Patent ID: 12245870

8 DETAILED DESCRIPTION OF EXAMPLES OF THE TECHNOLOGY

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.

The apparatus and methods described below are particularly suitable for the monitoring of cardio-pulmonary health, and are described in those terms. However, the described apparatus and methods may also be applied to monitoring other chronic diseases that affect a patient's respiration.

8.1 Monitoring Apparatus and Methods

8.1.1 Unobtrusive Monitoring Apparatus

FIG.7Aillustrates an unobtrusive monitoring apparatus7000according to one form of the present technology. The monitoring apparatus7000is positioned adjacent and relatively close to the sleeping patient1000(e.g. on a bedside table).

FIG.7Bis a block diagram illustrating the components of the monitoring apparatus7000ofFIG.7Ain more detail, according to one form of the present technology. In the monitoring apparatus7000, a contactless sensor unit1200includes a contactless motion sensor7010generally directed toward the patient1000. The motion sensor7010is configured to generate one or more signals representing bodily movement of the patient1000, from which may be derived one or more respiratory movement signals representing respiratory movement of the patient.

The sensor unit1200may also include a microcontroller unit (MCU)7001, and a memory7002(e.g. a memory card) for logging data. In one implementation, the sensor unit1200may include communications circuitry7004configured to transfer data to an external computing device7005, e.g. a local general purpose computer, or a remote server, via a connection7008. The connection7008may be wired or wireless, in which case the communications circuitry7004has wireless capability, and may be direct or indirect via a local network or a wide-area network (not shown) such as the Internet.

The sensor unit1200includes a processor7006configured to process the signals generated by the motion sensor7010as described in detail below.

The sensor unit1200includes a display device7015configured to provide visual feedback to a user. In one implementation, the display device7015comprises one or more warning lights (e.g., one or more light emitting diodes). The display device7015may also be implemented as a display screen such as an LCD or a touch-sensitive display. Operation of the display device7015is controlled by the processor7006based on an assessment of the patient's cardio-pulmonary health. The display device7015may be operated to show information to a user of the monitoring apparatus7000, such as the patient1000, or a physician or other clinician. The display device7015may also display a graphical user interface for operation of the monitoring apparatus7000.

The sensor unit1200may also include an audio output7017configured to provide acoustic feedback to a user under the control of the processor7006, e.g., a tone whose frequency varies with breathing, or an alarm which sounds when certain conditions are met.

User control of the operation of the monitoring apparatus7000may be based on operation of controls (not shown) that are sensed by the processor7006of the monitoring apparatus7000.

One example of a sensor unit1200is the SleepMinder device manufactured by ResMed Sensor Technologies Ltd, which contains a contactless Doppler radio-frequency (RF) motion sensor7010.

In one form of the present technology, such as when the SleepMinder device is used as the sensor unit1200, the motion sensor7010includes an RF transmitter7020configured to transmit an RF signal7060. The transmitted signal7060for example has the form
s(t)=u(t)cos(2πƒct+θ)  (Eq. 1)

In Eq. 1, the carrier frequency is ƒc(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, θ 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:

u⁡(t)={1,t∈[kT,kT+Tp],k∈Z0,otherwise(Eq.2)

where T is the period width, and Tpis the pulse width. Where Tp<<T, this becomes a pulsed continuous wave system. In one case, as Tpbecomes 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 signal7060can be varied (chirped) to produce a so-called frequency modulated continuous wave (FMCW) system.

The radio frequency signal7060may be generated by the transmitter7020using a local oscillator7040coupled with circuitry for applying the pulse gating. In the FMCW case, a voltage-controlled oscillator is used together with a voltage-frequency converter to produce the RF signal7060for transmission. The coupling of the transmitted RF signal7060to the air may be accomplished using an antenna7050. The antenna7050can 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 antenna7050in the apparatus7000so that transmitted and reflected energy are primarily coming from one direction. In one implementation of the apparatus7000, a single antenna7050is used for both the transmitter7020and the receiver7030, with a single carrier frequency. Alternatively, multiple receive and transmit antennas7050can be used, with multiple carrier frequencies.

The apparatus7000is compatible in various embodiments with various types of antenna7050such 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 apparatus7000can be operated in a manner which has been shown to be safe for human use. The apparatus7000has been demonstrated with a total system emitted average power of 1 mW (0 dBm) and lower. The recommended safety level for RF exposure is 1 mW/cm2. At a distance of 1 meter from a system transmitting at 0 dBm, the equivalent power density will be at least 100 times less than this recommended limit.

In use, the transmitted RF signal7060is reflected off objects that reflect radio waves (such as the air-body interface of the patient1000), and some of the reflected signal7070will be received at a receiver7030, which can be collocated with the transmitter7020, or which can be separate from the transmitter7020, in a so-called “bistatic” configuration. The received signal7070and the transmitted signal7060can be multiplied together in a mixer7080(either in an analog or digital fashion). This mixer7080can 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
m(t)=γ cos(2πƒct)cos(2πƒct+ϕ(t))  (Eq. 3)

where ϕ(t) is a phase term resulting from the path difference of the transmitted and received signals7060and7070(in the case where the reflection is dominated by a single reflective object), and γ is the attenuation experienced by the reflected signal7070. If the reflecting object is fixed, then ϕ(t) is fixed. In the apparatus7000, the reflecting object (e.g., the chest of the patient1000) is in general moving, and ϕ(t) will be time-varying. As a simple example, if the chest is undergoing a sinusoidal motion of frequency ƒmdue to respiration, then the mixed signal m(t) contains a component at ƒm(as well as a component centred at 2ƒcwhich can be simply removed by low pass filtering). The signal at the output of the low pass filter after mixing is referred to as the movement signal or the demodulated sensor movement signal7003, and contains information about gross bodily (non-respiratory) movement, and respiratory movement.

The amplitude of the demodulated sensor movement signal7003is affected by the mean path distance of the reflected signal, leading to detection nulls and peaks in the motion sensor7010(i.e. areas where the motion sensor7010is less or more sensitive). This effect can be minimised by using quadrature techniques in which the transmitter7020simultaneously transmits a signal 90 degrees out of phase (in quadrature) with the signal7060of Eq. 1. This results in two reflected signals, both of which can be mixed and lowpass filtered by the mixer7080, leading to two demodulated sensor signals, referred to as the “I signal” and the “Q signal” in respective I- and Q-“channels”. The movement signal7003may comprise one or both of these signals.

