Patent Description:
The present technology relates to one or more of the screening, diagnosis, monitoring, treatment, prevention and amelioration of respiratory-related disorders. The present technology also relates to medical devices or apparatus, and their use.

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 air into the venous blood and carbon dioxide to move out. 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 "<NPL>.

Obstructive Sleep Apnea (OSA), a form of Sleep Disordered Breathing (SDB), is characterized by events including occlusion or obstruction of the upper air passage during sleep. It results from a combination of an abnormally small upper airway and the normal loss of muscle tone in the region of the tongue, soft palate and posterior oropharyngeal wall during sleep. The condition causes the affected patient to stop breathing for periods typically of <NUM> to <NUM> seconds in duration, sometimes <NUM> to <NUM> 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 <CIT>).

Cheyne-Stokes Respiration (CSR) is another form of sleep disordered breathing. CSR is a disorder of a patient's respiratory controller in which there are rhythmic alternating periods of waxing and waning ventilation known as CSR cycles. CSR is characterised by repetitive de-oxygenation and re-oxygenation of the arterial blood. It is possible that CSR is harmful because of the repetitive hypoxia. In some patients CSR is associated with repetitive arousal from sleep, which causes severe sleep disruption, increased sympathetic activity, and increased afterload. See <CIT>).

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.

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.

Non-invasive ventilation (NIV) provides ventilatory support to a patient through the upper airways to assist the patient breathing and/or maintain adequate oxygen levels in the body by doing some or all of the work of breathing. The ventilatory support is provided via a non-invasive patient interface. NIV has been used to treat CSR and respiratory failure, in forms such as OHS, COPD, NMD and Chest Wall disorders. In some forms, the comfort and effectiveness of these therapies may be improved.

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. In some forms, the comfort and effectiveness of these therapies may be improved.

The above-mentioned therapies may be provided by a treatment system or device. Such systems and devices may also be used to screen, diagnose, or monitor a condition without treating it.

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

A patient interface may be used to interface respiratory equipment to its wearer, for example by providing a flow of air to an entrance to the airways. The flow of air may be provided via a mask to the nose and/or mouth, a tube to the mouth or a tracheostomy tube to the trachea of a patient. Depending upon the therapy to be applied, the patient interface may form a seal, e.g., with a region of the patient's face, to facilitate the delivery of gas at a pressure at sufficient variance with ambient pressure to effect therapy, e.g., at a positive pressure of about <NUM> cmH<NUM>O relative to ambient pressure. For other forms of therapy, such as the delivery of oxygen, the patient interface may not include a seal sufficient to facilitate delivery to the airways of a supply of gas at a positive pressure of about <NUM> cmH<NUM>O.

A respiratory pressure therapy (RPT) device may be used to deliver one or more of a number of therapies described above, such as by generating a flow of air for delivery to an entrance to the airways. The flow of air may be pressurised. Examples of RPT devices include a CPAP device and a ventilator.

Delivery of a flow of air without humidification may cause drying of airways. The use of a humidifier with an RPT device and the patient interface produces humidified gas that minimizes drying of the nasal mucosa and increases patient airway comfort. In addition in cooler climates, warm air applied generally to the face area in and about the patient interface is more comfortable than cold air.

Screening and diagnosis generally describe the identification of a disorder from its signs and symptoms. Screening typically gives a true / false result indicating whether or not a patient's disorder is severe enough to warrant further investigation, while diagnosis may result in clinically actionable information. Screening and diagnosis tend to be one-off processes, whereas monitoring the progress of a disorder can continue indefinitely. Some screening / diagnosis systems are suitable only for screening / diagnosis, whereas some may also be used for monitoring.

Polysomnography (PSG) is a conventional system for diagnosis / monitoring of cardio-pulmonary disorders, and typically involves expert clinical staff to apply the system. PSG typically involves the placement of <NUM> to <NUM> contact sensors on a person in order to record various biosignals such as electroencephalography (EEG), electrocardiography (ECG), electrooculograpy (EOG), electromyography (EMG), etc. PSG for sleep disordered breathing has involved two nights of observation of a patient in a clinic, one night of pure diagnosis and a second night of titration of treatment parameters by a clinician. Clinical experts may be able to diagnose or monitor patients adequately based on visual observation of PSG signals. However, there are circumstances where a clinical expert may not be available, or a clinical expert may not be affordable. PSG is therefore expensive and inconvenient. In particular it is unsuitable for in-home diagnosis / monitoring.

A more convenient screening / diagnosis / monitoring system for home use comprises a nasal cannula, a pressure sensor, a processing device, and recording means. A nasal cannula is a device comprising two hollow open-ended projections that are configured to be inserted non-invasively a little way into a patient's nares so as to interfere as little as possible with the patient's respiration. The hollow projections are in fluid communication with a pressure transducer via a Y-shaped tube. The pressure transducer provides a data signal representative of the pressure at the entrance to the patient's nares (the nasal pressure). It has been shown that a nasal pressure signal is a satisfactory proxy for the nasal flow rate signal generated by a flow rate transducer in-line with a sealed nasal mask, in that the nasal pressure signal is comparable in shape to the nasal flow rate signal. The processing device may be configured to analyse the nasal pressure signal from the pressure transducer in real time or near real time to detect and classify SDB events in order to monitor the patient's condition. Screening or diagnosis may require similar analysis but not necessarily in real time or near real time. The recording means is therefore configured to record the nasal pressure signal from the pressure transducer for later off-line or "batch" analysis by the processing device for screening / diagnosis purposes.

