WO2018006121A1 - Diagnosis and monitoring of cardio-respiratory disorders - Google Patents

Abstract

A patient monitoring system including a motion sensor, and a processor configured to analyse the in-phase (I) and quadrature (Q) channels from the sensor to estimate a patient's respiratory parameters such as respiratory rate, breath timing, and central apnea severity is disclosed. Periods of presence and absence of the patient may first be obtained by statistical analysis of the I and Q channels. Periods of absence may be discarded from subsequent analysis. During the presence periods, gross bodily movement periods may be detected and discarded from subsequent analysis. Robust demodulation of the I and Q channels to a signal representative of the instantaneous respiratory velocity of the patient's chest follows. From the respiratory chest velocity signal, breaths may be delineated and partitioned into inspiratory and expiratory phases by zero-crossing detection, yielding an instantaneous respiratory rate estimate and estimates of inspiratory and expiratory time. Peak detection of the respiratory chest velocity signal may provide an estimate of average respiratory rate over an interval. The difference between these two estimates of respiratory rate may be analysed to obtain an estimate of central apnea severity.

Description

DIAGNOSIS AND MONITORING OF CARDIO -RESPIRATORY DISORDERS

1 CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims the benefit of Australian Provisional Patent Application No. AU 2016902671, filed 7 July 2016, the entire disclosure of which is hereby incorporated herein by reference.

2 BACKGROUND OF THE TECHNOLOGY

2.1 FIELD OF THE TECHNOLOGY

[0002] The present technology relates to one or more of the detection, diagnosis, 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 inspired 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

Hyperventilation 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 (i.e. experience apnea) 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. 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 periodic alternating periods of waxing and waning ventilation known as CSR cycles. The waning periods may be severe enough to constitute central (open airway) apneas. 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. See US Patent No. 6,532,959 (Berthon-Jones).

[0009] Heart failure (HF) is a relatively common and severe cardio-respiratory condition, characterised by the inability of the heart to keep up with the oxygen demands of the body. Management of heart failure is a significant challenge to modern healthcare systems due to its high prevalence and severity. HF is a chronic condition, which is progressive in nature. The progression of HF is often

characterized as relatively stable over long periods of time (albeit with reduced cardiovascular function) punctuated by episodes of an acute nature. In these acute episodes, the patient experiences worsening of symptoms such as dyspnea (difficulty breathing), gallop rhythms, increased jugular venous pressure, and orthopnea. This is typically accompanied by overt congestion (which is the buildup of fluid in the pulmonary cavity). This excess fluid often leads to measurable weight gain of several kilograms. HF is also associated with Cheyne-Stokes respiration. In many cases, however, by the time overt congestion has occurred, there are limited options for the doctor to help restabilise the patients, and in many cases the patient requires hospitalization. In extreme cases, without timely treatment, the patient may undergo acute decompensated heart failure (ADHF) events, sometimes referred to as decompensations .

[0010] Respiratory failure is an umbrella term for cardio-respiratory disorders in which the lungs are unable to inspire sufficient oxygen or expire sufficient C0₂ to meet the patient's needs. Respiratory failure may encompass some or all of the following disorders.

[0011] A patient with respiratory insufficiency (a form of respiratory failure) may experience abnormal shortness of breath on exercise.

[0012] Obesity Hyperventilation Syndrome (OHS) is defined as the combination of severe obesity and awake chronic hypercapnia, in the absence of other known causes for hypoventilation. Symptoms include dyspnea, morning headache and excessive daytime sleepiness.

[0013] Chronic Obstructive Pulmonary Disease (COPD) encompasses any of a group of lower airway diseases that have certain characteristics in common. These include increased resistance to air movement, extended expiratory phase of respiration, and loss of the normal elasticity of the lung. Examples of COPD are emphysema and chronic bronchitis. COPD is caused by chronic tobacco smoking (primary risk factor), occupational exposures, air pollution and genetic factors.

Symptoms include: dyspnea on exertion, chronic cough and sputum production.

2.2.2 Therapy

[0014] Various therapies, such as Continuous Positive Airway Pressure (CPAP) therapy, Non-invasive ventilation (NIV) and Invasive ventilation (IV) have been used to treat one or more of the above respiratory disorders. [0015] 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.

[0016] 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.

[0017] 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.

2.2.3 Treatment Systems

[0018] These therapies may be provided by a treatment system or device. Such systems and devices may also be used to diagnose a condition without treating it.

[0019] A treatment system may comprise a Respiratory Pressure Therapy Device (RPT device), an air circuit, a humidifier, a patient interface, and data management.

2.2.4 Diagnosis and Monitoring Systems

[0020] Diagnosis is the identification of a condition from its signs and symptoms. Diagnosis tends to be a one-off process, whereas monitoring the progress of a condition can continue indefinitely. Some diagnosis systems are suitable only for diagnosis, whereas some may also be used for monitoring.

[0021] It is of interest to be able to monitor HF or COPD patients at home with a view to preventing or ameliorating potential clinical events such as HF

decompensations or COPD exacerbations. Characteristics that have been proposed or used for the purpose of predicting clinical events include body weight, levels of B natriuretic peptides (BNP), nocturnal heart rate, and changes in sleeping posture. Polysomnography (PSG) is a conventional system for diagnosis and monitoring of cardio-respiratory 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 biosignals such as electroencephalography (EEG), electrocardiography (ECG), electrooculograpy (EOG), electromyography (EMG), etc. PSG is therefore expensive and inconvenient. In particular it is unsuitable for home diagnosis and monitoring.

[0022] COPD and HF diagnosis / monitoring systems based on the sensor modalities described above tend to be unsatisfactory as they require good patient compliance (e.g. weight-based monitoring systems that rely on patients to record their daily weights), are wearable, which makes them unrealistic for long-term monitoring, or are invasive or obtrusive. The use of implantable devices is only feasible for a subset of HF patients eligible for such devices.

[0023] The S+ (ResMed Sensor Technologies Ltd, Dublin, Ireland) is a contactless bedside monitor suitable for long-term monitoring of chronic diseases such as HF and COPD. The S+ contains a biomotion transceiver sensor operating on radar principles in a licence-free band at ultra-low power (less than 1 mW). The S+ is capable of measuring bodily movement over a distance ranging from 0.3 to 1.5 metres; in the case of two people in a bed, a combination of sophisticated sensor design and intelligent signal processing allows the S+ to measure only the movement of the person nearest to the sensor. The S+ is suitable for long-term monitoring of chronic disease as it is unobtrusive and does not present significant compliance issues. However, processing the raw S+ signals to obtain respiratory parameters useful for chronic HF or COPD monitoring, such as respiratory rate, breath timing, and central apnea severity, is a difficult task.

3 BRIEF SUMMARY OF THE TECHNOLOGY

[0024] The present technology is directed towards providing medical devices used in the diagnosis, amelioration, treatment, or prevention of respiratory disorders having one or more of improved comfort, cost, efficacy, ease of use and

manuf acturability . [0025] A first aspect of the present technology relates to systems used in the diagnosis, amelioration, treatment or prevention of a respiratory disorder.

[0026] Another aspect of the present technology relates to methods used in the diagnosis, amelioration, treatment or prevention of a respiratory disorder.

[0027] One form of the present technology comprises a patient monitoring system including a motion sensor, and a processor configured to analyse the I and Q channels from the sensor to estimate a patient's respiratory parameters such as respiratory rate, breath timing, and central apnea severity. Periods of presence and absence of the patient may first be obtained by statistical analysis of the I and Q channels. Periods of absence may be discarded from subsequent analysis. During the presence periods, gross bodily movement periods may be detected and discarded from subsequent analysis. Robust demodulation of the I and Q channels to a signal representative of the instantaneous respiratory velocity of the patient's chest follows. From the respiratory chest velocity signal, breaths may be delineated and partitioned into inspiratory and expiratory phases by zero-crossing detection, yielding an instantaneous respiratory rate estimate and estimates of inspiratory and expiratory time. Peak detection of the respiratory chest velocity signal may provide an estimate of average respiratory rate over an interval. The difference between these two estimates of respiratory rate may be analysed to obtain an estimate of central apnea severity.

[0028] Another form of the present technology comprises a method of estimating a severity of central apneas experienced by a patient from a signal representing a respiratory chest velocity of the patient. The method comprises estimating an instantaneous respiratory rate over an interval of the respiratory chest velocity signal; estimating an average respiratory rate over the interval of the respiratory chest velocity signal; and computing an estimate of severity of central apneas experienced by the patient over the interval based on an absolute difference between the instantaneous respiratory rate estimate and the average respiratory rate estimate.

[0029] In some examples, estimating an instantaneous respiratory rate comprises: estimating a total cycle time of each breath within the interval, averaging the total cycle times estimated over the interval, and dividing by the average total cycle time. [0030] In other examples, estimating a total cycle time of a breath comprises: estimating an inspiratory time and an expiratory time of the breath, and summing the inspiratory time and the expiratory time.

[0031] In other examples, estimating the inspiratory time comprises subtracting a sampling instant of a positive-going zero-crossing from a sampling instant of the following negative-going zero-crossing of the respiratory chest velocity signal.

Additionally, estimating the expiratory time comprises subtracting a sampling instant of a negative-going zero-crossing from a sampling instant of the following positive- going zero-crossing of the respiratory chest velocity signal.

[0032] In additional examples, estimating the average respiratory rate comprises counting a number of peaks of the respiratory chest velocity signal over the interval and dividing by the length of the interval.

[0033] In some examples, the method further comprises demodulating an in- phase channel and a quadrature channel generated by a contactless motion sensor within whose measurement zone the chest of the patient is located, so as to generate the respiratory chest velocity signal.

[0034] In some examples, the method further comprises detecting presence of the patient in a measurement zone of the contactless motion sensor from at least one of the in-phase channel and the quadrature channel, and discarding periods of the in- phase channel and the quadrature channel in which presence of the patient was not detected.

[0035] In additional examples, detecting presence comprises: computing a plurality of L-moments over an epoch of at least one channel of the in-phase channel and the quadrature channel; computing an epoch feature from the L-moments; and detecting presence of the patient in the measurement zone within the epoch based on the epoch feature.