In the UWB implementation, an alternative method of acquiring a movement signal7003may be used. The path distance to the most significant air-body interface can be determined by measuring the delay between the transmitted pulse and peak reflected signal. For example, if the pulse width is 1 ns, and the distance from the motion sensor7010to the body is 0.5 metres, then the delay before a peak reflection of the pulse arrives at the receiver7030will be 1/(3×108) s=3.33 ns. By transmitting large numbers of pulses (e.g., a 1 ns pulse every 1 μs) and assuming that the path distance is changing slowly over a given period, a movement signal7003may be computed as the average of the time delays over that period.

In this way, the motion sensor7010, e.g., a radio-frequency sensor, can estimate the respiratory movement of the chest wall, or more generally the movement of the part of the body of the patient1000whom the apparatus7000is monitoring.

As mentioned above, the received signal7070can include large motion artefacts, 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 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.

In order to improve the quality of the respiratory movement signal, and more general bodily movement signals, the physical volume from which reflected energy is collected by the sensor unit1200can be restricted using various methods. For example, the sensor unit1200can be made “directionally selective” (that is, it transmits more energy in certain directions), as can the antenna of the receiver7030. Directional selectivity can be achieved using directional antennas7050, or multiple RF transmitters7020. 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 signals7070which arise from signals at a certain physical distance from the sensor unit1200. Frequency domain gating (filtering) can be used to ignore motions of the reflected object above a certain frequency.

In implementations of the apparatus7000using 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 unit1200, the apparatus7000collects information from the patient1000, and uses that information to determine respiratory movement, and more general bodily movement information.

The movement signal7003may be stored in memory7002of the sensor unit1200, and/or transmitted over a link (e.g., connection7008) for storage in the external computing device7005, for each monitoring session. In one implementation, each monitoring session is one night in duration.

The processor7006of the sensor unit1200, or that of the external computing device7005, may process the stored movement signal(s)7003according to a monitoring process such as those described in detail below. The instructions for the described processes may be stored on a computer-readable storage medium, e.g. the memory7002of the sensor unit1200, and interpreted and executed by a processor, e.g. the processor7006of the sensor unit1200.

8.1.2 Alternative Monitoring Apparatus

In other forms of the present technology, an RPT device4000that is configured to supply respiratory pressure therapy to the patient1000via an air circuit4170to a patient interface3000, as illustrated inFIG.1, may also be configured as a monitoring apparatus.

A patient interface3000may comprise the following functional aspects: a seal-forming structure3100, a plenum chamber3200, a positioning and stabilising structure3300, a vent3400, one form of connection port3600for connection to air circuit4170, and a forehead support3700. 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 structure3100is 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 device4000may comprise mechanical and pneumatic components4100, electrical components4200and is configured to execute one or more algorithms4300. The RPT device preferably has an external housing4010, preferably formed in two parts, an upper portion4012and a lower portion4014. Furthermore, the external housing4010may include one or more panel(s)4015. Preferably the RPT device4000comprises a chassis4016that supports one or more internal components of the RPT device4000. The RPT device4000may include a handle4018.

The pneumatic path of the RPT device4000preferably comprises one or more air path items, e.g. an inlet air filter4112, an inlet muffler4122, a pressure generator4140capable of supplying air at positive pressure (preferably a blower4142), an outlet muffler4124and one or more transducers4270, such as pressure sensors4272and flow sensors4274.

One or more of the air path items may be located within a removable unitary structure which will be referred to as a pneumatic block4020. The pneumatic block4020may be located within the external housing4010. In one form a pneumatic block4020is supported by, or formed as part of the chassis4016.

The RPT device4000preferably has an electrical power supply4210, one or more input devices4220, a central controller4230, a therapy device controller4240, a pressure generator4140, one or more protection circuits4250, memory4260, transducers4270, data communication interface4280and one or more output devices4290. Electrical components4200may be mounted on a single Printed Circuit Board Assembly (PCBA)4202. In an alternative form, the RPT device4000may include more than one PCBA4202.

In one form of the present technology, the central controller4230is one or a plurality of processors suitable to control an RPT device4000. Suitable processors may include an x86 INTEL processor, a processor based on ARM® Cortex®-M processor from ARM Holdings such as an STM32 series microcontroller from ST MICROELECTRONIC. In certain alternative forms of the present technology, a 32-bit RISC CPU, such as an STR9 series microcontroller from ST MICROELECTRONICS or a 16-bit RISC CPU such as a processor from the MSP430 family of microcontrollers, manufactured by TEXAS INSTRUMENTS may also be suitable.

In one form of the present technology, the central controller4230is a dedicated electronic circuit. In one form, the central controller4230is an application-specific integrated circuit. In another form, the central controller4230comprises discrete electronic components.

The central controller4230may be configured to receive input signal(s) from one or more transducers4270, and one or more input devices4220.

The central controller4230may be configured to provide output signal(s) to one or more of the output device4290, the therapy device controller4240, the data communication interface4280, and the humidifier5000.

In some forms of the present technology, the central controller4230is configured to implement the one or more processes described herein expressed as computer programs stored in a non-transitory computer readable storage medium, such as memory4260.

Data communication interface4280may be connectable to a remote external communication network4282and/or a local external communication network4284. The remote external communication network4282may be connectable to a remote external device4286. The local external communication network4284may be connectable to a local external device4288. The data communication interface4280may use wired communication (e.g. via Ethernet, or optical fibre) or a wireless protocol (e.g. CDMA, GSM, LTE) to connect to the Internet.

In one form, local external communication network4284utilises one or more communication standards, such as Bluetooth, or a consumer infrared protocol. The local external device4288may be a personal computer, mobile phone, tablet or remote control.

In one form, remote external communication network4282is the Internet. In one form, remote external device4286is one or more computers, for example a cluster of networked computers. In one form, remote external device4286may be virtual computers, rather than physical computers. In either case, such a remote external device4286may be accessible to an appropriately authorised person such as a clinician.

An output device4290may take the form of one or more of a visual, audio and haptic unit. A visual display may be a Liquid Crystal Display (LCD) or Light Emitting Diode (LED) display. A display driver4292receives as an input the characters, symbols, or images intended for display on the display4294, and converts them to commands that cause the display4294to display those characters, symbols, or images. A display4294is configured to visually display characters, symbols, or images in response to commands received from the display driver4292.

8.1.3 Monitoring Process

In one aspect of the present technology, a monitoring apparatus carries out a monitoring process to monitor the patient's cardio-pulmonary health from a respiratory signal that is indicative of the respiration of the patient1000.

