A SLEEP MONITORING SYSTEM AND METHOD

Abstract

A sleep monitoring system has a movement sensing arrangement and a controller for identifying from the movement sensing arrangement output signals sleep-disordered breathing events. Seismocardiography signals are recorded and analyzed to determine repetitive patterns, from which an inter-beat interval time series is derived without identifying specific elements of the repetitive patterns. The sleep-disordered breath events are derived from the inter-beat interval time series. In this way, SCG signals can be used for sleep monitoring in a robust and reliable way.

Claims

1. A sleep monitoring system, for monitoring a subject, comprising: a movement sensing arrangement; and a controller, which is adapted during a sleep monitoring period to identify from the movement sensing arrangement output signals sleep-disordered breathing events, wherein the movement sensing arrangement comprises an acceleration or gyroscope sensor arrangement for recording seismocardiography signals, and the controller is adapted to determine the sleep-disordered breathing events based on analysis of the seismocardiography signals, wherein the controller is adapted to determine repetitive patterns in the seismocardiography signals to determine an inter-beat interval time series and determine the sleep-disordered breath events from the inter-beat interval time series, wherein the inter-beat interval time series is computed without identifying specific elements of the repetitive patterns; and characterized in that the controller is further adapted to: determine from output signals of the movement sensing arrangement a sleep position out of a set of possible sleep positions; and provide an indication of the level or impact of sleep-disordered breathing for each encountered sleep position.

2. The system as claimed in claim 1, wherein the sleep-disordered breath events comprise sleep apnea events.

3. The system as claimed in claim 1, further comprising one or more of: a sensor arrangement for detecting respiratory effort; a microphone for detecting breathing sounds.

4. The system as claimed in claim 1, wherein the controller is adapted to detect snoring from the breathing sounds.

5. The system as claimed in claim 1, wherein the controller is adapted to determine sleep stages, and thereby determine a sleep time period and an awake time period during the monitoring period.

6. The system as claimed in claim 1, wherein the controller is adapted to extract from the movement sensing arrangement output signals separate signal components, for detection of sleep position, respiratory movements and seismocardiography signals.

7. (canceled)

8. The system as claimed in claim 1, wherein the level of sleep-disordered breathing for each encountered sleep position comprises an apnea-hypopnea index value.

9. The system as claimed in claim 1, further comprising a position therapy device for providing a stimulus to induce the subject to change sleep position.

10. A sleep monitoring method for monitoring a subject, comprising: monitoring movements of the subject using a movement sensing arrangement which collects seismocardiography signals; and identifying from the seismocardiography signals sleep-disordered breathing events by determining repetitive patterns in the seismocardiography signals to determine an inter-beat interval time series, wherein the method comprises computing the inter-beat interval time series without identifying specific elements of the repetitive patterns; determining from the monitored movements a sleep position out of a set of possible sleep positions; and providing an indication of the level or impact of sleep-disordered breathing for each encountered sleep position.

11. (canceled)

12. The method as claimed in claim 10, further comprising providing a stimulus to induce the subject to change sleep position.

13. The computer program comprising computer program code means which is adapted, when said program is run on a computer, to implement the method of claim 10.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0066] For a better understanding of the invention, and to show more clearly how it may be carried into effect, reference will now be made, by way of example only, to the accompanying drawings, in which:

[0067] FIG. 1 shows a sleep monitoring and position therapy system;

[0068] FIG. 2 shows the signals of interest for ballistocardiography;

[0069] FIG. 3 shows the signals of interest for a seismocardiography;

[0070] FIG. 4 shows how SCG signals can be used to provide an indication of the level or impact of sleep-disordered breathing;

[0071] FIG. 5 shows a modification to FIG. 4 which further takes account of respiration;

[0072] FIG. 6 shows the method carried out by the overall system of FIG. 1;

[0073] FIG. 7 shows the signals from the three axes of a DC-response tri-axial accelerometer, when there are changes in body position;

[0074] FIG. 8 shows the signals from the three axes measured with a gyroscope for the same period as FIG. 7; and

[0075] FIG. 9 shows a modification to FIG. 6 in which one or more microphones are used to measure breathing sounds.

