of two breaths is taken to be similar when the flow rate waveforms of the breaths normalised in time and amplitude are similar). Some simple examples include the median positive respiratory flow rate, the median of the absolute value of respiratory flow rate, and the standard deviation of flow rate. Arbitrary linear combinations of arbitrary order statistics of the absolute value of respiratory flow rate using positive coefficients, and even some using both positive and negative coefficients, are approximately proportional to ventilation. Another example is the mean of the respiratory flow rate in the middle K proportion (by time) of the inspiratory portion, where 0 < K < 1. There is an arbitrarily large number of measures that are exactly proportional to ventilation if the flow rate shape is constant. 5.4.3.2.4 Determination of Inspiratory Flow Limitation [0182] In one form of the present technology, the central controller 4230 executes an inspiratory flow limitation determination algorithm 4324 for the determination of the extent of inspiratory flow limitation. [0183] In one form, the inspiratory flow limitation determination algorithm 4324 receives as an input a respiratory flow rate signal Qr and provides as an output a metric of the extent to which the inspiratory portion of the breath exhibits inspiratory flow limitation. [0184] In one form of the present technology, the inspiratory portion of each breath is identified by a zero-crossing detector. A number of evenly spaced points (for example, sixty-five), representing points in time, are interpolated by an interpolator along the inspiratory flow rate-time curve for each breath. The curve described by the points is then scaled by a scalar to have unity length (duration/period) and unity area to remove the effects of changing breathing rate and depth. The scaled breaths are then compared in a comparator with a pre-stored template representing a normal unobstructed breath, similar to the inspiratory portion of the breath shown in Fig.6. Breaths deviating by more than a specified threshold (typically 1 scaled unit) at any time during the inspiration from this template, such as those due to coughs, sighs, swallows and hiccups, as determined by a test element, are rejected. For non-rejected data, a moving average of the first such scaled point is calculated by the central controller 4230 for the preceding several inspiratory events. This is repeated over the same inspiratory events for the second such point, and so on. Thus, for example, sixty-five scaled data points are generated by the central controller 4230, and represent a moving average of the preceding several inspiratory events, e.g., three events. The moving average of continuously updated values of the (e.g., sixty-five) points are hereinafter called the "scaled flow rate ", designated as Qs(t). Alternatively, a single inspiratory event can be utilised rather than a moving average. [0185] From the scaled flow rate, two shape factors relating to the determination of partial obstruction may be calculated. [0186] Shape factor 1 is the ratio of the mean of the middle (e.g. thirty-two) scaled flow rate points to the mean overall (e.g. sixty-five) scaled flow rate points. Where this ratio is in excess of unity, the breath will be taken to be normal. Where the ratio is unity or less, the breath will be taken to be obstructed. A ratio of about 1.17 is taken as a threshold between partially obstructed and unobstructed breathing, and equates to a degree of obstruction that would permit maintenance of adequate oxygenation in a typical patient. [0187] Shape factor 2 is calculated as the RMS deviation from unit scaled flow rate, taken over the middle (e.g. thirty-two) points. An RMS deviation of about 0.2 units is taken to be normal. An RMS deviation of zero is taken to be a totally flow–limited breath. The closer the RMS deviation to zero, the breath will be taken to be more flow limited. [0188] Shape factors 1 and 2 may be used as alternatives, or in combination. In other forms of the present technology, the number of sampled points, breaths and middle points may differ from those described above. Furthermore, the threshold values can be other than those described. 5.4.3.2.5 Determination of apneas and hypopneas [0189] In one form of the present technology, the central controller 4230 executes an apnea / hypopnea determination algorithm 4325 for the determination of the presence of apneas and/or hypopneas. [0190] In one form, the apnea / hypopnea determination algorithm 4325 receives as an input a respiratory flow rate signal Qr and provides as an output a flag that indicates that an apnea or a hypopnea has been detected. [0191] In one form, an apnea will be said to have been detected when a function of respiratory flow rate Qr falls below a flow rate threshold for a predetermined period of time. The function may determine a peak flow rate, a relatively short-term mean flow rate, or a flow rate intermediate of relatively short-term mean and peak flow rate, for example an RMS flow rate. The flow rate threshold may be a relatively long-term measure of flow rate. [0192] In one form, a hypopnea will be said to have been detected when a function of respiratory flow rate Qr falls below a second flow rate threshold for a predetermined period of time. The function may determine a peak flow, a relatively short-term mean flow rate, or a flow rate intermediate of relatively short-term mean and peak flow rate, for example an RMS flow rate. The second flow rate threshold may be a relatively long-term measure of flow rate. The second flow rate threshold is greater than the flow rate threshold used to detect apneas. 5.4.3.2.6 Determination of snore [0193] In one form of the present technology, the central controller 4230 executes one or more snore determination algorithms 4326 for the determination of the extent of snore. [0194] In one form, the snore determination algorithm 4326 receives as an input a respiratory flow rate signal Qr and provides as an output a metric of the extent to which snoring is present. [0195] The snore determination algorithm 4326 may comprise the step of determining the intensity of the flow rate signal in the range of 30-300 Hz. Further, the snore determination algorithm 4326 may comprise a step of filtering the respiratory flow rate signal Qr to reduce background noise, e.g., the sound of airflow in the system from the blower. 5.4.3.2.7 Determination of airway patency [0196] In one form of the present technology, the central controller 4230 executes one or more airway patency determination algorithms 4327 for the determination of the extent of airway patency. [0197] In one form, the airway patency determination algorithm 4327 receives as an input a respiratory flow rate signal Qr, and determines the power of the signal in the frequency range of about 0.75 Hz and about 3 Hz. The presence of a peak in this frequency range is taken to indicate an open airway. The absence of a peak is taken to be an indication of a closed airway. [0198] In one form, the frequency range within which the peak is sought is the frequency of a small forced oscillation in the treatment pressure Pt. In one implementation, the forced oscillation is of frequency 2 Hz with amplitude about 1 cmH2O. [0199] In one form, airway patency determination algorithm 4327 receives as an input a respiratory flow rate signal Qr, and determines the presence or absence of a cardiogenic signal. The absence of a cardiogenic signal is taken to be an indication of a closed airway. 5.4.3.2.8 Determination of target ventilation [0200] In one form of the present technology, the central controller 4230 takes as input the measure of current ventilation, Vent, and executes one or more target ventilation determination algorithms 4328 for the determination of a target value Vtgt for the measure of ventilation. [0201] In some forms of the present technology, there is no target ventilation determination algorithm 4328, and the target value Vtgt is predetermined, for example by hard-coding during configuration of the RPT device 4000 or by manual entry through the input device 4220. [0202] In other forms of the present technology, such as adaptive servo-ventilation (ASV), the target ventilation determination algorithm 4328 computes a target value Vtgt from a value Vtyp indicative of the typical recent ventilation of the patient. [0203] In some forms of adaptive servo-ventilation, the target ventilation Vtgt is computed as a high proportion of, but less than, the typical recent ventilation Vtyp. The high proportion in such forms may be in the range (80%, 100%), or (85%, 95%), or (87%, 92%). [0204] In other forms of adaptive servo-ventilation, the target ventilation Vtgt is computed as a slightly greater than unity multiple of the typical recent ventilation Vtyp. [0205] The typical recent ventilation Vtyp is the value around which the distribution of the measure of current ventilation Vent over multiple time instants over some predetermined timescale tends to cluster, that is, a measure of the central tendency of the measure of current ventilation over recent history. In one implementation of the target ventilation determination algorithm 4328, the recent history is of the order of several minutes, but in any case should be longer than the timescale of Cheyne-Stokes waxing and waning cycles. The target ventilation determination algorithm 4328 may use any of the variety of well-known measures of central tendency to determine the typical recent ventilation Vtyp from the measure of current ventilation, Vent. One such measure is the output of a low-pass filter on the measure of current ventilation Vent, with time constant equal to one hundred seconds. 5.4.3.2.9 Determination of therapy parameters [0206] In some forms of the present technology, the central controller 4230 executes one or more therapy parameter determination algorithms 4329 for the determination of one or more therapy parameters using the values returned by one or more of the other algorithms in the therapy engine module 4320. [0207] In one form of the present technology, the therapy parameter is an instantaneous treatment pressure Pt. In one implementation of this form, the therapy parameter determination algorithm 4329 determines the treatment pressure Pt using the equation P^{t = A Π ( Φ, t ) + P 0} ₍₁₎ • A is the amplitude, • Π(φ,t) is the waveform template value (in the range 0 to 1) at the current value φ of phase and t of time, and • P₀ is a base pressure. [0209] If the waveform determination algorithm 4322 provides the waveform template Π(φ,t) as a lookup table of values Π indexed by phase φ, the therapy parameter determination algorithm 4329 applies equation (1) by locating the nearest lookup table entry to the current value φ of phase returned by the phase determination algorithm 4321, or by interpolation between the two entries straddling the current value φ of phase. [0210] The values of the amplitude A and the base pressure P0 may be set by the therapy parameter determination algorithm 4329 depending on the chosen respiratory pressure therapy mode in the manner described below. [0211] The therapy parameter determination algorithms may include any or all of the algorithms for adjusting therapy that are described herein, such as those described in relation to Figs.8A to 16. 5.4.3.2.10 Mask-off determination [0212] It can be helpful to assess whether a patient is compliant with respiratory pressure therapy, i.e. whether they wear or remove the patient interface while sleeping. Accordingly, the therapy control module 4330 may periodically query the leak flow rate estimation algorithm 4316, which uses an estimate of pressure at the patient interface to determine how much air is leaking from the interface. In case the patient is not wearing / using the patient interface while the RPT device is operating, the leak flow rate will exceed a threshold value, and the therapy control module 4330 may assess a “mask-off” event. It should be understood that “mask-off” is a term of convenience and that the determination equally is applicable to other patient interfaces, e.g., nasal pillows. A mask-on event may be determined similarly such as by detecting a low leak flow rate or patient respiratory flow via the mask. 5.4.3.3 Therapy Control module [0213] The therapy control module 4330 in accordance with one aspect of the present technology receives as inputs the therapy parameters from the therapy parameter determination algorithm 4329 of the therapy engine module 4320, and controls the pressure generator 4140 to deliver a flow of air in accordance with the therapy parameters. [0214] In one form of the present technology, the therapy parameter is a treatment pressure Pt, and the therapy control module 4330 controls the pressure generator 4140 to deliver a flow of air whose interface pressure Pm at the patient interface 3000 or 3800 is equal to the treatment pressure Pt. 5.4.3.4 Detection of fault conditions [0215] In one form of the present technology, the central controller 4230 executes one or more methods 4340 for the detection of fault conditions. The fault conditions detected by the one or more methods 4340 may include at least one of the following: • Power failure (no power, or insufficient power) • Transducer fault detection • Failure to detect the presence of a component • Operating parameters outside recommended ranges (e.g. pressure, flow rate, temperature, PaO2) • Failure of a test alarm to generate a detectable alarm signal. [0216] Upon detection of the fault condition, the corresponding algorithm 4340 signals the presence of the fault by one or more of the following: • Initiation of an audible, visual &/or kinetic (e.g. vibrating) alarm • Sending a message to an external device • Logging of the incident 5.5 AIR CIRCUIT [0217] An air circuit 4170 in accordance with an aspect of the present technology is a conduit or a tube constructed and arranged to allow, in use, a flow of air to travel between two components such as RPT device 4000 and the patient interface 3000 or 3800. [0218] In particular, the air circuit 4170 may be in fluid connection with the outlet of the pneumatic block 4020 and the patient interface. The air circuit may be referred to as an air delivery tube. In some cases there may be separate limbs of the circuit for inhalation and exhalation. In other cases a single limb is used. [0219] In some forms, the air circuit 4170 may comprise 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 a form of a heated wire circuit, and may comprise one or more transducers, such as temperature sensors. In one form, the heated wire circuit may be helically wound around the axis of the air circuit 4170. The heating element may be in communication with a controller such as a central controller 4230. One example of an air circuit 4170 comprising a heated wire circuit is described in United States Patent 8,733,349, which is incorporated herewithin in its entirety by reference. 5.5.1 Supplementary gas delivery [0220] In one form of the present technology, supplementary gas, e.g. oxygen, 4180 is delivered to one or more points in the pneumatic path, such as upstream of the pneumatic block 4020, to the air circuit 4170, and/or to the patient interface 3000 or 3800. 5.6 HUMIDIFIER 5.6.1 Humidifier overview [0221] In one form of the present technology there is provided a humidifier 5000 (e.g. as shown in Fig.5A) to change the absolute humidity of air or gas for delivery to a patient relative to ambient air. Typically, the humidifier 5000 is used to increase the absolute humidity and increase the temperature of the flow of air (relative to ambient air) before delivery to the patient’s airways. [0222] The humidifier 5000 may comprise a humidifier reservoir 5110, a humidifier inlet 5002 to receive a flow of air, and a humidifier outlet 5004 to deliver a humidified flow of air. In some forms, as shown in Fig. 5A and Fig. 5B, an inlet and an outlet of the humidifier reservoir 5110 may be the humidifier inlet 5002 and the humidifier outlet 5004 respectively. The humidifier 5000 may further comprise a humidifier base 5006, which may be adapted to receive the humidifier reservoir 5110 and comprise a heating element 5240. 5.7 BREATHING WAVEFORMS Fig. 6 shows a model typical breath waveform of a person while sleeping. The horizontal axis is time, and the vertical axis is respiratory flow rate. While the parameter values may vary, a typical breath may have the following approximate values: tidal volume Vt 0.5L, inhalation time Ti 1.6s, peak inspiratory flow rate Qpeak 0.4 L/s, exhalation time Te 2.4s, peak expiratory flow rate Qpeak -0.5 L/s. The total duration of the breath, Ttot, is about 4s. The person typically breathes at a rate of about 15 breaths per minute (BPM), with Ventilation Vent about 7.5 L/min. A typical duty cycle, the ratio of Ti to Ttot, is about 40%. 5.8 SCREENING, DIAGNOSIS, MONITORING SYSTEMS 5.8.1 Respiratory polygraphy [0223] Fig.7 is a block diagram illustrating a screening / diagnosis / monitoring device 7200 that may be used to implement an RPG headbox in an RPG screening / diagnosis / monitoring system. The screening / diagnosis / monitoring device 7200 receives the three RPG channels mentioned above (a signal indicative of thoracic movement, a signal indicative of nasal flow rate, and a signal indicative of oxygen saturation) at a data input interface 7260. The screening / diagnosis / monitoring device 7200 also contains a processor 7210 configured to carry out encoded instructions. The screening / diagnosis / monitoring device 7200 also contains a non-transitory computer readable memory / storage medium 7230. [0224] Memory 7230 may be the screening / diagnosis / monitoring device 7200's internal memory, such as RAM, flash memory or ROM. In some implementations, memory 7230 may also be a removable or external memory linked to screening / diagnosis / monitoring device 7200, such as an SD card, server, USB flash drive or optical disc, for example. In other implementations, memory 7230 can be a combination of external and internal memory. Memory 7230 includes stored data 7240 and processor control instructions (code) 7250 adapted to configure the processor 7210 to perform certain tasks. Stored data 7240 can include RPG channel data received by data input interface 7260, and other data that is provided as a component part of an application. Processor control instructions 7250 can also be provided as a component part of an application program. The processor 7210 is configured to read the code 7250 from the memory 7230 and execute the encoded instructions. In particular, the code 7250 may contain instructions adapted to configure the processor 7210 to carry out methods of processing the RPG channel data provided by the interface 7260. One such method may be to store the RPG channel data as data 7240 in the memory 7230. Another such method may be to analyse the stored RPG data to extract features. The processor 7210 may store the results of such analysis as data 7240 in the memory 7230. [0225] The screening / diagnosis / monitoring device 7200 may also contain a communication interface 7220. The code 7250 may contain instructions configured to allow the processor 7210 to communicate with an external computing device (not shown) via the communication interface 7220. The mode of communication may be wired or wireless. In one such implementation, the processor 7210 may transmit the stored RPG channel data from the data 7240 to the remote computing device. In such an implementation, the remote computing device may be configured to analyse the received RPG data to extract features. In another such implementation, the processor 7210 may transmit the analysis results from the data 7240 to the remote computing device. [0226] Alternatively, if the memory 7230 is removable from the screening / diagnosis / monitoring device 7200, the remote computing device may be configured to be connected to the removable memory 7230. In such an implementation, the remote computing device may be configured to analyse the RPG data retrieved from the removable memory 7230 to extract the features. 5.9 RESPIRATORY THERAPY MODES [0227] Various respiratory therapy modes may be implemented by the disclosed respiratory therapy system. 5.9.1 CPAP therapy [0228] In some implementations of respiratory pressure therapy, the central controller 4230 sets the treatment pressure Pt according to the treatment pressure equation (1) as part of the therapy parameter determination algorithm 4329. In one such implementation, the amplitude A is identically zero, so the treatment pressure Pt (which represents a target value to be achieved by the interface pressure Pm at the current instant of time) is identically equal to the base pressure P0 throughout the respiratory cycle. Such implementations are generally grouped under the heading of CPAP therapy. In such implementations, there is no need for the therapy engine module 4320 to determine phase φ or the waveform template Π(φ,t). [0229] In CPAP therapy, the base pressure P0 may be a constant value that is hard-coded or manually entered to the RPT device 4000. Alternatively, the central controller 4230 may repeatedly compute the base pressure P0 as a function of indices or measures of sleep disordered breathing returned by the respective algorithms in the therapy engine module 4320, such as one or more of flow limitation, apnea, hypopnea, patency, and snore. This alternative is sometimes referred to as APAP therapy. [0230] Fig.4E is a flow chart illustrating a method 4500 carried out by the central controller 4230 to continuously compute the base pressure P0 as part of an APAP therapy implementation of the therapy parameter determination algorithm 4329, when the pressure support A is identically zero. [0231] The method 4500 starts at step 4520, at which the central controller 4230 compares the measure of the presence of apnea / hypopnea with a first threshold, and determines whether the measure of the presence of apnea / hypopnea has exceeded the first threshold for a predetermined period of time, indicating an apnea / hypopnea is occurring. If so, the method 4500 proceeds to step 4540; otherwise, the method 4500 proceeds to step 4530. At step 4540, the central controller 4230 compares the measure of airway patency with a second threshold. If the measure of airway patency exceeds the second threshold, indicating the airway is patent, the detected apnea / hypopnea is deemed central, and the method 4500 proceeds to step 4560; otherwise, the apnea / hypopnea is deemed obstructive, and the method 4500 proceeds to step 4550. [0232] At step 4530, the central controller 4230 compares the measure of flow limitation with a third threshold. If the measure of flow limitation exceeds the third threshold, indicating inspiratory flow is limited, the method 4500 proceeds to step 4550; otherwise, the method 4500 proceeds to step 4560. [0233] At step 4550, the central controller 4230 increases the base pressure P0 by a predetermined pressure increment ΔP, provided the resulting treatment pressure Pt would not exceed a maximum treatment pressure Pmax. In one implementation, the predetermined pressure increment ΔP and maximum treatment pressure Pmax are 1 cmH2O and 25 cmH2O respectively. In other implementations, the pressure increment ΔP can be as low as 0.1 cmH2O and as high as 3 cmH2O, or as low as 0.5 cmH2O and as high as 2 cmH2O. In other implementations, the maximum treatment pressure Pmax can be as low as 15 cmH2O and as high as 35 cmH2O, or as low as 20 cmH2O and as high as 30 cmH2O. The method 4500 then returns to step 4520. [0234] At step 4560, the central controller 4230 decreases the base pressure P0 by a decrement, provided the decreased base pressure P0 would not fall below a minimum treatment pressure Pmin. The method 4500 then returns to step 4520. In one implementation, the decrement is proportional to the value of P0-Pmin, so that the decrease in P0 to the minimum treatment pressure Pmin in the absence of any detected events is exponential. In one implementation, the constant of proportionality is set such that the time constant τ of the exponential decrease of P0 is 60 minutes, and the minimum treatment pressure Pmin is 4 cmH2O. In other implementations, the time constant τ could be as low as 1 minute and as high as 300 minutes, or as low as 5 minutes and as high as 180 minutes. In other implementations, the minimum treatment pressure Pmin can be as low as 0 cmH2O and as high as 8 cmH2O, or as low as 2 cmH2O and as high as 6 cmH2O. Alternatively, the decrement in P0 could be predetermined, so the decrease in P0 to the minimum treatment pressure Pmin in the absence of any detected events is linear. 