However, such pressure-based systems are not always able to reliably distinguish CSR from the repeated occurrences of obstructive apneas characteristic of OSA. Other sensor modalities have therefore been employed to supplement or replace the nasal pressure signal in more sophisticated screening / diagnosis / monitoring systems. However, such systems start to resemble full PSG as more sensors are added, with all the above-mentioned disadvantages. It is desirable to utilise a combination of sensors that are as unobtrusive as possible while maintaining accuracy of SDB screening / diagnosis / monitoring.

The present technology is directed towards providing medical devices used in the screening, diagnosis, or monitoring of respiratory disorders having one or more of improved comfort, cost, efficacy, ease of use and manufacturability.

Some versions of the present technology may include a device having an electrocardiogram (ECG) sensor; an accelerometer; and a photoplethysmograph (PPG). The device may also include a processor. The device may also include a memory. The memory may include processor control instructions adapted to configure the processor to detect sleep-disordered breathing (SDB) events of a patient. The processor, such as with the instructions, may be configured to control an analysis of ECG data of the patient from a signal generated by the ECG sensor, pulse oximetry data of the patient from a signal generated by the PPG, and three-dimensional (3D) accelerometry data of the patient from a signal generated by the accelerometer. The processor, such as with the instructions, may also be configured to detect the SDB events based on the analysis.

In some cases, the analysis may estimate a sleep stage of the patient from the ECG data. The device may also include a temperature sensor. The analysis that estimates the sleep stage may evaluate temperature data from a signal generated by the temperature sensor. The temperature data may represent temperature of skin of the patient. In some cases, the device may include a galvanic skin response (GSR) sensor. The analysis to estimate the sleep stage may evaluate sympathetic activity data of the patient from a signal generated by the GSR sensor. In some cases, with the instructions, the processor may be further configured to classify the detected SDB events into apneas and hypopneas, and/or into open and closed airway events.

In some cases, the device may include a galvanic skin response (GSR) sensor. With the instructions, the processor may be configured to classify the detected SDB events by evaluation of sympathetic activity data of the patient from a signal generated by the GSR sensor. The device may also include an acoustic sensor. With the instructions, the processor may be configured to classify the detected SDB events by evaluation of acoustic data representing heart sound of the patient from a signal generated by the acoustic sensor. In some versions, the device may be configured as a patch adapted to be worn on skin of a chest of the patient.

Some versions of the present technology may include a method of detecting sleep-disordered breathing (SDB) events of a patient. The method may include controlling, in one or more processors, an analysis of electrocardiogram (ECG) data of the patient from a signal generated by an ECG sensor, pulse oximetry data of the patient from a signal generated by a photoplethysmograph (PPG), and three-dimensional (3D) accelerometry data of the patient from a signal generated by an accelerometer. The method may include detecting, in the one or more processors, SDB events based on the analysis, to generate an output indication of SDB events.

In some cases, the analysis of the ECG data may include removing artefacts from the ECG data to produce artefact-removed ECG data. Removing artefacts from the ECG data may include identifying portions of the ECG data that differ from a typical portion. Analysing the ECG data may include estimating a sleep stage of the patient based on the artefact-removed ECG data. Estimating a sleep stage may include evaluating patient skin temperature data from a temperature signal generated by a temperature sensor. Estimating a sleep stage may include evaluating sympathetic activity data of the patient from a signal generated by a galvanic skin response (GSR) sensor. The analysis of the ECG data may include estimating a respiratory rate from the artefact-removed ECG data. The analysis of the ECG data may include extracting a respiratory-related component from the artefact-removed ECG data. The analysis of the 3D accelerometry data may include estimating a posture of the patient from the 3D accelerometry data. The analysis of the 3D accelerometry data may include estimating a respiratory effort of the patient from the 3D accelerometry data. The analysis of the 3D accelerometry data may include computing an activity index of the patient from the 3D accelerometry data. The activity index may represent gross bodily motion of the patient.

In some cases, the detecting of the SDB events may include: extracting features, and discriminating between normal breathing and SDB events by applying a classifier to the features. The method may include, in the one or more processors, classifying the detected SDB events into apneas and hypopneas, and into open and closed airway events. In some cases, the classifying the detected SDB events may evaluate the pulse oximetry data from the PPG. The classifying the detected SDB events may evaluate sympathetic activity data of the patient from a signal generated by a galvanic skin response (GSR) sensor. The classifying the detected SDB events may evaluate acoustic data representing heart sound of the patient from a signal generated by an acoustic sensor. The classifying the detected SDB events may include segmenting the acoustic data into phases of each heart cycle, and extracting heart sound features from the segmented acoustic data. The classifying the detected SDB events may use the heart sound features.

