Method and apparatus for determining information indicative of cardiac malfunctions and abnormalities

Abstract

An apparatus for determining information indicative of cardiac malfunctions and abnormalities includes a processing device (502) configured to detect amplitude variation from a signal indicative of cardiovascular motion, where the amplitude variation element variation of the amplitude of a wave pattern, e.g. the AO-peak, repeating on the heart-beat rate on the signal. The processing device is configured to determine, at least partly on the basis of the detected amplitude variation, an indicator of cardiac malfunction and abnormality.

Claims

1. An apparatus, comprising: a signal interface, that receives a signal indicative of cardiovascular motion, and that receives information indicative of a frequency of a respiratory rhythm; and a processing device coupled to the signal interface, wherein the processing device is configured to: detect peak-to-peak amplitude variation from the signal, the peak-to-peak amplitude variation being a variation of peak-to-peak amplitude of a wave pattern repeating on a heart-beat rate on the signal so that the peak-to-peak amplitude variation includes a plurality of increases of the peak-to-peak amplitude and a plurality of decreases of the peak-to-peak amplitude, compute a signal component of the peak-to-peak amplitude variation having the frequency of the respiratory rhythm, and produce a signal expressing atrial fibrillation in response to a situation in which the signal component of the peak-to-peak amplitude variation having the frequency of the respiratory rhythm is less than a threshold, and wherein, in detecting the peak-to-peak amplitude variations from the signal, the processing device operates to filter the signal, and the peak-to-peak amplitude variation is determined from peaks detected from the filtered signal.

2. The apparatus according to claim 1, wherein the apparatus further comprises: a sensor element that measures said signal, the sensor element being connectable to the signal interface.

3. The apparatus according to claim 2, wherein the sensor element provides to the signal interface the information indicative of the frequency of the respiratory rhythm.

4. The apparatus according to claim 2, wherein the sensor element comprises one selected from the group consisting of: an accelerometer, a piezo-electronic sensor, an inclinometer, and a pressure sensor.

5. The apparatus according to claim 1, wherein the processing device is configured to detect from the signal peak-to-peak values related to wave complexes, each of which being constituted by the AO-peak caused by opening of the aortic valve and the downward wave following the AO-peak, the wave pattern repeating on the heart-beat rate being constituted by the AO-peak and the downward wave following the AO-peak.

6. The apparatus according to claim 5, wherein the processing device is configured to low-pass filter the signal indicative of the cardiovascular motion and to detect the AO-peaks from the low-pass filtered signal.

7. The apparatus according to claim 6, wherein an upper limit frequency of the low-pass filtering is 30 Hz.

8. The apparatus according to claim 5, wherein the processing device is configured to band-pass filter the signal indicative of the cardiovascular motion and to detect AC-peaks from the band-pass filtered signal, the AC-peaks being caused by closures of the aortic valve.

9. The apparatus according to claim 8, wherein a pass-band of the band-pass filtering is from 40 Hz to 100 Hz.

10. A method, comprising: receiving a signal indicative of cardiovascular motion; applying a filter to said signal so as to generate a filtered signal indicative of said cardiovascular motion; detecting peak-to-peak amplitude variation from the filtered signal indicative of cardiovascular motion, the peak-to-peak amplitude variation being a variation of peak-to-peak amplitude of a wave pattern repeating on a heart-beat rate on the filtered signal, such that the peak-to-peak amplitude variation includes a plurality of increases of the peak-to-peak amplitude and a plurality of decreases of the peak-to-peak amplitude; computing a signal component of the peak-to-peak amplitude variation having a frequency of a respiratory rhythm; and producing a signal expressing atrial fibrillation in response to a situation in which the signal component of the peak-to-peak amplitude variation having the frequency of the respiratory rhythm is determined to be lower than a threshold.

11. The method according to claim 10, wherein the detecting of the peak-to-peak amplitude variation comprises detecting peak-to-peak values related to wave complexes, each of which being constituted by the AO-peak caused by opening of the aortic valve and the downward wave following the AO-peak.

12. The method according to claim 11, wherein the filter applies low-pass filtering to the signal indicative of the cardiovascular motion, and the detecting step detects the AO-peaks from the low-pass filtered signal.