5.9.2 Bi-level therapy [0235] In other implementations of this form of the present technology, the value of amplitude A in equation (1) may be positive. Such implementations are known as bi-level therapy, because in determining the treatment pressure Pt using equation (1) with positive amplitude A, the therapy parameter determination algorithm 4329 oscillates the treatment pressure Pt between two values or levels in synchrony with the spontaneous respiratory effort of the patient 1000. That is, based on the typical waveform templates Π(φ,t) described above, the therapy parameter determination algorithm 4329 increases the treatment pressure Pt to P₀ + A (known as the IPAP) at the start of, or during, or inspiration and decreases the treatment pressure Pt to the base pressure P0 (known as the EPAP) at the start of, or during, expiration. [0236] In some forms of bi-level therapy, the IPAP is a treatment pressure that has the same purpose as the treatment pressure in CPAP therapy modes, and the EPAP is the IPAP minus the amplitude A, which has a “small” value (a few cmH2O) sometimes referred to as the Expiratory Pressure Relief (EPR). Such forms are sometimes referred to as CPAP therapy with EPR, which is generally thought to be more comfortable than straight CPAP therapy. In CPAP therapy with EPR, either or both of the IPAP and the EPAP may be constant values that are hard-coded or manually entered to the RPT device 4000. Alternatively, the therapy parameter determination algorithm 4329 may repeatedly compute the IPAP and / or the EPAP during CPAP with EPR. In this alternative, the therapy parameter determination algorithm 4329 repeatedly computes the EPAP and / or the IPAP as a function of indices or measures of sleep disordered breathing returned by the respective algorithms in the therapy engine module 4320 in analogous fashion to the computation of the base pressure P0 in APAP therapy described above. [0237] In other forms of bi-level therapy, the amplitude A is large enough that the RPT device 4000 does some or all of the work of breathing of the patient 1000. In such forms, known as pressure support ventilation therapy, the amplitude A is referred to as the pressure support, or swing. In pressure support ventilation therapy, the IPAP is the base pressure P0 plus the pressure support A, and the EPAP is the base pressure P0. [0238] In some forms of pressure support ventilation therapy, known as fixed pressure support ventilation therapy, the pressure support A is fixed at a predetermined value, e.g.10 cmH2O. The predetermined pressure support value is a setting of the RPT device 4000, and may be set for example by hard-coding during configuration of the RPT device 4000 or by manual entry through the input device 4220. [0239] In other forms of pressure support ventilation therapy, broadly known as servo- ventilation, the therapy parameter determination algorithm 4329 takes as input some currently measured or estimated parameter of the respiratory cycle (e.g. the current measure Vent of ventilation) and a target value of that respiratory parameter (e.g. a target value Vtgt of ventilation) and repeatedly adjusts the parameters of equation (1) to bring the current measure of the respiratory parameter towards the target value. In a form of servo-ventilation known as adaptive servo- ventilation (ASV), which has been used to treat CSR, the respiratory parameter is ventilation, and the target ventilation value Vtgt is computed by the target ventilation determination algorithm 4328 from the typical recent ventilation Vtyp, as described above. [0240] In some forms of servo-ventilation, the therapy parameter determination algorithm 4329 applies a control methodology to repeatedly compute the pressure support A so as to bring the current measure of the respiratory parameter towards the target value. One such control methodology is Proportional-Integral (PI) control. In one implementation of PI control, suitable for ASV modes in which a target ventilation Vtgt is set to slightly less than the typical recent ventilation Vtyp, the pressure support A is repeatedly computed as: A^{= G} ^ ^{( Vent − Vtgt ) dt} (₂₎ [0241] where G is the gain of the PI control. Larger values of can result in positive feedback in the therapy engine module 4320. Smaller values of gain G may permit some residual untreated CSR or central sleep apnea. In some implementations, the gain G is fixed at a predetermined value, such as -0.4 cmH2O/(L/min)/sec. Alternatively, the gain G may be varied between therapy sessions, starting small and increasing from session to session until a value that substantially eliminates CSR is reached. Conventional means for retrospectively analysing the parameters of a therapy session to assess the severity of CSR during the therapy session may be employed in such implementations. In yet other implementations, the gain G may vary depending on the difference between the current measure Vent of ventilation and the target ventilation Vtgt. [0242] Other servo-ventilation control methodologies that may be applied by the therapy parameter determination algorithm 4329 include proportional (P), proportional-differential (PD), and proportional-integral-differential (PID). [0243] The value of the pressure support A computed via equation (2) may be clipped to a range defined as [Amin, Amax]. In this implementation, the pressure support A sits by default at the minimum pressure support Amin until the measure of current ventilation Vent falls below the target ventilation Vtgt, at which point A starts increasing, only falling back to Amin when Vent exceeds Vtgt once again. [0244] The pressure support limits Amin and Amax are settings of the RPT device 4000, set for example by hard-coding during configuration of the RPT device 4000 or by manual entry through the input device 4220. [0245] In pressure support ventilation therapy modes, the EPAP is the base pressure P0. As with the base pressure P0 in CPAP therapy, the EPAP may be a constant value that is prescribed or determined during titration. Such a constant EPAP may be set for example by hard-coding during configuration of the RPT device 4000 or by manual entry through the input device 4220. This alternative is sometimes referred to as fixed-EPAP pressure support ventilation therapy. Titration of the EPAP for a given patient may be performed by a clinician during a titration session with the aid of PSG, with the aim of preventing obstructive apneas, thereby maintaining an open airway for the pressure support ventilation therapy, in similar fashion to titration of the base pressure P0 in constant CPAP therapy. [0246] Alternatively, the therapy parameter determination algorithm 4329 may repeatedly compute the base pressure P0 during pressure support ventilation therapy. In such implementations, the therapy parameter determination algorithm 4329 repeatedly computes the EPAP as a function of indices or measures of sleep disordered breathing returned by the respective algorithms in the therapy engine module 4320, such as one or more of respiratory events of flow limitation, apnea, hypopnea, patency, and snore. Because the continuous computation of the EPAP resembles the manual adjustment of the EPAP by a clinician during titration of the EPAP, this process is also sometimes referred to as auto-titration of the EPAP, and the therapy mode is known as auto-titrating EPAP pressure support ventilation therapy, or auto-EPAP pressure support ventilation therapy. 5.10 FURTHER POTENTIAL CHANGES TO THERAPY SETTINGS [0247] Certain situations may arise in positive airway pressure therapy, such as CPAP or bi- level therapy, that would make it desirable to change therapy parameters. For example, there may be an excessive number of adverse events (e.g., apnea, hypopnea, snoring, mask-off) that suggests that at least some of the therapy parameters are not appropriate (i.e., set properly) for the patient. Accordingly, it may be helpful to implement (e.g., in the therapy control module 4330) one or more processes or algorithms in one or more processors (e.g., in any processor described herein such as a controller of the RPT, in a server and/or other external or remote computer) for recommending, such as to a clinician, or even implementing control to automate making changes to one or more therapy parameters, based on detections of any of various treatment issues. Such parameters may include, but are not limited to minimum therapeutic pressure (minCPAP), maximum therapeutic pressure (maxCPAP), ramp time and/or rate of pressure increase from StartCPAP pressure at initiation of therapy to minCPAP minimum therapeutic pressure, bridge time from minimum therapeutic pressure to a critical range of therapeutic pressures Pcrit, etc. For example, in some implementations of the technology, the process(es) described herein for detection of treatment issues, may use treatment information from one or more previous sessions to recommend and/or adjust parameters of an RPT controller (e.g., the therapy control module 4330) so that they may be better set to respond to respiratory events such as detections of snore, flow limitation and/or apnea, such as how fast or slow pressure changes (e.g., increase or decrease) should be made in light of detections of such respiratory events. Alternatively, the adjustment of one or more parameters may provide a more comfortable experience for the user, thus potentially improving or even improving the user’s compliance with the treatment. [0248] For example, such process(es) or algorithm(s) may be configured to detect if there are treatment issues (e.g., observed repetitively over a couple of sessions of therapy (e.g., a couple of nights of treatment for that patient with the RPT) which are likely to be a result of incorrect control parameter settings (such as Start CPAP, MinCPAP, MaxCPAP, ramp time / ramp rate). Such detections may serve as a basis for the process(s) to generate one more messages, such as to provide the user or a clinician, with an explanation of the treatment issues that have been detected and how the therapy parameters or settings may be adjusted in order to resolve or reduce the occurrences, or negative effects thereof, of those treatment issues. Such recommendations may be generated as, for example, as textual or graphic indication. In some implementations of the technology, the process(es) may be configured to automatically or semi-automatically implement the adjustment of such settings, such as by remotely communicating commands for the new parameter/setting adjustments to the RPT and/or setting them in the controller of the RPT, such as if the recommended adjustments are also approved by manual input or confirmation of the user/clinician. [0249] In this regard, such detections of treatment issues described herein may be made by evaluation of sensor data (e.g., pressure and/or flow rate data from transducer(s) described herein), and/or event data (e.g., respiratory events) determined from the sensor data, that may be generated by a controller of the RPT from therapy sessions with the RPT. Additionally, such detection of issues may also be made with an evaluation of additional information such as, for example, sleep information such as sleep time, scored sleep stage information, body position information, head position information, which may be input or otherwise detected from sensor data, such as by analysis of motion data from an accelerometer or other non-contact motion sensor such as radio frequency motion sensors. Any of such data may be received remotely from the RPT at one or more servers that store, in one or more database(s), the sensor data and/or event data so that the evaluations may be made, at least in part, by a server or computer (e.g., local or remote) accessing such database data. Thus, the evaluations to detect the treatment issues described herein may be made by any one or both of the controller of the RPT, such as without remote processing, and/or by a remote computer and/or server accessing the sensor and/or event data of the one or more databases. For example, the controller of the RPT may record sensor data, detect respiratory events from multiple sessions and evaluate the treatment issues as discussed herein. Similarly, a server or computer (local or remote) may access the sensor data such as from the database(s), detect respiratory events therefrom, and further evaluate the data for detecting the treatment issues described herein. Still further a server or computer (local or remote) may access the sensor data and/or detected respiratory events, such as from the database(s) and/or RPT, and evaluate the data for detecting the treatment issues described herein. The time during which the data is collected and processed, may include one or more treatment sessions. [0250] For example, in some implementations of the technology, the process(es) may be configured to detect a ramp related treatment issue, such as if it occurs repeatedly in a plurality of sessions. For example, the respiratory event data from one prior session or each of a plurality of previous sessions may be evaluated to detect whether a count of apnea-hypopnea index (AHI) events that occur particularly during a ramp period of each session may exceed a threshold value. Such a ramp period typically occurs during initiation of a therapy session (e.g., a “mask on” event) when lower pressures are provided to help a patient fall asleep using lower pressures of the ramp period, but may occur more than once in a session, such as every time the patient interrupts and then renews the treatment session. The pressure during a predetermined ramp period will gradually change to a predetermined minimum therapeutic pressure at the conclusion of the ramp period. Such a detection may be considered in relation to the example graph of FIG.8A. FIG.8A depicts a therapeutic pressure waveform 8100 from a sleep session. The waveform 8100 shows a ramp 8102 having a ramp time. If a number of AHI events occur in one or more ramp sessions, a ramp related treatment issue is detected. Based upon such a detection, the process(es) may generate a recommendation message of a change to a setting time for the ramp (e.g., an increase in rate of pressure increase during the ramp time, a reduction to a ramp time or removing the ramp time altogether). Similarly, based upon such a detection, the process(es) may generate a recommendation for a change (e.g., an increase) to the initial pressure used for the ramp (i.e., the startCPAP pressure). Moreover, the process(es) may be further configured to automatically or semi-automatically (such as with a manual input confirmation of a recommended change) implement any of such adjustments to such settings (e.g., the initial pressure and/or ramp period or rate of increase), such as by remotely communicating command(s) for the new parameter/setting adjustments to the RPT for the next session. [0251] Another example may be related to a “critical pressure range” (shown in Fig. 8C at 8301), which is a treatment pressure range in which generally no adverse respiratory events or no more than a predetermined maximum number of adverse respiratory events (e.g., apnea / hypopnea events (AHI events), snoring, or flow limitation events) occur for a particular patient. This range is deemed a “critical” range because it is generally efficacious for the particular patient over multiple sessions. The Pcrit range may be defined by a minimum Pcrit (Pcrit-min) 8305 (which can be above a minimum CPAP setting such as at or above a pressure range 8307 between the MinCPAP setting and the minimum Pcrit), and a maximum P_crit (Pcrit-max) 8303, which can be below a maximum CPAP setting. This Pcrit range of therapeutic pressures can typically maintain upper airway patency for the particular patient and the peak of the range may be designated as P_crit or P_critical and may optionally be determined by evaluation of density of breathing events per pressure range such as from one or a plurality of treatment sessions. Thus, data from several previous sessions of treatment, such as with an auto-titrating device, may be utilized to establish such a critical range of therapeutic pressures P_crit. The process(es) may be configured to detect related treatment issue (e.g., in the form of one or more respiratory events), such as if it occurs repeatedly in a plurality of sessions during a period 8104 wherein pressure increases from a minimum therapeutic pressure into a critical range of therapeutic pressures (see FIG.8A). In such a situation, it may be desirable to increase the minimum therapeutic pressure setting (MinCPAP), and/or to increase the rate of climb from the MinCPAP towards the Pcrit. range of pressures. For example, any processor described herein, such as a processor of controller (e.g., the therapy control module 4330), may have access to or be configured to identify a critical therapeutic pressure range. Data from several previous sessions of treatment, such as with an auto-titrating device, may be utilized to establish such a critical range of therapeutic pressures P_crit. [0252] In relation to such a range, a treatment issue may be detected, for example, if an evaluation of the respiratory event data from each of a plurality of sessions detects the occurrence of a predetermined number of respiratory events, or that a count of AHI events exceeds a threshold, during a time period of the session between the end of a ramp period and a first time when the delivered pressure reaches the Pcrit range, as pressure changes due to auto-titration (e.g., pressure responses to respiratory events). Based upon such a detection, the process(es) may generate a recommendation message of a change to a pressure setting parameter concerning the ramp approaching the Pcrit, (see treatment pressure range 8104 in Fig. 8A). For example, based upon such a detection, the process(es) may generate a recommendation for a change (e.g., an increase) to the end pressure (i.e., the MinCPAP pressure.) that is achieved by the aforementioned ramp. Moreover, the process(es) may be further configured to automatically or semi-automatically (such as with a manual input confirmation of a recommended change) implement such adjustment to such setting (e.g., the MinCPAP pressure), such as by remotely communicating command(s) for the new parameter/setting adjustments to the RPT for the next session. [0253] In another example, the process(es) may be configured to detect a maximum pressure related treatment issue, such as if it occurs repeatedly in a plurality of sessions. For example, the data from each of a plurality of sessions may be evaluated to detect whether the therapy pressure, such as during an auto-titrating therapy, repeatedly (e.g., a number of occurrences exceeding a threshold) reaches the limit of a maximum therapy pressure threshold setting (i.e., MaxCPAP) and/or maintains pressure at such a limit for an amount time that exceeds a time threshold that may be deemed significant. Moreover, such detection may be further conditioned on a count of AHI events that exceeds a threshold, that may be deemed significant. Based upon such a detection, the process(es) may generate a recommendation message of a change to a pressure setting parameter concerning the therapy. For example, based upon such a detection, the process(es) may generate a recommendation for a change (e.g., an increase) to the maximum pressure limit (i.e., the MaxCPAP pressure). Moreover, the process(es) may be further configured to automatically or semi-automatically (such as with a manual input confirmation of a recommended change) implement such an adjustment to such settings (e.g., the MaxCPAP pressure), such as by remotely communicating command(s) for the new parameter/setting adjustments to the RPT for the next session. [0254] In another example, the process(es) may be configured to detect a mask on/off related treatment issue, such as if it occurs repeatedly in a plurality of sessions. For example, as illustrated in FIG.8B, the graph depicts a therapeutic pressure waveform 8200 during a sleep session in which the patient removes their mask repeatedly (e.g., at 8202, 8204, 8206), which may be detected by detecting a drop in mask pressure. During such a sleep session, a count of such mask-off events may exceed a threshold value. As an additional parameter, one can observe if such mask-off events may occur in a similar pressure range. In such a situation, it may be desirable to reduce the maximum therapeutic pressure setting since the pressure may be waking the patient. For example, the data from each of a plurality of sessions may be evaluated to detect whether the number of detected mask-off events exceeds a threshold, such as that occur when the delivered therapy pressure is the same or similar pressure (e.g., within plus or minus a margin (e.g., 2 cm H₂O) of each other). Based upon such a detection, the process(es) may generate a recommendation message of a change to a pressure setting parameter concerning the therapy. For example, based upon such a detection, the process(es) may generate a recommendation for a change (e.g., a decrease) to the maximum pressure limit (i.e., the maximum of the Pcrit. Range (P_crit-max 8303) and/or the MaxCPAP pressure). Moreover, the process(es) may be further configured to automatically or semi-automatically (such as with a manual input confirmation of a recommended change) implement such an adjustment to such settings (e.g., the MaxCPAP pressure), such as by remotely communicating command(s) for the new parameter/setting adjustments to the RPT for the next session. [0255] In another similar example, the process(es) may be configured to detect a mask on/off related treatment issue, such as if it occurs repeatedly in a plurality of sessions. If a count of detected mask-off events in a session exceeds a corresponding threshold value and the RPT device is set to use a ramp period after each such event, it may be desirable to disable the ramp or increase the ramp rate of climb. For