In the form of the present technology in which the monitoring apparatus is the unobtrusive apparatus7000illustrated inFIG.7Band the respiratory signal is the respiratory movement signal derived from the movement signal7003, the monitoring process may be carried out by the processor7006of the contactless sensor unit1200, configured by instructions stored on computer-readable storage medium such as the memory7002. Alternatively, a processor of the external computing device7005may implement all or part of the described monitoring process, having obtained the required data, either raw or partly processed, from the sensor unit1200and any other sensors in the apparatus7000via the connection7008as described above. In such implementations, the above descriptions of the visual display7015and the audio output7017of the monitoring apparatus7000apply equally to comparable elements of the external computing device7005. In one example, the external computing device7005is a clinician-accessible device such as a multi-patient monitoring device that allows a clinician to review data from multiple remote patient data recording devices such as the monitoring apparatus7000. In these systems, a database may be provided to record patient monitoring data. Through such an external computing device7005, clinicians may receive a report or alert that a particular patient may require closer observation or should be brought to hospital.

In the form of the present technology in which the monitoring apparatus is the RPT device4000and the respiratory signal is a signal representing the respiratory flow rate Qr of the patient1000that is derived from one or more of the transducers4270, the monitoring process may be carried out by the central controller4230of the RPT device4000configured by instructions stored on computer-readable storage medium such as the memory4260. Alternatively, the local or remote external device4288or4286may implement all or part of the described processing, having obtained the required data, either raw or partly processed, from RPT device4000via the data communication interface4280as described above. In such implementations, the output functions of the output device4290of the RPT device4000are carried out by comparable elements of the local or remote external device4288or4286.

FIG.7Cis a flow chart illustrating a method7100that may be used to implement the monitoring process according to one form of the present technology. The method7100may be carried out at the end of each monitoring session on the stored respiratory signal corresponding to that session.

The method7100starts at step7110, at which the respiratory signal is pre-processed. The pre-processing step7110(shown dashed inFIG.7C) is optional and may be omitted from the method7100. At the next step7120, the (possibly pre-processed) respiratory signal is analysed to extract one or more respiratory features. The extracted respiratory features may be stored in a memory, for example the memory7002of the sensor unit1200or that of the external computing device7005.

The method7100then at step7130uses the extracted respiratory feature(s) from the just-completed monitoring session, and possibly respiratory features from one or more previous monitoring sessions, to compute a stability measure. The so-created stability measure, or a history of consecutively computed stability measures on a session-by-session basis, may be stored in one or more memories, for example the memory7002of the sensor unit1200or that of the external computing device7005or other memory associated with a processor that computes the stability measure. The stability measure computed at step7130may act as a predictor of potential clinical events, in that a change (e.g., rise) in the stability measure may indicate a deterioration in the patient's condition that may be a precursor to a clinical event. The stability measure also changes (e.g., increases) when the patient's condition improves, which is also an event of interest in monitoring a chronic disease.

The stability measure is then evaluated at step7140to determine whether it meets a criterion, such as by comparison with one or more thresholds. For example, the stability measure may be compared with a threshold at step7140, such as in a processor. If the stability measure exceeds the threshold, for example (“Y”), a change point is detected, and step7150may generate an alert. If not (“N”), the method7100concludes at step7160. The choice of the threshold affects the sensitivity and specificity of the monitoring process in detecting potential clinical events, and is chosen based on desired levels of sensitivity and specificity when the monitoring process is executed on training data. In some implementations, the threshold may be adjusted between monitoring sessions based on observed false positive and false negative detections. Other evaluations at step7140may determine whether the stability measure resides in a particular range, such by a comparison with one or more thresholds attributable to one or more ranges. Accordingly, the automated monitoring process effectively converts, through processing, respiratory signal data, which might appear to be innocuous, into a tool for patient monitoring, i.e., the stability measure, improving not only the monitoring apparatus but also the ability of clinicians in the field to more effectively monitor their patients, such as for making timely and necessary changes in treatment.

In the form of the present technology in which the monitoring apparatus is the unobtrusive monitoring apparatus7000, the contactless motion sensor7010is a Doppler RF motion sensor. As mentioned above, in such an implementation, the movement signal7003may comprise two signals, labelled I and Q signals, each generally indicative of bodily movement, but generally 90 degrees out of phase with each other.

Several approaches are possible when the movement signal7003comprises I and Q signals. In a “parallel” approach, the steps7110and7120are performed on each of the I and Q signals in parallel, and the separately obtained features are combined at the end of the feature extraction step7120. In one implementation of the parallel approach, the pre-processing step7110is omitted. In a “combined” approach, the I and Q signals are combined as part of the pre-processing step7110, and the processing steps7120to7130are carried out on the combined movement signal. The combined approach has the advantage of less computational complexity than the parallel approach, at the potential cost of lower accuracy.

Alternatively, the contactless motion sensor7010may provide a single movement signal7003. This is handled by an approach referred to as the “single-channel” approach.

The following sections describe implementations of the steps of the monitoring method7100ofFIG.7Cin more detail.

The implementations of steps7110and7120are described in terms of the form of the present technology in which the monitoring apparatus is the monitoring apparatus7000ofFIG.7B. For the form of the present technology in which the monitoring apparatus is the RPT device4000ofFIG.4A, the pre-processing step7110may be omitted, and the respiratory feature extraction step7120may be performed in conventional fashion on the respiratory flow signal Qr.

The described implementations of steps7130to7150are generic to both of the above forms of the present technology.

8.1.3.1 Pre-Processing

Under the combined approach, the pre-processing step7110begins by combining the I and Q signals in an adaptive geometrical manner into a combined movement signal c. In one implementation, the combination sub-step comprises three stages, applied to a window that slides along the I and Q signals (e.g., progressively processes an amount of data (window size) of the signals over time). In one implementation, the window is of 10 seconds duration with 50% overlap.a. Check if the signals are 180 degrees out of phase using a cross-correlation, and flip them back to the same quadrant if so.b. As the vectors (I, Q) form a cloud of points around a quasi-circular arc, subtract the mean of the cloud to centre the arc at (0, 0), locate the minimum mIQof the centred cloud of points in both directions, and compute the length m of each vector (I, Q) referred to mIQ.
mIQ=(mI,mQ)=(min[I−I],min[Q−Q])  (Eq. 4)
m=√{square root over ((I−mI)2+(Q−mQ)2)}  (Eq. 5)c. Subtract the mean of m to produce the (one dimensional) combined signal c.
c=m−m(Eq. 6)

The combined movement signal c is then (optionally) de-trended to remove baseline wandering. In one implementation, de-trending is implemented using a third-order polynomial:
c1=DTpoly,3[c](Eq. 7)

In another implementation, de-trending is implemented using double-pass median filtering.

The de-trended signal c1is (optionally) bandpass filtered with a Butterworth bandpass filter with range set to the frequency range of respiratory functioning, this being in one implementation [0.1 Hz, 0.8 Hz] (corresponding to 6 to 48 breaths per minute).