DETAILED DESCRIPTION OF THE EMBODIMENTS

[0076] The invention will be described with reference to the Figures.

[0077] It should be understood that the detailed description and specific examples, while indicating exemplary embodiments of the apparatus, systems and methods, are intended for purposes of illustration only and are not intended to limit the scope of the invention. These and other features, aspects, and advantages of the apparatus, systems and methods of the present invention will become better understood from the following description, appended claims, and accompanying drawings. It should be understood that the Figures are merely schematic and are not drawn to scale. It should also be understood that the same reference numerals are used throughout the Figures to indicate the same or similar parts.

[0078] The invention provides a sleep monitoring system which has a movement sensing arrangement and a controller for identifying from the movement sensing arrangement output signals sleep-disordered breathing events. Seismocardiography signals are recorded and analyzed to determine repetitive patterns, from which an inter-beat interval time series is derived without identifying specific elements of the repetitive patterns. The sleep-disordered breath events are derived from the inter-beat interval time series. In this way, SCG signals can be used for sleep monitoring in a robust and reliable way.

[0079] The sleep monitoring system may be used as part of as sleep monitoring and position therapy system, in which the movement sensing arrangement signals are also used to determine a sleep position. The level or impact of sleep-disordered breathing can then be determined for each encountered sleep position. In this way, information can be obtained about disordered breathing associated with different sleep positions, so that a position therapy system may provide improved results.

[0080] The invention is thus of particular interest for use in a sleep monitoring and position therapy system. However, the invention relates more generally to sleep monitoring only for the purpose of identifying sleep-disordered breathing events. Such a system may be used in applications other than sleep position therapy. The invention will, however, be described below in connection its used in a sleep monitoring and position therapy system.

[0081] FIG. 1 shows a sleep monitoring and position therapy system 10, for monitoring a subject 12. The system comprises a movement sensing arrangement 14 for monitoring movements induced by the beating of the heart and movements of the subject, for example between different sleeping positions. The movement sensing arrangement for example comprises an acceleration sensor such as an accelerometer or gyroscope sensor, mounted on a belt which positions the sensor on the chest of the subject over the heart 16. The movement sensing arrangement is thus positioned close to the location of the heart and is also preferably fixed to the surface of the chest, e.g. with an adhesive in contact with the skin, or by virtue of the integration in a stretchable belt. It may instead be integrated in a garment worn by the subject.

[0082] The movement sensing arrangement output signals (which will be termed “movement signals” below) are provided to a controller 18. The controller performs analysis of the movement signals to determine a sleep position out of a set of possible sleep positions and also to identify sleep-disordered breathing events.

[0083] The sleep positions for example include supine (facing upwards), prone (facing downwards) or on the left side or on the right side. These positions may be determined based on the position of the torso, or the neck/head position using a sensor mounted on the neck. More complex body positions could be detected, including for example when the head orientation differs from the torso orientation, which can also have an impact on the sleep apnea or the severity of sleep apnea. A combination of multiple sensors could for example be used for this purpose mounted on different parts of the body and/or or on or under the mattress.

[0084] In combination, this information is used to provide an indication of the level or impact of sleep-disordered breathing for each encountered sleep position. For this purpose, an output device 20 is shown. The system is also for providing position therapy, and for this purpose a position therapy device 22 is provided for generating a stimulus to induce the subject to change sleep position. This is shown as a speaker in FIG. 1, but any known position therapy device may be used as discussed above.

[0085] As discussed further below, the movement sensing arrangement 14 may include other sensing modalities, such as a sensor arrangement for detecting respiratory effort and a microphone for detecting breathing sounds.

[0086] The invention implements detection of breathing events based on movement signals. There are basically two different types of movement signal which may be used, described below.

[0087] Ballistocardiography (BCG) is a well-known method for measuring certain aspects of cardiac activity.

[0088] FIG. 2 shows the signals of interest. When the heart ejects blood (arrow 22), it does so mainly through the ascending aorta, in a longitudinal, upward direction. When the heart pulls blood from the body, it does so also in a longitudinal direction, parallel to the spine (arrow 24). These longitudinal forces exerted by the heart on the blood are, according to Newton's third law, matched by equal forces by the blood on the body, with opposite directions (arrows 26 and 28).