In some cases, the method may include detecting, in the one or more processors, Cheyne-Stokes respiration (CSR) from classified SDB events from the classifying. The detecting CSR may include template matching of a respiratory-related component extracted from the ECG data. The method may include controlling, with the one or more processors, (a) generating the signal by the ECG sensor, (b) generating the signal by the photoplethysmograph (PPG), and (c) generating the signal by the accelerometer. The method may include determining or controlling, with the one or more processors, a change to a therapy provided by a therapy device based on the output indication of SDB events.

In some cases, a processor-readable medium may have stored thereon processor-executable instructions which, when executed by a processor, cause the processor to detect detecting sleep-disordered breathing (SDB) events according to any of the methodologies described herein.

Some versions of the present technology may include a system. The system may include an electrocardiogram (ECG) sensor; an accelerometer; and a photoplethysmograph (PPG). The system may include one or more processors. The system may include a memory having processor control instructions adapted to configure the one or more processors to detect sleep-disordered breathing (SDB) events of a patient. The one or more processors, such as with the instructions, may be configured to control an analysis of ECG data of the patient from a signal generated by the ECG sensor, pulse oximetry data of the patient from a signal generated by the PPG, and three-dimensional (3D) accelerometry data of the patient from a signal generated by the accelerometer. The one or more processors, such as with the instructions, may be configured to detect the SDB events based on the analysis.

In some cases, the ECG sensor, the accelerometer, the PPG, the memory, and the one or more processors are co-located in one device. In some cases, the ECG sensor, the accelerometer, and the PPG are co-located in one device, and the one or more processors and the memory are located remotely from the device. In some cases, the device of the system may include a communication interface through which the device may be configured to communicate with the one or more processors. The device may be configured as a patch adapted to be worn on skin of a chest of the patient.

Some versions of the present technology may include an apparatus. The apparatus may include means for generating electrocardiogram (ECG) data of a patient. The apparatus may include means for generating three-dimensional (3D) accelerometry data of the patient. The apparatus may include means for generating pulse oximetry data of the patient. The apparatus may include means for analysing the ECG data of the patient, the pulse oximetry data of the patient, and the 3D accelerometry data of the patient to detect sleep-disordered breathing (SDB) events of the patient.

In some cases, the apparatus may include means for estimating sleep stage from the ECG data. The apparatus may include means for generating temperature data, wherein the means for estimating sleep stage estimates sleep stage based on the temperature data. The apparatus may include means for generating sympathetic activity data, wherein the means for estimating sleep stage estimates sleep stage based on the sympathetic activity data. The apparatus may include means for classifying detected SDB events. The apparatus may include means for generating heart sound data, wherein the means for classifying classifies the SDB events based on the heart sound data. The apparatus may include means for removing artefact from the ECG data. The apparatus may include means for mounting the apparatus to skin of a chest of the patient.

Another aspect of the present technology relates to apparatus and methods to analyse data from a patch device including ECG contacts, a three-axis accelerometer, an acoustic sensor, and a pulse oximeter, to detect and classify apneas and hypopneas and hence screen, diagnose and / or monitor SDB. As part of the analysis, the signal processing may estimate sleep / wake state. The patch sensor may also include a temperature sensor and a galvanic skin response (GSR) sensor whose signals are incorporated into the analysis to improve the accuracy.

In accordance with another aspect of the present technology, there is provided a device comprising: an electrocardiogram (ECG) sensor; an accelerometer; a photoplethysmograph (PPG); a processor; and a memory comprising instructions adapted to configure the processor to carry out a method of detecting sleep-disordered breathing (SDB) events of a patient. The method comprises analysing an ECG signal of the patient from the ECG sensor, pulse oximetry data of the patient from the PPG, and a three-dimensional (3D) accelerometry signal of the patient from the accelerometer to detect the SDB events.

In accordance with another aspect of the present technology, there is provided a method of detecting sleep-disordered breathing (SDB) events of a patient. The method comprises analysing an electrocardiogram (ECG) signal of the patient from an ECG sensor, pulse oximetry data of the patient from a photoplethysmograph (PPG), and a three-dimensional (3D) accelerometry signal of the patient from an accelerometer to detect the SDB events.

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 monitor and/or a respiratory therapy apparatus. Moreover, the described methods, systems, devices and apparatus can provide improvements in the technological field of automated management, monitoring and/or treatment of respiratory conditions, including, for example, sleep disordered breathing.

<FIG> shows a system including a patient <NUM> wearing a patient interface <NUM>, in the form of nasal pillows, receiving a supply of air at positive pressure from an RPT device <NUM>. Air from the RPT device <NUM> is humidified in a humidifier <NUM>, and passes along an air circuit <NUM> to the patient <NUM>. A bed partner <NUM> is also shown. The patient is sleeping in a supine sleeping position.

<FIG> 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.

<FIG> shows a patient interface in the form of a nasal mask in accordance with one form of the present technology.

<FIG> shows an RPT device in accordance with one form of the present technology.

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.

In one form, the present technology comprises an apparatus or device for treating a respiratory disorder. The apparatus or device may comprise an RPT device <NUM> for supplying pressurised air to the patient <NUM> via an air circuit <NUM> to a patient interface <NUM>.