A further (optional) sub-step in the pre-processing step7110is noise reduction. In one implementation, particularly suited to signals from Doppler RF motion sensors7010, which are non-stationary, the noise reduction sub-step is carried out in the wavelet transform domain on the (bandpass filtered) de-trended combined movement signal c2:
c3=W−1MWc2(Eq. 8)

where W represents a wavelet transform, for example the 30-coefficient “symmlet” wavelet up to the fifth dyadic level, and M is a masking matrix that passes certain wavelet coefficients and rejects others considered as “perturbative”.

The steps to implement the action of M are as follows:a. Select the dyadic scales for which the “artefactness” (see below) of the wavelet coefficients is above a first threshold TA;b. From this set of scales, perform a hard thresholding (with threshold TC) of the wavelet coefficients based on the standard deviation.

The “artefactness” at a scale quantifies the degree to which an artefact affects the signal at that scale. Artefactness is a measure of the skewness of the signal which can contain unlikely high amplitude values. The artefactness of a signal x may be computed as:

Art(x)=2⁢σxmax⁡(❘"\[LeftBracketingBar]"x❘"\[RightBracketingBar]")-min⁡(❘"\[LeftBracketingBar]"x❘"\[RightBracketingBar]")(Eq.9)

where σxis the standard deviation of the signal x. The further Art(x) is from 1, the larger the artefact is.

Under the parallel approach, the combination sub-step is omitted from the pre-processing step7110, and any or all of the subsequent sub-steps (de-trending, filtering, and noise reduction) are performed in parallel on each of the I and Q signals.

Under the single-channel approach, any or all of the de-trending, filtering, and noise reduction sub-steps are performed on the movement signal7003.

In the description below, the input(s) to the feature extraction step7120is /are referred to as (pre-processed) movement signal(s) to reflect the optional nature of the pre-processing step7110.

8.1.3.2 Respiratory Feature Extraction

FIG.7Dis a block diagram illustrating a method7200that may be used to implement the feature extraction step7120in the method ofFIG.7Cin one form of the present technology.

In the method7200, an activity estimation and movement detection module7210generates an activity count signal and a movement flag series from the (pre-processed) movement signal. (Under the combined or single-channel approach, there is only one (pre-processed) movement signal.) A presence/absence detection module7220generates a presence/absence flag series from the (pre-processed) movement signal and the movement flag series. A sleep/wake analysis module7230calculates a hypnogram from the presence/absence flag series, the movement flag series, and the activity count signal. A breathing rate estimation module7240generates a series of estimates of the breathing rate of the patient from the (pre-processed) movement signal and the hypnogram. A signal selection module7250selects sections of the (pre-processed) movement signal, using the movement flag series and the hypnogram.

A modulation cycle metrics calculation module7255generates an estimate of the modulation cycle length of the patient's respiration from the selected sections of the (pre-processed) movement signal. An envelope generation module7260generates envelopes of the selected sections of the (pre-processed) movement signal using the estimated breathing rate. An SDB event detection module7265generates candidate SDB events from the selected sections of the (pre-processed) movement signal using the estimated modulation cycle length. An SDB event confirmation module7270confirms the candidate SDB events generated by the SDB event detection module7265using the estimated modulation cycle length. Finally, a feature calculation module7280calculates respiratory feature values from the confirmed SDB events.

Under the parallel approach, the modules7210to7270of the method7200are simply duplicated to process the two (pre-processed) movement signals7003independently. A modified version of the feature calculation module7280combines the SDB events from the two parallel processing streams to calculating a single respiratory feature set for the two (pre-processed) movement signals.

The modules7210to7280of the method7200are described in detail in the co-pending PCT application no. PCT/AU2013/000564, published as WO 2013/177621, titled “Method and Apparatus for Monitoring Cardio-Pulmonary Health”, by ResMed Sensor Technologies Limited, the entire content of which is herein incorporated by reference.

In one implementation, the feature extraction step7120extracts four respiratory features for each monitoring session:The total number of SDB events;The 50thpercentile (median) of the breathing rate;The 75thpercentile of the breathing rate; andThe 75thpercentile of the duration of CSR cycles.
8.1.3.3 Computation of a Stability Measure

Clinical event prediction from respiratory features is an example of a highly imbalanced dataset with very small number of events within a large number of stable sessions, so a robust approach is needed to minimize the number of false positive predictions. The assumption underlying the present technology is that when the patient is stable, the respiratory feature follows one statistical distribution and, at some point before a clinical event, passes through a “change point” to follow a different distribution. The stability measure is therefore computed such that a change of distribution at monitoring session indexed by t results in a rise in the stability measure at or near the monitoring session indexed by t. In other words, the stability measure for a session is an indication of a change point having occurred in the distribution of the respiratory feature at that session. Step7130according to the present technology is therefore distribution-based in nature. The distribution-based approach to stability measure computation results in low false positives (high specificity) compared to classification-based approaches.

Two approaches to computing a stability measure at step7130are described below. An on-line or sequential approach can, in principle, detect a change point in the distribution of the respiratory feature as soon as it occurs. That is, sufficient data is available after monitoring session t is complete to compute the stability measure at the monitoring session t. The on-line approach is most suitable for ameliorating or preventing clinical events with rapid onset, i.e. a delay of the order of one week between the change point in the distribution and the occurrence of the clinical event.

A retrospective approach can detect a change point in the distribution of the respiratory feature with a delay of the order of one to two weeks, depending on the parameters chosen. The retrospective approach is most suitable for ameliorating or preventing clinical events with more gradual onset, i.e. a delay of the order of two to three weeks between the change point in the distribution and the occurrence of the clinical event.

Each approach forms and analyses a time series {y(t)} or {yt} of successive values or samples y of one of the extracted respiratory features indexed by the (integer) monitoring session numbers t. The (integer) monitoring session index t is sometimes shortened in what follows to “time t”, in which case it will be understood that time is measured in units of monitoring sessions.

8.1.3.3.1 On-Line or Sequential Approach

The on-line approach is based on Bayesian on-line change point detection (BOPCD). The on-line approach involves a quantity called the run length r at time t, written as rt, which is defined as the number of samples ytsince the last change point of the input sample distribution. Step7130under the on-line approach computes the posterior distribution of the run length rtat time t given all the samples ytup to and including time t. These samples are written in shorthand form as y1:t, so the posterior run length distribution to be computed is written as p(rt|y1:t). The samples yt−rt+1:tbelonging to the current run are written in shortened form as yt(r).