13. The method according to claim 12, wherein an upper limit frequency of the low-pass filtering is 30 Hz.

14. The method according to claim 11, wherein the filter applies band-pass filtering to the signal indicative of the cardiovascular motion, and the detecting step detects AC-peaks from the band-pass filtered signal, the AC-peaks being caused by closures of the aortic valve.

15. The method according to claim 14, wherein a pass-band of the band-pass filtering is from 40 Hz to 100 Hz.

16. A non-transitory computer readable medium encoded with a computer program comprising computer executable instructions for controlling a programmable processor, the computer executable instructions configured such to, upon execution by the programmable processor, cause the programmable processor to: receive a signal indicative of cardiovascular motion; apply a filter to said signal so as to generate a filtered signal indicative of said cardiovascular motion; detect peak-to-peak amplitude variation from the filtered signal indicative of cardiovascular motion, the peak-to-peak amplitude variation being variation of peak-to-peak amplitude of a wave pattern repeating on a heart-beat rate on the filtered signal, such that the peak-to-peak amplitude variation includes a plurality of increases of the peak-to-peak amplitude and a plurality of decreases of the peak-to-peak amplitude; compute a signal component of the amplitude variation having a frequency of a respiratory rhythm; determine whether the signal component of the peak-to-peak amplitude variation having the frequency of the respiratory rhythm is lower than a threshold; and in the event the signal component of the peak-to-peak amplitude variation having the frequency of the respiratory rhythm is determined to be lower than the threshold, produce a signal expressing atrial fibrillation.

Description

BRIEF DESCRIPTION OF THE FIGURES

(1) The exemplifying embodiments of the invention and their advantages are explained in greater detail below with reference to the accompanying drawings, in which:

(2) FIG. 1a illustrates an example of an ECG waveform and FIG. 1b illustrates a waveform of an exemplifying signal indicative of cardiovascular motion and measured with an accelerometer in the head-to-foot-direction that is typically referred to as the y-direction,

(3) FIG. 2a illustrates a flow chart of a method according to an exemplifying embodiment of the invention for determining information indicative of cardiac malfunctions and abnormalities,

(4) FIG. 2b illustrates a flow chart of a method according to an exemplifying embodiment of the invention for extracting AO data and/or AC data from a signal indicative of the cardiovascular motion,

(5) FIG. 3a illustrates a waveform of an exemplifying signal indicative of cardiovascular motion over several heart-beat periods in a normal case when a person under consideration holds breath, FIG. 3b illustrates a waveform of an exemplifying signal indicative of cardiovascular motion over several heart-beat periods in a case of atrial fibrillation when a person under consideration holds breath, these waveforms have been measured with an accelerometer in the through chest-direction that is typically referred to as the z-direction,

(6) FIG. 3c shows a part of the waveform shown in FIG. 3b,

(7) FIG. 3d illustrates a waveform of an exemplifying signal indicative of cardiovascular motion over several heart-beat periods in a normal case when a person under consideration is breathing, FIG. 3e illustrates a waveform of an exemplifying signal indicative of cardiovascular motion over several heart-beat periods in a case of atrial fibrillation when a person under consideration is breathing, these waveforms have been measured with an accelerometer in the through chest-direction that is typically referred to as the z-direction,

(8) FIG. 4 illustrates an exemplifying time variation in a normal case, solid line, and an exemplifying time variation in a case of atrial fibrillation, dashed line, and

(9) FIG. 5 shows a schematic illustration of an apparatus according to an exemplifying embodiment of the invention for determining information indicative of cardiac malfunctions and abnormalities.

(10) FIGS. 1a and 1b have already been explained when describing the background of the invention.

DESCRIPTION OF THE EXEMPLIFYING EMBODIMENTS

(11) FIG. 2a illustrates a flow chart of a method according to an exemplifying embodiment of the invention for determining information indicative of cardiac malfunctions and abnormalities, e.g. atrial fibrillation. The method comprises in a phase 201 detecting amplitude variation from a signal indicative of cardiovascular motion, where the amplitude variation means variation of amplitude of a wave pattern repeating on the heart-beat rate on the signal. The method comprises in a phase 202 determining, at least partly on the basis of the detected amplitude variation, an indicator of cardiac malfunction and abnormality.