example, the process(es) may be configured to detect whether a count of mask-off events exceeds a threshold and that ramp function is enabled. Based upon such a detection, the process(es) may generate a recommendation message of a change to the ramp (e.g., disabling the ramp, such as after detected mask off events, decreasing a ramp time or increasing the ramp end pressure (MinCPAP) or increasing a rate of a ramp pressure increase). Moreover, the process(es) may be further configured to automatically or semi-automatically (such as with a manual input confirmation of a recommended change) implement any of such adjustments to such settings (e.g., disabling the ramp, such as after detected mask off events, decreasing a ramp time or increasing the ramp end pressure (MinCPAP) or increasing rate of ramp pressure increase), such as by remotely communicating command(s) for the new parameter/setting adjustments to the RPT for the next session. In some cases, the above recommendations/changes may be made only if there is an indication that the mask-off events and/or the subsequent ramp time increase the number of adverse respiratory events. [0256] Additional processes for avoiding treatment issues may be considered in relation to FIG. 8C. Such processes may be configured to evaluate data (e.g., sensor data, such as pressure and/or flow data, and/or respiratory event data) from previous sessions (e.g., previous nights of treatment) for modifying automated therapy pressure changes in response to detections of respiratory events, such as snore or flow limitation or apnoea, and/or how fast or slow pressure increase or decreases. Such processes may be based on the aforementioned determination of a critical pressure (Pcrit) range. [0257] For example, FIG.8C depicts a graph 8300 of therapeutic pressure waveforms from a plurality of sessions and a determined Pcrit range. The previously determined Pcrit range may be implemented as a control input(s), such as the therapy parameter determination module 4329. The Pcrit and/or the Pcrit range or a value therein, determined from one or more prior sessions, may serve as an additional control parameter or pressure control parameter for controlling therapy of a subsequent session. For example, a value of the Pcrit or Pcrit range may serve as a soft limit to pressure increases or decreases, or modify the rate of change of pressure within certain pressure ranges. The system may be programmed to focus on operating within this range and to resist moving the treatment pressure out of the range, such as by implementing of one or more biasing functions as described in more detail herein. [0258] For example, in some implementations, the controller of the RPT may increase pressure generally in response to a detection of a respiratory event, such as any one or more flow limitation, snore, and apnea as previously described. However, if such a change in pressure ΔP would increase the treatment pressure above the maximum Pcrit (PcritMax 8303), the Pcrit range may serve as a further constraint to resist such a change. For example, such a change in pressure ΔP may be reduced, such as by some proportion, if the resulting pressure would be outside the Pcrit range. Similarly, application of such a change in pressure ΔP may be delayed until of another similar respiratory event is detected. In this way, a value of the Pcrit or Pcrit range may serve as a soft limit to pressure increases. Such increases may occur but the controller operates to maintain the pressure within, or closer to, the Pcrit range. By modifying the controller’s response to detected respiratory events so as to resist pressure changes above the Pcrit, the controller may, in part, permit such increases but may minimize or avoid unnecessary overshoot events 8302 that could might lead to any of patient discomfort, leak and/or patient arousal. [0259] Similarly, the controller of the RPT may decrease pressure over time generally in the absence of a detection of a respiratory event, such an absence of any one or more flow limitation, snore, and apnea as previously described. However, if such a change in pressure ΔP would decrease the treatment pressure below the Pcrit range, the Pcrit range may serve as a further constraint to resist such a change. For example, such a change in pressure ΔP may be reduced, such as by some proportion, if the resulting pressure would fall below the Pcrit range. Similarly, application of such a change in pressure ΔP due to an absence of events may be delayed for a longer period of time without detection of respiratory events. In this way, a value of the minimum Pcrit (P_crit-Min 8305) or Pcrit range may serve as a soft limit to pressure decreases. Such decreases may occur but the controller operates to maintain the pressure closer to or within the Pcrit range. [0260] Similarly, the Pcrit range may serve as a magnet for changes to the treatment pressure, with the controller in some cases being programmed to more rapidly approach the Pcrit range when the treatment pressure is not within the range. For example, for a detection of a respiratory event requiring an increase in pressure if detected when the treatment pressure is below the range, the change in pressure ΔP for such event may be larger, such as by a factor, when compared to an increase in pressure for an event detected when the treatment pressure is within the Pcrit range. Similarly, in an absence of detections of respiratory events that requires a decrease in treatment pressure, if occurring when the treatment pressure is above the range, the change in pressure ΔP for such an absence of events may be larger, such as by a factor, when compared to a decrease in pressure for an absence of events when the treatment pressure is within the Pcrit range. [0261] In another example, such as during a ramp period, if respiratory events are detected, the controller may modify one or more control parameters of the ramp so that the pressure of the ramp approaches a value in the Pcrit range, such as a minimum pressure value of the Pcrit range. FIG.8C depicts an example of such a ramp 8304 in which a processor of the controller of the RPT detects a number of AHI events, which number during the ramp exceeds or meets a threshold value. In response, the controller adjusts one or more of the control parameter(s) of the ramp (e.g., the rate of increase and/or end pressure for the ramp) so that the ramp approaches a value of the Pcrit range, which is larger than the end pressure target of the ramp that had been set at the beginning of the ramp (i.e., MinCPAP), or that would have been set otherwise. 5.11 AUTOMATIC PRESSURE ADJUSTMENTS [0262] In some forms of the present technology, a respiratory therapy system, which may comprise an RPT device 4000 for supplying a flow of air to the patient 1000 via an air circuit 4170 and a patient interface 3000 or 3800, is configured or otherwise embodied as an auto-adjusting pressure device (or self-adjusting device) comprising advanced event detection and auto-adjusting mechanisms. The event detection mechanism may detect one or more respiratory events from a flow signal measured and recorded during a respiratory therapy period (RTP), such as a night of sleep, or portions thereof. The auto-adjusting mechanism may automatically adjust respiratory therapy parameters, such as the pressure settings used by the respiratory therapy system to supply pressure to the patient during the RTP. [0263] The auto-adjusting mechanism, in one form of the present technology, may provide adaptive respiratory therapies, such as in relation to one or more biasing functions as described in more detail herein. The adaptive respiratory therapies may reduce pressure variability including RTP-to-RTP pressure variability, reduce unintentional leakage, maintain acceptable AHI, and reduce the number of sleep disturbances experienced by the patient. In addition to improving the operation and performance of the respiratory therapy system itself, the auto-adjusting mechanism allows the adaptive respiratory therapies to be personalized to a specific patient. This personalization may improve long-term adherence to prescribed therapeutic protocols and thereby improve overall patient health and safety. [0264] The auto-adjusting mechanism, in one form of the present technology, may provide a hybrid approach between CPAP and APAP respiratory therapy algorithms. In these ways, the auto- adjusting mechanism may borrow the strengths from both algorithms, such as the pressure stability provided by CPAP algorithms and the adaptability of APAP algorithms. Additionally, the auto- adjusting mechanism may learn the Pcrit for each patient and bias respiratory pressure towards the learned Pcrit values. In one form of the present technology, the further the deviation from the optimal Pcrit, auto-adjusting mechanism may have a stronger resistance and greater the tendency to return to the optimal Pcrit values. [0265] In some forms of the present technology, the auto-adjusting mechanism may learn a range of optimal pressures from a patient’s previous RTP, or from a set of previous RTPs, and may bias a current respiratory therapy pressure towards the learned range, such as by implementing one or more biasing functions, such as to selectively moderate pressure increases and/or decreases, which may be based on one or more learned critical pressures. This may reduce the fluctuations in pressure, allowing the patient to more easily adapt to different pressure levels. [0266] Conventional pressure treatment algorithms that may reduce the therapy pressure to a specified minimum pressure, which often drives patients to lower pressure levels where apneas and hypopneas may occur. The conventional pressure treatment algorithms may also increase the pressure until a respiratory event is resolved. This may be an overreaction, especially if the pressure is raised above the patient’s optimal pressure range where events do not typically occur. Higher pressures typically increase the chances of mask leaks, which is a leading issue for PAP users. This repeating cycle of raising and lowering the respiratory therapy pressure throughout the therapy period makes it more difficult for patient to be comfortable, leads to more sleep disturbances, and may result in less ideal outcomes, from a therapeutic perspective. [0267] By contrast, the auto-adjusting mechanism, in one form, may reduce the fluctuations in pressure. For example, in one form, the auto-adjusting mechanism may slow dipping below ideal therapy pressures by, for example, slowing the decay of the pressure level below ideal pressures for the patient. Furthermore, the auto-adjusting mechanism, in one form, may avoid excessive pressure increases that may lead to sleep disturbances. In these ways, the auto-adjusting mechanism may reduce the likelihood of respiratory events from occurring during the therapy period (i.e., during the patient’s sleep period). [0268] Fig.9A shows an example automatic pressure adjustment system 900 (also referred to herein as the “auto-adjusting system 900” or the “auto-adjusting mechanism 900”). In one form of the present technology, the auto-adjusting system 900 may be instantiated in therapy engine code of the therapy engine 4320. For example, some of the depicted components of the auto-adjusting system 900 may be instantiated as individual components of the therapy engine 4320, and other components of the auto-adjusting system 900 may be instantiated as part of one or more components of the therapy engine 4320 shown by Fig. 4D. The auto-adjusting system 900 may include an APAP 910 that operates during a current therapy session 901 based on pressure settings across one or more therapy sessions 902. The APAP 910 includes an event detection component 912 (also referred to herein as “event detector 912”), a prescription component 914, a rolling AHI calculator 915, and a floor pressure component 917. [0269] During operation, a flow therapy signal 911 is provided to the event detection component 912. In one form of the present technology, the event detection component 912 may include multiple event detectors such as, for example, a flow limitation detector, an apnea detector, a snore detector, and/or the like. In one form, the flow limitation detector may be, or may be part of the inspiratory flow limitation determination entity 4324, the apnea detector may be, or may be part of the apnea / hypopnea determination entity 4325, and the snore detector may be, or may be part of the snore determination 4326. Additional or alternative detectors may be included other implementations. Each event detector may be configured to determine or identify a corresponding respiratory event from the flow signal 911. For example, the flow limitation detector may determine or identify zero or more flow limitation events from the flow signal 911, the apnea detector may determine or identify zero or more apnea events from the flow signal 911, and the snore detector may determine or identify zero or more snoring events from the flow signal 911. Each event signal may be a separate event detection signal 913 or a component part of an event detection signal 913. For example, each event detector may output a respective event detection signal 913 indicating the determined/identified events that occurred at respective points in time in the flow signal 911. In another example, the event signals from each detector may be combined into an individual event detection signal 913 that is provided to the rolling AHI calculator 915 and the prescription component 914. In either form, the event detection signal(s) 913 may indicate, for each detected respiratory event, an event type, such as flow limitation, apnea, hypopnea, snore, and/or the like, and a timestamp of when the event occurred during the RTP. Such detections may be implemented by algorithms as described in more detail in this specification. In some implementations, the event detection signal(s) 913 may indicate a likelihood or probability that such event has occurred. The likelihood or probability may be expressed as a confidence level, a percentage, and/or using some other value or label. In one form, the event detection signal(s) 913 may also indicate a severity indication for at least some detected event types, such as flow limitation and/or other event types. The severity may also be expressed as a severity level, a percentage, a pressure value, and/or using some other value or label. [0270] The event detection component 912 provides the event detection signal(s) 913 to the prescription component 914 and the rolling AHI calculator 915. The rolling AHI calculator 915 calculates a rolling AHI 916. Typically, an AHI is used to classify the severity of sleep disordered breathing based on counts of apnea and hypopnea event represented by an AHI count. For example, the severity of the condition may be classified as mild, moderate, or severe based on the number of AHI events that occur during the RTP. The rolling AHI 916 may be implemented as a moving average time window of the number of AHI events that are detected. For example, the rolling AHI calculator 915 calculates the average AHI value from a set of AHIs measured or calculated within a specified, continuously shifting time frame. In one example, the time window is a 30 minute window, and rolling AHI 916 may represent an average AHI calculated for a previous 30 minute period. It should be noted that, in one form, the rolling AHI 916 determination is on a per-session basis. The rolling AHI 916 is provided by the rolling AHI calculator 915 to a floor pressure component 917. Other time window periods may be implemented such as 20 minutes, 40 minutes 60 minutes, etc. [0271] The floor pressure component 917 determines a floor pressure 918 based on the rolling AHI 916. The floor pressure 918 may be a minimum pressure setting to be provided to the patient, based on the rolling AHI 916. The floor pressure 918 is provided by the floor pressure component 917 to the prescription component 914. Before, after, or simultaneously as the event signal 913 and/or the floor pressure 917, the prescription component 914 also obtains pressure adjustment factors 924 (also referred to herein as “optimal pressure settings 924”) from learning module 921. [0272] The prescription component 914 determines the current pressure settings 919 based at least on the event detection signal(s) 912, floor pressure 918, and the pressure adjustment factors 924, which can be implemented by any of the biasing functions described herein. The pressure settings 919 may be used to adjust the respiratory therapy pressure provided to the patient during the current, or on-going, respiratory therapy session. The current pressure settings 919 may also be provided to the N-day memory 921, which may serve to store the pressure settings as a plurality of prior treatment session pressure curves. Such memory of prior session pressure curves may also be evaluated for determining the biasing functions that produce the pressure adjustment factors 924. As discussed in more detail with respect to Fig.16, the learning module 923 may learn a patient-optimal pressure treatment algorithm 922 that uses a biasing function for the patient to produce the pressure adjustment factors 924 based on a current pressure or other parameters of the current pressure settings 919 (or a current pressure trace 1610, see Fig. 9B) and/or based on a set of previous pressure traces 940 (also referred to herein as “previous curves 940”). [0273] Thus, the previous curves 940 may have been measured and recorded during previous RTPs, such as where pressures were adjusted during such sessions in accordance with an auto- titrating process that may change therapy pressure over the course of the session in response to detection of events of sleep disordered breathing, such as any of apnea, hypopnea, snoring, flow limitation, etc., as described in more detail herein in reference to the automated algorithm detectors or "doctors". The previous curves 940 may be stored in an N-day memory 921 or may be stored separately from the N-day memory 921. The current pressure 919 may also be stored as a pressure trace 940 in the N-day memory 921 or other storage unit. The N-day memory 921 may also store one or more pressure treatment algorithms 922, such as pressure treatment algorithms 1200A, 1200B, and 1200C of Figs. 12A, 12B, and 12C, respectively that may each include a different biasing function. Optionally, the N-day memory may also store one or more metrics or statistics derived from the plurality of previous pressure curves 940. The metrics or statistics, such as a pressure distribution or density and/or one or more local maxima thereof, may be derived/computed by the learning module 923 as described in more detail herein. [0274] The learning module 923 may learn pressure adjustment factors, such as an increase factor and a decrease (decay) factor, based on a learned biasing functions, which may be part of one of the pressure treatment algorithms 922 that may optionally be stored in the n-day memory or other memory. In one example, the biasing function(s) may be determined and/or selected based on a computed pressure distribution, such as pressure distribution 1612. For instance, the biasing function may be determined and/or selected based on one or more local maxima of the pressure distribution. The pressure distribution may be generated from a set of previous pressure curves 940, such as one or more previous pressure traces 1610. [0275] For example, if the flow limitation detector detects a flow limitation event 913, the prescription component 914 may generate pressure settings 919 or a pressure trace 1610 during a RTP. A pressure distribution, such as pressure distribution 1612, may be generated for storage in the N-day memory 921 based on the set of previous curves 940. The learning module 923 may select or generate a biasing function based on a distribution type of the pressure distribution. In one form of the present technology, the distribution type may be based on the number of local maxima in the pressure distribution. The learning module may determine, from the selected/generated biasing function, the pressure adjustment factors 924, such as an increase factor and a decrease factor, to be applied to the current pressure settings 919 or to an adjustment to the current pressure setting. The bias function with its pressure adjustment factors may then be used to generate an appropriate pressure response in the pressure settings 919 such as to achieve patient specific pressure control based on a critical pressure or patient pressure range as previously discussed. [0276] Thus, the pressure adjustment factors 924, or a biasing function(s), may be provided from the learning module 923 (or the N-day memory 921) to the prescription component 914 for determining the pressure response in the next pressure setting 919 so as to influence changes in pressure that are based on detected events (e.g., flow limitation, snoring, hypopnea) or an absence thereof. As mentioned previously, the pressure adjustment factors 924 may include an increase factor and a decrease factor. In one form of the present technology, the increase factor may be a moderation factor, such as EPAP MF 957 shown by Fig. 9B. Such a moderation factor may influence the increase behavior of the pressure settings 919. Aspects of such a moderation factor are discussed infra with respect to Figs.13A, 13B, and 13C. In one form of the present technology, the decrease factor may be a decay factor or decay constant, such as decay parameters 953 shown by Fig.9B. The decay factor/constant may influence the decrease behavior of the pressure settings 919. Aspects of such a decay factor/constant are discussed infra with respect to Figs. 14A, 14B, and 14C. [0277] The floor pressure 918 based on rolling AHI 916 may relate to a re-baselining feature 935 (see FIG. 9B). Here, if the rolling AHI 916 (or flow limitation) exceeds some predefined or configured threshold, the floor pressure component 917 may be triggered to (re)set a minimum pressure value to be temporarily raised to a new higher pressure value. This new higher pressure value may be expressed as the floor pressure 918. In one form, the new higher pressure value (floor pressure 918) may be the pressure at which the device operates when the rolling AHI threshold has been exceeded. For example, if at the beginning of a respiratory therapy session, the event detection component 912 identifies two consecutive apneas (as indicated by the event detection signal(s) 913) within the first hour of treatment, then the minimum pressure the device approached after those two events 913 may be used as the minimum (floor) pressure 918. The new higher minimum pressure may be imposed as the pressure setting 919 for some predefined or configured length of time. A suitable timer may be used to impose this pressure setting 919. This timer may be set to a predefined timer value. In one example, the predefined timer value may be 10 minutes, or 15 minutes or 20 minutes etc. After expiration of the timer, the prescription component 914 may revert the pressure settings 919 back to the minimum pressure set on the respiratory therapy system by the clinician or patient. In one form, each time this re-baselining 935 is triggered, the aforementioned timer may be