[0036] In other examples, computing the plurality of L-moments comprises computing a second-order L-moment and a third-order L-moment over the epoch of the at least one channel. Additionally, computing the epoch feature comprises multiplying the second-order L-moment by the third-order L-moment. In further examples, detecting presence of the patient comprises determining whether the epoch feature exceeds a threshold.

[0037] In other examples, the method may further comprise: detecting gross bodily movement of the patient in the measurement zone of the contactless motion sensor from at least one of the in-phase channel and the quadrature channel, and discarding sections of the in-phase channel and the quadrature channel in which gross bodily movement of the patient was detected.

[0038] In other examples, computing an estimate of severity comprises averaging the absolute difference between the instantaneous respiratory rate estimate and the average respiratory rate estimate over an interval.

[0039] Another form of the present technology may comprise a patient monitoring system. The system comprises a motion sensor configured to generate a signal representing respiratory chest velocity of a patient; and a processor configured to analyse the chest velocity signal. The analysis performed by the processor comprises estimating an instantaneous respiratory rate over an interval of the respiratory chest velocity signal; estimating an average respiratory rate over the interval of the respiratory chest velocity signal; and computing an estimate of severity of central apneas experienced by the patient over the interval based on an absolute difference between the instantaneous respiratory rate estimate and the average respiratory rate estimate.

[0040] In some examples, the processor is co-located with the motion sensor.

[0041] In some examples, the system further comprises an external computing device coupled to the motion sensor via a connection. In other examples, the processor is a processor of the external computing device.

[0042] In some examples, the motion sensor is a radio-frequency sensor that generates the chest velocity signal by processing of signals representing transmitted radio-frequency waves and received reflected ones of the transmitted radio -frequency waves. [0043] In another form, the present technology comprises a patient monitoring system. The system comprises means for generating a signal representing respiratory chest velocity of a patient; and means for analysing the chest velocity signal. The means for analysing the chest velocity signal may comprise estimating an

instantaneous respiratory rate over an interval of the respiratory chest velocity signal; estimating an average respiratory rate over the interval of the respiratory chest velocity signal; and computing an estimate of severity of central apneas experienced by the patient over the interval based on an absolute difference between the instantaneous respiratory rate estimate and the average respiratory rate estimate.

[0044] Another form of the present technology comprises a method of detecting presence of a patient in a measurement zone of a contactless motion sensor, the method comprising: computing a plurality of L-moments over an epoch of at least one channel representing movement of the patient within the measurement zone, the at least one channel being generated by the contactless motion sensor; computing an epoch feature from the L-moments; and detecting presence of the patient in the measurement zone within the epoch based on the epoch feature.

[0045] In some examples, computing the plurality of L-moments comprises computing a second-order L-moment and a third-order L-moment over the epoch of the at least one channel.

[0046] In other examples, computing the epoch feature comprises multiplying the second-order L-moment by the third-order L-moment. Further, detecting presence of the patient comprises determining whether the epoch feature exceeds a threshold.

[0047] In other examples, the method further comprises detecting gross bodily movement of the patient in the measurement zone of the contactless motion sensor from the at least one channel, and discarding sections of the at least one channel in which gross bodily movement of the patient was detected.

[0048] In other examples, the contactless motion sensor is a radio-frequency motion sensor and the at least one channel comprises at least one of an in-phase channel and a quadrature channel. [0049] In some examples, the method may further comprise demodulating the in- phase channel and the quadrature channel to generate a signal representing a respiratory chest velocity of the patient. Additionally, in other examples, the method may further comprise estimating an instantaneous respiratory rate over an interval of the respiratory chest velocity signal; estimating an average respiratory rate over the interval of the respiratory chest velocity signal; and computing an estimate of severity of central apneas experienced by the patient over the interval based on an absolute difference between the instantaneous respiratory rate estimate and the average respiratory rate estimate.

[0050] In one form the present technology comprises a patient monitoring system. The system comprising a contactless motion sensor configured to generate at least one channel representing movement of a patient within a measurement zone; and a processor configured to analyse the at least one channel, the analysis comprising: computing a plurality of L-moments over an epoch of the at least one channel;

computing an epoch feature from the L-moments; and detecting presence of the patient in the measurement zone within the epoch based on the epoch feature.

[0051] In some examples, the processor is co-located with the motion sensor.

[0052] In some examples, the system further comprises an external computing device coupled to the motion sensor via a connection. In other examples, the processor is a processor of the external computing device. In other examples, the motion sensor is a radio-frequency sensor that generates the at least one channel by processing of signals representing transmitted radio-frequency waves and received reflected ones of the transmitted radio-frequency waves.

[0053] In one form, the present technology may also comprise a patient monitoring system, the system comprising means for generating at least one channel representing movement of a patient within a measurement zone; and means for analysing the at least one channel, the analysis comprising: computing a plurality of L-moments over an epoch of the at least one channel; computing an epoch feature from the L-moments; and detecting presence of the patient in the measurement zone within the epoch based on the epoch feature. [0054] In another form, the present technology comprises a method of demodulating an in-phase channel and a quadrature channel generated by a contactless motion sensor to generate a velocity signal representing a velocity of an object within a measurement zone of the contactless motion sensor, the method comprising: forming a lag signal from products of consecutive samples of a complex signal whose real part is the in-phase channel and whose imaginary part is the quadrature channel; computing a phase difference signal representing the difference in phase between consecutive samples of the lag signal; and removing a baseline component of the phase difference signal to generate a velocity signal representing a velocity of the object.

[0055] In some examples, removing the baseline component comprises:

numerically differentiating the phase difference signal to obtain a derivate of the phase difference signal, and cumulatively summing the derivative of the phase difference signal.

[0056] In some examples, numerically differentiating comprises applying a Holoborodko numeric differentiator.

[0057] In some examples, the object is the chest of a patient, and the method further comprises applying a respiratory band -pass filter to the velocity signal to obtain a signal representing a respiratory chest velocity of the patient. Additionally, in some examples, the respiratory band-pass filter has a passband of 0.08 Hz to 0.75 Hz.

[0058] In some examples, the method further comprises estimating an instantaneous respiratory rate over an interval of the respiratory chest velocity signal. Additionally, estimating an instantaneous respiratory rate in some examples comprises estimating a total cycle time of each breath within the interval, averaging the total cycle times estimated over the interval; and dividing by the average total cycle time.

[0059] In some examples, the method further comprises estimating an average respiratory rate over the interval of the respiratory chest velocity signal. In additional examples, estimating the average respiratory rate comprises counting a number of peaks of the respiratory chest velocity signal over the interval and dividing by the length of the interval.

[0060] In some examples, the method further comprises computing an estimate of severity of central apneas experienced by the patient over the interval based on an absolute difference between the instantaneous respiratory rate estimate and the average respiratory rate estimate. In additional examples, computing an estimate of severity comprises averaging the absolute difference between the instantaneous respiratory rate estimate and the average respiratory rate estimate over an interval.

[0061] Another form the present technology comprises a patient monitoring system. The system comprises a contactless motion sensor configured to generate an in-phase channel and a quadrature channel, wherein the in-phase channel and the quadrature channel each represent movement of an object within a measurement zone of the contactless motion sensor; and a processor configured to demodulate the in- phase channel and the quadrature channel, the demodulating comprising: forming a lag signal from products of consecutive samples of a complex signal whose real part is the in-phase channel and whose imaginary part is the quadrature channel;

computing a phase difference signal representing the difference in phase between consecutive samples of the lag signal; and removing a baseline component of the phase difference signal to generate a velocity signal representing a velocity of the object.

[0062] In some examples, the processor is co-located with the motion sensor.

[0063] In some examples, the system further comprises an external computing device coupled to the motion sensor via a connection.

[0064] In some examples, the processor is a processor of the external computing device. In other examples, the contactless motion sensor is a radio-frequency sensor that generates the in-phase channel and the quadrature channel by processing of signals representing transmitted radio-frequency waves and received reflected ones of the transmitted radio-frequency waves.

[0065] In another form, the present technology may comprise a patient monitoring system. The system comprises means for generating an in-phase channel and a quadrature channel, wherein the in-phase channel and the quadrature channel each represent movement of an object; and means for demodulating the in-phase channel and the quadrature channel, the demodulating comprising: forming a lag signal from products of consecutive samples of a complex signal whose real part is the in-phase channel and whose imaginary part is the quadrature channel; computing a phase difference signal representing the difference in phase between consecutive samples of the lag signal; and removing a baseline component of the phase difference signal to generate a velocity signal representing a velocity of the object.

[0066] In one form the present technology comprises a method of demodulating an in-phase channel and a quadrature channel generated by a contactless motion sensor to generate a velocity signal representing a velocity of an object within a measurement zone of the contactless motion sensor, the method comprising:

computing a Fourier transform spectrum of a complex signal whose real part is the in- phase channel and whose imaginary part is the quadrature channel; symmetrising the Fourier transform spectrum; and computing an inverse Fourier transform of the symmetrised spectrum to generate a velocity signal representing a velocity of the object.

[0067] In some examples, symmetrising comprises replacing each negative- frequency component of the spectrum with the complex conjugate of a corresponding positive-frequency component of the spectrum.

[0068] In some examples, the object is the chest of a patient, and further comprising smoothing the velocity signal using a low-pass filter to generate a signal representing a respiratory chest velocity of the patient. Additional examples comprise estimating an instantaneous respiratory rate over an interval of the respiratory chest velocity signal.

[0069] In some examples, estimating an instantaneous respiratory rate comprises: estimating a total cycle time of each breath within the interval, averaging the total cycle times estimated over the interval, and dividing by the average total cycle time.

[0070] In other examples, the method further comprises estimating an average respiratory rate over the interval of the respiratory chest velocity signal. [0071] In other examples, estimating the average respiratory rate comprises counting a number of peaks of the respiratory chest velocity signal over the interval and dividing by the length of the interval. In other examples, the method further comprises computing an estimate of severity of central apneas experienced by the patient over the interval based on an absolute difference between the instantaneous respiratory rate estimate and the average respiratory rate estimate. Additionally in some examples computing an estimate of severity comprises averaging the absolute difference between the instantaneous respiratory rate estimate and the average respiratory rate estimate over an interval.