(12) FIG. 3a illustrates an exemplifying waveform of the above-mentioned signal over several heart-beat periods in an exemplifying normal case when a person under consideration holds breath, and FIG. 3b illustrates an exemplifying waveform of the signal over several heart-beat periods in an exemplifying case of atrial fibrillation when a person under consideration holds breath. The waveforms shown in FIGS. 3a and 3b have been measured with an accelerometer in the through chest-direction that is typically referred to as the z-direction. A coordinate system 550 shown in FIG. 5 illustrates the z-direction.

(13) The amplitude variation in the normal case is illustrated by an envelope curve 311 shown in FIG. 3a, and the amplitude variation in the exemplifying case of atrial fibrillation is illustrated with an envelope curve 312 shown in FIGS. 3b and 3c. As can be seen from FIG. 3b, the amplitude variation includes a plurality of increases of the amplitude and a plurality of decreases of the amplitude within about ten successive heart-beat periods HB, and the increases and the decreases are interleaved with each other in a substantially stochastic way. As illustrated by FIGS. 3a and 3b, the strength of the amplitude variation represents information indicative of cardiac malfunction and abnormality.

(14) FIG. 3d illustrates an exemplifying waveform of the above-mentioned signal over several heart-beat periods in an exemplifying normal case when a person under consideration is breathing, and FIG. 3e illustrates an exemplifying waveform of the signal over several heart-beat periods in an exemplifying case of atrial fibrillation when a person under consideration is breathing. The waveforms shown in FIGS. 3d and 3e have been measured with an accelerometer in the through chest-direction that is typically referred to as the z-direction. A coordinate system 550 shown in FIG. 5 illustrates the z-direction. The amplitude variation in the normal case is illustrated by an envelope curve 321 shown in FIG. 3d, and the amplitude variation in the exemplifying case of atrial fibrillation is illustrated with an envelope curve 322 shown in FIG. 3e. As can be seen from FIG. 3d, the amplitude variation in the normal case has a clear signal component having the frequency of the breathing rhythm. As can be seen from FIG. 3e, there is no clear signal component having the frequency of the breathing rhythm in the case of atrial fibrillation.

(15) In a method according to an exemplifying embodiment of the invention: the detection of the amplitude variation, i.e. the phase 201 shown in FIG. 2a, comprises computing the above-mentioned signal component of the amplitude variation having the frequency of the respiratory rhythm, and the determining of the indicator of cardiac malfunction and abnormality, i.e. the phase 202, comprises determining the indicator of cardiac malfunction and abnormality at least partly on the basis of the strength of the signal component of the amplitude variation having the frequency of the respiratory rhythm.

(16) The signal component of the amplitude variation having the frequency of the respiratory rhythm can be computed, for example, in the same way as a Fourier component having a given frequency is computed, i.e. by correlating a time trend of the amplitude with sine and/or cosine functions having the frequency of the respiratory rhythm.

(17) The above-mentioned signal component of the amplitude variation having the frequency of the respiratory rhythm is indicative of cardiac malfunction and abnormality, and it can be compared to a threshold in order to detect occurrence of cardiac malfunction and abnormality. The threshold can be determined on the basis of empirical data gathered from a group of patients and/or other persons. The threshold is not necessary a constant but the threshold can be changing according to the individual under consideration, according to time, and/or according to some other factors. It is also possible to construct a series of thresholds so that each threshold represents a specific probability of atrial fibrillation or some other cardiac malfunction and/or abnormality.

(18) FIG. 3c shows a part of the waveform shown in FIG. 3b. The waveform comprises AO-peaks caused by openings of the aortic valve and downwards waves MA each of which takes place just after the respective AO-peak.

(19) In a method according to an exemplifying embodiment of the invention: the detection of the amplitude variation, i.e. the phase 201 shown in FIG. 2a, comprises detecting peak-to-peak values related to wave complexes each of which being constituted by the AO-peak and the downward wave MA following the AO-peak, and the determining of the indicator of cardiac malfunction and abnormality, i.e. the phase 202, comprises computing an amplitude variation quantity indicative of the strength of the variation of the detected peak-to-peak values.