A non-invasive patient interface <NUM> in accordance with one aspect of the present technology comprises the following functional aspects: a seal-forming structure <NUM>, a plenum chamber <NUM>, a positioning and stabilising structure <NUM>, a vent <NUM>, one form of connection port <NUM> for connection to air circuit <NUM>, and a forehead support <NUM>. 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 <NUM> 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.

An RPT device <NUM> 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 <NUM> 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.

The RPT device may have an external housing <NUM>, formed in two parts, an upper portion <NUM> and a lower portion <NUM>. Furthermore, the external housing <NUM> may include one or more panels <NUM>. The RPT device <NUM> comprises a chassis <NUM> that supports one or more internal components of the RPT device <NUM>. The RPT device <NUM> may include a handle <NUM>.

The pneumatic path of the RPT device <NUM> may comprise one or more air path items, e.g., an inlet air filter <NUM> and a pressure generator capable of supplying air at positive pressure (e.g., a blower <NUM>).

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 <NUM>. The pneumatic block <NUM> may be located within the external housing <NUM>. In one form a pneumatic block <NUM> is supported by, or formed as part of the chassis <NUM>.

The RPT device <NUM> may have an electrical power supply <NUM> and one or more input devices <NUM>. Electrical components <NUM> may be mounted on a single Printed Circuit Board Assembly (PCBA) <NUM>.

An air circuit <NUM> 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 <NUM> and the patient interface <NUM>.

In particular, the air circuit <NUM> may be in fluid connection with the outlet of the pneumatic block <NUM> and the patient interface. The air circuit may be referred to as an air delivery tube. In some cases there may be separate limbs of the circuit for inhalation and exhalation. In other cases a single limb is used.

In one form of the present technology there is provided a humidifier <NUM> (e.g. as shown in <FIG>) to change the absolute humidity of air or gas for delivery to a patient relative to ambient air. Typically, the humidifier <NUM> 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.

The humidifier <NUM> may comprise a humidifier reservoir <NUM>, a humidifier inlet <NUM> to receive a flow of air, and a humidifier outlet <NUM> to deliver a humidified flow of air. In some forms, as shown in <FIG>, an inlet and an outlet of the humidifier reservoir <NUM> may be the humidifier inlet <NUM> and the humidifier outlet <NUM> respectively. The humidifier <NUM> may further comprise a humidifier base <NUM>, which may be adapted to receive the humidifier reservoir <NUM> and comprise a heating element <NUM>.

<FIG> shows a model typical breath waveform of a person while sleeping. The horizontal axis is time, and the vertical axis is respiratory flow rate. While the parameter values may vary, a typical breath may have the following approximate values: tidal volume, Vt, <NUM> litres, inhalation time, Ti, <NUM>, peak inspiratory flow rate, Qpeak, <NUM>/s, exhalation time, Te, <NUM>, peak expiratory flow rate, Qpeak, -<NUM>/s. The total duration of the breath, Ttot, is about <NUM>. The person typically breathes at a rate of about <NUM> breaths per minute (BPM), with Ventilation, Vent, about <NUM>/minute. A typical duty cycle, the ratio of Ti to Ttot, is about <NUM>%.

<FIG> shows patient data from a patient during non-REM sleep breathing normally over a period of about ninety seconds comprising about <NUM> breaths, being treated with Automatic PAP, and the mask pressure being about <NUM> cmH<NUM>O. The top channel shows oximetry (oxygen saturation or SpO<NUM>), the scale having a range of saturation from <NUM> to <NUM>% in the vertical direction. The patient maintained a saturation of about <NUM>% throughout the period shown. The second channel shows quantitative respiratory airflow, and the scale ranges from -<NUM> to +<NUM> litres per second in a vertical direction, and with inspiration positive. Thoracic and abdominal movement are shown in the third and fourth channels.

<FIG> shows polysomnography of a patient before treatment. There are eleven signal channels from top to bottom with a <NUM> minute horizontal span. The top two channels are both EEG (electoencephalogram) from different scalp locations. Periodic spikes in the second EEG represent cortical arousal and related activity. The third channel down is submental EMG (electromyogram). Increasing activity around the time of arousals represents genioglossus recruitment. The fourth & fifth channels are EOG (electro-oculogram). The sixth channel is an electocardiogram. The seventh channel shows oxygen saturation with repetitive desaturations to below <NUM>% from about <NUM>%. The eighth channel is respiratory flow rate from a nasal cannula connected to a differential pressure transducer. Repetitive apneas of <NUM> to <NUM> seconds alternate with <NUM> to <NUM> second bursts of recovery breathing coinciding with EEG arousal and increased EMG activity. The ninth channel shows thoracic movement and the tenth shows abdominal movement. The abdomen shows a crescendo of movement over the length of the apnea leading to the arousal. Both become untidy during the arousal due to gross bodily movement during recovery hyperpnea. The apneas are therefore obstructive, and the condition is severe. The lowest channel is posture, and in this example it does not show change.

<FIG> shows patient flow rate data where the patient is experiencing a series of total obstructive apneas. The duration of the recording is approximately <NUM> seconds. Flow rates range from about +<NUM>/s to about -<NUM>/s. Each apnea lasts approximately <NUM> to <NUM> seconds.