[0072] In another form, the present technology comprises a patient monitoring system comprising: a contactless motion sensor configured to generate an in-phase channel and a quadrature channel, wherein the in-phase channel and the quadrature channel each represent movement of an object within a measurement zone of the contactless motion sensor; and a processor configured to demodulate the in-phase channel and the quadrature channel, the demodulating comprising: computing a Fourier transform spectrum of a complex signal whose real part is the in-phase channel and whose imaginary part is the quadrature channel; symmetrising the Fourier transform spectrum; and computing an inverse Fourier transform of the symmetrised spectrum to generate a velocity signal representing a velocity of the object.

[0073] In some examples, the processor is co-located with the motion sensor. In other examples, the system further comprises an external computing device coupled to the motion sensor via a connection. In further examples, wherein the processor is a processor of the external computing device. In additional examples, the contactless motion sensor is a radio-frequency sensor that generates the in-phase channel and the quadrature channel by processing of signals representing transmitted radio-frequency waves and received reflected ones of the transmitted radio-frequency waves.

[0074] In yet another form, the present technology may comprise a patient monitoring system comprising means for generating an in-phase channel and a quadrature channel, wherein the in-phase channel and the quadrature channel each represent movement of an object; and means for demodulating the in-phase channel and the quadrature channel, the demodulating comprising: computing a Fourier transform spectrum of a complex signal whose real part is the in-phase channel and whose imaginary part is the quadrature channel; symmetrising the Fourier transform spectrum; and computing an inverse Fourier transform of the symmetrised spectrum to generate a velocity signal representing a velocity of the object.

[0075] The methods, systems, devices and apparatus described herein can provide improved functioning in a processor, such as of a processor of a specific purpose computer, respiratory monitoring system and/or a respiratory therapy device. 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.

[0076] Of course, portions of the aspects, forms or examples may form sub- aspects, sub-forms or sub-examples of the present technology. Also, various ones of the sub-aspects, etc., and/or aspects, etc., may be combined in various manners and also constitute additional aspects or sub-aspects of the present technology.

[0077] 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

[0078] 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 TREATMENT SYSTEMS

[0079] Fig. 1 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. 4.2 RESPIRATORY SYSTEM AND FACIAL ANATOMY

[0080] Fig. 2 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.

4.3 PATIENT INTERFACE

[0081] Fig. 3 shows a patient interface in the form of a nasal mask in accordance with one form of the present technology.

4.4 RPT DEVICE

[0082] Fig. 4A shows an RPT device in accordance with one form of the present technology.

[0083] 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.

4.5 HUMIDIFIER

[0084] Fig. 5A shows an isometric view of a humidifier in accordance with one form of the present technology.

[0085] 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

[0086] Fig. 6A shows a typical respiratory flow rate waveform of a person while sleeping.

[0087] Fig. 6B shows selected polysomnography channels (pulse oximetry, flow rate, thoracic movement, and abdominal movement) of a patient during non-REM sleep breathing normally over a period of about ninety seconds.

4.7 DIAGNOSIS AND MONITORING SYSTEMS

[0088] Fig. 7A shows a patient undergoing polysomnography (PSG). The patient is sleeping in a supine sleeping position. [0089] Fig. 7B shows a monitoring system monitoring a sleeping patient in accordance with one form of the present technology.

[0090] Fig. 7C is a block diagram illustrating the monitoring system of Fig. 7B in more detail.

[0091] Fig. 8 is a flow chart illustrating a method that may be used to implement an analysis process carried out by the monitoring system of Fig. 7B in accordance with one form of the present technology.

[0092] Fig. 9 is a flow chart illustrating a method that may be used to implement the presence / absence detection step of the method of Fig. 8.

[0093] Fig. 10 contains a graph illustrating the performance of the method of Fig. 9 on example I and Q channel data.

[0094] Fig. 11 is a flow chart illustrating a method that may be used to implement the gross bodily movement detection step of the method of Fig. 8.

[0095] Fig. 12 contains a graph illustrating the performance of the method of Fig. 11 on example I and Q channel data.

[0096] Fig. 13 is a flow chart illustrating a method that may be used to implement a robust pulse pairing implementation of the demodulation step of the method of Fig. 8.

[0097] Fig. 14 contains a graph illustrating the performance of the method of Fig. 13 on example I and Q channel data.

[0098] Fig. 15 is a flow chart illustrating a method that may be used to implement a Fourier-domain implementation of the demodulation step of the method of Fig. 8.

[0099] Fig. 16 contains a graph illustrating the performance of the method of Fig. 15 on example I and Q channel data.

[0100] Fig. 17 contains two graphs illustrating different sections of the respiratory chest velocity signal obtained by the demodulation step of the method of Fig. 8. [0101] Fig. 18 contains a graph illustrating the performance of the method of Fig. 8 on an example respiratory chest velocity signal obtained by the demodulation step of the method of Fig. 8.

5 DETAILED DESCRIPTION OF EXAMPLES OF THE

TECHNOLOGY

[0102] 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.

[0103] 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

[0104] In one form, the present technology comprises a method for treating a respiratory disorder comprising the step of applying positive pressure to the entrance of the airways of a patient 1000.

[0105] 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.

[0106] In certain examples of the present technology, mouth breathing is limited, restricted or prevented.

5.2 TREATMENT SYSTEMS

[0107] In one form, the present technology comprises an apparatus or device for treating a respiratory disorder. The apparatus or device may comprise, see, e.g., Fig. 1, an RPT device 4000 for supplying pressurised air to the patient 1000 via an air circuit 4170 to a patient interface 3000. 5.3 PATIENT INTERFACE

[0108] A non-invasive patient interface 3000 (see, e.g., Figs. 1 or 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 facilitate the supply of air at positive pressure to the airways.

5.4 RPT DEVICE

[0109] An RPT device 4000 (see, e.g., Figs. 4A or 4B) 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. 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.

[0110] 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 6 cmH₂0, or at least 10cmH₂O, or at least 20 cmH₂0.

[0111] 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.

[0112] 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. [0113] 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.

[0114] 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.

5.5 AIR CIRCUIT

[0115] 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.

5.6 HUMIDIFIER

[0116] 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.

[0117] 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

[0118] Fig. 6A 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 respiratory parameter values may vary, a typical breath may have the following approximate values: tidal volume, Vt, 0.5 L, inspiration time, Ti, 1.6 seconds, peak inspiratory flow rate, Qpeak, 0.4 L/s, expiration time, Te, 2.4 seconds, peak expiratory flow rate, Qpeak, -0.5 L/s. The total cycle time of the breath, 7c, is about 4 seconds, the I:E ratio Ti I Te is about 1: 1.5, and the duty cycle, the ratio of Ti to Tc, is about 40%. The person breathes at a respiratory rate of about 15 breaths per minute (BPM), with Ventilation, Vent, about 7.5 L/min. In normal individuals, the I:E ratio ranges from 1:2 to 1:3 (expiration being about two to three times longer than inspiration). In patients with expiratory airflow obstruction (e.g., COPD patients), the expiratory time is typically prolonged. This results in lower I:E ratios, such as 1:4 or 1:5. A prolonged expiratory time and low I:E ratio is a cardinal sign of expiratory airflow obstruction.

[0119] Fig. 6B shows selected polysomnography channels (pulse oximetry, flow rate, thoracic movement, and abdominal movement) of a patient during non-REM sleep breathing normally over a period of about ninety seconds, with about 34 breaths. The top channel shows blood oxygen saturation (Sp0₂), the scale has a range of saturation from 90 to 99% in the vertical direction. The patient maintained a saturation of about 95% throughout the period shown. The second channel shows quantitative respiratory flow rate, and the scale ranges from -1 to +1 LPS in a vertical direction, and with inspiration positive. Thoracic and abdominal movement are shown in the third and fourth channels.

5.8 MONITORING SYSTEMS 5.8.1 Polysomnography

[0120] Fig. 7A shows a patient 1000 undergoing polysomnography (PSG). A PSG system comprises a headbox 2000 which receives and records signals from the following contact sensors: an EOG electrode 2015; an EEG electrode 2020; an ECG electrode 2025; a submental EMG electrode 2030; a snore sensor 2035; a respiratory inductance plethysmogram (respiratory effort sensor) 2040 on a chest band; a respiratory inductance plethysmogram (respiratory effort sensor) 2045 on an abdominal band; an oro-nasal cannula 2050 with oral thermistor; a

photoplethysmograph (pulse oximeter) 2055; and a body position sensor 2060. The electrical signals are referred to a ground electrode (ISOG) 2010 positioned in the centre of the forehead.

5.8.2 Unobtrusive monitoring system

[0121] One example of an unobtrusive monitoring system 7000 for monitoring a sleeping patient 1000 is illustrated in Fig. 7B. The monitoring system 7000 contains a contactless motion sensor generally directed toward the chest of the patient 1000. The motion sensor is configured to generate one or more signals representing bodily movement of the patient 1000 within a measurement zone.

[0122] Fig. 7C is a block diagram illustrating the components of the monitoring system 7000 of Fig. 7B in more detail, according to one form of the present technology. The monitoring system 7000 includes a contactless sensor device 7007 containing a contactless motion sensor 7010. The motion sensor 7010 is configured to generate one or more signals representing the positions and motions of objects within a measurement zone. The monitoring system 7000 may also include an external computing device 7005, e.g. a local general purpose computer, or a remote server.

[0123] The sensor device 7007 may also include a microcontroller unit (MCU) 7001, and a memory 7002 (e.g. a memory card) for recording data. In one

implementation, the sensor device 7007 may include communications circuitry 7004 configured to transfer data to the external computing device 7005 via a connection 7008. The connection 7008 may be wired or wireless, in which case the

communications circuitry 7004 has wireless capability, and may be direct or indirect via a local network or a wide-area network (not shown) such as the Internet.

[0124] The sensor device 7007 includes a processor 7006 configured to process the signals generated by the motion sensor 7010 as described in detail below.