The posterior run length distribution p(rt|y1:t) may be computed by normalising the joint likelihood p(rt, y1:t) over run length rt:

p⁡(rt|y1:t)=p⁡(rt,y1:t)∑rtp⁡(rt,y1:t)(Eq.10)

The joint likelihood p(rt, y1:t), shortened to γt, may be computed from its previous value γt−1by writing γtas p(rt, rt−1, y1:t), which may be expanded as a marginalisation over rt−1:

γt=p⁡(rt,rt-1,y1:t)=∑rt-1p⁡(rt,yt|rt-1,y1:t-1)⁢p⁡(rt-1,y1:t-1)=∑rt-1p⁡(rt|rt-1)⁢p⁡(yt|rt-1,y1:t-1)⁢γt-1(Eq.11)

The first factor p(rt|rt−1) in the summed term of Eq. 11 is the conditional prior on the run length, also referred to as the change point prior. In one implementation, the change point prior p(rt|rt−1) has only two non-zero values:

p⁡(rt⁢❘"\[LeftBracketingBar]"rt-1)={H⁡(rt-1+1),rt=01-H⁡(rt-1+1),rt-1+10,otherwise(Eq.12)

The first non-zero value (for rt=0) is the probability H(rt−1+1) of a change point occurring after a run of length rt−1, and the other non-zero value (for rt=rt−1+1) is its complement, the probability of rtbeing one greater than rt−1, i.e. for the current run to grow by one session. The function H(rt−1+1) is termed the “hazard function”. In one implementation, the hazard function H(rt−1+1) may be set to a predetermined constant value h (known as the “hazard rate”) that is independent of rt−1(a so-called “memoryless” process) giving rise to a geometric distribution of run lengths with timescale 1/h. In one implementation, the hazard rate h is set to 1/90, that is, one change point per a timescale of 90 monitoring sessions.

The second factor p(yt|rt−1,y1:t−1) in the summed term of Eq. 11 is known as the posterior predictive probability, since it is the probability of observing the current sample ytgiven all the previous samples y1:t−1and the previous run length rt−1. The posterior predictive probability p(yt|rt−1,y1:t−1) may be abbreviated to p(yt|yt−1(r)) since it depends only on the previous samples yt−1(r)belonging to the current run.

The Underlying Predictive Model (UPM) is a model of the time series {yt} that is used to compute the posterior predictive probability p(yt|yt−1(r)). In one implementation of the on-line approach, the UPM is based on independent and identically distributed Gaussian samples:
yt˜N(μ,σ2)  (Eq. 13)

with mean μ and variance σ2that change at every change point. In one implementation, the mean and variance μ and σ2are drawn from normal and inverse-Gamma distributions respectively:

μ∼N⁢(μ0,σ2k)σ-2∼Γ⁡(α,β)(Eq.14)

For this reason such a UPM is referred to as a normal-inverse-Gamma (NIG) model. The parameters μ0, κ0, α0, and β0of the (NIG) model are determined by fitting a normal-inverse-Gamma model to a training data set.

To compute the posterior predictive probability at time t, the parameters of the UPM are first updated for all times from 1 to the current time t:

μ1:t=[μ0⁢κ1:t-1⁢μ1:t-1+ytκ1:t-1+1]κ1:t=[κ01+κ1:t-1]α1:t=[α0α1:t-1+0.5]β1:t=[β0β1:t-1+κ1:t-1(yt-μ1:t-1)22⁢(κ1:t-1+1)](Eq.15)

The variance σ2of the NIG model is then computed as

σ2=βt(κt+1)αt+κt(Eq.16)

Finally, the posterior predictive probability p(yt|yt−1(r)) is computed as

p⁡(yt⁢❘"\[LeftBracketingBar]"yt-1(r))=Γ⁡(αt+0.5)Γ⁡(αt)⁢12⁢αt⁢π⁢σ2[1+12⁢αt⁢σ2⁢(yt-μt)2]-(at+0.5)(Eq.17)

As an aside, the posterior run length distribution p(rt|y1:t) at time t may be used to compute the marginal predictive distribution p(yt+1|y1:t) of the next sample yt+1, given all the samples y1:t. The marginal predictive distribution p(yt+1|y1:t) may be computed in a way that preserves uncertainty in the run length rtby marginalisation of the posterior predictive probability p(yt+1|yt(r)) over the run length rt:

p⁡(yt+1⁢❘"\[LeftBracketingBar]"y1:t)=∑rtp⁡(yt+1⁢❘"\[LeftBracketingBar]"yt(r))⁢p⁡(rt⁢❘"\[LeftBracketingBar]"y1:t)(Eq.18)

The posterior predictive probability p(yt+1|yt(r)) for each value of run length rtmay be computed from the UPM as described above.

The stability measure Stunder the on-line approach may be computed as the probability at time t of a change point having occurred since the last change point was detected. This probability is computed as the sum of the values of the posterior run length distribution p(rt|y1:t) over all possible run lengths since the previous change point was detected. That is, the sum is computed over all values of run length rtfrom zero up to the current time t less the time of the previous change point:

St=∑rt=0t-t0p⁡(rt⁢❘"\[LeftBracketingBar]"y1:t)(Eq.19)

where the previous change point before time t was detected at time t0. (The previous alert time t0is initialised to zero before any change points are detected.)

In one form of the present technology, as described below, a clinician is able to issue manual alerts to the monitoring apparatus7000through the user interface of their associated external computing device7005based on the clinician's inspection of the respiratory features being received at, or computed by, the external computing device7005. If such a manual alert is issued, the value of the previous alert time t0is updated to the time at which the manual alert was issued.

FIG.7Econtains a flow chart illustrating a method7300that may be used to implement the stability measure computation step7130of the method7100ofFIG.7Cunder the on-line approach according to one form of the present technology. The method7300is carried out iteratively, one iteration after each monitoring session.

The method7300starts at step7310, where the joint likelihood p(rt, y1:t) (i.e. γt) is initialised (i.e. assigned a value for t=0), to 1 in one implementation. The step7310is only carried out at the first iteration of the method7300, and hence is shown dashed inFIG.7E. At step7320, the current time t is incremented and the current sample ytis received. Step7330follows, at which the method7300computes the current posterior predictive probability p(yt|yt−1(r)) using the UPM and the current sample ytusing Eqs. 15 to 17. Step7340then computes the current joint likelihood p(rt, y1:t)=γtfrom the previous joint likelihood γt−1and the current posterior predictive probability p(yt|yt−1(r)) using Eq. 11. For run length rtequal to zero, there are t terms in the sum of Eq. 11. However, for values of run length rtthat are greater than zero, there is only one term in the sum of Eq. 11, as there is only one value of rt−1at which the change point prior (Eq. 12) is non-zero, namely rt−1=rt−1. This fact gives the on-line approach its computational efficiency.