<FIG> shows patient data from a patient with Cheyne-Stokes respiration. There are three channels: pulse oximetry (SpO<NUM>); a signal indicative of flow rate; and thoracic movement. The data span six minutes. The signal representative of flow rate was measured using a pressure sensor connected to a nasal cannula. The patient exhibits apneas of about <NUM> seconds and hyperpneas of about <NUM> seconds. The higher frequency low amplitude oscillation in the flow rate signal during apnea is cardiogenic.

<FIG> shows patient data from a patient with another example of Cheyne-Stokes respiration, using the same three channels as in <FIG>. The data span ten minutes. The patient exhibits hyperpneas of about <NUM> seconds and hypopneas of about <NUM> seconds.

<FIG> shows a patient <NUM> undergoing polysomnography (PSG). The PSG system illustrated in <FIG> comprises a headbox <NUM> which receives and records signals from the following sensors: an EOG electrode <NUM>; an EEG electrode <NUM>; an ECG electrode <NUM>; a submental EMG electrode <NUM>; a snore sensor <NUM>; a respiratory inductance plethysmogram (thoracic movement sensor) <NUM> on a chest band; a respiratory inductance plethysmogram (abdominal movement sensor) <NUM> on an abdominal band; an oro-nasal cannula <NUM> with oral thermistor; a photoplethysmograph (pulse oximeter) <NUM>; and a body position sensor <NUM>. The electrical signals from the electrodes (<NUM>, <NUM>, <NUM>, <NUM>) are referenced to a ground electrode (ISOG) <NUM> on the patient such as one positioned in the centre of the forehead.

<FIG> is a block diagram illustrating a screening / diagnosis / monitoring device <NUM> according to one form of the present technology. The device <NUM> may be configured as a patch, adapted to be worn on the skin of the chest of the patient <NUM>, preferably on the upper left chest.

The device <NUM> comprises multiple biometric sensors <NUM> to <NUM>, each configured to generate a signal representing one or more physiological parameters of a patient <NUM>. The ECG sensor <NUM> comprises one or more electrical contacts which, when in contact with the skin, generate a signal known as the electrocardiogram (ECG or EKG) representative of the electrical activity of the heart. The three-axis accelerometer <NUM> generates a three-component signal (referred to as the 3D accelerometry signal), each component of which represents the acceleration of the device <NUM> along a corresponding orthogonal axis. In a typical orientation, the z-axis is perpendicular to the skin. The x- and y-axes may be aligned in any direction in the plane of the skin. However, the orientation of the x- and y-axes in relation to the main axes of the body (superior-inferior and medial-lateral) may be taken into account by the accelerometry signal analysis (described below). When the accelerometer <NUM> is at rest, it can detect the influence of gravity and hence provide an absolute vertical direction. The photoplethysmograph (PPG or pulse oximeter) <NUM> uses light to estimate the blood oxygen saturation (SpO<NUM>, or oximetry) of the patient, usually represented as a percentage (%). The temperature sensor <NUM> generates a signal representing the temperature of the patient's skin. The acoustic sensor <NUM> (e.g. a microphone) generates a signal representing the heart sounds of the patient <NUM>. The galvanic skin response (GSR) sensor <NUM> generates a signal representative of the conductivity of the skin in the region of the device <NUM>, which in turn is indicative of sympathetic nervous system activity (which among other physiological effects activates the sweat glands).

The device <NUM> also comprises an input/output (I/O) interface such as a sensor interface <NUM> (e.g., with multiple ports) that may receive the signals from the sensors <NUM> to <NUM> during a screening / diagnosis / monitoring session. The signals from the sensors may be generated by the sensors in analog or digital form. Thus, the interface may have analog and/or digital ports. For example, one or more of the sensor signals may arrive at the sensor interface <NUM> as sequences of discrete samples ("sensor data") at respective sampling rates. The sensor interface <NUM> may discretise those of the sensor signals not arriving in this form into respective sequences of discrete samples at respective sampling rates, so that all signals provided by the sensor interface <NUM> are in discrete form.

In some cases, the device may be formed with or include a controller, such as a microcontroller with a processor or CPU. In some cases, the controller may be formed with a microprocessor. Thus, the device <NUM> typically contains a processor <NUM> configured to carry out the methods described herein such as with encoded instructions. The device <NUM> may also contain a non-transient computer readable memory / storage medium <NUM>. The memory <NUM> may be the internal memory of the device <NUM>, such as RAM, flash memory or ROM. In some implementations, memory <NUM> may also be a removable or external memory linked to the device <NUM>, such as an SD card, server, USB flash drive or optical disc, for example. In other implementations, memory <NUM> can be a combination of external and internal memory. The contents of the memory <NUM> include stored data <NUM> and processor control instructions (code) <NUM> adapted to configure the processor <NUM> to perform certain tasks. Stored data <NUM> can include sensor data from sensor interface <NUM> during a session, and other data that is provided as a component part of an application. Processor control instructions <NUM> can also be provided as a component part of an application. The processor <NUM> is adapted to read the code <NUM> from the memory <NUM> and execute the encoded instructions. In particular, the code <NUM> may contain instructions that configure the processor <NUM> to carry out methods of processing the sensor data signals from the sensor interface <NUM>. One such method may be to record the sensor data for the session in the memory <NUM> as data <NUM>. Another such method may be to analyse the session recording to extract SDB features. One such analysis method is described in detail below. The processor <NUM> may store the results of such analysis (the SDB features) as data <NUM> in the memory <NUM>.