[0125] The sensor device 7007 includes a display device 7015 configured to provide visual feedback to a user. In one implementation, the display device 7015 comprises one or more warning lights (e.g., one or more light emitting diodes). The display device 7015 may also be implemented as a display screen such as an LCD or a touch-sensitive display used, for example, to display information and/or operate the sensor device 7007. Operation of the display device 7015 is controlled by the processor 7006 based on an assessment of the patient' s cardio-respiratory disorder. The display device 7015 may be operated to show information to a user of the monitoring system 7000, such as the patient 1000, or a physician or other clinician. The display device 7015 may also display a graphical user interface for operation of the monitoring system 7000.

[0126] The sensor device 7007 may also include an audio output 7017 configured to provide acoustic feedback to a user under the control of the processor 7006, e.g., a tone whose frequency varies with respiratory rate, or an alarm which sounds when certain conditions are met.

[0127] The above descriptions of the visual display 7015 and the audio output 7017 of the sensor device 7007 apply equally to comparable elements of the external computing device 7005.

[0128] User control of the operation of the monitoring system 7000 may be based on operation of controls (not shown) that are sensed by the processor 7006 of the monitoring system 7000.

[0129] One example of a sensor device 7007 is the S+ device manufactured by ResMed Sensor Technologies Ltd, which contains a contactless radio-frequency (RF) motion sensor 7010.

[0130] In one form of the present technology, such as when the S+ device is used as the sensor device 7007, the motion sensor 7010 includes an RF transmitter 7020 configured to transmit an RF signal 7060. The transmitted signal 7060 for example has the form s(t) = u(t)∞s faf_ct + 0)

[0131] In Eq. 1, the carrier frequency is /_c (typically in the range 100 MHz to 100 GHz, e.g. 3 GHz to 12 GHz, e.g. 5.8 GHz or 10.5 GHz), t is time, 6> is an arbitrary phase angle, and u{t) is a pulse shape. In a continuous wave system, the magnitude of u(t) may be unitary, and can be omitted from Eq. 1. More generally, the pulse u(t) may be defined as in Eq. 2:

[0132] where is the period width, and T_p is the pulse width. Where T_p «T, this becomes a pulsed continuous wave system. In one case, as T_p becomes very small, the spectrum of the emitted signal becomes very wide, and the system is referred to as an ultra- wideband (UWB) radar or impulse radar. Alternatively, the carrier frequency of the RF transmitted signal 7060 can be varied (chirped) to produce a so-called frequency modulated continuous wave (FMCW) system.

[0133] The radio-frequency signal 7060 may be generated by the transmitter 7020 using a local oscillator 7040 coupled with circuitry for applying pulse gating. In the FMCW case, a voltage-controlled oscillator is used together with a voltage- frequency converter to produce the RF signal 7060 for transmission. The coupling of the transmitted RF signal 7060 to the air may be accomplished using an antenna 7050. The antenna 7050 can be omnidirectional (transmitting power more or less equally in all directions) or directional (transmitting power preferentially in certain directions). It may be advantageous to use a directional antenna 7050 in the sensor device 7007 so that transmitted and reflected energy are primarily coming from one direction. In one implementation of the sensor device 7007, a single antenna 7050 is used for both the transmitter 7020 and the receiver 7030, with a single carrier frequency. Alternatively, multiple receive and transmit antennas 7050 can be used, with multiple carrier frequencies.

[0134] The sensor device 7007 is compatible in various embodiments with various types of antenna 7050 such as simple dipole antennas, patch antennas, and helical antennas, and the choice of antenna can be influenced by factors such as the required directionality, size, shape, or cost. It should be noted that the sensor device 7007 can be operated in a manner which has been shown to be safe for human use. The sensor device 7007 has been demonstrated with a total emitted average power of 1 mW (0 dBm) and lower. The recommended safe power density level for RF 2

exposure is 1 mW/cm . At a distance of 1 metre from a system transmitting at 0 dBm, the equivalent power density will be at least 100 times less than this recommended limit.

[0135] In use, the transmitted RF signal 7060 is reflected off objects that reflect radio waves (such as the air-body interface of the patient 1000), and some of the reflected signal 7070 will be received at a receiver 7030, which can be collocated with the transmitter 7020, or which can be separate from the transmitter 7020, in a so- called "bistatic" configuration. The received signal 7070 and the transmitted signal 7060 can be multiplied together in a mixer 7080 (either in an analog or digital fashion). This mixer 7080 can be of the form of a multiplier (as denoted below in (Eq. 3)) or in a circuit which approximates the effect of a multiplier (e.g., an envelope detector circuit which adds sinusoidal waves). For example, in the CW case, the mixed signal will equal ιη(ί) = χ∞$ (2π/_εή∞$ (2π/_εί + (ή) ₃₎

[0136] where (ΐ) is a phase term resulting from the path difference of the transmitted and received signals 7060 and 7070 (in the case where the reflection is dominated by a single reflective object), and yis the attenuation experienced by the reflected signal 7070. If the reflecting object is fixed, then (ΐ) is fixed. In the system 7000, the reflecting object (e.g., the chest wall of the patient 1000) is in general moving, and (ΐ) will be time-varying. As a simple example, if the chest wall is undergoing only a sinusoidal respiratory movement of frequency f_m, then the mixed signal m(f) contains a component at/_m (as well as a component centred at 2f_c which can be removed by low-pass filtering, e.g. at 1.6 Hz). The signal at the output of the low-pass filter after mixing is referred to as the baseband signal 7003, and in general contains information about bodily movement. In some implementations, the mixer 7080 contains an analog-to-digital converter at its output, so the baseband signal 7003 may be a discrete signal (sequence of samples), e.g. with sampling rate equal to 16 Hz, or 64 Hz.

[0137] The amplitude of the baseband signal 7003 is affected by the mean path distance of the reflected signal, leading to detection nulls and peaks in the motion sensor 7010 (i.e. areas where the motion sensor 7010 is less or more sensitive). This effect can be minimised by using quadrature techniques in which the transmitter 7020 simultaneously transmits a signal 90 degrees out of phase (in quadrature) with the signal 7060 of Eq. 1. This results in two reflected signals, both of which can be mixed and low-pass filtered by the mixer 7080, leading to two signals, referred to as the "I channel" and the "Q channel". The baseband signal 7003 may comprise one or both of these channels.

[0138] In this way, the motion sensor 7010, e.g., a radio-frequency motion sensor, can observe the movement of the chest wall, or more generally the movement of the part of the body of the patient 1000 whom the system 7000 is monitoring.

[0139] The received signal 7070 can include large non-respiratory components, e.g. as the result of gross bodily movement. This is due to the fact that the reflected signals from the body can contain more than one reflection path, and lead to complex signals (for example if one hand is moving towards the sensor, and the chest wall is moving away). The reception of such signals is useful as it can indicate that the upper body is in motion, which is useful in determining sleep state.

[0140] In order to improve the quality of the bodily movement signals, the physical volume from which reflected energy is collected by the sensor device 7007 (the measurement zone) can be restricted using various methods. For example, the sensor device 7007 can be made "directionally selective" (that is, it transmits more energy in certain directions), as can the antenna of the receiver 7030. Directional selectivity can be achieved using directional antennas 7050, or multiple RF transmitters 7020. In alternative forms of the present technology, a continuous wave, an FMCW, or a UWB radar is used to obtain similar signals. A technique called "time-domain gating" can be used to only measure reflected signals 7070 which arise from signals at a certain physical distance from the sensor device 7007. Frequency domain gating (filtering) can be used to ignore motions of the reflected object above a certain frequency.

[0141] In implementations of the sensor device 7007 using multiple frequencies (e.g., at 500 MHz and 5 GHz), the lower frequency can be used to determine large motions accurately without phase ambiguity, which can then be subtracted from the higher- frequency sensor signals (which are more suited to measuring small motions). Using such a sensor device 7007, the system 7000 collects information from the patient 1000, and uses that information to determine bodily movement information.

[0142] Data representing the baseband signal 7003 may be stored in memory 7002 of the sensor device 7007, and / or transmitted over a link (e.g., connection 7008) for storage in the external computing device 7005, for each monitoring session. In one implementation, each monitoring session is one night in duration.

[0143] The processor 7006 of the sensor device 7007, or that of the external computing device 7005, may analyse the stored data representative of the baseband signal 7003 according to an analysis process such as those described in detail below. The instructions for carrying out the described processes may be stored on a computer-readable storage medium, e.g. the memory 7002 of the sensor device 7007, and interpreted and executed by a processor, e.g. the processor 7006 of the sensor device 7007. The instructions are operable to cause the computing device 7005 or processor 7006 to analyse data representative of the baseband signal 7003 using one or more of the processes, algorithms or methods discussed below. Such data itself may also be stored on a computer-readable medium. The outcome of the analysis may comprise an output of respiratory parameters or estimates of central apneas experienced by a monitored subject. As explained below, this data may take the form of a report for eventual use by a clinician.

5.8.3 Baseband signal analysis

[0144] One aspect of the present technology comprises an analysis process to obtain respiratory parameters from a signal representing bodily movement of the patient 1000.

[0145] In the form of the present technology in which the monitoring system is the unobtrusive monitoring system 7000 illustrated in Fig. 7B and the analysed signal is the baseband signal 7003, an analysis process may be implemented by the processor 7006 of the contactless sensor device 7007, configured by instructions stored on computer-readable storage medium such as the memory 7002. The results of the analysis, i.e. the respiratory parameters, may be transmitted to the external computing device 7005 via the connection 7008 as described above. [0146] Alternatively, a processor of the external computing device 7005 may implement all or part of the described analysis process, having obtained the required data, either raw or partly analysed, from the sensor device 7007 and any other sensors in the system 7000 via the connection 7008 as described above.

[0147] In one example, the external computing device 7005 is a clinician- accessible device such as a patient monitoring device that allows a clinician to review the respiratory parameters, whether these are received from the sensor device 7007 or obtained by the external computing device 7005 itself. In this example, a database may also be provided to record the respiratory parameters. Through such an external computing device 7005, a clinician may monitor the patient's cardio-respiratory disorder and issue a report or alert that the patient may require closer observation or hospitalisation.