=−

[0089] By means of an accelerometer, the accelerations on the body caused by these forces are measured. According to Newton's second law, and since the mass can be considered constant during a small interval of a ballistocardiographic measurement, acceleration is directly related to the force exerted on a certain mass through the equation:

F=m.Math.a

[0090] It follows immediately that the acceleration measured on a body resting on a moving surface is related to the acceleration of the blood,

[00001]$a_{\mathrm{blood}}=\frac{-m_{\mathrm{body}+\mathrm{surface}}.Math.a_{\mathrm{body}+\mathrm{surface}}}{m_{\mathrm{blood}}}$

[0091] Since the mass of the body, surface and blood will stay approximately constant during a measurement, it follows that:

a.sub.blood=a.sub.body+surface.Math.c

[0092] where c is a negative constant.

[0093] Early ballistocardiograms were recorded with rather complicated apparatus, whereby a patient was laid down on either a suspended surface or on a surface with very low friction and a sensor would then measure longitudinal displacement, velocity or acceleration of that surface. Developments in digital electronics, signal processing and sensor technology have enabled more convenient and simpler set-ups. Since then, it has been shown that the ballistocardiogram can also be measured under the subject's body with sensors such as strain gauges, pressures sensors and load cells under the feet of a bed.

[0094] The example of FIG. 1 is based on an accelerometer mounted on the chest of the subject and set up to measure longitudinal accelerations. This can be used to measure the acceleration of the blood caused by cardiac activity.

[0095] It is known to make use of BCG signals for determining sleep apnea events. In particular, peaks of the BCG signal can be associated with different phases of the heart beat signal, making the measurement of the heart rate variability possible.

[0096] While BCG measures the ballistic effect of the beating heart, mainly in a direction approximately coinciding with a longitudinal direction, seismocardiography (SCG) measures the small vibrations produced on the surface of the chest by the heart contractions and blood ejection. These vibrations are mostly measurable in a direction orthogonal to the surface of the chest. This is shown as arrow 30 in FIG. 3 and can be picked up, for example, by sensitive accelerometers configured to respond to orthogonal accelerations when mounted on the chest, close to the approximate location of the heart.

[0097] FIG. 4 shows how SCG signals can be used to provide an indication of the level or impact of sleep-disordered breathing.

[0098] The movement sensing arrangement provides movement signals, in particular acceleration signals, in step 40. This is achieved with a sensor which measures seismocardiographic forces resulting from cardiac activity. An accelerometer or a gyroscope is configured to respond to forces and/or accelerations orthogonal to the surface of the chest wall for SCG. Longitudinal forces, along the direction of the body (or the bed), would be used for BCG.

[0099] The movement signals are converted to a measure of the degree of sleep-disordered breathing in step 42. In this example, the AHI measure described above is obtained.

[0100] Step 42 comprises the sub-steps of obtaining the SCG signals in step 44, and determining the inter-beat intervals (IBI) in step 46. This for example involves detecting the timing of each individual heart beat pattern, and computing a time series based on the distance between consecutive heart beats.

[0101] It is noted that the processing to obtain the IBI is different for the SCG signals compared to the known processing of BCG signals. Although seismocardiography (SCG) is, to a certain extent, related to ballistocardiography (BCG) insofar as both measure the mechanical (as opposed to electrical) activity of the heart, the determination of the IBI for an SCG is less straightforward, since the signal is far more complex than a BCG signal. Thus, different algorithms are needed.

[0102] One approach is to find repetitive patterns in the SCG signal which have an expected length (determined by the shortest and longest duration between heart beats) without relying on the presence of peaks per se, as is used in the analysis of BCG (and ECG) signals. In particular, analysis of a BCG signal involves identifying the presence of components of the signal which are related to specific events of the cardiac cycle (e.g. the J-peak in the BCG). The SCG signal has a more complex and variable shape, so instead of relying on the identification of a specific peak or component of the signal, the distance between these repetitive patterns is identified in order to determine the inter-beat interval. Thus, the step of detecting the precise location of heart beats in the signal based on specific elements of the signal is not carried out (as for BCG). From the repetitive occurrence of a pattern, the inter-beat intervals can be computed without identifying specific elements of that pattern.