At the next step7350, the method7300computes the current posterior run length distribution p(rt|y1:t) by normalising the current joint likelihood p(rt, y1:t) as in Eq. 10. Step7360then computes the current stability measure Stfrom the current posterior run length distribution p(rt|y1:t) using Eq. 19. The method7300then concludes.

8.1.3.3.2 Retrospective Approach

The retrospective approach to step7130works by comparing the probability distributions of sub-sequences of the time series {y(t)} before and after a certain time. Step7130under the retrospective approach computes the stability measure at that time as the dissimilarity between the two distributions.

Notationally, a sub-sequence Y(t) of length k of the time series {y(t)} is defined as
Y(t)[y(t) . . .y(t+k−1)]  (Eq. 20)

where k is a parameter of the retrospective approach. Each sub-sequence Y(t) is treated as a k-component vector that is a sample from an underlying k-dimensional joint distribution. A set Ψ(t) of n consecutive sub-sequences Y(t) at time t is defined as
y(t){Y(t),Y(t+1),Y(t+2), . . .Y(t+n−1)}  (Eq. 21)

where n is a further parameter of the retrospective approach. The probability distribution of the set Ψ(t) of n sub-sequences Y(t) is written as Pt.

The retrospective approach computes a symmetrical dissimilarity Dsbetween the distribution Ptand the distribution Pt+nof the set Ψ(t+n) of n sub-sequences, n samples later than the set Ψ(t). In one implementation, the symmetrical dissimilarity Dsmakes use of a measure of dissimilarity Dƒ(P∥P′) between two distributions P and P′ known as the f-divergence, and defined as

Df(P⁢P′)=Δ∫p′(Y)⁢f⁡(p⁡(Y)p′(Y))⁢d⁢Y(Eq.22)

where ƒ is a convex function such that ƒ(1)=0, and p(Y) and p′(Y) are the probability density functions (densities) of the distributions P and P′ respectively. The symmetrical dissimilarity Dsmay be computed as the sum of the f-divergence Dƒ(Pt∥Pt+n) between the distribution Ptand the distribution Pt+n, and the f-divergence Dƒ(Pt+n∥Pt) between the distribution Pt+nand the distribution Pt:
Ds(Pt∥Pt+n)=Dƒ(Pt∥Pt+n)+Dƒ(Pt+n∥Pt)  (Eq. 23)

The symmetrical dissimilarity Dsis so termed because the symmetrical dissimilarity Dsbetween Ptand Pt+nis the same as the symmetrical dissimilarity Dsbetween Pt+nand Pt. In general, the f-divergence Dƒof Eq. 22 is not symmetrical in this sense. The symmetrical dissimilarity Dscomputed according to Eq. 23 takes a high value when either the f-divergence Dƒbetween Ptand Pt+nor the f-divergence Dƒbetween Pt+nand Ptis high. The symmetrical dissimilarity Dsis therefore more sensitive in detecting change points than either the f-divergence Dƒbetween Ptand Pt+nalone or the f-divergence Dƒbetween Pt+nand Ptalone. The symmetrical dissimilarity Dsbetween Ptand Pt+nmay therefore be used as a stability measure St+nfor the patient1000at time t+n. A high value of the symmetrical dissimilarity Dsbetween Pt, a probability distribution of a set Ψ(t) comprising sub-sequences Y(t) substantially composed of samples of the time series {y(t)} before the monitoring session t+n, and Pt+n, a probability distribution of a set Ψ(t+n) comprising sub-sequences Y(t+n) substantially composed of samples of the time series {y(t)} after the monitoring session t+n, indicates that a change point in the time series {y(t)} is likely to be present between monitoring sessions (t+n−1) and (t+n).

To evaluate the stability measure St+nat the monitoring session t+n using the definition in Eq. 23 requires samples y(t) from times up to and including the time t+2n+k−2. In other words, when sample y(T) is received at time T, the stability measure S according to the retrospective approach may be computed at time T−n−k+2. Therefore, a rise in the stability measure S computed using the most recent sample y(T) at time T according to the retrospective approach indicates that a change point occurred approximately n+k−2 monitoring sessions before the time T. The retrospective approach is therefore said to have a delay of n+k−2 samples.

In one implementation, the convex function ƒ used in the definition of the f-divergence (Eq. 22) is the Kullback-Leibler divergence defined as ƒ(t)=t log(t). In another implementation, the convex function ƒ is the Pearson divergence defined as a quadratic function:

f⁡(t)=12⁢(t-1)2(Eq.24)

Substituting the Pearson divergence ƒ(t) of Eq. 24 into Eq. 22 gives the Pearson dissimilarity DPE.

Since the densities p(Y) and p′(Y) of the distributions Ptand Pt+nare unknown, the symmetrical dissimilarity Dscannot be computed directly. One implementation of the retrospective approach uses conventional methods to estimate the densities p(Y) and p′(Y) from the sets Ψ(t) and Ψ(t+n) respectively, and then applies Eqs. 22 and 23 to compute the symmetrical dissimilarity Dsfrom the estimated densities. However, conventional density estimation methods tend to be less accurate as the number of dimensions (in this case k) increases.

An alternative implementation of the retrospective approach estimates the ratio between the densities p(Y) and p′(Y). Density ratio estimation is easier than estimation of the separate densities p(Y) and p′(Y) to comparable accuracy.

The density ratio g(Y)=p(Y)/p′(Y) may be approximated by a weighted sum of kernel basis functions:

gˆ(Y)=∑l=1nθl⁢K⁡(Y,Yl)(Eq.25)

where the kernel basis function K is a Gaussian function:

K⁡(Y,Y′)=exp⁡(-12⁢σ2⁢Y-Y′2)(Eq.26)

with a kernel width σ that is determined based on cross-validation, and the weights or coefficients θlare elements of a parameter n-vector θ. The kernel centres Yl(l=1, . . . , n) are the n sub-sequences Y(t), . . . , Y(t+n−1) making up the set Ψ(t).

The optimal parameter vector {circumflex over (θ)} of the approximation given in Eq. 25 to the density ratio g(Y) may be found by fitting the approximation ĝ to the true density ratio g under squared loss. This is equivalent to minimising the following objective function O over the parameter vector θ:

O⁡(θ)=12⁢θT⁢H⁢θ-hT⁢θ+λ2⁢θT⁢θ(Eq.27)

where H is an n-by-n matrix with (l, l′)-th element H(l, l′) given by

H⁡(l,l′)=1n⁢∑j=1nK⁡(Yj′,Yl)⁢K⁡(Yj′,Yl′)(Eq.28)

where the Y′j(j=1, . . . , n) are the n sub-sequences Y(t+n), . . . , Y(t+2n−1) making up the set Ψ(t+n). The vector h is an n-vector with l-th element h(l) given by

h⁡(l)=1n⁢∑i=1nK⁡(Yi,Yl)(Eq.29)

The last term in the objective function of Eq. 27 is a penalty term, included for regularisation purposes, with λ as the regularisation parameter.