The device <NUM> may also contain a communication interface <NUM>. The code <NUM> may contain instructions adapted to configure the processor <NUM> to communicate with a remote external computing device (not shown) such as an RPT device <NUM> via the communication interface <NUM>. The mode of communication may be wired or wireless. In one such implementation, the processor <NUM> may transmit the real time or session-by-session recording information from the data <NUM> to the remote computing device via the communication interface <NUM>. In such an implementation, a processor of the remote computing device may be configured to analyse the received session recording to extract SDB features. In another such implementation, the processor <NUM> may transmit the analysis results (e.g., indications of the SDB features or other detected or estimated patient related information) from the data <NUM> to the remote computing device via the communication interface <NUM>. In yet another such implementation, the processor <NUM> may partially analyse the session data, store and transmit the results of the partial analysis to the remote computing device via the communication interface <NUM>, and the processor of the remote computing device may complete the analysis to obtain the SDB features.

Alternatively, if the memory <NUM> is removable from the device <NUM>, the remote computing device may be configured to be connected to the removable memory <NUM>. In such an implementation, the remote computing device may be configured to retrieve the session data from the removable memory <NUM> and analyse the session data to extract SDB features.

In the case of transmission of data via the communication interface to an RPT device <NUM>, the data may serve as a basis for making control changes in an automated therapy adjustment process of the RPT device <NUM> based on the data. For example, based on the extracted SDB features and/or sleep state, a therapy change, such as a change to pressure or flow (e.g., an increase or decrease) may be controlled by the RPT device <NUM>.

<FIG> is a flow chart illustrating example processes that may be implemented in a method <NUM> of screening / diagnosing / monitoring SDB making use of the device <NUM> in one form of the present technology. The method <NUM> may be implemented by the processor <NUM> of the device <NUM>, a processor of a remote external computing device in communication with the device <NUM>, or a combination of both as described above. The method <NUM> may be carried out in real time during a session, in which case it is more appropriately described as a monitoring method, or in "batch" mode on recorded data after the session, in which case it is more appropriately described as a screening or diagnosis method.

Process <NUM> removes movement and other artefacts from the ECG signal generated by the ECG sensor <NUM>. Process <NUM> may optionally evaluate the 3D accelerometry signal from the accelerometer <NUM> to detect timing of such artefacts for removal of corresponding portions of the ECG signal based on timing of such detection of artefacts. Such removal may include adjusting the ECG, such as by interpolation, smoothing or other technique, to produce an ECG signal that reduces or eliminates the effect of the artefact.

<FIG> is a flow chart illustrating an example method <NUM> that may be implemented to perform the artefact removal process <NUM> of the method <NUM> in one form of the present technology. In this example, the method <NUM> does not use the 3D accelerometry signal from the accelerometer <NUM>, but is based on the assumption that most portions of the ECG signal resemble the other portions, and identifies those portions that differ substantially from the typical. The method <NUM> starts at process <NUM>, which band-pass filters the ECG signal such as within the frequency range of about <NUM> to <NUM>. Process <NUM> then differentiates (e.g., by derivative function) and squares the band-pass filtered ECG signal (e.g., by squaring function). The next process <NUM> finds the upper envelope of the squared derivative of the band-pass filtered ECG signal.

Process <NUM> then works sequentially through a sequence of nonoverlapping windows of fixed duration into which the envelope is partitioned. In one implementation, the windows are of duration on the order of a predetermined number of seconds, (e.g., <NUM> seconds, <NUM> seconds, <NUM> seconds, etc.). For each window, process <NUM> computes the autocorrelation function of the envelope, up to a lag of a predetermined number of samples (e.g., <NUM> samples). The next process <NUM> computes a threshold by averaging the peak values of the autocorrelation functions over all windows, and dividing by a predetermined number (e.g., <NUM>). Process <NUM> then, for each window, sets those samples of the autocorrelation function whose values are less than the threshold to zero. Generally, the process <NUM> removes small "noisy" values from the autocorrelation of each window, leaving only the "significant" features of the autocorrelation.

The final process <NUM> determines whether each window contains artefacts. To do this, process <NUM> compares the autocorrelation function (thresholded at process <NUM>) of a current window with the autocorrelation functions of a plurality of other windows (e.g., some or all other windows). In one implementation, the metric of comparison between two autocorrelation functions is the cosine function, computed as the dot product of the two autocorrelation functions divided by the product of their respective Euclidean norms. The cosine function ranges between <NUM> (for wholly dissimilar functions) and <NUM> (for identical functions). Process <NUM> averages this metric for the similarity of the current window to other windows over the plurality of other windows to obtain a "normality" metric for the current window. If the normality metric for the current window falls below a threshold (e.g. <NUM>), this indicates the current window is "outlying" enough to be discarded as an artefact.