reset back to its predefined timer value. [0278] Fig. 9B shows an example of the auto-adjusting system 900 in more detail. In particular, Fig.9B shows how the outputs of the learning module 923 are brought into the control algorithm over several therapy periods. In Fig. 9B, the prescription component 914 includes a doctor layer 930, a moderation layer 931, a gain layer 932, a limitation layer 933, and a summation layer 934 (also referred to herein as “∑ 934”). The doctor layer 930 includes a respective doctor component for different types of events. In the example of Fig. 9B, the doctor layer 930 includes a flow limitation doctor (also referred to as a “fuzzy flow-limitation doctor” or “FFL doctor”), an apnea doctor, and a snore doctor. These "doctors" will be understood to be automated algorithms executed by one or more processors of an RPT for detection of each of an event of flow limitation, an event of snoring and an event of apnea. Additional or alternative doctors may be included in the doctor layer 930 in other implementations. Each "doctor" provides respective prescriptions (e.g., an amount of a change to a pressure) to the moderation layer 931, where the prescription of each doctor is moderated by one or more moderation factors (MFs) which are produced by one or more biasing functions. The MFs may influence the size or magnitude of a pressure increase based on the current pressure level, which may, for example, provide “softer responses” to respiratory events 913. [0279] Thus, in the moderation layer 931, various MFs are applied to (e.g., by multiplication or subtraction or other suitable operation, etc.) the prescription signals. For example, an EPAP MF 957 and/or 0 to M other flow-related MFs (where M is a number) may be combined into a flow limitation MF at a product point 9311. Some or all of the components of the flow limitation MF, such as the EPAP MF 957, may be provided by an EPAP moderation curve parameterizer 956 which selects or generates one or more of the biasing functions as discussed in more detail herein. The 0 to M flow-related MFs may include, for example, a mean leak MF, a valvelike mouthleak MF, a jamming MF, a normalized expiratory peak location (NEPL) MF, a pressure MF, and/or the like. The flow limitation MF may be combined with the flow limitation prescription signal at summation point 9312. At summation point 9312, the flow limitation MF may be applied to adjust the flow limitation prescription signal. For example, a suitable MF value may be subtracted from, or multiplied with, the flow limitation prescription signal. [0280] By way of another example, an apnea prescription signal may be combined (such as by subtraction or multiplication, or other suitable operation etc.) with an apnea MF at product point 9313 such as to modify the apnea prescription signal. By way of yet another example, a snore prescription signal may be similarly combined with a vent MF and a leak MF at product point 9314. The moderated snore prescription signal may be combined with a snore threshold (Ths) and an EPR threshold (Th_EPR) at summation point 9315. The snore and EPR thresholds may be subtracted from the combined snore prescription signal at summation point 9315 such that the moderated snore prescription signal does not exceed the snore and/or EPR thresholds. [0281] In the optional gain layer 932, the moderated flow prescription signal is combined with a gain at product point 9321 and the moderated snore prescription signal is combined with another gain at product point 9322. The gain may represent a constant factor by which the moderated flow prescription signal and/or the moderated snore prescription signal is multiplied. The gain may scale the amplitude of such signals up or down or may adjust the strength of such signals in order to achieve a desired response from the system. The gain applied at product point 9321 may be the same value as the gain applied at product point 9322, or the two gains may be different values. [0282] In the optional limitation layer 933, limits or floor values may be applied to the prescription signals. For example, at summation point 9331, a fuzzy flow-limitation (FFL) limit (FFL-L) may be applied to the flow limitation prescription signal output by product point 9321 if, for example, the flow limitation prescription signal is lower than the FFL-L. By way of another example, an apnea limit (A-L) may be applied to the apnea prescription signal at point 9332 if, for example, the snore prescription signal is below the A-L. By way of yet another example, a snore limit (S-L) may be applied to the snore prescription signal at point 9333 if, for example, the snore prescription signal is below the S-L. The signals output by points 9331, 9332, and 9333 are then supplied to the summation layer 934. [0283] In any of the aforementioned examples, the product points 9311, 9313, 9314, 9321, 9322 may perform multiplication, calculate a dot product, or combine the MFs/signals in some other manner. Additionally, in any of the aforementioned examples the summation points 9312, 9315 may perform addition, subtraction, or a logical OR operation. Furthermore, in any of the aforementioned examples the points 9331, 9332, 9333 may perform logical OR operations or some other mathematical or logical operation(s). [0284] Additionally, the moderated prescription signal output by summation point 9312 may be provided to a decay curve 954 such as in the absence of detection of any particular event or all events (e.g., no flow limitation is detected (i.e., no FFL) and/or no apnea is detected and/or no snore is detected). In some implementations, the decay curve 954 may be used when there is no flow limitation (or when there is no fuzzy flow limitation FFL). The decay serves to permit a reduction in pressure when higher pressures are no longer needed due to alleviation of previous events. The decay curve 954 may be influenced by decay parameters 953, such as the decay constant that may be provided by a learned biasing function as previously discussed, and discussed in more detail with respect to the biasing functions of Figs. 14A, 14B, and 14c, that may be produced by the decay curve parameterizer 952. The decay curve 954 may be provided to influence the pressure settings 919, and may be provided in the summation layer 934 to influence the prescription signals, if any. [0285] At the summation layer 934, the prescription signals output from the limitation layer 933 and the decay curve 954 are combined to produce changes to therapy pressure that is controlled and delivered to the patient for therapy. The pressure settings may also be recorded to provide a pressure trace 1610 (a pressure curve) which may then be stored in the N-memory as previously discussed. Furthermore, the rolling AHI calculator 915 may also provide the rolling AHI 916 to a re-baseline module 935. The re-baseline module 935 may correspond to the floor pressure component 917. The rolling AHI 916 may redefine a baseline critical pressure, such as floor pressure 918, based on the rolling AHI 916. The baseline critical pressure, such as floor pressure 918, may also affect the pressure trace 1610 by creating a floor or minimum pressure to be supplied to the patient. The pressure trace 1610 may be one form of the pressure settings 919 discussed previously. [0286] As previously mentioned, the pressure trace 1610 may be stored in the N-day memory 921. In the N-day memory 921, the pressure trace 1610 is combined with one or more other stored pressure traces, such as one or more previous curves 940, at product point 9231. As previously discussed, the previous pressure curves 940 may be combined to compute a pressure distribution 1612. The pressure distribution 1612 may then be implemented to learn the pressure patterns that the patient experiences after a number of therapy periods, such as for producing one or more biasing functions. In one example, the number of therapy periods may be between 7 and 10 therapy periods. As discussed in more detail herein, the learned pressure patterns may include uni-modal, bi-modal, or pressure band-based patterns. The pressure change of the flow limitation doctor may be tuned or adjusted based on the learned pressure patterns, such as with a biasing function. Alternatively, the decay curve 954, such as from a biasing function, may be used in absence of a flow limitation. [0287] For example, various parameters of the pressure distribution 1612 are fed back for modifying the adjustment of the flow limitation doctor. In this regard, a feature extractor 950 may extract various features from the pressure distribution 2001, such as the pressure pattern/mode, percentiles, one or more local maxima, and the like. These features are fed into the decay curve parameterizer 952 and the EPAP moderation curve parameterizer 956, along with previous respiratory therapy curves 940 (or parameters of such curves) such as for the selection or generation of one or more biasing functions in each of the parameterizers. The previous curves 940 may be pressure traces obtained during previous RTPs. The EPAP moderation curve parameterizer 956 may select or generate a one or more biasing functions, for generating the EPAP MF 957, based on the extracted features and the previous curves 940, which are then used to moderate the flow limitation prescription signal. The decay curve parameterizer 952 may select or generate a one or more biasing functions (such as a decay biasing function 954) or provide decay parameters 953 (e.g., decay constants) for control of pressure decay. The decay parameters 953 such as the decay constant discussed infra, may be used to adjust the pressure trace 1610 and/or the current pressure settings 919. [0288] In some implementations, the feature extractor 950, decay curve parameterizer 952, and the EPAP moderation curve parameterizer 956 are components of the learning module 923. Additionally or alternatively, the feature extractor 950, decay curve parameterizer 952, and the EPAP moderation curve parameterizer 956 may be instantiated in therapy engine code of the therapy engine module 4320 or some other controller or processor discussed herein. In either of these implementations, the EPAP MF 957 and the decay parameters 953 (or the decay biasing functions 954) may be part of the pressure adjustment factors 924 discussed previously in relation to the one or more biasing functions learned by the learning module 923. [0289] In some implementations, 7 to 10 previous curves 940, or previously stored pressure traces, are used to generate the pressure distribution(s) 1612. In other implementations, more or fewer previous curves 940 may be used to generate the pressure distribution(s) 1612. For example, in cases where only two previous curves 940 are stored in N-day memory 921, the two previous curves 940 may be used to generate the pressure distribution 1612. Additionally, the pressure distribution 1612 may be repeatedly regenerated using additional pressure traces 1610 that are measured and stored. In these implementations, a confidence level can be associated with each generated pressure distribution 1612, and the system can stop generating new pressure distributions 1612 once a certain confidence level is eventually reached. Furthermore, default settings and/or the standard automatic pressure system, without influence of the biasing functions, can be used when there is an insufficient amount of data to generate the pressure distribution 1612 and/or learn the pressure patterns. [0290] In the example of Fig. 9B, the extracted features may be used to influence the Pcrit based pressure adjustments, and these Pcrit based pressure adjustments may only applied to the flow limitation doctor. Modification of the flow limitation doctor in these ways may decrease the likelihood of experiencing apneas or snoring events. However, similar Pcrit based pressure adjustments could also be made to the other doctors in other implementations. In these ways, a feedback loop that dynamically changes the MF(s) may be implemented. [0291] Figs. 10A and 10B show examples of biasing functions of the parameterization of EPAP and decay adjustment factors. EPAP moderation, such as the EPAP MF 957 of Fig. 9B, and decay constants, allows the pressure to be biased towards particular patient specific ranges. Fig. 10A shows a graphs 1000A of a pressure moderation factor (MF) versus water pressure, expressed in centimeters of water (cmH2O), for a curve 1001A and curve 1002A. The pressure MF may correspond to any of the MFs discussed previously. Curve 1001A may correspond to a standard automatic adjustment feature that is not influenced by the patient's prior therapy such that it is not a biasing function, and curve 1002A may be a biasing function as previously discussed because it corresponds to an enhanced auto-adjusting mechanism, such as the auto-adjusting system 900 discussed previously since its parameters are influenced by the patient's prior therapy (e.g., the patient's critical pressures and/or a pressure therapy range). [0292] Graph 1000A shows how the MFs are applied to pressure increase increments. For example, a patient may be prescribed a response of X cmH2O for a detected flow limitation. In one example, X = 0.5cmH2O. As shown by curve 1002A, when there is 0cmH2O to 10cmH2O of pressure being delivered to the patient, there is a pressure MF of 1 output by the biasing function and the algorithm will pass the full X cmH2O for pressure adjustment. When the pressure being delivered to the patient is above 10cmH2O, such as when the pressure being delivered to the patient is between 10cmH2O and 20cmH2O, the biasing function algorithm may apply a suitable MF. For example, curve 1002A shows that, when the pressure reaches 15cmH2O, an MF of 0.5 may be applied for modifying the pressure increase adjustment. This is in contrast with curve 1001A, which shows that an MF of approximately 0.85 may be applied to the pressure adjustment when the pressure reaches 15cmH2O. Thus, the MF applied by the biasing function influenced by the patient's prior therapy may cause the pressure treatment to increase by about 0.25cmH2O (i.e., 0.5 cmH20 x 0.5) whereas the MF applied by the standard automatic adjustment feature may cause the pressure treatment to increase by 0.425cmH2O (i.e., 0.5 cmH2O x 0.85). Therefore, curves 1001A and 1002A show that the biasing function allows the pressure treatment to increase more gradually in comparison to the standard automatic adjustment feature. [0293] Fig. 10B illustrates how a patient's prior therapy (e.g., local maxima of a distribution of prior pressure therapy curves and/or limits of the distribution) influences the shape of each biasing function. Fig. 10B has a one mode example 1001B and a multi-mode example 1002B. The one mode example 1001B has a biasing function 1011B that is formed in correspondence with a uni-mode pressure distribution 1010B. Graph 1010B shows a stable pressure, which is effectively a single Pcrit value that has been detected, which may be detected by finding the local maxima. The multi-mode example 1002B has a biasing function 1021B that is formed in correspondence with a dual-mode pressure distribution 1020B. The dotted lines in each example 1001B, 1002B show the correspondence between the pressure distribution 1010B, 1020B and the graphs of the biasing functions 1011B, 1021B. For example, a first dotted line through graphs 1010B and 1011B shows a correspondence between a peak pressure in the pressure distribution 1010B and a pressure moderation factor of 1 in biasing function 1011B, and so forth. The pressure distribution 1020B includes two peak pressures, where a first dotted line through graphs 1020B and 1021B shows a correspondence between a first peak pressure in the pressure distribution 1020B and a pressure moderation factor of 1 in biasing function 1021B, a second dotted line through graphs 1020B and 1021B shows a correspondence between the second peak pressure in the pressure distribution 1020B and a corresponding pressure MF in biasing function1021B, and so forth. [0294] The examples 1001B and 1002B show that the pressure MF of the biasing function does not change until a threshold is reached with the threshold corresponding to the local maxima of the pressure distribution. For example, as shown by graphs 1010B and 1011B, the pressure MF of the biasing function does not change for pressures under the local maxima distribution pressure. Under the local maxima pressure, the biasing function does not modify any prescription pressure increment that may be applied to the pressure settings since that factor may be unity. A decline in the pressure MF of the biasing function exists above the local maxima pressure(s). Other features of the distribution may then be applied to forming of the shape of the curve of the biasing function to achieve the desired influence of the patient's prior therapy (e.g., the patient's critical pressure(s)) on changes in therapy effected by the pressure MFs produced by the biasing function. [0295] By way of another example, as shown by graphs 1020B and 1021B, the pressure MF of the biasing function does not modify any pressure change until delivered pressure is above a first maxima pressure. Further modifications to the pressure MF exist in correspondence with a second local maxima of the pressure distribution. In this example, both maxima are influencing the pressure MF, where the distribution maxima is a target that can be approached faster on an increase in pressure and the MF is applied to attempt to stay closer to that target above the distribution maxima. It will be understood that other shapes of the curve of the biasing function may be implemented to bias the pressure changes with the pressure MFs to bias delivered pressure to approach and adhere to a range associated with the local maxima(s) of the distribution. [0296] Fig.11 depicts graphs 1100a and 1100b demonstrating an example of biasing pressure ranges with the biasing functions f(p) for both pressure increases and pressure decreases. Graph 1100a is a pressure increase graph (pressure_MF = f(p) where p = delivered pressure setting), showing how the pressure MF changes for adjusting pressure increases based on a current pressure (i.e., delivered pressure). Graph 1100a may demonstrate the pressure MF behavior of the examples of Figs 10A and 10B. Graph 1100b is a pressure decrease graph (pressure_Decay_Time_constant = f(p) where p = delivered pressure) showing a decay time constant versus delivered pressure settings. Graph 1100b shows how the decay time constant changes for decaying delivered pressure based on a currently delivered pressure. Graph 1100b may represent the behavior of the decay curve 954 discussed previously. The decay time constant may operate on similar principles as the pressure MF such that the shape of the curve of the biasing function for the decay constant is formed from the pressure distribution and/or its maxima(s). Depending on the pressure distribution, the decay time constant attempts to bias the pressure toward or near the critical/learned pressure(s) or a range thereof as determined by the distribution when there are no events 913 detected, which may make the decay time constant relatively slow. Slowing down the decay may reduce the magnitude of fluctuations between pressure settings. Additionally, the pressure decreases more slowly when the pressure is closer to the lower limit of learned pressures, as can be seen by graph 1100b. In other words, the pressure MF may prevent the pressure settings from jumping too high too fast, and the decay time constant may prevent the pressure settings from dropping too low too fast. In these ways, the pressure MF and decay time constant, prevent pressure “overshoots” and maintains pressure stability at upper and lower pressure limits that are attributable to a particular patient's therapy history. This pressure stability may help maintain the pressures that that the particular patient requires to keep their airway open. [0297] Figs.12A, 12B, and 12C show respective pressure treatment algorithms using different biasing functions. In particular, Fig.12A shows an example elastic APAP (elPAP) algorithm 1200A involving biasing function(s) (e.g., one for increases and/or one for decreases) that are based on a single local maxima of the patient's distribution of pressures. Fig.12B shows an example orbital APAP (oPAP) algorithm 1200B involving biasing function(s) (e.g., one for increases and/or one for decreases) that are based on a dual local maxima of the patient's distribution of pressures. Fig. 12C shows an example optimal pressure range (OPR) algorithm 1200C involving biasing function(s) (e.g., one for increases and/or one for decreases) that are based on an absence of local maxima of the patient's distribution of pressures and a previously determined range of delivered pressure. As such, the algorithms shown by Figs. 12A to 12C are based on common trends that have been observed from the disordered sleep patterns exhibited by many patients. Thus, a majority of patients experience disordered sleep patterns that can be alleviated using respiratory therapy that is modulated according to at least one of the algorithms shown by Figs. 12A to 12C. Therefore, the learning module 923 may learn an appropriate algorithm to apply to a particular patient based on the pressure distribution(s) of the pressure trace(s) 1610 from that patient’s previous therapy periods. However, there may still be a minority of patients that may have disordered sleep patterns that are difficult to classify, and thus, it may be challenging to classify such patients as being candidates for receiving therapy according to one of algorithms 1200A, 1200B, 1200C. In some cases, the inability to classify such disordered sleep patterns may be due to lack of a sufficient amount of data. In these cases, a default algorithm can be applied until a sufficient amount of data is obtained to properly classify the patient’s disordered sleep pattern. Therefore, in some implementations, the learning module 923 may apply a default pressure therapy algorithm, such as without using biasing functions, when it is unable to classify the pressure distribution of the patient’s pressure trace(s) 1610. [0298] Furthermore, as previously noted, the specific algorithm to be applied may depend on the number of critical pressures that are observed in a pressure distribution. For example, the elPAP algorithm 1200A may be applied when one Pcrit is observed in a pressure distribution, the oPAP algorithm 1200B may be applied when two Pcrits are observed in a pressure distribution, and the OPR algorithm 1200C may be applied when no Pcrits are observed in a pressure distribution but an optimal range determined by a lower limit and upper limit of the previously delivered pressures are learned. [0299] Referring now to Fig.12A, the elPAP algorithm 1200A attempts to keep the pressure settings 919 around a single critical pressure (Pcrit) 1210A. A patient receiving therapy according to the elPAP algorithm 1200A may be a patient that does not exhibit much variation in the pressures that are required to maintain their airway open. The pressure distribution 1201A of such patients may include a single Pcrit value, such as Pcrit 1210A. [0300] The arrows above and below the critical pressure 1210A may illustrate