The objective function in Eq. 27 is minimised by the parameter vector {circumflex over (θ)} given by:
{circumflex over (θ)}=(H+λIn)−1h(Eq. 30)

The Pearson dissimilarity DPE(Pt∥Pt+n) of Eq. 22 may be approximated as

DˆPE(Pt⁢Pt+n)=-12⁢n⁢∑j=1n(gˆ(Yj′))2+1n⁢∑i=1ngˆ(Yi)-12(Eq.3⁢1)

The Pearson dissimilarity DPE(Pt+n∥Pt) may be approximated in similar fashion to {circumflex over (D)}PE(Pt∥Pt+n) by interchanging the sub-sequences Yi(i=1, . . . , n) and Y′j(j=1, . . . , n). The resulting approximation {circumflex over (D)}PE(Pt+n∥Pt) is then added to the approximation {circumflex over (D)}PE(Pt∥Pt+n) to obtain the symmetrical dissimilarity Ds(Pt∥Pt+n), which is the stability measure St+nunder the retrospective approach.

FIG.7Fcontains a flow chart illustrating a method7400that may be used to implement the stability measure computation step7130of the method7100ofFIG.7Cunder the retrospective approach according to one form of the present technology.

The method7400starts at step7410, which forms the two sets of sub-sequences Yi(i=1, . . . , n) and Y′j(j=1, . . . , n) making up the sets Ψ(t) and Ψ(t+n) respectively according to Eq. 21. Because of the definitions of these sub-sequences, the method7400is carried out on or after the time t+2n+k−2. Step7420follows, at which the matrix H and the vector h are computed using Eqs. 28 and 29 and the kernel definition Eq. 26. At the next step7430, Eq. 30 is applied to compute the parameter vector {circumflex over (θ)}. Step7440follows, at which the method7400uses the parameter vector {circumflex over (θ)} to compute the approximation to the Pearson dissimilarity DPE(Pt∥Pt+n) using Eqs. 31 and 25.

At step7450, the sub-sequences Yi(i=1, . . . , n) and Y′j(j=1, . . . , n) are interchanged. Steps7460,7470, and7480repeat the processing of steps7420,7430, and7440on the interchanged sub-sequences Y′j(j=1, . . . , n) and Yi(i=1, . . . , n) to obtain an approximation to the Pearson dissimilarity DPE(Pt+n∥Pt). Finally, at step7490the approximations to DPE(Pt∥Pt+n) and DPE(Pt+n∥Pt) are added together to obtain the symmetrical dissimilarity DS(Pt∥Pt+n), which is a stability measure St+nat the time t+n according to the retrospective approach.

In one implementation of the method7400, the parameters n and k are 10 and 5 respectively, so the delay of the retrospective approach is n+k−2=13 samples. The kernel width σ and the regularisation parameter λ are obtained by cross-validation, as the values from two discrete candidate sets that minimise the objective function in Eq. 27 over plural randomly chosen subsets of the sets Ψ(t) and Ψ(t+n) of sub-sequences Yiand Y′j.

8.1.3.3.3 Combined Approach

The combined approach to step7130computes both the on-line stability measure and the retrospective stability measure as described above to generate alerts. In one implementation of the combined approach, the combined approach generates an alert when the on-line stability measure meets a first criterion (e.g., one or more threshold comparisons) and the retrospective stability measure at the same time meets a second criterion (e.g., one or more threshold comparisons). In another implementation of the combined approach, the combined approach generates an alert when either the on-line stability measure meets a first criterion (e.g., one or more threshold comparisons) or the retrospective stability measure at the same time meets a second criterion (e.g., one or more threshold comparisons).

8.1.3.4 Alert Generation

The clinical alert generated at step7150may include a warning or alert message taking a number of forms. For example, the processor7006, to generate a clinical alert to the patient1000, may activate a status light (e.g., an LED or an icon on the display device7015) of the monitoring apparatus7000. A more detailed message concerning the assessment of the indicator may also be displayed to the patient1000on the display device7015. Optionally, the processor7006may also, or alternatively, send an alert message via the connection7008to the external computing device7005associated with a clinician. Such a message may take the form of a wired or wireless communication. For example, the processor7006may generate an alert message via a paging system such as by automatically dialing a paging system. The processor7006may also be configured to generate an automated voice phone call message. The processor7006may also send the alert message by a fax transmission. In some embodiments, the processor7006may also send an alert message via any internet messaging protocol, such as an email message, or by any other internet data file transport protocol. The alert messages may even be encrypted to keep patient information confidential. A typical alert message may identify the patient. Such a message may also include data recorded by the monitoring apparatus7000or any other recorded patient information. Optionally, in some embodiments, the alert message may even express that the patient should be considered for additional treatment, hospitalization, or an evaluation due to the detection of a potential clinical event.

While alert messages may be directed by the processor7006to the patient via the display device7015of the monitoring apparatus7000and to the clinician via the connection7008, in some embodiments, the alert messages could be directed more selectively. For example, a first alert message may be only transmitted to a clinician by only transmitting the alert message to an external computing device7005through the connection7008without showing any alert on the display device7015. However, a second alert message, which may be a more urgent message, could then be actively displayed on the display device7015in addition to being transmitted to the external computing device7005. An audible alarm from an optional speaker controlled by the processor7006may also be implemented. Use of an audible alarm may depend on the urgency of the alert message.

In one form of the present technology, a clinician is able to issue manual alerts to the monitoring apparatus7000through the user interface of their associated external computing device7005based on the clinician's inspection of the respiratory features being received at the external computing device7005.

8.1.3.4.1 Queries

In another form of the present technology, the processor7006may condition an alert on responses to a patient query that may serve to avoid unnecessary alerts. In a variant of the method7100, upon the stability measure meeting a criterion (step7140), rather than immediately generating an alert, as at step7150, the processor7006may prompt the patient1000to take an action, such as take their prescribed medication, or trigger a presentation of a query to the patient1000to provide a response. The display device7015under control of the processor7006may present the query to the patient1000, prompting the patient1000to input a response via a user interface. The presented question or questions of the query may be selected from a database, or other data structure of questions, such as a data structure in the memory7002of the monitoring apparatus7000. The processor7006may then evaluate the received responses to the query. Based on this evaluation, the processor7006may generate an alert as at step7150, abort an alert, and/or delay generation of an alert pending responses to one or more additional queries. Such additional queries may be triggered after a certain time, after a further detected change point, or after a further use of the monitoring apparatus7000. In the forms of the present technology in which step7150comprises sending an alert message to the external computing device7005associated with a clinician, the received query responses may instead be forwarded to the external computing device7005for manual evaluation by the clinician. Based on their evaluation, the clinician may decide to maintain or to cancel the alert.