<FIG> is a graph illustrating the operation of the method <NUM> on an example ECG signal. The graph contains an ECG trace <NUM> lasting approximately four minutes. Each vertical graticule shown in the bottom signal trace of <FIG> represents thirty seconds. The other traces <NUM> to <NUM> represent intermediate products of the method <NUM> on the ECG trace <NUM>. The trace <NUM> represents the band-pass filtered ECG from process <NUM>. The trace <NUM> represents the derivative of the band-pass filtered ECG from process <NUM>. The trace <NUM> represents the square of the derivative of the band-pass filtered ECG from process <NUM>. The trace <NUM> represents the upper envelope of the square of the derivative of the band-pass filtered ECG from process <NUM>. The trace <NUM> represents the "normality" metric of the envelope computed as part of process <NUM>. The binary-valued trace <NUM> is high for "outlying" windows whose normality metric falls below <NUM>, and <NUM> otherwise. It may be seen that the trace <NUM> is high for two windows <NUM> and <NUM>, both of which coincide with patterns of unusual activity in the ECG trace <NUM>, while the remainder of the ECG trace <NUM> is relatively stationary.

Returning to the method <NUM>, the ECG signal, though primarily representative of heart activity, contains a component that is related to respiration. Process <NUM> therefore extracts the respiratory-related component of the artefact-removed ECG signal, resulting in an EDR (ECG-derived respiratory) signal. In one implementation, process <NUM> generates the EDR based on a determination of the amplitude of the R-wave of the ECG signal. In another implementation, process <NUM> generates the EDR based on a determination of the area covered by the QRS complex of the ECG signal.

Process <NUM> of the method <NUM> evaluates the artefact-removed ECG signal to estimate the respiratory rate of the patient <NUM>. In one form, process <NUM> applies a wavelet-based approach. In another form, process <NUM> applies a combination of any two or more of differentiation, moving-average, and thresholding of the ECG signal.

Process <NUM> evaluates the estimated respiratory rate from process <NUM> and the artefact-removed ECG signal from process <NUM> to detect the AFib burden of the patient (a measure of irregularity of the heart rhythm or atrial fibrillation and a classification of the type of irregularity: paroxysmal, persistent, or permanent).

Process <NUM> removes artefacts from the 3D accelerometry signal generated by the accelerometer <NUM>. Process <NUM> then evaluates the artefact-removed 3D accelerometry signal to estimate posture (e.g. prone, supine, upright), based on the absolute vertical direction provided by the accelerometer. For this step, the orientation of the axes of the accelerometer <NUM> relative to the main axes of the body may be taken into account.

Process <NUM> evaluates the artefact-removed 3D accelerometry signal to estimate respiratory effort. In one implementation, process <NUM> applies a principal component analysis (PCA)-based method to the 3D accelerometry signal. For this step, the orientation of the axes of the accelerometer <NUM> relative to the main axes of the body may be taken into account.

Process <NUM> evaluates the artefact-removed 3D accelerometry signal to compute an activity index representative of the non-cardio-respiratory activity of the body, e.g. activity resulting from gross bodily motion.

Process <NUM> segments the "heart sounds" acoustic signal from the acoustic sensor <NUM>. The segmentation partitions the acoustic signal into individual heart cycles and phases of each cycle (S1, systole, S2, diastole). Example implementations of process <NUM> may apply any one or more of wavelet decomposition, Shannon energy, and peak location. Process <NUM> then extracts heart sound features from the segmented heart sounds signal provided by the segmentation process <NUM>. Process <NUM> may extract time domain features such as duration and amplitude, and frequency domain features such as power spectral density.

Processes <NUM> to <NUM> may be carried out in parallel or sequentially in any order, with the exceptions that process <NUM> should follow processes <NUM> and <NUM>, process <NUM> should follow process <NUM>, processes <NUM>, <NUM>, and <NUM> should follow process <NUM>, and process <NUM> should follow process <NUM>.

Process <NUM> evaluates the artefact-removed ECG signal from process <NUM>, the posture estimate from process <NUM>, the respiratory effort estimate from process <NUM>, the activity index from step <NUM>, the oxygen saturation (SpO<NUM>) signal from the PPG <NUM>, the skin temperature signal from the temperature sensor <NUM>, and the sympathetic activity signal from the GSR sensor <NUM> to estimate the sleep stage of the patient (e.g. wake, REM, non-REM (NREM)). Process <NUM> may also detect brief arousals within the non-REM and REM stages. In one implementation, process <NUM> extracts one or more features from its input signals, followed by classification such as by linear discriminant analysis (LDA), a support vector machine (SVM), or neural network to estimate the sleep stage and detect arousals.

Following process <NUM>, process <NUM> evaluates the EDR signal and the respiratory rate estimate from processes <NUM> and <NUM>, the posture estimate, the respiratory effort estimate, and the activity index from processes <NUM>, <NUM>, and <NUM>, and the SpO<NUM> signal from the PPG <NUM> to detect SDB events, e.g., apneas and hypopneas (undifferentiated from each other). Process <NUM> may also consider the estimated sleep stage from process <NUM>. In one implementation, process <NUM> extracts features from its input signals and applies a classifier to the features to discriminate between normal breathing and SDB events in each of successive time windows, e.g. of duration <NUM> seconds or other predetermined window duration on the order of seconds or minutes.