an amount or magnitude of an adjustment factor, such as an increase factor or decrease factor (e.g., decay time constant), to be applied to the pressure settings 919 according to the elPAP algorithm 1200A as produced by the biasing functions. For example, a single arrow represents a smaller adjustment factor magnitude than the magnitude represented by the double arrows, and the double arrows represent a smaller adjustment factor magnitude than the magnitude represented by the triple arrows. The sets of arrows shown by Fig. 12A represent that, as the pressure moves further away from the critical pressure 1210A, the greater the adjustment factor is applied to the pressure settings 919 in order to bring the pressure settings 919 closer to the critical pressure 1210A. For example, the single arrow below the critical pressure 1210A represents a relatively small increase factor that is applied to changes to pressure settings 919, the double arrow below the critical pressure 1210A represents a relatively moderate-sized increase factor that is applied to changes to the pressure settings 919, and the triple arrow below the critical pressure 1210A represents a relatively large increase factor that is applied to changes to the pressure settings 919. In other words, the more the pressure drops below the critical pressure 1210A, the greater the magnitude of an increase factor may be applied to the pressure. By way of another example, the single arrow above the critical pressure 1210A represents a relatively small decay factor that is applied to changes to the pressure settings 919, the double arrow above the critical pressure 1210A represents a relatively moderate-sized decay factor that is applied to changes to the pressure settings 919, and the triple arrow above the critical pressure 1210A represents a relatively large decay factor that is applied to changes to the pressure settings 919. In other words, the more the pressure goes above the critical pressure 1210A, the greater the magnitude of the decrease (decay) factor may be applied to changes to the pressure for a quicker return to the critical pressure. [0301] Referring now to Fig. 12B, the oPAP algorithm 1200B may bias pressure changes to two critical pressure values as produced by the biasing functions that are formed in relation to an upper Pcrit 1210B and a lower Pcrit 1220B. Patients utilizing the oPAP algorithm 1200B may have issues related to positional OSA and/or may experience relatively stronger effects due to changes between sleep stages. The pressure distribution 1201B of such patients may include two Pcrit values, such as Pcrit 1210B and Pcrit 1220B, which may be determined from a local maxima of the distribution. [0302] Additionally, the arrows above, below, and between the upper Pcrit 1210B and the lower Pcrit 1220B may indicate an amount or magnitude of an adjustment factor (or decay factor) applied to change the pressure settings 919 according to the oPAP algorithm 1200B as produced by suitable biasing functions. For example, a single arrow represents a smaller adjustment factor magnitude than the magnitude represented by the double arrows, and the double arrows represent a smaller adjustment factor magnitude than the magnitude represented by the triple arrows. Here, as the pressure moves further away from the Pcrit 1210B or 1220B, the greater the adjustment factor is applied to the change in pressure in order to bring the pressure settings closer to one of the Pcrit 1210B or 1220B. For example, the more the pressure drops below the lower Pcrit 1220B, the greater the magnitude of a positive adjustment factor may be applied to the change in pressure. By way of another example, the more the pressure goes above the upper Pcrit 1210B, the greater the magnitude of a negative adjustment factor may be applied to the change in pressure. [0303] Similarly, the more the pressure drops below the upper Pcrit 1210B, the greater the magnitude of a positive adjustment factor may be applied to changes to the pressure, and the more the pressure goes above the lower Pcrit 1220B, the greater the magnitude of a negative adjustment factor may be applied to the pressure. However, a threshold value may be used to switch between the two Pcrits 1210B, 1220B that are the target of the bias of the biasing functions. For example, when the pressure goes above the lower Pcrit 1220B past a threshold value, the greater the magnitude of a positive adjustment factor may be applied to the pressure so that the pressure may reach the upper Pcrit 1210B. By way of another example, when the pressure goes below the upper Pcrit 1210B past the threshold value, the greater the magnitude of a negative adjustment factor may be applied to the changes to pressure so that the pressure may reach the lower Pcrit 1220B. In some implementations, the threshold may be a middle or median value between the upper Pcrit 1210B and the lower Pcrit 1220B. In other implementations, the threshold may be a range of middle or median values between the upper Pcrit 1210B and the lower Pcrit 1220B. [0304] Referring now to Fig.12C, the OPR algorithm 1200C based on two pressure limits or bounds, including an upper pressure bound 1210C and a lower pressure bound 1220C such as where no local maxima is detectable in a distribution. That is, the OPR algorithm 1200C may be applied to pressure distributions, such as pressure distribution 1201C, that do not show a clear shift from one bound to the other bound, such as bounds1210C and 1220C. Here, there is no clear trend other than the pressure distribution 1201C generally being between the two bounds 1210C, 1220C. A patient exhibiting such a pressure distribution may appear to have a stable upper airway, but there is no particular trend as to whether the patient spends more time towards the upper pressure bound 1210C or towards the lower pressure bound 1220C. The pressure distribution 1201C of such patients may be generally considered to include Pcrit values between the bounds, which may be the limits of a pressure range provided from prior therapy. Additionally, it should be noted that the bounds 1210C and 1220C may represent ‘soft boundaries’ within which an optimal pressure may be maintained, and ‘hard boundaries’ residing outside the bounds 1210C and 1220C (not shown by Fig.12C) may represent maximum and minimum pressure settings. [0305] Similar to the examples of Figs. 12A and 12B, the arrows above the upper pressure bound 1210C and below the lower pressure bound 1220C may indicate an amount or magnitude of an adjustment factor (or decay factor) applied to the change in pressure settings 919 according to the OPR algorithm 1200C. For example, a single arrow represents a smaller adjustment factor magnitude than the magnitude represented by the double arrows, and the double arrows represent a smaller adjustment factor magnitude than the magnitude represented by the triple arrows. Here, the more the pressure moves outside of a region defined by the two bounds 1210C, 1220C, the greater the adjustment factor is applied to the change in pressure in order to bring the pressure settings back inside the two bounds 1210C, 1220C. For example, the more the pressure drops below the lower pressure bound 1220C, the greater the magnitude of a positive adjustment factor may be applied to changes to the pressure settings. By way of another example, the more the pressure goes above the upper pressure bound 1210C, the greater the magnitude of a negative adjustment factor may be applied to the change in pressure settings. Moreover, when the pressure is within the bounds, no adjustment factors need be applied such that standard increase and decrease behaviors of the therapy device may be applied without adjustment factors. [0306] Figs. 13A, 13B, and 13C illustrate bias functions suitable for the adjustment bias behavior of the three pressure adjustment algorithms of Figs.12A, 12B, and 12C, respectively. In particular, graphs 1300A, 1300B, and 1300C in Figs. 13A, 13B, and 13C show the increase behaviors for the elPAP algorithm 1200A, the oPAP algorithm 1200B, and the OPR algorithm 1200C, respectively. While each graph 1300A, 1300B, and 1300C demonstrates different increase behaviors, each graph 1300A, 1300B, and 1300C shows moderate pressure increases based on the current pressure level. [0307] Referring to Fig, 13A, graph 1300A includes a first coordinate (x1, y1), which may represent a patient critical pressure, such as critical pressure 1210A in Fig. 12A. At the first coordinate, a pressure MF of 1 may be applied to changes to the pressure settings. A second coordinate (x2, y2) shows a pressure cut off where the pressure MF is set to 0.2 so that the pressure increases beyond the patient critical pressure more slowly. [0308] Referring to Fig, 13B, graph 1300B includes a first coordinate (x1, y1), which may represent a lower Pcrit value, such as the lower critical pressure 1220B in Fig. 12B. A third coordinate (x3, y3) may represent an upper Pcrit value, such as upper critical pressure 1210B in Fig.12B. A second coordinate (x2, y2) may represent a midway point between the first and third coordinates, such as a midway value between the upper critical pressure 1210B and the lower critical pressure 1220B. At the first and third coordinates, a pressure MF of 1 may be applied to the changes in pressure settings to keep the pressure at those Pcrit values. At the second coordinate (x2, y2), the MF is set to approximately 0.4 so that the pressure increases more slowly so as to avoid oscillations between the upper and lower Pcrit. A fourth coordinate (x4, y4) shows an upper pressure cut off where the pressure MF is set to approximately 0.1 so that the pressure increases more slowly as the pressure raises above the upper Pcrit. [0309] Referring to Fig, 13C, graph 1300C includes a first coordinate (x1, y1), which may represent a lower cut off value and a fourth coordinate (x4, y4) may represent an upper cut off value. A second coordinate (x2, y2) may represent a lower bound value, such as the lower bound 1220C in Fig.12C. A third coordinate (x3, y3) may represent an upper bound value, such as upper bound 1210C in Fig.12C. At the first coordinate, a pressure MF of 1 may be applied to the pressure settings, which may allow the pressure to increase closer to the lower bound. At the second and third coordinates, the MF is set to approximately 0.3 so that the pressure increases more slowly so as to avoid moving outside of the upper and lower bounds. At the fourth coordinate, the pressure MF is set to approximately 0.1 so that the pressure increases more slowly as the pressure raises above the upper bound. [0310] The pressure change behaviors demonstrated by Figs. 13A, 13B, and 13C may be selected or generated by the EPAP moderation curve parameterizer 956 discussed previously. Variations of biasing function curves 1300A, 1300B, and 1300C may exist for each doctor, such as the doctors in doctor layer 930. Additionally, the curve coordinates (x, y) can be repeatedly tuned based on changes in critical pressure observed from previous RTPs. [0311] Figs. 14A, 14B, and 14C demonstrate the decrease behavior of the three pressure adjustment algorithms of Figs. 12A, 12B, and 12C, respectively produced by example biasing functions as described herein. In particular, graphs 1400A, 1400B, and 1400C in Figs.14A, 14B, and 14C show the decrease (decay) behaviors using decrease biasing functions for the elPAP algorithm 1200A, the oPAP algorithm 1200B, and the OPR algorithm 1200C, respectively. While each graph 1400A, 1400B, and 1400C demonstrates different decrease (decay) behaviors, each graph 1400A, 1400B, and 1400C shows moderation or adjustment to pressure decreases based on the current pressure level. In particular, the graphs 1400A, 1400B, and 1400C show pressure decreases that are controlled through variation of a decay constant. In some implementations, the decay constant may be a fixed decay constant, but the enhanced auto-adjusting system 900 uses biasing function(s) that implement a dynamic time constant that determines the speed of decay. Additionally, in one form, the decay constant is a decay parameter, such as the decay parameter 953 produced by a biasing function of the decay parameterizer 952. [0312] Referring to Fig, 14A, the biasing function represented by graph 1400A includes a first coordinate 1410a and a second coordinate 1420a. The first coordinate 1410a may represent a patient critical pressure, such as a determined critical pressure 1210A in Fig. 12A. The second coordinate 1420a may represent an upper cut off value. At the first coordinate 1410a, the decay constant is relatively high so that the decay away from the critical pressure happens slower. At the second coordinate 1420a, the decay constant is relatively low so that the decay away from this cut off point pressure happens faster. [0313] Referring to Fig, 14B, biasing function graph 1400B includes a first coordinate 1410b, a second coordinate 1420b, a third coordinate 1430b, and a fourth coordinate 1440b. The first coordinate 1410b may represent a lower Pcrit value, such as the lower critical pressure 1220B in Fig. 12B. The third coordinate 1430b may represent an upper Pcrit value, such as upper critical pressure 1210B in Fig. 12B. The second coordinate 1420b may represent a midway pressure between the upper and lower Pcrit values, such as a midway value between the upper critical pressure 1210B and the lower critical pressure 1220B. At the first and third coordinates, the decay constant may be relatively high so that the pressure decays slower such that the pressure moves away from the Pcrit values more slowly. At the second coordinate 1420b, the decay constant is relatively low so that the pressure decreases faster so as to avoid oscillations between the upper and lower Pcrit. The fourth coordinate 1440b may represent an upper or lower pressure cut off where the pressure decay is set relatively low so that the pressure decays faster when the pressure is further from the upper or lower Pcrit values. [0314] Referring to Fig, 14C, biasing function represented by graph 1400C includes a first coordinate 1410c, a second coordinate 1420c, a third coordinate 1430c, and a fourth coordinate 1440c. The first coordinate 1410c may represent a lower cut off value and the fourth coordinate 1440c may represent an upper cut off value. The second coordinate 1420c may represent a lower bound value, such as the lower bound 1220C in Fig. 12C. The third coordinate 1430c may represent an upper bound value, such as upper bound 1210C in Fig. 12C. The first coordinate 1410c may have a relatively high decay constant, which may allow the pressure to decrease slower, and thus, the pressure may decrease from the lower cutoff more slowly, and thus, may help keep the pressure closer to the lower bound. The second coordinate 1420c and third coordinate 1430c may have a lower decay constant than the first cutoff value. Thus, the pressure may move from the upper bound towards the lower bound somewhat faster than when the pressure is at the lower cutoff. Additionally, the pressure may move from the lower bound towards the lower cutoff somewhat faster than when the pressure is at the lower cutoff. The fourth coordinate 1440c may have a lower decay constant than the other coordinates, which may allow the pressure to decrease faster when the pressure is at the upper cutoff. Thus, the pressure may decrease from the upper cutoff faster, and thus, may help keep the pressure closer to the upper bound. [0315] Figs. 15A and 15B show respective pressure graphs or pressure curves 1500a and 1500b each for a therapy session and respective pressure distribution graphs 1510a and 1510b each determined from a pressure curves 1500a and 1500b. The pressure curves 1501a and 1501b in Figs 15A and 15B may be examples of the pressure curves 1610 discussed with respect to Figs.9B, 16, and 17. The pressure graph 1500a in Fig. 15A includes pressure event 1504a for an RTP. Here, the pressure curve 1501a reaches a maximum pressure at 1504a. Graph 1500a also includes a set of event bars 1505a. Each event bar 1505a may correspond to a detected respiratory event 913, such as those determined/identified by respective event detectors (doctors) during the RTP. A respiratory event 913 associated with event bar 1505a-m may correspond to the maximum pressure reached at 1504a. Note that not all event bars 1505a are labeled in Fig.15A. [0316] The pressure density or distribution graph 1510a in Fig. 15A includes a pressure density curve 1511a that corresponds to the single critical pressure. A local maximum 1520a in the pressure density curve 1511a corresponds to the concentrated pressures in the pressure curve 1501a. The pressure density or distribution graph 1510a also includes a set of pressure event bars 1515a. The pressure event bars 1515a may represent the pressure that was delivered during a corresponding respiratory event 913. Each pressure event bar 1515a may correspond to one of the event bars 1505a. Note that not all event bars 1515a are labeled in Fig. 15A. In the depicted example, the labeled event bar 1515a may correspond to the local maximum 1520a, the maximum pressure event bar 1505a-m, and/or point 1504a. [0317] The pressure curve graph 1500b in Fig. 15B shows a pressure curve with a double critical pressure for an RTP. Graph 1500b also includes a set of event bars 1505b. Each event bar 1505b may correspond to a detected respiratory event 913, such as those determined/identified by respective event detectors 930 (see e.g., Fig. 9B), during the RTP. A respiratory event 913 associated with event bar 1505b-m may correspond to the maximum pressure reached at point 1504b-2. Note that not all event bars 1505b are labeled in Fig.15B. [0318] The pressure density or distribution graph 1510b in Fig. 15B includes a pressure density curve 1511b that corresponds to the double critical pressure of pressure curve 1501b. Local maxima 1520b-1 and 1520b-2 in the pressure density or distribution curve 1511b corresponds to the concentrated pressures in the pressure curve 1501b. The pressure density graph 1510b also includes a set of pressure event bars 1515b. The pressure event bars 1515b may represent the pressure that was measured during a corresponding respiratory event 913. Each pressure event bar 1515b may correspond to one of the event bars 1505b. Note that not all event bars 1515b are labeled in Fig. 15B. In the depicted example, the labeled event bars 1515b-1 and 1515b-2 may correspond to the local maxima 1520b-1 1520b-2, respectively. Additionally, the labeled event bars 1515b-1 and 1515b-2 may correspond to points 1504b-1 and 1504b-2, respectively. Furthermore, the event bar 1515b-2 may correspond to pressure event bar 1505a-m. [0319] Fig. 16 shows an example process 1600 for operating the learning module 923 to determine an algorithm such as with one or more biasing functions, such one of the algorithms shown by Figs.12A to 12C, to apply to a particular patient based on therapy pressure(s) provided during previous RTPs. The process 1600 optionally begins at a current therapy session level 1601 where ramp removal 1611 may be performed on the pressure trace 1610 before determining a pressure distribution 1612 of the pressure trace 1610. After the pressure distribution 1612 is determined, the learning module 912 determines whether the pressure distribution 1612 is a uni- modal distribution 1613. The pressure distribution 1612 may be a uni-model distribution 1613 if a stable intra-session pressure (such as a single local maxima of a distribution is detected) is detected within the pressure distribution 1612 and/or if the pressure distribution 1612 includes a single Pcrit. If the pressure distribution 1612 is a uni-model distribution 1613, then the single Pcrit is provided to a patient level 1602. [0320] If the pressure distribution 1612 is not a uni-model distribution 1613, then the learning module 923 determines whether the pressure distribution 1612 is a bi-modal distribution 1614. The pressure distribution 1612 may be a bi-modal distribution 1614 if the pressure distribution 1612 includes two Pcrits (such as two local maxima of the distribution are detected). If the pressure distribution 1612 is a bi-modal distribution 1614, then the two Pcrits are provided to the patient level 1602 such that the pressure levels attributed to the local maxima are provided. [0321] If the pressure distribution 1612 is not a bi-model distribution 1614, then the learning module 923 determines whether the pressure distribution 1612 is a multi-modal distribution 1615. The pressure distribution 1612 may be a multi-modal distribution 1615 if the pressure distribution 1612 does not have a single or double local maxima (e.g., greater than two critical pressures). If the pressure distribution 1612 is a multi-modal distribution 1615, then the distribution statistics (e.g., a minimum and a maximum of delivered pressure) of the pressure distribution 1612 are provided to the patient level 1602. [0322] At the patient level 1602, the learning module 923 determines whether there is only one Pcrit provided 1616 from the current therapy level 1601 (e.g., whether the pressure distribution 1612 is a uni-model distribution 1613). If there only one Pcrit is provided 1616 to the patient level 1602, then the elPAP algorithm 1200A is applied for adjusting the pressure 919 using one or more biasing functions derived from at least the Pcrit. If more than one Pcrits are provided 1616 to the patient level 1602, then the learning module 923 determines whether there two Pcrits were provided 1617 from the current therapy level 1601 (e.g., whether the pressure distribution 1612 is a bi-model distribution 1614). If two Pcrits were provided 1617 to the patient level 1602, then the learning module 923 determines whether there is a lower inter-session pressure 1618. If there is no lower inter-session pressure 1618, then the OPR algorithm 1200C is applied for adjusting the pressure 919 using one or more biasing functions derived from at least the two Pcrits. If there is a lower inter-session pressure 1618, then the oPAP algorithm 1200B is applied as the pressure 919. Otherwise, if two Pcrits were not provided 1617 to the patient level 1602, then the OPR algorithm 1200C is applied for adjusting the pressure 919 using one or more biasing functions derived from minimum and maximum bounds for a provided range. [0323] Fig.17 shows an example control loop 1700. The enhanced auto-adjusting system 900 may operate according to the control loop 1700. The control loop 1700 begins at operation 1700 where the standard automatic adjustment feature is enabled. For example, the standard automatic adjustment feature may be set as a default setting when there is an insufficient amount of data to generate the pressure distribution 1612 and/or learn the pressure patterns. At operation 1702, a pressure signal, such as flow signal 911, may be analyzed according to the various embodiments discussed herein. [0324] At operation 1703, the determined or identified Pcrit values and/or bounds are stored in the N-day memory 921. For example, for a uni-modal pressure distribution 1612, 1500a, a single Pcrit value and the Pcrit value may be stored in the N-day memory 921. By way of another example, for a bi-modal pressure distribution 1612, 1500b, the two Pcrit values and the upper and lower cutoff pressures may be stored in the N-day memory 921. By way of yet another example, for a multi-modal pressure distribution 1612, the more than 2 Pcrit values may be stored in the N- day memory along with a median pressure value as well as other statistical values, such as percentile pressures such as 5, 25, 75 and/or 95 percentile pressures. [0325] At operation 1704, an appropriate algorithm is selected from the N-day memory 921. For example, the elPAP algorithm 1200A may be selected for a uni-modal pressure distribution 1612, 1500a. By way of another example, the oPAP algorithm 1200B may be selected for a bi- modal pressure distribution 1612, 1500B. By way of yet another example, the OPR algorithm 1200C may be selected for a bi-modal pressure distribution 1612. At operation 1705, the selected algorithm is provided to the analysis engine for further analysis at operation 1702. The control loop 1700 may continue until the respiratory therapy system is powered down, or until some predefined or configured condition(s) are met. For example, the control loop 1700 may end when a specified number of previous curves 940, or previously stored pressure traces, have been analyzed. In some implementations, this specified number of previous curves 940, or previously stored pressure traces, may be a value between 7 to 10. 