[0103] In step 48, there is the detection of breathing events (BE). The inter-beat interval (IBI) time series is used as an input to a pre-trained machine learning model, by which the system automatically detects the presence of breathing events, such as obstructive apneas, central apneas, and hypopneas.

[0104] The detection of breathing events can be performed using any or a combination of known methods which are based on ECG signals. Although the sensing modality is fundamentally different (electrical activity for ECG, in contrast with mechanical activity for SCG), the resulting inter-beat intervals are comparable and as such, known methods can be applied.

[0105] These can be divided in two main categories, which can be used alternatively, or in combination:

[0106] A first approach is to use the IBI series directly as the input to a machine learning classifier. In this case the classifier is trained to recognize certain patterns directly of the IBI time series and use those patterns to detect the presence of breathing events. Modern machine deep learning methods make this a viable option, with the advantage of requiring less development time and less domain knowledge to achieve the end goal. They have the disadvantage of requiring a large (and representative) enough training data set.

[0107] A second approach is to use the IBI series to derive a number of intermediate, manually engineered parameters which are known to describe different patterns and behaviors of IBIs during and following breathing events. These features, commonly describing heart rate variability (HRV) parameters, can be based on the time and frequency analysis of the IBI series and have been described in literature to be successful for this task. This approach has the advantage of using domain knowledge to reduce the complexity and training requirements of the machine learning classifier, often requiring less data to achieve a good performance.

[0108] In step 50 the AHI is calculated, reflecting the average number of breathing events per hour.

[0109] FIG. 5 shows a modification to FIG. 4 which further takes account of respiration.

[0110] The derivation of the AHI from the movement signals further comprises the sub-step of obtaining a respiration signal in step 52.

[0111] Respiratory effort may be measured with sensors placed on the subject's body. The air inhaled via the nose or the mouth causes the lungs to inflate and, consequently, the chest circumference to expand. Because the volume of the chest will increase, it will exert a downwards force on whatever surface the subject is lying on and an upward force on whatever sensor might be mounted on the chest of the subject. Thus, respiration will also generate a force as shown by arrow 30 in FIG. 3, but with a different frequency spectrum to the forces created by the heart beat.

[0112] For measuring BCG signals, a movement sensing arrangement configured to respond to both orthogonal and well as longitudinal forces, placed on the chest of the subject, close to the heart, could be be used simultaneously to measure BCG signals as well as respiratory activity. The separation of the BCG signals from the respiratory activity signals is then simply based on the direction of sensing.

[0113] For measuring SCG signals, orthogonal forces alone may be used to measure simultaneously SCG signals as well as respiratory activity. The separation of the SCG signals from the respiratory activity signals is then based on a spectral analysis.

[0114] As shown in FIG. 5, the respiratory movement signals are used as an additional input for the detection of breathing events in step 48. This can be done with the “raw” respiratory movement signal (i.e. without the optional step 54), in which case the machine learning (deep) model can learn the patterns in the respiratory movements that are associated with the presence of breathing events, or based on defined features describing time (amplitude, volume, depth, etc.) or frequency (rate, regularity, etc.) characteristics of the signal.

[0115] The optional step 54 in FIG. 5 is to determine inter-breath intervals (IBrI).

[0116] FIG. 5 shows the separation of the different components of the movement signal into two branches prior to the estimation of AHI. This serves more than the mere purpose of filtering the SCG signal to be able to detect inter-beat intervals. A further component is also used for position sensing (as shown in FIG. 6, described below).