Process <NUM> classifies the detected SDB events from process <NUM> into apneas and hypopneas, and into open airway and closed airway (obstructive) events, by evaluation of the SpO<NUM> signal from the PPG <NUM>, the sympathetic activity signal from the GSR sensor <NUM>, and the heart sound features extracted by process <NUM>. In one example of classification, an obstructive event may trigger sympathetic drive resulting in increased amplitude of heart sound(s) during the S1 phase of each heart cycle. An event coinciding with increased amplitude of heart sound(s) during the S1 phase of each heart cycle may therefore be classified as obstructive.

Process <NUM> considers the detected events from process <NUM> and their classifications from process <NUM> and detects Cheyne-Stoke respiration (CSR) and (optionally) other forms of periodic breathing (PB). Process <NUM> may be based on the sequencing and periodicity of the classified SDB events, and may also apply template matching of the EDR signal (from process <NUM>) during hyperpneas with a sinusoidal template in its evaluation.

From the classified SDB events from process <NUM> and / or the CSR / PB detections from process <NUM>, various indices of SDB severity may be computed over a screening / diagnosis / monitoring session, e.g. apnea / hypopnea index (AHI), total duration of CSR episodes, etc. The computation of the indices may take into account the total sleep time (TST) of the patient during the session, as estimated from the sleep stage information provided by process <NUM>. The computed indices may be used for screening, diagnostic, or monitoring purposes in conventional fashion.

Ambient: In certain forms of the present technology, the term ambient will be taken to mean (i) external of the treatment system or patient, and (ii) immediately surrounding the treatment system or patient.

Continuous Positive Airway Pressure (CPAP) therapy: Respiratory pressure therapy in which the treatment pressure is approximately constant through a respiratory cycle of a patient. In some forms, the pressure at the entrance to the airways will be slightly higher during exhalation, and slightly lower during inhalation. In some forms, the pressure will vary between different respiratory cycles of the patient, for example, being increased in response to detection of indications of partial upper airway obstruction, and decreased in the absence of indications of partial upper airway obstruction.

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'.

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

Pressure: Force per unit area. Pressure may be expressed in a range of units, including cmH<NUM>O, g-f/cm<NUM> and hectopascal. <NUM> cmH<NUM>O is equal to <NUM>-f/cm<NUM> and is approximately <NUM> hectopascal. In this specification, unless otherwise stated, pressure is given in units of cmH<NUM>O.

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.

Apnea: According to some definitions, an apnea is said to have occurred when flow falls below a predetermined threshold for a duration, e.g. <NUM> seconds. An obstructive apnea will be said to have occurred when, despite patient effort, some obstruction of the airway does not allow air to flow. A central apnea will be said to have occurred when an apnea is detected that is due to a reduction in breathing effort, or the absence of breathing effort, despite the airway being patent. A mixed apnea occurs when a reduction or absence of breathing effort coincides with an obstructed airway.

Breathing rate (respiratory rate): The rate of spontaneous respiration of a patient, usually measured in breaths per minute.

Duty cycle: The ratio of inhalation time, Ti to total breath time, Ttot.

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

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

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.

Types of flow limited inspiratory waveforms:.

Hypopnea: According to some definitions, a hypopnea is taken to be a reduction in flow, but not a cessation of flow. In one form, a hypopnea may be said to have occurred when there is a reduction in flow below a threshold rate for a duration. A central hypopnea will be said to have occurred when a hypopnea is detected that is due to a reduction in breathing effort. In one form in adults, either of the following may be regarded as being hypopneas:.

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

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.

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 (<NUM>) being patent, and a value of zero (<NUM>), being closed (obstructed).

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

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

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.

Tidal volume (Vt): The volume of air inhaled or exhaled during normal breathing, when extra effort is not applied.

(inhalation) Time (Ti): The duration of the inspiratory portion of the respiratory flow rate waveform.

(exhalation) Time (Te): The duration of the expiratory portion of the respiratory flow rate waveform.

(total) Time (Ttot): The total duration between the start of one inspiratory portion of a respiratory flow rate waveform and the start of the following inspiratory portion of the respiratory flow rate waveform.

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.

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).

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.

Claim 1:
A system comprising:
an electrocardiogram, ECG, sensor;
an accelerometer;
a photoplethysmograph, PPG;
a temperature sensor and/or a galvanic skin response, GSR, sensor;
one or more processors; and
a memory comprising instructions adapted to configure the one or more processors to detect sleep-disordered breathing, SDB, events of a patient, wherein by the instructions, the one or more processors are configured to:
control an analysis of ECG data of the patient from a signal generated by the ECG sensor, pulse oximetry data of the patient from a signal generated by the PPG, and three-dimensional accelerometry data of the patient from a signal generated by the accelerometer; and
detect the SDB events based on the analysis,
wherein the analysis estimates a sleep stage of the patient from the ECG data and characterized in that
to estimate the sleep stage, the analysis evaluates temperature data from a signal generated by the temperature sensor, the temperature data representing temperature of skin of the patient; and/or
to estimate the sleep stage, the analysis evaluates sympathetic activity data of the patient from a signal generated by the GSR sensor.