5.12 GLOSSARY [0326] For the purposes of the present technology disclosure, in certain forms of the present technology, one or more of the following definitions may apply. In other forms of the present technology, alternative definitions may apply. 5.12.1 General [0327] Air: In certain forms of the present technology, air may be taken to mean atmospheric air, and in other forms of the present technology air may be taken to mean some other combination of breathable gases, e.g. oxygen enriched air. [0328] Ambient: In certain forms of the present technology, the term ambient will be taken to mean (i) external of the treatment system or patient, and (ii) immediately surrounding the treatment system or patient. [0329] For example, ambient humidity with respect to a humidifier may be the humidity of air immediately surrounding the humidifier, e.g. the humidity in the room where a patient is sleeping. Such ambient humidity may be different to the humidity outside the room where a patient is sleeping. [0330] In another example, ambient pressure may be the pressure immediately surrounding or external to the body. [0331] In certain forms, ambient (e.g., acoustic) noise may be considered to be the background noise level in the room where a patient is located, other than for example, noise generated by an RPT device or emanating from a mask or patient interface. Ambient noise may be generated by sources outside the room. [0332] Automatic Positive Airway Pressure (APAP) therapy: CPAP therapy in which the treatment pressure is automatically adjustable, e.g. from breath to breath, between minimum and maximum limits, depending on the presence or absence of indications of SDB events. [0333] Continuous Positive Airway Pressure (CPAP) therapy: Respiratory pressure therapy in which the treatment pressure is approximately constant through a respiratory cycle of a patient. In some forms, the pressure at the entrance to the airways will be slightly higher during exhalation, and slightly lower during inhalation. In some forms, the pressure will vary between different respiratory cycles of the patient, for example, being increased in response to detection of indications of partial upper airway obstruction, and decreased in the absence of indications of partial upper airway obstruction. [0334] Flow rate: The volume (or mass) of air delivered per unit time. Flow rate may refer to an instantaneous quantity. In some cases, a reference to flow rate will be a reference to a scalar quantity, namely a quantity having magnitude only. In other cases, a reference to flow rate will be a reference to a vector quantity, namely a quantity having both magnitude and direction. Flow rate may be given the symbol Q. ‘Flow rate’ is sometimes shortened to simply ‘flow’ or ‘airflow’. [0335] In the example of patient respiration, a flow rate may be nominally positive for the inspiratory portion of a breathing cycle of a patient, and hence negative for the expiratory portion of the breathing cycle of a patient. Device flow rate, Qd, is the flow rate of air leaving the RPT device. Total flow rate, Qt, is the flow rate of air and any supplementary gas reaching the patient interface via the air circuit. Vent flow rate, Qv, is the flow rate of air leaving a vent to allow washout of exhaled gases. Leak flow rate, Ql, is the flow rate of leak from a patient interface system or elsewhere. Respiratory flow rate, Qr, is the flow rate of air that is received into the patient's respiratory system. [0336] Flow therapy: Respiratory therapy comprising the delivery of a flow of air to an entrance to the airways at a controlled flow rate referred to as the treatment flow rate that is typically positive throughout the patient’s breathing cycle. [0337] Humidifier: The word humidifier will be taken to mean a humidifying apparatus constructed and arranged, or configured with a physical structure to be capable of providing a therapeutically beneficial amount of water (H₂O) vapour to a flow of air to ameliorate a medical respiratory condition of a patient. [0338] Leak: The word leak will be taken to be an unintended flow of air. In one example, leak may occur as the result of an incomplete seal between a mask and a patient's face. In another example leak may occur in a swivel elbow to the ambient. [0339] Noise, conducted (acoustic): Conducted noise in the present document refers to noise which is carried to the patient by the pneumatic path, such as the air circuit and the patient interface as well as the air therein. In one form, conducted noise may be quantified by measuring sound pressure levels at the end of an air circuit. [0340] Noise, radiated (acoustic): Radiated noise in the present document refers to noise which is carried to the patient by the ambient air. In one form, radiated noise may be quantified by measuring sound power/pressure levels of the object in question according to ISO 3744. [0341] Noise, vent (acoustic): Vent noise in the present document refers to noise which is generated by the flow of air through any vents such as vent holes of the patient interface. [0342] Oxygen enriched air: Air with a concentration of oxygen greater than that of atmospheric air (21%), for example at least about 50% oxygen, at least about 60% oxygen, at least about 70% oxygen, at least about 80% oxygen, at least about 90% oxygen, at least about 95% oxygen, at least about 98% oxygen, or at least about 99% oxygen. “Oxygen enriched air” is sometimes shortened to “oxygen”. [0343] Medical Oxygen: Medical oxygen is defined as oxygen enriched air with an oxygen concentration of 80% or greater. [0344] Patient: A person, whether or not they are suffering from a respiratory condition. [0345] Pressure: Force per unit area. Pressure may be expressed in a range of units, including cmH₂O, g-f/cm² and hectopascal. 1 cmH₂O is equal to 1 g-f/cm² and is approximately 0.98 hectopascal (1 hectopascal = 100 Pa = 100 N/m² = 1 millibar ~ 0.001 atm). In this specification, unless otherwise stated, pressure is given in units of cmH₂O. [0346] The pressure in the patient interface is given the symbol Pm, while the treatment pressure, which represents a target value to be achieved by the interface pressure Pm at the current instant of time, is given the symbol Pt. [0347] Respiratory Pressure Therapy: The application of a supply of air to an entrance to the airways at a treatment pressure that is typically positive with respect to atmosphere. [0348] Ventilator: A mechanical device that provides pressure support to a patient to perform some or all of the work of breathing. [0349] Seal: May be a noun form ("a seal") which refers to a structure, or a verb form (“to seal”) which refers to the effect. Two elements may be constructed and/or arranged to ‘seal’ or to effect ‘sealing’ therebetween without requiring a separate ‘seal’ element per se. [0350] Shell: A shell will be taken to mean a curved, relatively thin structure having bending, tensile and compressive stiffness. For example, a curved structural wall of a mask may be a shell. In some forms, a shell may be faceted. In some forms a shell may be airtight. In some forms a shell may not be airtight. [0351] Stiffener: A stiffener will be taken to mean a structural component designed to increase the bending resistance of another component in at least one direction. [0352] Strut: A strut will be taken to be a structural component designed to increase the compression resistance of another component in at least one direction. [0353] Swivel (noun): A subassembly of components configured to rotate about a common axis, preferably independently, preferably under low torque. In one form, the swivel may be constructed to rotate through an angle of at least 360 degrees. In another form, the swivel may be constructed to rotate through an angle less than 360 degrees. When used in the context of an air delivery conduit, the sub-assembly of components preferably comprises a matched pair of cylindrical conduits. There may be little or no leak flow of air from the swivel in use. [0354] Optimal: the best or most desirable, favorable, functional, satisfactory, advantageous, efficient, or effective design(s), configuration, arrangement, operation(s), decision(s), condition(s), criteria, parameter(s), allocation(s), variable(s), solution(s), result(s), and/or output(s) considering a set of constraints, criteria, and/or parameters. An “optimum” may refer to an amount or degree to which something is optimal. The term “optimization” may refer to an act, process, algorithm, or methodology of making something as optimal as possible. Optimization typically includes mathematical procedures, such as finding the maximum or minimum of a function, or solving one or more loss functions and/or objective functions. 5.12.2 Respiratory cycle [0355] Apnea: According to some definitions, an apnea is said to have occurred when flow falls below a predetermined threshold for a duration, e.g. 10 seconds. An obstructive apnea will be said to have occurred when, despite patient effort, some obstruction of the airway does not allow air to flow. A central apnea will be said to have occurred when an apnea is detected that is due to a reduction in breathing effort, or the absence of breathing effort, despite the airway being patent. A mixed apnea occurs when a reduction or absence of breathing effort coincides with an obstructed airway. [0356] Breathing rate: The rate of spontaneous respiration of a patient, usually measured in breaths per minute. [0357] Duty cycle: The ratio of inhalation time, Ti to total breath time, Ttot. [0358] Effort (breathing): The work done by a spontaneously breathing person attempting to breathe. [0359] Expiratory portion of a breathing cycle: The period from the start of expiratory flow to the start of inspiratory flow. [0360] Flow limitation: Flow limitation will be taken to be the state of affairs in a patient's respiration where an increase in effort by the patient does not give rise to a corresponding increase in flow. Where flow limitation occurs during an inspiratory portion of the breathing cycle it may be described as inspiratory flow limitation. Where flow limitation occurs during an expiratory portion of the breathing cycle it may be described as expiratory flow limitation. [0361] Types of flow limited inspiratory waveforms: (i) Flattened: Having a rise followed by a relatively flat portion, followed by a fall. (ii) M-shaped: Having two local peaks, one at the leading edge, and one at the trailing edge, and a relatively flat portion between the two peaks. (iii) Chair-shaped: Having a single local peak, the peak being at the leading edge, followed by a relatively flat portion. (iv) Reverse-chair shaped: Having a relatively flat portion followed by single local peak, the peak being at the trailing edge. [0362] Hypopnea: According to some definitions, a hypopnea is taken to be a reduction in flow, but not a cessation of flow. In one form, a hypopnea may be said to have occurred when there is a reduction in flow below a threshold rate for a duration. A central hypopnea will be said to have occurred when a hypopnea is detected that is due to a reduction in breathing effort. In one form in adults, either of the following may be regarded as being hypopneas: (i) a 30% reduction in patient breathing for at least 10 seconds plus an associated 4% desaturation; or (ii) a reduction in patient breathing (but less than 50%) for at least 10 seconds, with an associated desaturation of at least 3% or an arousal. [0363] Hyperpnea: An increase in flow to a level higher than normal. [0364] Inspiratory portion of a breathing cycle: The period from the start of inspiratory flow to the start of expiratory flow will be taken to be the inspiratory portion of a breathing cycle. [0365] Patency (airway): The degree of the airway being open, or the extent to which the airway is open. A patent airway is open. Airway patency may be quantified, for example with a value of one (1) being patent, and a value of zero (0), being closed (obstructed). [0366] Positive End-Expiratory Pressure (PEEP): The pressure above atmosphere in the lungs that exists at the end of expiration. [0367] Peak flow rate (Qpeak): The maximum value of flow rate during the inspiratory portion of the respiratory flow waveform. [0368] Respiratory flow rate, patient airflow rate, respiratory airflow rate (Qr): These terms may be understood to refer to the RPT device’s estimate of respiratory flow rate, as opposed to “true respiratory flow rate” or “true respiratory flow rate”, which is the actual respiratory flow rate experienced by the patient, usually expressed in litres per minute. [0369] Tidal volume (Vt): The volume of air inhaled or exhaled during normal breathing, when extra effort is not applied. In principle the inspiratory volume Vi (the volume of air inhaled) is equal to the expiratory volume Ve (the volume of air exhaled), and therefore a single tidal volume Vt may be defined as equal to either quantity. In practice the tidal volume Vt is estimated as some combination, e.g. the mean, of the inspiratory volume Vi and the expiratory volume Ve. [0370] Inhalation Time (Ti): The duration of the inspiratory portion of the respiratory flow rate waveform. [0371] Exhalation Time (Te): The duration of the expiratory portion of the respiratory flow rate waveform. [0372] Total Time (Ttot): The total duration between the start of one inspiratory portion of a respiratory flow rate waveform and the start of the following inspiratory portion of the respiratory flow rate waveform. [0373] Typical recent ventilation: The value of ventilation around which recent values of ventilation Vent over some predetermined timescale tend to cluster, that is, a measure of the central tendency of the recent values of ventilation. [0374] Upper airway obstruction (UAO): includes both partial and total upper airway obstruction. This may be associated with a state of flow limitation, in which the flow rate increases only slightly or may even decrease as the pressure difference across the upper airway increases (Starling resistor behaviour). [0375] Ventilation (Vent): A measure of a rate of gas being exchanged by the patient’s respiratory system. Measures of ventilation may include one or both of inspiratory and expiratory flow, per unit time. When expressed as a volume per minute, this quantity is often referred to as “minute ventilation”. Minute ventilation is sometimes given simply as a volume, understood to be the volume per minute. 5.12.3 Ventilation [0376] Adaptive Servo-Ventilator (ASV): A servo-ventilator that has a changeable, rather than fixed target ventilation. The changeable target ventilation may be learned from some characteristic of the patient, for example, a respiratory characteristic of the patient. [0377] Backup rate: A parameter of a ventilator that establishes the minimum breathing rate (typically in number of breaths per minute) that the ventilator will deliver to the patient, if not triggered by spontaneous respiratory effort. [0378] Cycled: The termination of a ventilator's inspiratory phase. When a ventilator delivers a breath to a spontaneously breathing patient, at the end of the inspiratory portion of the breathing cycle, the ventilator is said to be cycled to stop delivering the breath. [0379] Expiratory positive airway pressure (EPAP): a base pressure, to which a pressure varying within the breath is added to produce the desired interface pressure which the ventilator will attempt to achieve at a given time. [0380] End expiratory pressure (EEP): Desired interface pressure which the ventilator will attempt to achieve at the end of the expiratory portion of the breath. If the pressure waveform template Π(φ) is zero-valued at the end of expiration, i.e. Π(φ) = 0 when φ = 1, the EEP is equal to the EPAP. [0381] Inspiratory positive airway pressure (IPAP): Maximum desired interface pressure which the ventilator will attempt to achieve during the inspiratory portion of the breath. [0382] Pressure support: A number that is indicative of the increase in pressure during ventilator inspiration over that during ventilator expiration, and generally means the difference in pressure between the maximum value during inspiration and the base pressure (e.g., PS = IPAP – EPAP). In some contexts, pressure support means the difference which the ventilator aims to achieve, rather than what it actually achieves. [0383] Servo-ventilator: A ventilator that measures patient ventilation, has a target ventilation, and which adjusts the level of pressure support to bring the patient ventilation towards the target ventilation. [0384] Spontaneous/Timed (S/T): A mode of a ventilator or other device that attempts to detect the initiation of a breath of a spontaneously breathing patient. If however, the device is unable to detect a breath within a predetermined period of time, the device will automatically initiate delivery of the breath. [0385] Swing: Equivalent term to pressure support. [0386] Triggered: When a ventilator, or other respiratory therapy device such as an RPT device or portable oxygen concentrator, delivers a volume of breathable gas to a spontaneously breathing patient, it is said to be triggered to do so. Triggering usually takes place at or near the initiation of the respiratory portion of the breathing cycle by the patient's efforts. 5.13 OTHER REMARKS [0387] A portion of the disclosure of this patent document contains material which is subject to copyright protection. The copyright owner has no objection to the facsimile reproduction by anyone of the patent document or the patent disclosure, as it appears in Patent Office patent files or records, but otherwise reserves all copyright rights whatsoever. [0388] Unless the context clearly dictates otherwise and where a range of values is provided, it is understood that each intervening value, to the tenth of the unit of the lower limit, between the upper and lower limit of that range, and any other stated or intervening value in that stated range is encompassed within the technology. The upper and lower limits of these intervening ranges, which may be independently included in the intervening ranges, are also encompassed within the technology, subject to any specifically excluded limit in the stated range. Where the stated range includes one or both of the limits, ranges excluding either or both of those included limits are also included in the technology. [0389] Furthermore, where a value or values are stated herein as being implemented as part of the technology, it is understood that such values may be approximated, unless otherwise stated, and such values may be utilized to any suitable significant digit to the extent that a practical technical implementation may permit or require it. [0390] Furthermore, “approximately”, “substantially”, “about”, or any similar term used herein means +/- 5-10% of the recited value. [0391] Unless defined otherwise, 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 belongs. Although any methods and materials similar or equivalent to those described herein can also be used in the practice or testing of the present technology, a limited number of the exemplary methods and materials are described herein. [0392] When a particular material is identified as being used to construct a component, obvious alternative materials with similar properties may be used as a substitute. Furthermore, unless specified to the contrary, any and all components herein described are understood to be capable of being manufactured and, as such, may be manufactured together or separately. [0393] It must be noted that as used herein and in the appended claims, the singular forms "a", "an", and "the" include their plural equivalents, unless the context clearly dictates otherwise. [0394] All publications mentioned herein are incorporated herein by reference in their entirety to disclose and describe the methods and/or materials which are the subject of those publications. The publications discussed herein are provided solely for their disclosure prior to the filing date of the present application. Nothing herein is to be construed as an admission that the present technology is not entitled to antedate such publication by virtue of prior invention. Further, the dates of publication provided may be different from the actual publication dates, which may need to be independently confirmed. [0395] The terms "comprises" and "comprising" should be interpreted as referring to elements, components, or steps in a non-exclusive manner, indicating that the referenced elements, components, or steps may be present, or utilized, or combined with other elements, components, or steps that are not expressly referenced. [0396] The subject headings used in the detailed description are included only for the ease of reference of the reader and should not be used to limit the subject matter found throughout the disclosure or the claims. The subject headings should not be used in construing the scope of the claims or the claim limitations. [0397] Although the technology herein has been described with reference to particular examples, it is to be understood that these examples are merely illustrative of the principles and applications of the technology. In some instances, the terminology and symbols may imply specific details that are not required to practice the technology. For example, although the terms "first" and "second" may be used, unless otherwise specified, they are not intended to indicate any order but may be utilised to distinguish between distinct elements. Furthermore, although process steps in the methodologies may be described or illustrated in an order, such an ordering is not required. Those skilled in the art will recognize that such ordering may be modified and/or aspects thereof may be conducted concurrently or even synchronously. [0398] It is therefore to be understood that numerous modifications may be made to the illustrative examples and that other arrangements may be devised without departing from the spirit and scope of the technology.