[0148] In another example, the external computing device 7005 may be a cell phone, more typically a smartphone, that communicates with the sensor device 7007 to obtain data representing the baseband signal 7003 or other raw or partially processed data collected by the sensor. Communication between the sensor device 7007 and cell/smartphone may take place using any number of wireless protocols including for example Bluetooth or WiFi. The cell/smartphone upon receipt of this data may process it as would any other external computing device 7005 or may partially process the data and then further send it to another external computing device, e.g., a server, over a local area (e.g., IEEE 802.3) or wide area (e.g., the Internet) for further processing. In another implementation, the processing power discussed herein as resident on the sensor device 7007, may reside in the

cell/smartphone so that the sensor device 7007 processing capability is lessened and it functions more as a data collector. In addition, using the display on a

cell/smartphone, various information collected by the sensor device may be displayed after being processed by the cell/smartphone or by another external computing device 7005. That data may include any of the output of the processing described in detail below.

[0149] Fig. 8 contains a flow chart illustrating a method 8000 that may be used to implement the analysis process mentioned above. The respiratory parameters that may be obtained by the method 8000 from the I and Q channels of the baseband signal 7003 are one or more of respiratory rate (instantaneous and average), breath timing (inspiratory time Ti and expiratory time Te), and central apnea severity.

[0150] As mentioned above, the method 8000 may be carried out by the processor 7006 of the sensor device 7007 or a processor of the external computing device 7005 in communication therewith.

[0151] The method 8000 starts at step 8010, which detects the presence or absence of a patient from the measurement zone of the motion sensor 7010 from one or both of the I and Q channels. In one implementation, step 8010 uses a statistical approach based on L-moments [1] . In statistics, L-moments are a sequence of statistics used to summarize the shape of a probability distribution. L-moments can be defined for any random variable whose mean exists, and form the basis of a general theory which covers the summarization and description of theoretical probability distributions, the summarization and description of observed data samples, estimation of parameters and quantiles of probability distributions, and hypothesis tests for probability distributions. L-moments are usually defined as linear combinations of conventional order statistics. For a real-valued random variable X with cumulative distribution F{x) and quantile function x{F) (the inverse function of F x)), order statistics of a random sample of size n drawn from F(x) are defined as

[0152] for r = 1, n and j = 1, r. The L-moment λ_τ of order r (r = 1, 2, n) of X is defined as

[0153] L-moments can be used to calculate quantities analogous to standard deviation, skewness and kurtosis, termed the L-scale, L-skewness and L-kurtosis respectively (the L-mean is identical to the conventional mean). L-moments have the theoretical advantages over conventional moments of being able to characterize a wider range of distributions and, when estimated from a sample, of being more robust to the presence of outliers in the sample. Compared with conventional moments, L- moments are less subject to bias in estimation and approximate their asymptotic normal distribution more closely in finite samples. Parameter estimates obtained from L-moments are sometimes more accurate in small samples than even the maximum likelihood estimates.

[0154] Substituting Eq. 4 into Eq. 5, the L-moment λ_τ of order r (r = 1, 2, n) may be computed as

[0155] where P^* ( ) is the n-t shifted Legendre polynomial of F, related to the usual Legendre polynomials P„(u) by P^* (u) = P_n (2M -l) . Using Eq. 6 and the definitions of Legendre polynomials, it may be shown that the first three L-moments may be computed as linear combinations of conventional order statistics:

1

= EX_l = j^" x(F)dF (Eq. 7) o 2F - l) dF (Eq. 8)

A₃ = 6F² - 6F + l) dF (Eq. 9)

[0156] Fig. 9 is a flow chart illustrating a method 9000 that may be used to implement the presence / absence detection step 8010 of the method 8000 in accordance with one form of the present technology. The method 9000 starts at step 9010, which analyses either or both of the I and Q channels within a current epoch. An epoch is an interval of time. Its duration may be predetermined or selected based on the particular application or desired accuracy or granularity. In one

implementation, the epoch is of duration 5 seconds. Step 9010 extracts the second- order and third-order L-moments /¾ and λ of the I channel or the Q channel, or the I and Q channels jointly, within the current epoch, e.g. using Eqs. 8 and 9. Step 9020 then forms a feature for the current epoch by multiplying λι by -/¾. Step 9030 determines whether the epoch feature exceeds a threshold, set in one implementation to 0.01. If so ("Y"), step 9050 marks the current epoch as one in which the patient 1000 was present. Otherwise ("N"), step 9040 marks the current epoch as one in which the patient 1000 was absent. After either step 9040 or step 9050, step 9060 determines whether the end of the I and Q channels has been reached. If so ("Y"), the method concludes at step 9080. Otherwise, step 9070 slides the epoch along the I and Q channels. In one implementation, the slide distance is equal to the epoch duration, so the epochs are non-overlapping. The method 9000 then returns to step 9010 to analyse the next epoch.

[0157] Fig. 10 contains a graph 1010 illustrating the performance of the method 9000 on example I and Q channel data. The upper trace 1020 shows the I and Q channels recorded by a sensor device 7007 (carrier frequency 10.5 GHz) over a monitoring session of 20 hours (evening followed by a night's sleep followed by the next morning). The lower trace 1030 has two levels, 0 representing presence and 1 representing absence. As can be seen from Fig. 10, the method 9000 is capable of accurately denoting the regions of absence/presence across the monitoring session.

[0158] Returning to the method 8000, the next step 8020 detects gross bodily movement in I and Q channels. Optionally, epochs of the I and Q channels in which the presence of the patient was not detected by step 8010 may be discarded before carrying out step 8020. The main idea of step 8020 is that the estimation of respiratory parameters should not be biased by gross bodily movements incorrectly treated as big breaths and therefore one should mark the gross bodily movements and remove their corresponding effect from the subsequent respiratory parameter estimation. An example of a gross bodily movement is when the patient gets up and walks away from the measurement zone of the motion sensor 7010. In such a case, the corresponding baseband signal section might have a significant effect on the estimated respiratory parameters, and therefore discarding such a section is of significant importance to producing accurate estimates. Step 8020 is described in greater detail below with reference to Fig. 11.

[0159] Fig. 11 is a flow chart illustrating a method 1100 that may be used to implement the gross bodily movement step 8020 of the method 8000 in accordance with one form of the present technology. The method 1100 starts at step 1110 which removes the DC component (mean) of each of the I and Q channels. This can be done either by simply removing the mean of the signals across the two channels or by using an appropriate filter. A single-pole single-zero IIR filter may be utilised with both the pole and the zero located at zero angle. The zero of the filter may lie on the unit circle, while the pole radius may be from 0.80 to 0.99. The next step 1120 includes differentiating each of the DC-removed I and Q channels and summing the resulting derivatives to form a summed-derivative channel.

[0160] Step 1130 then calculates the mean and standard deviation over the whole duration of the summed-derivative channel and marks the samples of the channel whose absolute value is greater than the mean plus some multiple (e.g. 4) of the standard deviation as gross bodily movement samples. For each sample so marked, an interval surrounding the sample (e.g. two seconds before and after) is marked as a gross bodily movement section.

[0161] Fig. 12 contains a graph 1200 illustrating the performance of the method 9000 on example I and Q channel data. The trace 1210 shows the I channel recorded by a sensor device 7007 (carrier frequency 10.5 GHz) over approximately five minutes of a monitoring session during which the patient 1000 was both present in and absent from the measurement zone of the motion sensor 7010. The gross bodily movement trace 1220 has two levels, 0 representing no gross bodily movement and 1 representing gross bodily movement. The trace 1220 shows that the sections of large, chaotic variation in the I channel were detected as gross bodily movement. The quasi-periodic section 1230 represents steady breathing, which the trace 1220 shows was not detected as gross bodily movement.

[0162] Returning to the method 8000, step 8030 demodulates the sections of the I and Q channels in which the presence of the patient was detected by step 8010 and no gross bodily movement was detected by step 8020. Step 8030 produces a signal representing the respiratory velocity of the chest of the patient 1000. One

implementation of the demodulation step 8030 uses robust pulse pairing. The robust pulse pairing implementation is described in more detail below with reference to Fig. 13. Another implementation of the demodulation step 8030 uses a Fourier-domain approach. The Fourier-domain implementation is described in more detail below with reference to Fig. 15. [0163] Fig. 13 is a flow chart illustrating a method 1300 that may be used to implement the robust pulse pairing implementation of the demodulation step 8030 of the method 8000. The method 1300 starts at step 1310 which forms a complex discrete signal z[n] as I[n]+jQ[n]. Step 1320 of the method 1300 forms a signal Lag[n] by summing the products of consecutive samples of z[n] over a neighbourhood of S samples around the sample index n:

S-l

Lag[n] = z[i + n]z[i + n + l] (Eq. 10)

i=0

[0164] S may be as small as 1 or as large as the whole length of the monitoring session; in one implementation, S is set to 1.

[0165] Step 1330 then computes the phase difference ΔΦ[π] between consecutive samples of Lag[n], which is a scaled version of the chest velocity superimposed on a low-frequency baseline. Respiratory chest velocity by definition has no baseline component. Step 1340 therefore removes the baseline component of the phase difference ΔΦ[π] by numeric differentiation followed by cumulatively summing the derivative of the phase difference signal. In one implementation, step 1340 uses an Appoint Holoborodko noise-robust numeric differentiator, which is defined for a discrete signal j n as M

fW = T∑c_k (f [* + *] - f [* - ]) (^Eq- I D

[0166] where M = (ΛΜ)/2 is the half-width of the differentiator, and the coefficients are given by

[0167] where m - M- 1. Larger values of N give more high frequency noise suppression; in one implementation, N is set to 41.

[0168] The final (optional) step 1350 applies a respiratory band-pass filter to the baseline-removed phase difference, i.e. the chest velocity signal, to reduce

components of the chest velocity signal outside the respiratory rate frequency range. In one implementation, the passband of the respiratory band-pass filter is 0.08 Hz to 0.75 Hz, which corresponds to a respiratory rate of 5 to 45 breaths per minute. The result of step 1350 is a signal that is representative of respiratory chest velocity.