(20) The amplitude variation quantity is indicative of cardiac malfunction and abnormality and it can be compared to a threshold in order to detect occurrence of cardiac malfunction and abnormality. The threshold can be determined on the basis of empirical data gathered from a group of patients and/or other persons. The threshold is not necessary a constant but the threshold can be changing according to the individual under consideration, according to time, and/or according to some other factors. It is also possible to construct a series of thresholds so that each threshold represents a specific probability of atrial fibrillation or some other cardiac malfunction and/or abnormality.

(21) The amplitude variation quantity can be, for example:
RMS.sub.p-pAVE.sub.p-p,
where RMS.sub.p-p is the root-mean-square RMS of the detected peak-to-peak values and AVE.sub.p-p is the arithmetic average of the detected peak-to-peak values. For another example, the strength of the amplitude variation can be expressed with the aid of the standard deviation of the detected peak-to-peak values, i.e. amplitude variation quantity can be the standard deviation of the detected peak-to-peak values.

(22) Furthermore, the amplitude variation quantity can be indicative of variation of a ratio S1/S2 or of a difference S1S2, where: S1 is a signal indicative of cardiovascular motion measured in the through chest-direction, i.e. the z-direction, and S2 is a signal indicative of cardiovascular motion measured in the head-to-foot-direction, i.e. the y-direction.

(23) The coordinate system 550 shown in FIG. 5 illustrates the y- and z-directions.

(24) Especially, the signal S1 can be the OA-peak shown in FIG. 3c and the signal S2 can be the J-peak shown in FIG. 1b.

(25) Each peak value, e.g. the height of a single AO-peak shown in FIG. 3c, can be taken as a single point by searching a local maximum. Alternatively, the peak value can be obtained so that many samples are taken first from a time-window covering the peak under consideration and then the peak value is computed as a mathematical function, e.g. an arithmetic mean, of the samples in order to mitigate the effect of noise. The time window can be e.g. 100 ms, and the number of samples within the time window can be e.g. ten or more. The method based on the time-window is an example of digital filtering. Generally, there are numerous digital and analogue signal processing methods that can be used for mitigating the effect of noise in the signals indicative of cardiovascular motion.

(26) It is to be noted that there are numerous ways to express the strength of the amplitude variation and the present invention is not limited to any particular ways of expressing the strength of the amplitude variation.

(27) In a method according to another exemplifying embodiment of the invention: the detection of the amplitude variation, i.e. the phase 201 shown in FIG. 2a, comprises detecting maximum signal values of the AO-peaks, and the determining of the indicator of cardiac malfunction and abnormality, i.e. the phase 202, comprises computing an amplitude variation quantity indicative of the strength of the variation of the detected maximum signal values.

(28) A solid curve 401 in FIG. 4 illustrates variation of temporal lengths of successive heart-beat periods, i.e. the time variation, in an exemplifying normal case. The temporal length of a single heart-beat period is denoted with HB in FIG. 3b. A dashed curve 402 in FIG. 4 illustrates the time variation in an exemplifying case of atrial fibrillation. As illustrated by FIG. 4, also the strength of the time variation represents information indicative of cardiac malfunction and abnormality.

(29) A method according to an exemplifying embodiment of the invention comprises, in addition to utilizing the amplitude variation, detecting the time variation from the signal indicative of cardiovascular motion. The indicator of cardiac malfunction and abnormality is advantageously determined on the basis of both the amplitude variation and the time variation in order to improve the reliability of the information indicative of cardiac malfunctions and abnormalities.

(30) In a method according to an exemplifying embodiment of the invention, the detection of the time variation comprises detecting temporal lengths of time intervals between successive AO-peaks, and computing a time variation quantity that is indicative of the strength of the variation of the detected temporal lengths.