[0117] Thus, the movement signal will have different components in different directions, and the processing of different components is able to separate SCG signals from BCG signals (since both may be picked up by the motion sensing arrangement) such that the SCG signals can be processed in accordance with the invention. Different components are also caused by the influence of different physiological mechanisms on the signal, such as the influence of body position, where the gravitational component has an influence on signals of all acceleration directions not orthogonal to the direction of gravity, and the influence of breathing and chest movements which are most visible on signals orthogonal to the surface of the torso/chest. These components can be identified and separated in different ways. For example, a tri-axial accelerometer will respond to displacements and accelerations in well-defined directions. The alignment of the accelerometer can be such that one axis is aligned in a direction orthogonal to the surface of the chest, and hence also along the direction of gravity when lying in a supine or prone position. Another axis could be aligned along the gravity direction when lying on the side. The third axis could be mounted in a direction perfectly orthogonal to gravity when in those two positions, responding only to displacements/accelerations in the longitudinal direction. In this case, the first axis would have components of SCG, breathing and gravity (when lying on supine or prone positions), the second would have components of gravity when lying on the side, and the third would have components of BCG.

[0118] The different physiological components in the signal may also be separated using different band-pass filtering strategies (configured to respond to expected signal frequency bands for each physiological component), time-frequency analysis techniques such as wavelet decompositions of the accelerometer signal(s), or other non-linear techniques such as empirical mode decomposition.

[0119] The aim of these techniques is to separate the different physiological processes (body position and body movements, breathing, and cardiac activity) in the signals for further (separate) processing.

[0120] FIG. 6 shows the method carried out by the overall system.

[0121] The determination of AHI is carried out in steps 40 and 42 as explained above with reference to FIGS. 4 and 5.

[0122] In addition, the movement signals are used to determine the body position (BP) of the subject in step 60. This can be achieved when tri-axial accelerometers are used, so that the lying position of the subject can be identified. This body position is used to implement position therapy (PT) in step 62. A position therapy system may be passive (by providing a mechanism to prevent a subject sleeping in a supine position) or active. An active approach involves applying a stimulus to the subject to induce them to move position when they are in a supine position.

[0123] As mentioned above, this active stimulus may involve an alarm system or vibro-tactile feedback technology. Sensors used for the position therapy may be at additional body locations (not only on the chest) such as for measuring head position (with a tri-axial accelerometer mounted on the head (e.g. on a head-band, or on another face-mounted device such as a CPAP mask), or even mounted outside the body, for example on a sensor mattress which is able to measure the body position.

[0124] An output is provided in step 64 to users (i.e. the subject) and/or clinicians in order to give feedback about the effectiveness of the position therapy.

[0125] Furthermore, the output provides an indication of the level or impact of sleep-disordered breathing for each encountered sleep position. This takes place in step 66, wherein the AHI value obtained in step 42 is processed using the body position information from step 60. In the example of FIG. 6, this is represented as an AHI value for each body position, i.e. an AHI value which is a function of body position (AHI=f(BP)). In other words, position-specific AHI values are determined.

[0126] In standard position therapy PT, after each night or after a number of nights, the subject and/or the referring clinician can have information about the percentage of time the subject was lying in a supine position, and the percentage of time lying sideways; however, this information might not be sufficient to assess the efficacy of the therapy. The system of FIG. 6 enables the information to be complemented with information about the overall AHI (which should reflect residual—i.e. untreated apneas/hypopneas) and position-specific AHI, which should highlight whether and how position therapy is helping prevent additional breathing events.

[0127] Different sensor options are possible to measure the SCG signals. These sensors can consist of a selected one of different sensor types, or a combination of different types of sensors, including accelerometers (AC- or DC-response) or gyroscopes. As is clear from the discussion above, they may respond to the forces and accelerations in a direction orthogonal to the chest wall and/or the forces and accelerations in the longitudinal direction (along the direction of the body/bed).

[0128] The same sensor used for the SCG (and optionally also BCG) measurements may be used to measure body position, needed for position therapy, and for this purpose a multi-axis (preferably tri-axial) DC-response accelerometer is preferable.

[0129] AC-response accelerometers, even when mounted on the body, cannot measure static accelerations such as those related to gravity. This makes them unsuitable for measuring body position. If AC-response accelerometers are desired, the system may comprise at least two accelerometers, one configured to measure body position (a DC-response accelerometer) and another (which may then be an AC-response accelerometer) configured to measure cardiac and respiratory information. The same is true when using gyroscopes instead of accelerometers. Gyroscopes may be used for SCG (and optionally also BCG) measurements. Gyroscopes do not respond in the same way to the pull of gravity, and cannot (easily) be used for measuring body position.