[0169] Fig. 14 contains a graph 1400 illustrating the performance of the method 1300 on example I and Q channel data. The trace 1410 shows the I channel recorded by a sensor device 7007 (carrier frequency 10.5 GHz) over approximately ninety seconds of a monitoring session during which the patient 1000 was present with no gross bodily movement, while the trace 1420 shows the corresponding Q channel. The horizontal graticules are at intervals of 12 seconds. The lower trace 1430 shows the respiratory chest velocity signal obtained by the robust pulse pairing method 1300. The traces 1410 and 1420 are highly affected by folding, whereas the respiratory chest velocity signal 1430 is "unfolded" with the individual breaths clearly discernible.

[0170] Fig. 15 is a flow chart illustrating a method 1500 that may be used to implement the Fourier-domain implementation of the demodulation step 8030 of the method 8000. The method 1500 starts at step 1510 which, like step 1310 of the method 1300, forms a complex discrete signal z[n] as I[n]+jQ[n]. Step 1520 computes the forward discrete Fourier transform Z[k] of z[n]. Step 1530 then

"symmetrises" the discrete Fourier spectrum Z[k] by replicating the right half of the spectrum (positive frequencies) onto the left half (negative frequencies) with the sign of the imaginary component reversed. That is, step 1530 forms a symmetrised discrete Fourier spectrum Z '[k] by replacing the negative frequency component Z[k] by Z*[-£] (the complex conjugate of the corresponding positive-frequency component Z[-k]) for all k<0. This operation ensures that the inverse discrete Fourier transform of the symmetrised discrete Fourier spectrum Z'[k] (performed in step 1540) produces a real-valued signal z n , which may then be smoothed in step 1550 (e.g. using a moving- average low-pass filter) to generate a signal representative of respiratory chest velocity. The method 1500 works by capturing the "dominant" channel of the I and Q channels at a given sampling instant, which can vary in relative amplitude as the patient 1000 moves about within the measurement zone. Capturing the

"dominant" channel at any given sampling instant ensures greater robustness to noise, in addition to "unfolding" the dominant channel. [0171] Fig. 16 contains a graph 1600 illustrating the performance of the method 1500 on example I and Q channel data. The trace 1610 shows the I channel recorded by a sensor device 7007 (carrier frequency 10.5 GHz) over approximately two and a half minutes of a monitoring session during which the patient 1000 was present with no gross bodily movement, while the trace 1620 shows the corresponding Q channel. (The traces 1610 and 1620 contain the I and Q channel data traces 1410 and 1420 of Fig. 14 respectively.) The horizontal graticules are at intervals of 30 seconds. The lower trace 1630 shows the respiratory chest velocity signal obtained by the Fourier- domain method 1500. The traces 1610 and 1620 are highly affected by folding, whereas the respiratory chest velocity signal 1630 is "unfolded" with the individual breaths clearly discernible.

[0172] After step 8030, the method 8000 bifurcates into two parallel processing strands that may take place simultaneously or in sequence. Step 8040 detects zero- crossings in the respiratory chest velocity signal obtained by the demodulation step 8030. The result of step 8040 is an alternating sequence of sampling instants at which the respiratory chest velocity changes from negative to positive (positive-going zero- crossings), and sampling instants at which the respiratory chest velocity changes from positive to negative (negative-going zero-crossings). The positive-going zero- crossing instants are the locations of the start of inspiration of each breath, and the negative-going zero-crossing instants are the locations of the start of expiration of each breath. Step 8040 may first apply a causal low-pass filter to the respiratory chest velocity signal, e.g. with time constant equal to thirty seconds, to further reduce any non-respiratory components of the respiratory chest velocity. Step 8050 then subtracts the identified successive sampling instants to obtain the inspiratory time Ti and the expiratory time Te of each breath, whose sum is the total cycle time Tc of the breath. The inspiratory time Ti, the expiratory time Te, and the I:E ratio Ti I Te of each breath are examples of breath timing parameters. The instantaneous respiratory rate for a breath in BPM may be computed as 60 divided by the total cycle time Tc of the breath. The instantaneous respiratory rate in BPM may be computed over an interval as 60 divided by the average cycle time of the breaths during the interval.

[0173] Meanwhile, or subsequently, step 8060 detects peaks in the respiratory chest velocity signal obtained by the demodulation step 8030. One implementation of step 8060 uses scale-space filtering. Instead of focusing on local derivative information as is conventional in peak detection, the scale-space filtering approach is more global. It performs iterative smoothing of the respiratory chest velocity signal with increasing length-scales and then defines a peak as a sampling instant that remains a local maximum for many such smoothing processes. Formally, the local maxima are identified after each smoothing operation and then associated to the maxima identified with the previous length-scales. A score is then added to the criterion for these latter instants that depends on the length-scale. This strategy emphasises peaks that remain local maxima even after many smoothing operations. At the end of the smoothing operations, the peaks are identified as the sampling instants having the largest score.

[0174] Another implementation of step 8060 uses persistence filtering. The presence of noise can cause a wealth of topological structures (often referred to as "over-segmentation"), because every local extremum causes the creation of corresponding topological structures. Topological simplification is a powerful tool to handle noise and over-segmentation. The goal is to rank the structures of the skeleton according to some importance measure and successively remove all structures ranked below a certain threshold - under the side condition that the skeleton is in a topologically consistent state after each simplification step. A commonly used stable importance measure for critical points is persistence. In combination with topological simplification, persistence can be defined locally as the smallest deviation between the saddle and its connected extrema. A topological simplification is an iterative process of repeated cancellations where the saddle-extremum pair with the currently lowest persistence is cancelled until a certain persistence is reached or no further cancellations are possible.

[0175] After step 8060 detects peaks in the respiratory chest velocity signal, step 8070 estimates the average respiratory rate based on the peaks detected at step 8060. Since each peak corresponds to one breath, step 8070 counts the number of peaks over an interval and divides by the length of the interval in minutes to obtain an average respiratory rate in BPM. The interval may be of length, for example, 15, 30, or 60 seconds. The average respiratory rate estimate may be updated more frequently than the length of the interval, e.g. every second, every two seconds, every five seconds, etc., by estimating over overlapping intervals.

[0176] After both step 8050 and step 8070 have been carried out, step 8080 calculates a measure of severity of central apneas in the patient by comparing the average respiratory rate estimate and the instantaneous respiratory rate estimated over the same interval as the average respiratory rate. Under normal breathing conditions, the average respiratory rate and the instantaneous respiratory rate should be very similar (with the instantaneous value being more accurate than the average value). However, the existence of apneic breathing patterns may separate these estimates, and the average value may become more representative of the actual respiratory rate. An example is illustrated in Fig. 17. Fig. 17 shows two traces 1710 and 1720

representing the respiratory chest velocity signal computed by the demodulation step 8030 over two different one-minute intervals. For the upper trace 1710, the average respiratory rate estimate is 17 BPM while the instantaneous respiratory rate estimate is 17.5 BPM. For the lower trace 1720, the average respiratory rate estimate is 13 BPM, while the instantaneous respiratory rate estimate is 17.5 BPM, as for the trace 1710. The significant difference between the average respiratory rate estimate and the instantaneous respiratory rate estimate in the trace 1720 is caused by the section 1730 of central apnea in which there is no detectable respiratory movement of the chest and hence no breaths whose cycle time may be estimated by zero-crossing detection. The section 1730 therefore does not contribute to the average cycle time over the interval, which is similar to that for the trace 1710 because of the similar breath timing during the non-apneic section of the trace 1720. However, the absence of detectable peaks during the centrally apneic section 1730 means the section 1730 does pull the average respiratory rate estimate over the entire interval down.

[0177] A section of obstructive apnea would not have the same separating effect on the respiratory rate estimates, since some respiratory movement would still occur during the obstructive apnea as the patient tried to breathe. The section would still therefore yield peaks which would be detectable by the peak detection step 8060 and counted by the average respiratory rate estimation step 8070, so the average respiratory rate estimate would therefore not be pulled down in the same way as by the centrally apneic section 1730. [0178] A further example may be seen in Fig. 18, which contains a graph 1800 illustrating approximately an hour of data. The trace 1810 represents the respiratory chest velocity signal, the trace 1820 represents the instantaneous respiratory rate estimate, and the trace 1830 represents the average respiratory rate estimate, each computed by the method 8000. The graph 1800 clearly demonstrates the differences between the two respiratory rate estimates on a respiratory chest velocity signal section containing central apneas. The average respiratory rate trace 1830 falls below the instantaneous respiratory rate trace 1820 due to the central apneas, which do not affect the instantaneous respiratory rate estimate as explained above.

[0179] Step 8080 therefore calculates a measure of severity of central apneas based on the difference between the instantaneous respiratory rate estimate and the average respiratory rate estimate. In one implementation of step 8080, an instantaneous measure of severity is simply the absolute value of the difference between the instantaneous respiratory rate estimate and the average respiratory rate estimate at each instant. In an alternative implementation, giving a longer-term measure of central apnea severity, the absolute value of the difference is averaged over an interval. The averaging interval may be as long as the full duration of the monitoring session, giving an estimate of central apnea severity over the monitoring session.

[0180] Steps 8040 to 8070 may be carried out on a respiratory chest velocity signal originating otherwise than from the demodulation step 8030. For example, steps 8040 to 8070 may be carried out on a respiratory chest velocity signal obtained from a chest band respiratory effort sensor 2040.

5.9 GLOSSARY

[0181] 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.

5.9.1 General

[0182] 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. atmospheric air enriched with oxygen.

[0183] 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' .

[0184] 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. Total flow rate, Qt, is the flow rate of air leaving the RPT device. Vent flow rate, Qv, is the flow rate of air leaving a vent to allow washout of expired gases. Leak flow rate, Ql, is the flow rate of leak from a patient interface or elsewhere. Respiratory flow rate, Qr, is the flow rate of air that is received into the patient's respiratory system.

[0185] 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 (H₂0) vapour to a flow of air to ameliorate a medical respiratory condition of a patient.

[0186] Patient: A person, whether or not they are suffering from a respiratory condition.