(31) The indicator of cardiac malfunction and abnormality can be formed, for example, with the aid of a mathematical or logical operation from the amplitude variation quantity indicative of the strength of the amplitude variation and the time variation quantity indicative of the strength of the time variation. It is also possible that the amplitude variation quantity and the time variation quantity are used separately; e.g. each of them is compared to its own threshold.

(32) A method according to an exemplifying embodiment of the invention comprises producing a signal expressing that atrial fibrillation is taking place in response to a situation in which at least one of the following takes place: the strength of the amplitude variation exceeds a first threshold, the strength of the time variation exceeds a second threshold. In this exemplifying embodiment, the risk that atrial fibrillation remains undetected is reduced by indicating the occurrence of the atrial fibrillation if at least one of the amplitude variation and the time variation indicates the occurrence.

(33) A method according to another exemplifying embodiment of the invention comprises producing a signal expressing that atrial fibrillation is taking place if and only if the strength of the amplitude variation exceeds a first threshold and the strength of the time variation exceeds a second threshold. In this exemplifying embodiment, the risk of false alarms is reduced by indicating the occurrence of the atrial fibrillation if and only if both the amplitude variation and the time variation indicate the occurrence.

(34) FIG. 2b illustrates a flow chart of a method according to an exemplifying embodiment of the invention for extracting AO data and/or AC data from a signal indicative of the cardiovascular motion. The signal indicative of the cardiovascular motion is advantageously measured with an accelerometer in the through chest-direction that is typically referred to as the z-direction. The method comprises the following actions: action 211: low-pass filtering the signal indicative of the cardiovascular motion and/or band-pass filtering the signal indicative of the cardiovascular motion, and action 212: detecting the AO-peaks from the low-pass filtered signal and/or detecting the AC-peaks from the band-pass filtered signal.

(35) The upper limit frequency of the low-pass filtering can be, for example but not necessarily, 30 Hz, and the pass-band of the band-pass filtering can be, for example but not necessarily, from 40 Hz up to 100 Hz. The low-pass filtering and/or the pass-band filtering facilitate the detection of the AO- and/or AC-peaks. The detected AO- and/or AC-peaks can be utilized when detecting for example the above-mentioned amplitude variation and/or the above-mentioned time variation. The detected AO- and/or AC-peaks can be used for many other purposes too, e.g. for detecting the heart-beat rate, the systolic intervals, and/or the diastolic intervals.

(36) A method according to an exemplifying embodiment of the invention comprises detecting temporal lengths of AC-AO intervals and computing a time variation quantity indicative of strength of variation of the detected temporal lengths of the AC-AO intervals, where each of the AC-AO intervals is a time interval from one of the AC-peaks to the following one of the AO-peaks and the time variation quantity is indicative of cardiac malfunction and abnormality.

(37) A method according to an exemplifying embodiment of the invention comprises detecting temporal lengths of AC-AO intervals and temporal lengths of AO-AO intervals and computing a ratio quantity indicative of the ratio between the temporal length of the AC-AO interval and the temporal length of the AO-AO interval within a same heart-beat period. Each of the AC-AO intervals is a time interval from one of the AC-peaks to the following one of the AO-peaks, each of the AO-AO intervals is a time interval from one of the AO-peaks to the following one of the AO-peaks, and the ratio quantity is indicative of cardiac malfunction and abnormality.

(38) A method according to an exemplifying embodiment of the invention comprises optionally measuring the signal indicative of cardiovascular motion with a sensor element from an individual's body. A method according to another exemplifying embodiment of the invention comprises reading this signal from a memory, in which case the signal has been measured earlier and recorded to the memory. A method according to an exemplifying embodiment of the invention comprises receiving the signal from an external data transfer system. Hence, the measuring is not an essential and necessary step of methods according to embodiments of the invention.

(39) FIG. 5 illustrates a schematic illustration of an apparatus according to an exemplifying embodiment of the invention for determining information indicative of cardiac malfunctions and abnormalities. The apparatus comprises a signal interface 501 for receiving a signal indicative of cardiovascular motion, and a processing device 502 coupled to the signal interface. The processing device is configured to: detect amplitude variation from the signal, the amplitude variation being variation of amplitude of a wave pattern repeating on the heart-beat rate on the signal, and determine, at least partly on the basis of the amplitude variation, an indicator of cardiac malfunction and abnormality.