Claims

6 CLAIMS 1. A system for monitoring treatment of a respiratory disorder provided by a therapy apparatus comprising: a pressure device configured to deliver a flow of pressurised air to a patient interface that is, in use, connected to a patient airway; one or more sensors to monitor one or more characteristics of the pressurised air; and one or more processors configured to: (a) control the pressure device to deliver the flow of pressurised air at treatment pressures responsive to adverse events, wherein the delivered treatment pressures are maintained within a range of treatment pressures between a minimum therapeutic pressure and a maximum therapeutic pressure (b) detect adverse events, based on the monitored one or more characteristics of the air during each of a plurality of treatment sessions, (c) evaluate counts of the detected adverse events that occur during pressure delivery within a given range of treatment pressures for the plurality of treatment sessions, and (d) generate output comprising a parameter change, for use in a subsequent therapy session, to the minimum therapeutic pressure setting or the maximum therapeutic pressure setting, in response to the counts of detected adverse events, at or exceeding a threshold value.

2. The system of claim 1, wherein the output comprises any one or more of: a recommendation for the parameter change, and an automatic control operation of the pressure device with the parameter change.

3. The system of any one of claims 1 and 2, wherein the evaluated counts are apnea and/or hypopnea events.

4. The system of any one of claims 2 to 3, wherein the output comprises an increase to the minimum therapeutic pressure, in response to the evaluated counts exceeding a threshold value, wherein the given range of treatment pressures are between the minimum therapeutic pressure and a critical range of treatment pressures.

5. The system of any one of claims 1 to 4, wherein the one or more processors are further configured to: control ramping of the delivered pressurised air from an initial ramp pressure to the minimum therapeutic pressure in ramp periods of the plurality of treatment sessions; and generate a recommendation for a parameter change, for use in a subsequent therapy session, to a profile of a controlled ramping of the delivered pressure over a ramp period, wherein the parameter change comprises: (a) an increase to a rate of pressure increase for the profile and/or (b) an increase to an initial pressure for the profile, or (c) disabling a ramp period, wherein the recommendation is based on the evaluated counts exceeding a threshold value, wherein the given range of treatment pressures are between the initial ramp pressure and the minimum therapeutic pressure and occur during the ramp periods.

6. The system of any one of claims 1 to 6, wherein the one or more processors are further configured to generate a recommendation for a parameter change, for use in a subsequent therapy session, to increase the maximum therapeutic pressure in response to the evaluated counts exceeding a threshold value during a period when the treatment pressure remains at or near the maximum therapeutic pressure.

7. The system of any one of claims 1 to 7, wherein the evaluated counts are mask off events, and wherein the one or more processors are further configured to generate a recommendation for a parameter change, for use in a subsequent therapy session, to reduce the maximum therapeutic pressure in response to the evaluated counts exceeding a threshold value, wherein each of the mask off events occur when pressure rises to a particular pressure.

8. The system of any one of claims 1 to 7, wherein the one or more processors are further configured to determine a critical range of therapeutic pressures during which a count of adverse events is below a threshold value.

9. The system of claim 8, wherein the one or more processors are further configured to generate a recommendation for a parameter change, for use in a ramp period of a subsequent therapy session, for ramping of pressure to a therapeutic pressure that is above the minimum therapeutic pressure and is in the determined critical range of therapeutic pressures.

10. The system of any one of claims 8 to 9, wherein the one or more processors are further configured to reduce an intended change in pressure to be made in response to a detected adverse event comprising any of snore, flow limitation or obstructive apnea, when a currently delivered pressure is in the determined critical range of therapeutic pressures and the intended change in pressure takes the pressure outside of the determined critical range of therapeutic pressures.

11. The system of any one of claims 8 to 10, wherein the evaluated counts are mask off events, and wherein the one or more processors are further configured to generate a recommendation for, or to automatically introduce, a parameter change, for use in a subsequent therapy session, the introduced change being related to reduction of the maximum value of the determined critical range of therapeutic pressures.

12. The system of any one of claims 1 to 11, wherein the one or more processors consist of one or more processors in a controller of a respiratory pressure therapy device.

13. The system of any one of claims 1 to 11, wherein the one or more processors comprise a processor of a server and a processor in a controller of a respiratory pressure therapy device.

14. The system of claim 13, wherein the server is configured to evaluate the counts of the detected adverse events and to generate the output for a parameter change.

15. The system of claim 14, wherein the server is configured to detect the adverse events.

16. The system of claim 14, wherein the controller is configured to control the pressure device to deliver the flow of pressurised air and to detect the adverse events.

17. The system of any one of claims 1 to 16, wherein the one or more processors are further configured to apply a generated recommendation for a parameter change for control of the pressure device in response to the generated recommendation.

18. The system of claim 17, wherein the one or more processors are further configured to apply the generated recommendation in response to manual input on a user interface.

19. The system of any one of claims 2 to 18, wherein the automatic control operation of the pressure device with the parameter change includes a set of pressure adjustment factors.

20. The system of claim 19, wherein the set of pressure adjustment factors includes a set of increase factors.

21. The system of claim 20, wherein each increase factor in the set of increase factors corresponds to respective pressure values of a set of pressure values.

22. The system of claim 21, wherein increase factors in the set of increase factors that cause fast increases in pressure correspond to smaller pressure values in the set of pressure values.

23. The system of any one of claims 21 to 22, wherein increase factors in the set of increase factors that cause slow increases in pressure correspond to larger pressure values in the set of pressure values.

24. The system of any one of claims 21 to 23, wherein increase factors in the set of increase factors having larger magnitudes correspond to respective pressure values in the set of pressure values having smaller magnitudes.

25. The system of any one of claims 21 to 24, wherein the set of pressure adjustment factors includes a set of decrease factors.

26. The system of claim 25, wherein each decrease factor in the set of decrease factors corresponds to respective pressure values in the set of pressure values.

27. The system of claim 26, wherein decrease factors in the set of decrease factors that cause slow decreases in pressure correspond to smaller pressure values in the set of pressure values.

28. The system of any one of claims 26 to 27, wherein decrease factors in the set of decrease factors that cause fast decreases in pressure correspond to larger pressure values in the set of pressure values.

29. The system of any one of claims 25 to 28, wherein each decrease factor in the set of decrease factors is a decay time constant.

30. The system of any one of claims 19 to 29, wherein the one or more processors are configured to: determine the set of pressure adjustment factors based on a biasing function.

31. The system of claim 30, wherein the one or more processors are configured to: determine the biasing function from a pressure distribution.

32. The system of claim 31, wherein the one or more processors are configured to: generate the pressure distribution from a set of stored pressure traces.

33. The system of claim 32, wherein each pressure trace in the set of stored pressure traces was measured during respective respiratory therapy period.

34. The system of any one of claims 32 to 33, wherein the set of stored pressure traces includes at least seven pressure traces.

35. The system of any one of claims 32 to 34, wherein the set of stored pressure traces includes between seven and ten pressure traces.

36. The system of any one of claims 31 to 35, wherein, to determine the biasing function, the one or more processors are configured to: select the adjustment function from among a plurality of biasing functions based on the pressure distribution.

37. The system of claim 36, wherein the one or more processors are configured to: select the biasing function from among the plurality of biasing functions based on a number of local maxima in the pressure distribution.

38. The system of any one of claims 36 to 37, wherein the selected biasing function is an elastic automatic positive airway pressure function when the pressure distribution has only one local maxima.

39. The system of any one of claims 36 to 37, wherein the selected biasing function is an orbital automatic positive airway pressure function when the pressure distribution has two local maxima.

40. The system of any one of claims 36 to 37, wherein the selected biasing function is an optimal pressure range function when the pressure distribution has more than two local maxima.

41. The system of any one of claims 19 to 40, wherein the automatic control operation of the pressure device with the parameter change includes the set of pressure adjustment factors when at least one of the detected adverse events includes a flow limitation respiratory event.

42. A method in one or more processors for monitoring treatment of a respiratory disorder provided by a therapy apparatus comprising: detecting adverse events, based on monitoring of one or more characteristics of pressurized air delivered during each of a plurality of treatment sessions wherein a pressure device is controlled to deliver a flow of pressurized air to a patient interface at treatment pressures responsive to respiratory events, wherein the delivered treatment pressures are maintained within a range of treatment pressures between a minimum therapeutic pressure and a maximum therapeutic pressure; evaluating counts of the detected adverse events that occur during pressure delivery within a given range of treatment pressures for the plurality of treatment sessions; and generating output comprising a parameter change, for use in a subsequent therapy session, to the minimum therapeutic pressure setting or the maximum therapeutic pressure setting, in response to the counts of detected adverse events, at or exceeding a threshold value.

43. The method of claim 42, wherein the output comprises any one or more of: a recommendation for the parameter change, and an automatic control operation of the pressure device with the parameter change.

44. The method of any one of claims 42 to 43, wherein the evaluated counts are apnea and/or hypopnea events.

45. The method of any one of claims 42 to 44, wherein the output comprises an increase to the minimum therapeutic pressure, in response to the evaluated counts exceeding a threshold value, wherein the given range of treatment pressures are between the minimum therapeutic pressure and a critical range of treatment pressures.

46. The method of any one of claims 42 to 45, further comprising, by the one or more processors: controlling ramping of the delivered pressurized air from an initial ramp pressure to the minimum therapeutic pressure in ramp periods of the plurality of treatment sessions; and generating a recommendation for a parameter change, for use in a subsequent therapy session, to a profile of a controlled ramping of the delivered pressure over a ramp period, wherein the parameter change comprises: (a) an increase to a rate of pressure increase for the profile and/or (b) an increase to an initial pressure for the profile, or (d) disabling the ramp period, wherein the recommendation is based on the evaluated counts exceeding a threshold value, wherein the given range of treatment pressures are between the initial ramp pressure and the minimum therapeutic pressure and occur during the ramp periods.

47. The method of any one of claims 42 to 46, further comprising generating a recommendation for a parameter change, for use in a subsequent therapy session, to increase the maximum therapeutic pressure in response to the evaluated counts exceeding a threshold value during a period when the treatment pressure remains at or near the maximum therapeutic pressure.

48. The method of any one of claims 42 to 47, wherein the evaluated counts are mask off events, and the method further comprises generating a recommendation for a parameter change, for use in a subsequent therapy session, to reduce the maximum therapeutic pressure in response to the evaluated counts exceeding a threshold value, wherein each of the mask off events occur when pressure rises to a particular pressure.

49. The method of any one of claims 42 to 48, further comprising determining a critical range of therapeutic pressures during which a count of adverse events is below a threshold value.

50. The method of claim 49, further comprising generating a recommendation for a parameter change, for use in a ramp period of a subsequent therapy session, for ramping of pressure to a therapeutic pressure that is above the minimum therapeutic pressure and is in the determined critical range of therapeutic pressures.

51. The method of any one of claims 49 to 50, further comprising reducing an intended change in pressure to be made in response to a detected adverse event comprising any of snore, flow limitation or obstructive apnea, when a currently delivered pressure is in the determined critical range of therapeutic pressures and the intended change in pressure takes the pressure outside of the determined critical range of therapeutic pressures.