[0187] Respiratory Pressure Therapy (RPT): 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.

[0188] Ventilator: A mechanical device that provides pressure support to a patient to perform some or all of the work of breathing.

5.9.2 Respiratory cycle

[0189] 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 due to a reduction in breathing effort, or the absence of breathing effort.

[0190] Duty cycle: The ratio of inspiration time, Ti, to total cycle time, Tc.

[0191] Effort (breathing): The work done by a spontaneously breathing person attempting to breathe.

[0192] Expiratory portion of a breathing cycle: The period from the start of expiratory flow to the start of inspiratory flow.

[0193] 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.

[0194] 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.

[0195] Hyperpnea: An increase in flow to a level higher than normal.

[0196] 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.

[0197] 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). [0198] Positive End-Expiratory Pressure (PEEP): The pressure above atmosphere in the lungs that exists at the end of expiration.

[0199] Peak flow rate ( Qpeak): The maximum value of flow rate during the inspiratory portion of the respiratory flow waveform.

[0200] 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 airflow rate, as opposed to "true respiratory flow rate" or "true respiratory airflow rate", which is the actual respiratory flow rate experienced by the patient, usually expressed in litres per minute.

[0201] Respiratory rate: The rate of spontaneous respiration of a patient, usually measured in breaths per minute.

[0202] Tidal volume (Vt): The volume of air inspired or expired during normal breathing, when extra effort is not applied.

[0203] Inspiratory time (Ti): The duration of the inspiratory portion of the respiratory cycle.

[0204] Expiratory time (Te): The duration of the expiratory portion of the respiratory cycle.

[0205] Total cycle time (Tc): The total duration between the start of one inspiratory portion of a respiratory cycle and the start of the following inspiratory portion of the respiratory cycle.

[0206] 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.

[0207] 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). [0208] 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.

5.9.3 Ventilation

[0209] 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.

[0210] Backup rate: A parameter of a ventilator that establishes the minimum respiratory rate (typically in number of breaths per minute) that the ventilator will deliver to the patient, if not triggered by spontaneous respiratory effort.

[0211] 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.

[0212] Expiratory positive airway pressure (EPAP): a base pressure, to which a pressure varying within the breath is added to produce the desired mask pressure which the ventilator will attempt to achieve at a given time.

[0213] End expiratory pressure (EEP): Desired mask 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.

[0214] Inspiratory positive airway pressure (IPAP): Maximum desired mask pressure which the ventilator will attempt to achieve during the inspiratory portion of the breath.

[0215] 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.

[0216] 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.

[0217] 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.

[0218] Swing: Equivalent term to pressure support.

[0219] Triggered: When a ventilator delivers a breath of air to a spontaneously breathing patient, it is said to be triggered to do so at the initiation of the respiratory portion of the breathing cycle by the patient's efforts.

[0220] Typical recent ventilation: The typical recent ventilation Vtyp is the value around which recent measures of ventilation over some predetermined timescale tend to cluster. For example, a measure of the central tendency of the measures of ventilation over recent history may be a suitable value of a typical recent ventilation.

5.10 OTHER REMARKS

[0221] 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.

[0222] 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.

[0223] 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.

[0224] 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.

[0225] 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.

[0226] 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.

[0227] 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.

[0228] 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.

[0229] 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.

[0230] 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.

[0231] 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.

5.11 REFERENCE SIGNS LIST

patient 1000

graph 1010

trace 1020

method 1100

step 1110

step 1120

step 1130 graph 1200 trace 1210 trace 1220 section 1230 method 1300 step 1310 step 1320 step 1330 step 1340 step 1350 graph 1400 trace 1410 trace 1420 respiratory chest velocity signal 1430 method 1500 step 1510 step 1520 step 1530 step 1540 step 1550 graph 1600 trace 1610 trace 1620 trace 1630 trace 1710 trace 1720 section 1730 graph 1800 trace 1810 trace 1820 trace 1830 headbox 2000 ground electrode ISOG 2010

EOG electrode 2015

EEG electrode 2020

ECG electrode 2025 submental EMG electrode 2030 snore sensor 2035 respiratory effort sensor 2040 respiratory effort sensor 2045 oro - nasal cannula 2050 photoplethysmograph pulse oximeter 2055 body position sensor 2060 patient interface 3000 seal - forming structure 3100 plenum chamber 3200 structure 3300 vent 3400 connection port 3600 forehead support 3700 RPT device 4000 external housing 4010 upper portion 4012 portion 4014 panel 4015 chassis 4016 handle 4018 pneumatic block 4020 inlet air filter 4112 inlet muffler 4122 outlet muffler 4124 pressure generator 4140 blower 4142 air circuit 4170 electrical components 4200 PCBA 4202 electrical power supply 4210 input devices 4220 central controller 4230 therapy device controller 4240 protection circuits 4250 memory 4260 transducers 4270 pressure sensors 4272 flow rate sensors 4274 data communication interface 4280 output devices 4290 humidifier 5000 humidifier inlet 5002 humidifier outlet 5004 humidifier base 5006 humidifier reservoir 5110 humidifier reservoir dock 5130 heating element 5240 unobtrusive monitoring system 7000 microcontroller unit 7001 memory 7002 baseband signal 7003 communications circuitry 7004 external computing device 7005 processor 7006 sensor device 7007 connection 7008

motion sensor 7010

display device 7015

audio output 7017

transmitter 7020

receiver 7030

local oscillator 7040

antenna 7050

radio - frequency signal 7060

signals 7070

mixer 7080

method 8000

step 8010

step 8020

step 8030

step 8040

step 8050

step 8060

step 8070

step 8080

method 9000

step 9010

step 9020

step 9040

step 9050

step 9060

step 9070

step 9080

CITATIONS

Hosking, J.R.M. L-Moments: Analysis and Estimation of Distributions Using Linear Combinations of Order Statistics, Journal of the Royal Statistical Society, Series B (Methodological), vol. 52, no. 1, pp. 105-124, 1990.

Claims

7 CLAIMS

1. A method of estimating a severity of central apneas experienced by a patient from a signal representing a respiratory chest velocity of the patient, the method comprising: estimating an instantaneous respiratory rate over an interval of the respiratory chest velocity signal; estimating an average respiratory rate over the interval of the respiratory chest velocity signal; and computing an estimate of severity of central apneas experienced by the patient over the interval based on an absolute difference between the instantaneous respiratory rate estimate and the average respiratory rate estimate.

2. A method according to claim 1, wherein estimating an instantaneous respiratory rate comprises: estimating a total cycle time of each breath within the interval, averaging the total cycle times estimated over the interval, and dividing by the average total cycle time.

3. A method according to claim 2, wherein estimating a total cycle time of a breath comprises: estimating an inspiratory time and an expiratory time of the breath, and summing the inspiratory time and the expiratory time.

4. A method according to claim 3, wherein estimating the inspiratory time comprises subtracting a sampling instant of a positive-going zero-crossing from a sampling instant of the following negative-going zero-crossing of the respiratory chest velocity signal.

5. A method according to claim 3, wherein estimating the expiratory time comprises subtracting a sampling instant of a negative-going zero-crossing from a sampling instant of the following positive-going zero-crossing of the respiratory chest velocity signal.

6. A method according to any one of claims 1 to 5, wherein estimating the average respiratory rate comprises counting a number of peaks of the respiratory chest velocity signal over the interval and dividing by the length of the interval.

7. A method according to any one of claims 1 to 6, further comprising demodulating an in-phase channel and a quadrature channel generated by a contactless motion sensor within whose measurement zone the chest of the patient is located, so as to generate the respiratory chest velocity signal.

8. A method according to claim 7, further comprising: detecting presence of the patient in a measurement zone of the contactless motion sensor from at least one of the in-phase channel and the quadrature channel, and discarding periods of the in-phase channel and the quadrature channel in which presence of the patient was not detected.

9. A method according to claim 8, wherein detecting presence comprises: computing a plurality of L-moments over an epoch of at least one channel of the in-phase channel and the quadrature channel; computing an epoch feature from the L-moments; and detecting presence of the patient in the measurement zone within the epoch based on the epoch feature.

10. A method according to claim 9, wherein computing the plurality of L-moments comprises computing a second-order L-moment and a third-order L-moment over the epoch of the at least one channel.

11. A method according to claim 10, wherein computing the epoch feature comprises multiplying the second-order L-moment by the third-order L-moment.

12. A method according to claim 11, wherein detecting presence of the patient comprises determining whether the epoch feature exceeds a threshold.

13. A method according to any one of claims 7 to 12, further comprising: detecting gross bodily movement of the patient in the measurement zone of the contactless motion sensor from at least one of the in-phase channel and the quadrature channel, and discarding sections of the in-phase channel and the quadrature channel in which gross bodily movement of the patient was detected.

14. A method according to any of claims 1 to 13, wherein computing an estimate of severity comprises averaging the absolute difference between the instantaneous respiratory rate estimate and the average respiratory rate estimate over an interval.

15. A patient monitoring system comprising: a motion sensor configured to generate a signal representing respiratory chest velocity of a patient; and a processor configured to analyse the chest velocity signal, the analysis comprising: estimating an instantaneous respiratory rate over an interval of the respiratory chest velocity signal; estimating an average respiratory rate over the interval of the respiratory chest velocity signal; and computing an estimate of severity of central apneas experienced by the patient over the interval based on an absolute difference between the

instantaneous respiratory rate estimate and the average respiratory rate estimate.

16. A system according to claim 15, wherein the processor is co-located with the motion sensor.

17. A system according to claim 15, further comprising an external computing device coupled to the motion sensor via a connection.

18. A system according to claim 17, wherein the processor is a processor of the external computing device.

19. A system according to any one of claims 15 to 18, wherein the motion sensor is a radio-frequency sensor that generates the chest velocity signal by processing of signals representing transmitted radio-frequency waves and received reflected ones of the transmitted radio-frequency waves.