[0130] For example, FIG. 7 shows signals from the three axes of a DC-response tri-axial accelerometer, where changes in body position are clearly visible as “steps” in the signal.

[0131] FIG. 8 shows the signals from the three axes measured with a gyroscope for the same period as FIG. 7. It is less trivial to measure, on each point in the recording, the body position of the subject. Thus, the system may combine a gyroscope and a DC-response accelerometer mounted on the body.

[0132] It is instead possible to use a DC-response accelerometer mounted on the body as a single sensor. However, there are advantages to using an AC-response accelerometer or a gyroscope for the SCG measurement. By construction, because they do not respond to the component of gravity, they can use the entire dynamic range after analog-to-digital conversion without requiring dedicated filtering before conversion. This means that they can be more sensitive than DC-response accelerometers for a comparable resolution.

[0133] FIG. 9 shows a modification to FIG. 6 in which one or more microphones are used, in step 90, to measure breathing sounds. The microphone or microphones are preferably mounted as part of the movement sensing arrangement, but alternatively they can be contactless, for example mounted on the night table next to the sleeping subject, as a separate device connected to the system, or alternatively using a device with a microphone that the user might have (e.g. smartphone). The microphone or microphones are used to measure breathing sounds, in particular those associated with relevant breathing events that are known to be associated with OSAS, such as snoring, gasping, etc.

[0134] FIG. 9 also shows a step 92 of detecting snoring from the breathing sounds. Since positional therapy is known also to relieve snoring, this information can be further provided to the user and/or clinician, highlighting the efficacy of the therapy also to this (secondary) aspect.

[0135] The presence of these breathing sounds can also be used to further improve the detection of breathing events. This is shown in FIG. 9 by the dotted arrow from step 90 to step 42.

[0136] A further option is to use cardiac and respiratory information to automatically detect sleeping and wakening periods, and/or sleep stages. This is shown as step 94 in FIG. 9. This can be performed using many different known methods, for example as described in P. Fonseca, N. den Teuling, X. Long, and R. M. Aarts, ‘Cardiorespiratory sleep stage detection using conditional random fields,’ IEEE Journal of Biomedical and Health Informatics, vol. 21, no. 4, pp. 956-66, 2017.

[0137] For example, heart rate variability (HRV) may be computed from an ECG signal, but equally from the heart beats detected using the SCG or BCG signals, and respiratory variability (RV) for example derived from an accelerometer mounted on the torso. Based on HRV and RV characteristics that are known to be discriminative of different sleep stages, a pre-trained machine learning classifier may be used to predict, e.g. for each segment (e.g. of 30 seconds) in a recording, the sleep state (awake vs. asleep, or even awake vs. REM vs. N1 vs. N2).

[0138] Body movements may also be used to discriminate between awake and sleeping states.

[0139] This information can in turn be used to compute the total sleep time during each recording.

[0140] The total sleep time can then be used, together with a calculation of the number of apneas and hypopneas, to compute the sleeping AHI, by dividing the number of detection events (apneas plus hypopneas) by the total sleep time, arriving at the number of events per hour of sleep.

[0141] This estimation is more accurate than the estimation based on total recorded AHI (number of events per hour of recording). The difference in accuracy is particularly visible for subjects that spend a large part of the night awake, thereby reducing the recorded AHI value, and hence giving an underestimated view on the severity of their condition.

[0142] The detected sleep stages can also be used to provide additional feedback to users and/or clinicians regarding their sleep architecture and overall quality of sleep.

[0143] The invention makes use of motion sensing for both cardiac monitoring and position detection. For example, heart beats are measured unobtrusively and possibly without direct skin contact, and potentially embodied in the same module as used for PT.

[0144] The SCG signals are obtained based on a sensor mounted against the subject in the examples above. However, these signals can also be measured with bed sensors, mounted on or below the mattress. This has the disadvantage of requiring additional elements separate from the main PT device.