Such queries may serve to reduce false positives (e.g., when the alert results in clinical intervention with the patient and the clinical intervention is later found to have been unnecessary). Some false positives may be due to changes in patient behavior, which may be corrected without clinical intervention. Such behaviors may include missed or incorrect dosage of medication, non-compliance with dietary instructions and/or rest requirements, and the like. The query questions may address pharmaceutical and/or lifestyle compliance by the patient (e.g., has the patient been taking prescribed medication and/or following a physician's treatment advice, etc.). Optionally, in some cases, one or more questions may address the operational integrity of the monitoring apparatus7000to ensure that the received respiratory signal is valid. Optionally, the processor7006may pursue a series of queries over a predetermined span of time (such as one or more monitoring sessions) and generate an alert only after the predetermined span of time has elapsed.

Under the on-line approach, if the processor7006aborts an alert in response to a detected change point, or a generated alert is subsequently manually cancelled by a clinician, the processor7006may revert the previous alert time to (used in Eq. 19) to the time of the last-but-one generated alert.

8.2 Example Results

FIG.8contains a graph8000showing example results obtained from the monitoring apparatus7000using the method7100. The upper trace8010shows one of the above-mentioned respiratory features, namely 75thpercentile of respiratory rate over the session, over 400 sessions indexed by t. The grey band8015shows a 28-session interval symmetrically surrounding an ADHF event (indicated by the upward arrow8020) experienced by the patient at approximately session number182. The lower trace8050shows peaks at the sessions t where the stability measure Stcomputed by the retrospective approach exceeded a threshold, and hence step7150generated an alert. In particular the double peak8060coincides with the ADHF event. Other peaks, e.g.8070, do not coincide with ADHF events and therefore represent “false positives”.

8.3 Glossary

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.

8.3.1 General

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.

Continuous Positive Airway Pressure (CPAP): CPAP treatment will be taken to mean the application of a supply of air to the entrance to the airways at a pressure that is continuously positive with respect to atmosphere, and preferably 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.

8.3.2 Aspects of the Respiratory Cycle

Apnea: Preferably, apnea will be said to have occurred when flow falls below a predetermined threshold for a duration, e.g. 10 seconds. An obstructive apnea will be said to have occurred when, despite patient effort, some obstruction of the airway does not allow air to flow. A central apnea will be said to have occurred when an apnea is detected that is due to a reduction in breathing effort, or the absence of breathing effort, despite the airway being patent. A mixed apnea occurs when a reduction or absence of breathing effort coincides with an obstructed airway.

Breathing 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): Preferably breathing effort will be said to be 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: Preferably, 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.

Hypopnea: Preferably, a hypopnea will be 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 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.

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

Inspiratory portion of a breathing cycle: Preferably 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 (1) being patent, and a value of zero (0), being closed (obstructed).

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

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

Respiratory flow, airflow, patient airflow, respiratory airflow (Qr): These synonymous terms may be understood to refer to the RPT device's estimate of respiratory airflow, as opposed to “true respiratory flow” or “true respiratory airflow”, which is the actual respiratory flow 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 waveform.

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

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

Typical recent ventilation: The value of ventilation around which recent values 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 level of flow increases only slightly or may even decrease as the pressure difference across the upper airway increases (Starling resistor behaviour).

Ventilation (Vent): A measure of the total amount of gas being exchanged by the patient's respiratory system, including both 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.

8.3.3 RPT Device Parameters

Flow rate (or flow): The instantaneous volume (or mass) of air delivered per unit time. While flow rate and ventilation have the same dimensions of volume or mass per unit time, flow rate is measured over a much shorter period of time. 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. Where it is referred to as a signed quantity, 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. Flow rate will be given the symbol Q. Total flow, Qt, is the flow rate of air leaving the RPT device. Vent flow, Qv, is the flow rate of air leaving a vent to allow washout of exhaled gases. Leak flow, Ql, is the flow rate of unintentional leak from a patient interface system. Respiratory flow, Qr, is the flow rate of air that is received into the patient's respiratory system.

Leak: Preferably, the word leak will be taken to be a flow of air to the ambient. Leak may be intentional, for example to allow for the washout of exhaled CO2. Leak may be unintentional, for example, as the result of an incomplete seal between a mask and a patient's face. In one example leak may occur in a swivel elbow.

8.4 Other Remarks

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 the Patent and Trademark Office patent file or records, but otherwise reserves all copyright rights whatsoever.

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.

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.

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.

When a particular material is identified as being preferably 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.

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.

All publications mentioned herein are incorporated by reference 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.

Moreover, in interpreting the disclosure, all terms should be interpreted in the broadest reasonable manner consistent with the context. In particular, 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.

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.

Although the technology herein has been described with reference to particular embodiments, it is to be understood that these embodiments 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.

It is therefore to be understood that numerous modifications may be made to the illustrative embodiments and that other arrangements may be devised without departing from the spirit and scope of the technology.

8.5 Reference Signs List

patient1000sensor unit1200patient interface3000seal-forming structure3100plenum chamber3200stabilising structure3300vent3400connection port3600forehead support3700RPT device4000external housing4010upper portion4012portion4014panel4015chassis4016handle4018pneumatic block4020pneumatic component4100inlet air filter4112inlet muffler4122outlet muffler4124pressure generator4140blower4142air circuit4170electrical component4200PCBA4202electrical power supply4210input device4220central controller4230therapy device controller4240protection circuit4250memory4260transducer4270pressure sensor4272flow sensor4274data communication interface4280remote external communication network4282local external communication network4284remote external device4286local external device4288output device4290display driver4292display4294algorithm4300humidifier5000monitoring apparatus7000microcontroller unit7001memory7002movement signal7003communications circuitry7004external computing device7005processor7006connection7008motion sensor7010display device7015audio output7017transmitter7020receiver7030local oscillator7040antenna7050radio frequency signal7060reflected signal7070mixer7080monitoring method7100step7110step7120step7130step7140step7150step7160method7200movement detection module7210presence/absence detection module7220sleep/wake analysis module7230breathing rate estimation module7240signal selection module7250modulation cycle metrics calculation module7255envelope generation module7260SDB event detection module7265SDB event confirmation module7270feature calculation module7280method7300step7310step7320step7330step7340step7350step7360method7400step7410step7420step7430step7440step7450step7460step7470step7480step7490graph8000trace8010grey band8015upward arrow8020trace8050peak8060