20. A patient monitoring system comprising: means for generating a signal representing respiratory chest velocity of a patient; and means for analysing the chest velocity signal, the analysis comprising: estimating an instantaneous respiratory rate over an interval of the respiratory chest velocity signal; estimating an average respiratory rate over the interval of the respiratory chest velocity signal; and computing an estimate of severity of central apneas experienced by the patient over the interval based on an absolute difference between the

instantaneous respiratory rate estimate and the average respiratory rate estimate.

21. A method of detecting presence of a patient in a measurement zone of a contactless motion sensor, the method comprising: computing a plurality of L-moments over an epoch of at least one channel representing movement of the patient within the measurement zone, the at least one channel being generated by the contactless motion sensor; computing an epoch feature from the L-moments; and detecting presence of the patient in the measurement zone within the epoch based on the epoch feature.

22. A method according to claim 21, wherein computing the plurality of L- moments comprises computing a second-order L-moment and a third-order L-moment over the epoch of the at least one channel.

23. A method according to claim 22, wherein computing the epoch feature comprises multiplying the second-order L-moment by the third-order L-moment.

24. A method according to claim 23, wherein detecting presence of the patient comprises determining whether the epoch feature exceeds a threshold.

25. A method according to any one of claims 21 to 24, further comprising: detecting gross bodily movement of the patient in the measurement zone of the contactless motion sensor from the at least one channel, and discarding sections of the at least one channel in which gross bodily movement of the patient was detected.

26. A method according to claim 25, wherein the contactless motion sensor is a radio- frequency motion sensor and the at least one channel comprises at least one of an in- phase channel and a quadrature channel.

27. A method according to claim 26, further comprising demodulating the in -phase channel and the quadrature channel to generate a signal representing a respiratory chest velocity of the patient.

28. A method according to claim 27, further comprising: estimating an instantaneous respiratory rate over an interval of the respiratory chest velocity signal; estimating an average respiratory rate over the interval of the respiratory chest velocity signal; and computing an estimate of severity of central apneas experienced by the patient over the interval based on an absolute difference between the instantaneous respiratory rate estimate and the average respiratory rate estimate.

29. A patient monitoring system comprising: a contactless motion sensor configured to generate at least one channel representing movement of a patient within a measurement zone; and a processor configured to analyse the at least one channel, the analysis comprising: computing a plurality of L-moments over an epoch of the at least one channel; computing an epoch feature from the L-moments; and detecting presence of the patient in the measurement zone within the epoch based on the epoch feature.

30. A system according to claim 29, wherein the processor is co-located with the motion sensor.

31. A system according to claim 29, further comprising an external computing device coupled to the motion sensor via a connection.

32. A system according to claim 31, wherein the processor is a processor of the external computing device.

33. A system according to any one of claims 29 to 32, wherein the motion sensor is a radio-frequency sensor that generates the at least one channel by processing of signals representing transmitted radio-frequency waves and received reflected ones of the transmitted radio-frequency waves.

34. A patient monitoring system comprising: means for generating at least one channel representing movement of a patient within a measurement zone; and means for analysing the at least one channel, the analysis comprising: computing a plurality of L-moments over an epoch of the at least one channel; computing an epoch feature from the L-moments; and detecting presence of the patient in the measurement zone within the epoch based on the epoch feature.

35. A method of demodulating an in-phase channel and a quadrature channel generated by a contactless motion sensor to generate a velocity signal representing a velocity of an object within a measurement zone of the contactless motion sensor, the method comprising: forming a lag signal from products of consecutive samples of a complex signal whose real part is the in-phase channel and whose imaginary part is the quadrature channel; computing a phase difference signal representing the difference in phase between consecutive samples of the lag signal; and removing a baseline component of the phase difference signal to generate a velocity signal representing a velocity of the object.

36. A method according to claim 35, wherein removing the baseline component comprises: numerically differentiating the phase difference signal to obtain a derivate of the phase difference signal, and cumulatively summing the derivative of the phase difference signal.

37. A method according to claim 36, wherein numerically differentiating comprises applying a Holoborodko numeric differentiator.

38. A method according to any of claims 35 to 37, wherein the object is the chest of a patient, and further comprising applying a respiratory band-pass filter to the velocity signal to obtain a signal representing a respiratory chest velocity of the patient.

39. A method according to claim 38, wherein the respiratory band-pass filter has a passband of 0.08 Hz to 0.75 Hz.

40. A method according to any of claims 38 to 39, further comprising estimating an instantaneous respiratory rate over an interval of the respiratory chest velocity signal.

41. A method according to claim 40, wherein estimating an instantaneous respiratory rate comprises: estimating a total cycle time of each breath within the interval, averaging the total cycle times estimated over the interval; and dividing by the average total cycle time.

42. A method according to any of claims 40 to 41, further comprising estimating an average respiratory rate over the interval of the respiratory chest velocity signal.

43. A method according to claim 42, wherein estimating the average respiratory rate comprises counting a number of peaks of the respiratory chest velocity signal over the interval and dividing by the length of the interval.

44. A method according to any of claims 42 to 43, further comprising computing an estimate of severity of central apneas experienced by the patient over the interval based on an absolute difference between the instantaneous respiratory rate estimate and the average respiratory rate estimate.

45. A method according to claim 44, wherein computing an estimate of severity comprises averaging the absolute difference between the instantaneous respiratory rate estimate and the average respiratory rate estimate over an interval.

46. A patient monitoring system comprising: a contactless motion sensor configured to generate an in-phase channel and a quadrature channel, wherein the in-phase channel and the quadrature channel each represent movement of an object within a measurement zone of the contactless motion sensor; and a processor configured to demodulate the in-phase channel and the quadrature channel, the demodulating comprising: forming a lag signal from products of consecutive samples of a complex signal whose real part is the in-phase channel and whose imaginary part is the quadrature channel; computing a phase difference signal representing the difference in phase between consecutive samples of the lag signal; and removing a baseline component of the phase difference signal to generate a velocity signal representing a velocity of the object.

47. A system according to claim 46, wherein the processor is co-located with the motion sensor.

48. A system according to claim 46, further comprising an external computing device coupled to the motion sensor via a connection.

49. A system according to claim 48, wherein the processor is a processor of the external computing device.

50. A system according to any one of claims 46 to 49, wherein the contactless motion sensor is a radio-frequency sensor that generates the in-phase channel and the quadrature channel by processing of signals representing transmitted radio-frequency waves and received reflected ones of the transmitted radio-frequency waves.

51. A patient monitoring system comprising: means for generating an in-phase channel and a quadrature channel, wherein the in-phase channel and the quadrature channel each represent movement of an object; and means for demodulating the in-phase channel and the quadrature channel, the demodulating comprising: forming a lag signal from products of consecutive samples of a complex signal whose real part is the in-phase channel and whose imaginary part is the quadrature channel; computing a phase difference signal representing the difference in phase between consecutive samples of the lag signal; and removing a baseline component of the phase difference signal to generate a velocity signal representing a velocity of the object.

52. A method of demodulating an in-phase channel and a quadrature channel generated by a contactless motion sensor to generate a velocity signal representing a velocity of an object within a measurement zone of the contactless motion sensor, the method comprising: computing a Fourier transform spectrum of a complex signal whose real part is the in-phase channel and whose imaginary part is the quadrature channel; symmetrising the Fourier transform spectrum; and computing an inverse Fourier transform of the symmetrised spectrum to generate a velocity signal representing a velocity of the object.

53. A method according to claim 52, wherein symmetrising comprises replacing each negative-frequency component of the spectrum with the complex conjugate of a corresponding positive-frequency component of the spectrum.

54. A method according to claim 53, wherein the object is the chest of a patient, and further comprising smoothing the velocity signal using a low-pass filter to generate a signal representing a respiratory chest velocity of the patient.

55. A method according to claim 54, further comprising estimating an instantaneous respiratory rate over an interval of the respiratory chest velocity signal.

56. A method according to claim 55, wherein estimating an instantaneous respiratory rate comprises: estimating a total cycle time of each breath within the interval, averaging the total cycle times estimated over the interval, and dividing by the average total cycle time.

57. A method according to any of claims 55 to 56, further comprising estimating an average respiratory rate over the interval of the respiratory chest velocity signal.

58. A method according to claim 57, wherein estimating the average respiratory rate comprises counting a number of peaks of the respiratory chest velocity signal over the interval and dividing by the length of the interval.

59. A method according to any of claims 57 to 58, further comprising computing an estimate of severity of central apneas experienced by the patient over the interval based on an absolute difference between the instantaneous respiratory rate estimate and the average respiratory rate estimate.

60. A method according to claim 59, wherein computing an estimate of severity comprises averaging the absolute difference between the instantaneous respiratory rate estimate and the average respiratory rate estimate over an interval.

61. A patient monitoring system comprising: a contactless motion sensor configured to generate an in-phase channel and a quadrature channel, wherein the in-phase channel and the quadrature channel each represent movement of an object within a measurement zone of the contactless motion sensor; and a processor configured to demodulate the in-phase channel and the quadrature channel, the demodulating comprising: computing a Fourier transform spectrum of a complex signal whose real part is the in-phase channel and whose imaginary part is the quadrature channel; symmetrising the Fourier transform spectrum; and computing an inverse Fourier transform of the symmetrised spectrum to generate a velocity signal representing a velocity of the object.

62. A system according to claim 61, wherein the processor is co-located with the motion sensor.

63. A system according to claim 61, further comprising an external computing device coupled to the motion sensor via a connection.

64. A system according to claim 63, wherein the processor is a processor of the external computing device.

65. A system according to any one of claims 61 to 64, wherein the contactless motion sensor is a radio-frequency sensor that generates the in-phase channel and the quadrature channel by processing of signals representing transmitted radio-frequency waves and received reflected ones of the transmitted radio-frequency waves.

66. A patient monitoring system comprising: means for generating an in-phase channel and a quadrature channel, wherein the in-phase channel and the quadrature channel each represent movement of an object; and means for demodulating the in-phase channel and the quadrature channel, the demodulating comprising: computing a Fourier transform spectrum of a complex signal whose real part is the in-phase channel and whose imaginary part is the quadrature channel; symmetrising the Fourier transform spectrum; and computing an inverse Fourier transform of the symmetrised spectrum to generate a velocity signal representing a velocity of the object.