(40) The indicator can be, for example, a message shown on a display screen 506.

(41) In an apparatus according to an exemplifying embodiment of the invention, the signal interface 501 is further configured to receive information indicative of frequency of a respiratory rhythm and the processing device 502 is configured to: compute a signal component of the amplitude variation having the frequency of the respiratory rhythm, and determine the indicator of cardiac malfunction and abnormality at least partly on the basis of the signal component of the amplitude variation having the frequency of the respiratory rhythm.

(42) In an apparatus according to an exemplifying embodiment of the invention, the processing device 502 is configured to produce a signal expressing atrial fibrillation in response to a situation in which the signal component of the amplitude variation having the frequency of the respiratory rhythm is less than a pre-determined threshold.

(43) An apparatus according to an exemplifying embodiment of the invention further comprises a sensor element 503 for measuring the signal indicative of cardiovascular motion from individual's body 510. The sensor element is connected to the signal interface via a data transfer link that can be, for example, a radio link or a corded link. The data transfer from the sensor element 503 to the signal interface 501 may take place either directly or via a data transfer network 505 such as e.g. a telecommunications network. It is also possible that the apparatus comprising the processing device is integrated with the sensor element. In this case, the signal interface is actually a simple wiring between the sensor element 503 and the processing device 502. In an apparatus according to an exemplifying embodiment of the invention, the sensor element 503 is also suitable for providing the information indicative of the frequency of the respiratory rhythm.

(44) The sensor element 503 may comprise, for example, an accelerometer, a piezo-electronic sensor, an inclinometer, a pressure sensor, or any other element suitable for measuring force, acceleration, displacement, or any other physical quantity related to and indicative of cardiovascular motion. The sensor element may further comprise, for example, an amplifier, a signal filter, and/or an analog-to-digital AD converter. An accelerometer can be, for example, a three-axis accelerometer which is capable of measuring movements independently in three mutually orthogonal directions x, y, and z of e.g. the coordinate system 550 shown in FIG. 5. In this case, the signal indicative of cardiovascular motion comprises three components and the signal can be, for example, pre-processed by forming its Euclidian norm, i.e. the absolute value of the three component vector indicative of cardiovascular motion.

(45) An apparatus according to an exemplifying embodiment of the invention is configured to record the signal within a time window having a fixed temporal start point and a fixed temporal end point or within a sliding time window having a fixed temporal length and moving along with elapsing time. The apparatus may comprise an internal memory 507 for recording the signal or the apparatus may comprise a data port for connecting to an external memory.

(46) In an apparatus according to an exemplifying embodiment of the invention, the processing device 502 is configured to detect peak-to-peak values related to wave complexes each of which being constituted by the AO-peak caused by opening of the aortic valve and the downward wave following the AO-peak, and to compute an amplitude variation quantity indicative of the strength of the variation of the detected peak-to-peak values.

(47) In an apparatus according to an exemplifying embodiment of the invention, the processing device 502 is configured to detect maximum signal values of the AO-peaks, and to compute an amplitude variation quantity indicative of the strength of the variation of the detected maximum signal values.

(48) In an apparatus according to an exemplifying embodiment of the invention, the processing device 502 is configured to detect time variation from the signal and to determine the indicator of cardiac malfunction and abnormality on the basis of both the amplitude variation and the time variation.

(49) In an apparatus according to an exemplifying embodiment of the invention, the processing device 502 is configured to detect temporal lengths of time intervals between successive AO-peaks, and to compute a time variation quantity indicative of the strength of the variation of the detected temporal lengths.

(50) In an apparatus according to an exemplifying embodiment of the invention, the processing device 502 is configured to produce a signal expressing atrial fibrillation in response to a situation in which at least one of the following takes place: the strength of the amplitude variation exceeds a first threshold, the strength of the time variation exceeds a second threshold, the signal component of the amplitude variation having the frequency of the respiratory rhythm is less than a third threshold.