[0145] The examples above derive an AHI value. However, instead of calculating an estimate of AHI, a similar algorithm could be configured to provide an alternative measure that reflects the impact of apneas on the overall quality of sleep; such a measure could combine a quantification or qualification of one or more of the following elements:

[0146] a measure of the intensity and/or frequency of disordered breathing events;

[0147] a measure of the effect of breathing events on the architecture of sleep, for example, on the number of awakenings, the number of transitions between sleep stages, and/or percentage of sleep stages known to be affected by the presence of disordered breathing, such as N3 (also known as slow wave sleep or deep sleep) or REM sleep; and

[0148] a measure of the number of (autonomic) arousals following disordered breathing events.

[0149] Although these alternative measures would not have the same meaning and definition as AHI, it should be clear that they also reflect the effect of sleep apnea on the quality of sleep, and in turn, the improved effect arising from the use of PT.

[0150] Thus, the information of relevance to the subject or clinician is the level or impact of sleep-disordered breathing. This is derived for each encountered sleep position (i.e. each sleep position which the subject adopted during the sleep monitoring period). In this way, the position-specific impact/level is obtained as explained above.

[0151] As discussed above, embodiments make use of a controller. The controller can be implemented in numerous ways, with software and/or hardware, to perform the various functions required. A processor is one example of a controller which employs one or more microprocessors that may be programmed using software (e.g., microcode) to perform the required functions. A controller may however be implemented with or without employing a processor, and also may be implemented as a combination of dedicated hardware to perform some functions and a processor (e.g., one or more programmed microprocessors and associated circuitry) to perform other functions.

[0152] Examples of controller components that may be employed in various embodiments of the present disclosure include, but are not limited to, conventional microprocessors, application specific integrated circuits (ASICs), and field-programmable gate arrays (FPGAs).

[0153] In various implementations, a processor or controller may be associated with one or more storage media such as volatile and non-volatile computer memory such as RAM, PROM, EPROM, and EEPROM. The storage media may be encoded with one or more programs that, when executed on one or more processors and/or controllers, perform the required functions. Various storage media may be fixed within a processor or controller or may be transportable, such that the one or more programs stored thereon can be loaded into a processor or controller.

[0154] Another aspect provides a sleep monitoring and position therapy system, for monitoring a subject, comprising:

[0155] a movement sensing arrangement (14); and

[0156] a controller (28), which is adapted during a sleep monitoring period to: [0157] determine from the movement sensing arrangement output signals a sleep position out of a set of possible sleep positions; [0158] identify from the movement sensing arrangement output signals sleep-disordered breathing events; and [0159] provide an indication of the level or impact of sleep-disordered breathing for each encountered sleep position.

[0160] According to this aspect, the movement sensing arrangement (14) may comprise an acceleration or gyroscope sensor arrangement for recording ballistocardiography signals, and the controller is adapted to determine the sleep-disordered breathing events based on analysis of the ballistocardiography signals. The timing of each heart beat can be obtained from the movement sensing arrangement signals the sleep-disordered breath events are determined from an inter-beat interval time series.

[0161] The movement sensing arrangement (14) may instead comprise an acceleration or gyroscope sensor arrangement for recording seismocardiography signals, and the controller is adapted to determine the sleep-disordered breathing events based on analysis of the seismocardiography signals. Repetitive patterns in the movement sensing arrangement signals may then be derived to determine an inter-beat interval time series and determine the sleep-disordered breath events from the inter-beat interval time series.

[0162] Variations to the disclosed embodiments can be understood and effected by those skilled in the art in practicing the claimed invention, from a study of the drawings, the disclosure and the appended claims. In the claims, the word “comprising” does not exclude other elements or steps, and the indefinite article “a” or “an” does not exclude a plurality. A single processor or other unit may fulfill the functions of several items recited in the claims. The mere fact that certain measures are recited in mutually different dependent claims does not indicate that a combination of these measures cannot be used to advantage. The computer program discussed above may be stored/distributed on a suitable medium, such as an optical storage medium or a solid-state medium supplied together with or as part of other hardware, but may also be distributed in other forms, such as via the Internet or other wired or wireless telecommunication systems. If the term “adapted to” is used in the claims or description, it is noted the term “adapted to” is intended to be equivalent to the term “configured to”. Any reference signs in the claims should not be construed as limiting the scope.