52. The method of any one of claims 26 to 51, wherein the evaluated counts are mask off events, and further comprising generating a recommendation for, or automatically introducing, a parameter change, for use in a subsequent therapy session, the introduced change being related to reduction of the maximum value of the determined critical range of therapeutic pressures.

53. The method of any one of claims 42 to 52, wherein the one or more processors consist of one or more processors in a controller of a respiratory pressure therapy device.

54. The method of any one of claims 42 to 52, wherein the one or more processors comprise a processor of a server and a processor in a controller of a respiratory pressure therapy device.

55. The method of claim 54, wherein the server evaluates the counts of the detected adverse events and generates the output for a parameter change.

56. The method of claim 55, wherein the server detects the adverse events.

57. The method of claim 55, wherein the controller controls the pressure device to deliver the flow of pressurised air and detects the adverse events.

58. The method of any one of claims 42 to 57, further comprising applying a generated recommendation for a parameter change for control of the pressure device in response to the generated recommendation.

59. The method of claim 58, further comprising applying the generated recommendation in response to manual input on a user interface.

60. The method of any one of claims 43 to 59, wherein the automatic control operation of the pressure device with the parameter change includes a set of pressure adjustment factors.

61. The method of claim 60, wherein the set of pressure adjustment factors includes a set of increase factors.

62. The method of claim 61, wherein each increase factor in the set of increase factors corresponds to respective pressure values of a set of pressure values.

63. The method of claim 62, wherein increase factors in the set of increase factors that cause fast increases in pressure correspond to smaller pressure values in the set of pressure values.

64. The method of any one of claims 62 to 63, wherein increase factors in the set of increase factors that cause slow increases in pressure correspond to larger pressure values in the set of pressure values.

65. The method of any one of claims 62 to 64, wherein increase factors in the set of increase factors having larger magnitudes correspond to respective pressure values in the set of pressure values having smaller magnitudes.

66. The method of any one of claims 62 to 65, wherein the set of pressure adjustment factors includes a set of decrease factors.

67. The method of claim 66, wherein each decrease factor in the set of decrease factors corresponds to respective pressure values in the set of pressure values.

68. The method of claim 67, wherein decrease factors in the set of decrease factors that cause slow decreases in pressure correspond to smaller pressure values in the set of pressure values.

69. The method of any one of claims 67 to 68, wherein decrease factors in the set of decrease factors that cause fast decreases in pressure correspond to larger pressure values in the set of pressure values.

70. The method of any one of claims 25 to 69, wherein each decrease factor in the set of decrease factors is a decay time constant.

71. The method of any one of claims 60 to 70, further comprising: determining the set of pressure adjustment factors based on a biasing function.

72. The method of claim 71, further comprising: determining the biasing function from a pressure distribution.

73. The method of claim 72, further comprising: generating the pressure distribution from a set of stored pressure traces.

74. The method of claim 73, wherein each pressure trace in the set of stored pressure traces was measured during respective respiratory therapy period.

75. The method of any one of claims 73 to 74, wherein the set of stored pressure traces includes at least seven pressure traces.

76. The method of any one of claims 73 to 75, wherein the set of stored pressure traces includes between seven and ten pressure traces.

77. The method of any one of claims 31 to 76, wherein determining the biasing function comprises: selecting the biasing function from among a plurality of biasing functions based on the pressure distribution.

78. The method of claim 77, further comprising: selecting the biasing function from among the plurality of biasing functions based on a number of local maxima in the pressure distribution.

79. The method of any one of claims 77 to 78, wherein the selected biasing function is an elastic automatic positive airway pressure function when the pressure distribution has only one local maxima.

80. The method of any one of claims 77 to 78, wherein the selected biasing function is an orbital automatic positive airway pressure function when the pressure distribution has two local maxima.

81. The method of any one of claims 77 to 78, wherein the selected biasing function is an optimal pressure range function when the pressure distribution has more than two local maxima.

82. The method of any one of claims 60 to 81, wherein the automatic control operation of the pressure device with the parameter change includes the set of pressure adjustment factors when at least one of the detected adverse events includes a flow limitation respiratory event.

83. A computer-readable medium encoded with computer-readable instructions, which when executed by one or more processors implement the method of any one of claims 42 to 82.

84. A system for monitoring treatment of a respiratory disorder provided by a therapy apparatus comprising: a pressure device configured to deliver a flow of pressurised air to a patient interface that is, in use, connected to a patient airway; one or more sensors to monitor one or more characteristics of the pressurised air; and one or more processors configured to: (a) control the pressure device to deliver the flow of pressurised air at treatment pressures responsive to adverse events, wherein the delivered treatment pressures are maintained within a range of treatment pressures between a minimum therapeutic pressure and a maximum therapeutic pressure; (b) detect adverse events, based on the monitored one or more characteristics of the pressurized air during each of a plurality of treatment sessions; (c) evaluate counts of the detected adverse events that occur during pressure delivery within a given range of treatment pressures for the plurality of treatment sessions; and (d) generate an output comprising a parameter change, for use in a subsequent therapy session, in response to the counts of detected adverse events, at or exceeding a threshold value.

85. A method in one or more processors for monitoring treatment of a respiratory disorder provided by a therapy apparatus comprising: detecting adverse events, based on monitoring of one or more characteristics of pressurized air delivered during each of a plurality of treatment sessions wherein a pressure device is controlled to deliver a flow of pressurized air to a patient interface at treatment pressures responsive to respiratory events, wherein the delivered treatment pressures are maintained within a range of treatment pressures between a minimum therapeutic pressure and a maximum therapeutic pressure; evaluating counts of the detected adverse events that occur during pressure delivery within a given range of treatment pressures for the plurality of treatment sessions; and generating output comprising a parameter change, for use in a subsequent therapy session, in response to the counts of detected adverse events, at or exceeding a threshold value.

86. A computer-readable medium encoded with computer-readable instructions, which when executed by one or more processors implement the method of claim 85.

87. A system for monitoring treatment of a respiratory disorder provided by a therapy apparatus comprising: a pressure device configured to deliver a flow of pressurised air to a patient interface that is, in use, connected to a patient airway; one or more sensors to monitor one or more characteristics of the pressurised air; and one or more processors configured to: (a) control the pressure device to deliver the flow of pressurised air at treatment pressures responsive to adverse events, wherein the delivered treatment pressures are maintained within a range of treatment pressures between a minimum therapeutic pressure and a maximum therapeutic pressure; (b) detect adverse events, based on the monitored one or more characteristics of the air during each of a plurality of treatment sessions; (c) evaluate counts of the detected adverse events that occur during pressure delivery within a given range of treatment pressures for the plurality of treatment sessions; (d) control ramping of the delivered pressurised air from an initial ramp pressure to the minimum therapeutic pressure in ramp periods of the plurality of treatment sessions; and (e) generate an output for a parameter change, for use in a subsequent therapy session, to a profile of a controlled ramping of the delivered pressure over a ramp period, wherein the parameter change comprises (i) an increase to a rate of pressure increase for the profile and/or (ii) an increase to an initial pressure for the profile, or (iii) disabling a ramp period, wherein the output is based on the evaluated counts exceeding a threshold value, wherein the given range of treatment pressures are between the initial ramp pressure and the minimum therapeutic pressure and occur during the ramp periods.

88. A method in one or more processors for monitoring treatment of a respiratory disorder provided by a therapy apparatus comprising: detecting adverse events, based on monitoring of one or more characteristics of pressurized air delivered during each of a plurality of treatment sessions wherein a pressure device is controlled to deliver a flow of pressurized air to a patient interface at treatment pressures responsive to respiratory events, wherein the delivered treatment pressures are maintained within a range of treatment pressures between a minimum therapeutic pressure and a maximum therapeutic pressure; evaluating counts of the detected adverse events that occur during pressure delivery within a given range of treatment pressures for the plurality of treatment sessions; controlling ramping of the delivered pressurized air from an initial ramp pressure to the minimum therapeutic pressure in ramp periods of the plurality of treatment sessions; and generating an output for a parameter change, for use in a subsequent therapy session, to a profile of a controlled ramping of the delivered pressure over a ramp period, wherein the parameter change comprises: (a) an increase to a rate of pressure increase for the profile and/or (b) an increase to an initial pressure for the profile, or (d) disabling the ramp period, wherein the output is based on the evaluated counts exceeding a threshold value, wherein the given range of treatment pressures are between the initial ramp pressure and the minimum therapeutic pressure and occur during the ramp periods.

89. A computer-readable medium encoded with computer-readable instructions, which when executed by one or more processors implement the method of claim 88.

90. A system for monitoring treatment of a respiratory disorder provided by a therapy apparatus comprising: a pressure device configured to deliver a flow of pressurised air to a patient interface that is, in use, connected to a patient airway; one or more sensors to monitor one or more characteristics of the pressurised air; and one or more processors configured to: (a) control the pressure device to deliver the flow of pressurised air at treatment pressures responsive to adverse events, wherein the delivered treatment pressures are maintained within a range of treatment pressures between a minimum therapeutic pressure and a maximum therapeutic pressure; (b) detect adverse events, based on the monitored one or more characteristics of the air during each of a plurality of treatment sessions; (c) evaluate counts of the detected adverse events that occur during pressure delivery within a given range of treatment pressures for the plurality of treatment sessions; (d) control ramping of the delivered pressurised air from an initial ramp pressure to the minimum therapeutic pressure in ramp periods of the plurality of treatment sessions; and (e) generate an output for a parameter change, for use in a ramp period of a subsequent therapy session, for ramping of pressure to a therapeutic pressure that is above the minimum therapeutic pressure and is in a determined critical range of therapeutic pressures.

91. A method in one or more processors for monitoring treatment of a respiratory disorder provided by a therapy apparatus comprising: detecting adverse events, based on monitoring of one or more characteristics of pressurized air delivered during each of a plurality of treatment sessions wherein a pressure device is controlled to deliver a flow of pressurized air to a patient interface at treatment pressures responsive to respiratory events, wherein the delivered treatment pressures are maintained within a range of treatment pressures between a minimum therapeutic pressure and a maximum therapeutic pressure; evaluating counts of the detected adverse events that occur during pressure delivery within a given range of treatment pressures for the plurality of treatment sessions; controlling ramping of the delivered pressurized air from an initial ramp pressure to the minimum therapeutic pressure in ramp periods of the plurality of treatment sessions; and generating an output for a parameter change, for use in a ramp period of a subsequent therapy session, for ramping of pressure to a therapeutic pressure that is above the minimum therapeutic pressure and is in a determined critical range of therapeutic pressures.

92. A computer-readable medium encoded with computer-readable instructions, which when executed by one or more processors implement the method of claim 91.

93. A system for providing therapy for a respiratory disorder using a therapy apparatus comprising: a pressure device configured to deliver a flow of pressurised air to a patient interface that is, in use, connected to a patient airway; one or more sensors to monitor one or more characteristics of the pressurised air; and one or more processors configured to: (a) control the pressure device to deliver the flow of pressurised air at treatment pressures responsive to adverse events for a plurality of therapy periods; (b) evaluate delivered treatment pressures from the plurality of treatment sessions to determine one or more concentrations of treatment pressure, (c) based on the determination of one or more concentrations of treatment pressure, activate one or more biasing functions to bias adjustments to treatment pressure with respect to one or more critical treatment pressures of the plurality of therapy periods; (d) control the pressure device to deliver the flow of pressurised air at treatment pressures in a subsequent therapy period; and (e) adjust changes to the treatment pressures in the subsequent therapy period according to the one or more biasing functions.

94. The system of claim 93, wherein to evaluate the delivered treatment pressures the one or more processors are configured to compute a distribution from the delivered treatment pressures.

95. The system of claim 94, wherein to evaluate the delivered treatment pressures the one or more processors are configured to detect one or more local maxima in the distribution from the delivered treatment pressures.

96. The system of claim 95, wherein a biasing function of the one or more biasing functions biases adjustments to treatment pressure with respect to a treatment pressure attributable to a single local maxima in the distribution.

97. The system of claim 95, wherein a biasing function of the one or more biasing functions biases adjustments to treatment pressure with respect to treatment pressures attributable to a dual local maxima in the distribution.

98. The system of claim 95, wherein a biasing function of the one or more biasing functions biases adjustments to treatment pressure with respect to treatment pressures attributable to an upper and lower bound of delivered pressures.

99. The system of any one of claims 93 to 98, wherein to adjust changes to the treatment pressures in the subsequent therapy period according to the one or more biasing functions, a biasing function decreases a determined pressure increase amount, wherein the determined pressure increase amount is a prescribed pressure increase for a detected event comprising any one of an event of flow limitation, an event of snoring and a hypopnea.

100. The system of any one of claims 93 to 99, wherein to adjust changes to the treatment pressures in the subsequent therapy period according to the one or more biasing functions, a biasing function changes a time constant of a pressure decay, wherein the pressure decay is provided in a recent absence of a detection of any one of an event of flow limitation, an event of snoring and a hypopnea.

101. The system of any one of claims 93 to 100, wherein to bias adjustments to treatment pressure with respect to the one or more treatment pressures of the plurality of therapy periods, the one more biasing functions are configured to slow pressure changes away from the one or more critical treatment pressures.

102. The system of any one of claims 93 to 101, wherein to bias adjustments to treatment pressure with respect to the one or more treatment pressures of the plurality of therapy periods, the one more biasing functions are configured to accelerate pressure changes toward the one or more critical treatment pressures.

103. The system of any one of claims 93 to 102, wherein the one or more critical treatment pressures comprises a treatment pressure attributable to a local maxima of a distribution determined from delivered treatment pressures from one or more treatment sessions of the plurality of treatment sessions. 103. The system of any one of claims 93 to 103, wherein the one or more critical treatment pressures comprises two treatment pressures each attributable to a local maxima of a distribution determined from delivered treatment pressures from one or more treatment sessions of the plurality of treatment sessions.

104. The system of any one of claims 93 to 103, wherein the one or more critical treatment pressures comprises two treatment pressures attributable to upper and lower pressure bounds from delivered treatment pressures from one or more treatment sessions of the plurality of treatment sessions, wherein a distribution determined from delivered treatment pressures from the one or more treatment sessions of the plurality of treatment sessions lacks a local maxima.

105. A method for providing therapy for a respiratory disorder using a therapy apparatus comprising a pressure device configured to deliver a flow of pressurized air to a patient interface that is, in use, connected to a patient airway, the method comprising: controlling, with one or more processors, the pressure device to deliver the flow of pressurised air at treatment pressures responsive to adverse events for a plurality of therapy periods; evaluating, with the one or more processors, delivered treatment pressures from the plurality of treatment sessions to determine one or more concentrations of treatment pressure, based on the determination of one or more concentrations of treatment pressure, activating in a subsequent therapy period, with the one or more processors, one or more biasing functions to bias adjustments to treatment pressure with respect to one or more critical treatment pressures of the plurality of therapy periods; controlling, with the one or more processors, the pressure device to deliver the flow of pressurised air at treatment pressures in the subsequent therapy period; adjusting, with the one or more processors, changes to the treatment pressures in the subsequent therapy period according to the one or more biasing functions.

106. The method of claim 105, wherein the evaluating the delivered treatment pressures comprises computing a distribution from the delivered treatment pressures.

107. The method of claim 106, wherein evaluating the delivered treatment pressures comprises detecting one or more local maxima in the distribution from the delivered treatment pressures.

108. The method of claim 107, wherein a biasing function of the one or more biasing functions biases adjustments to treatment pressure with respect to a treatment pressure attributable to a single local maxima in the distribution.

109. The method of claim 106, wherein a biasing function of the one or more biasing functions biases adjustments to treatment pressure with respect to treatment pressures attributable to a dual local maxima in the distribution.

110. The method of claim 106, wherein a biasing function of the one or more biasing functions biases adjustments to treatment pressure with respect to treatment pressures attributable to an upper and lower bound of delivered pressures.

111. The method of any one of claims 105 to 110, wherein adjusting changes to the treatment pressures in the subsequent therapy period according to the one or more biasing functions comprises decreasing a determined pressure increase amount, wherein the determined pressure increase amount is a prescribed pressure increase for a detected event comprising any one of an event of flow limitation, an event of snoring and a hypopnea.

112. The method of any one of claims 105 to 111, wherein adjusting changes to the treatment pressures in the subsequent therapy period according to the one or more biasing functions comprises changing a time constant of a pressure decay, wherein the pressure decay is provided in a recent absence of a detection of any one of an event of flow limitation, an event of snoring and a hypopnea.

113. The method of any one of claims 105 to 112, wherein biasing adjustments to treatment pressure with respect to the one or more treatment pressures of the plurality of therapy periods comprises slowing pressure changes away from the one or more critical treatment pressures.

114. The method of any one of claims 105 to 113, wherein biasing adjustments to treatment pressure with respect to the one or more treatment pressures of the plurality of therapy periods comprises accelerating pressure changes toward the one or more critical treatment pressures.

115. The method of any one of claims 105 to 114, wherein the one or more critical treatment pressures comprises a treatment pressure attributable to a local maxima of a distribution determined from delivered treatment pressures from one or more treatment sessions of the plurality of treatment sessions.

116. The method of any one of claims 105 to 115, wherein the one or more critical treatment pressures comprises two treatment pressures that are each attributable to a local maxima of a distribution determined from delivered treatment pressures from one or more treatment sessions of the plurality of treatment sessions.

117. The method of any one of claims 105 to 116, wherein the one or more critical treatment pressures comprises two treatment pressures that are attributable to upper and lower pressure bounds from delivered treatment pressures from one or more treatment sessions of the plurality of treatment sessions, wherein a distribution determined from delivered treatment pressures from the one or more treatment sessions of the plurality of treatment sessions lacks a local maxima.

118. A computer-readable medium encoded with computer-readable instructions, which when executed by one or more processors implement the method of any one of claims 105 to 117.