(51) In an apparatus according to an exemplifying embodiment of the invention, the processing device 502 is configured to low-pass filter the signal indicative of the cardiovascular motion and to detect the above-mentioned AO-peaks from the low-pass filtered signal. A functional block 520 shown in FIG. 5 represents of the low-pass filtering, and a functional block 522 represents the detection of the AO-peaks.

(52) In an apparatus according to an exemplifying embodiment of the invention, the processing device 502 is configured to band-pass filter the signal indicative of the cardiovascular motion and to detect AC-peaks from the band-pass filtered signal, the AC-peaks being caused by the closures of the aortic valve. A functional block 521 shown in FIG. 5 represents of the band-pass filtering, and the functional block 522 represents the detecting the AC-peaks. In the exemplifying case illustrated in FIG. 5, there are both the low-pass filtering and the band-pass filtering and the functional block 522 represents the detection of both the AO- and AC-peaks. The upper limit frequency of the low-pass filtering can be, for example but not necessarily, 30 Hz, and the pass-band of the band-pass filtering can be, for example but not necessarily, from 40 Hz up to 100 Hz. The low-pass filtering and/or the pass-band filtering facilitate the detection of the AO- and/or AC-peaks. The detected AO- and/or AC-peaks can be utilized when detecting for example the above-mentioned amplitude variation and/or the above-mentioned time variation. The detected AO- and/or AC-peaks can be used for many other purposes too, e.g. for detecting the heart-beat rate, the systolic intervals, and/or the diastolic intervals.

(53) In an apparatus according to an exemplifying embodiment of the invention, the processing device 502 is configured to detect temporal lengths of AC-AO intervals and to compute a time variation quantity indicative of strength of variation of the detected temporal lengths of the AC-AO intervals. Each of the AC-AO intervals is a time interval from one of the AC-peaks to the following one of the AO-peaks and the time variation quantity is indicative of cardiac malfunction and abnormality.

(54) In an apparatus according to an exemplifying embodiment of the invention, the processing device 502 is configured to detect temporal lengths of AC-AO intervals and temporal lengths of AO-AO intervals and to compute a ratio quantity indicative of the ratio between the temporal length of the AC-AO interval and the temporal length of the AO-AO interval within a same heart-beat period. Each of the AC-AO intervals is a time interval from one of the AC-peaks to the following one of the AO-peaks, each of the AO-AO intervals is a time interval from one of the AO-peaks to the following one of the AO-peaks, and the ratio quantity is indicative of cardiac malfunction and abnormality.

(55) The processing device 502 can be, for example, implemented with one or more processor circuits, each of which can be a programmable processor circuit provided with appropriate software, a dedicated hardware processor such as, for example, an application specific integrated circuit ASIC, or a configurable hardware processor such as, for example, a field programmable gate array FPGA.

(56) An apparatus according to an exemplifying embodiment of the invention comprises means for pre-processing the signal indicative of cardiovascular motion prior to the detection of the amplitude variation. The pre-processing may comprise, for example, cancellation of noise caused by e.g. breathing, non-cardiovascular movements of the individual, tremble caused by external reasons, etc. The means for the pre-processing can be, for example, implemented with the processing device 502 or there can be one or more separate processing devices for the pre-processing.

(57) A computer program according to an exemplifying embodiment of the invention comprises software modules for determining information indicative of cardiac malfunctions and abnormalities. The software modules comprise computer executable instructions for controlling a programmable processor to: detect amplitude variation from a signal indicative of cardiovascular motion, the amplitude variation being variation of amplitude of a wave pattern repeating on the heart-beat rate on the signal, and determine, at least partly on the basis of the amplitude variation, an indicator of cardiac malfunction and abnormality.

(58) In a computer program according to an exemplifying embodiment of the invention, the software modules further comprise computer executable instructions for controlling the programmable processor to: compute a signal component of the amplitude variation having a frequency of a respiratory rhythm, and determine the indicator of cardiac malfunction and abnormality at least partly on the basis of the signal component of the amplitude variation having the frequency of the respiratory rhythm.

(59) The software modules can be e.g. subroutines or functions implemented with a suitable programming language and with a compiler suitable for the programming language and the programmable processor.

(60) In a computer program according to an exemplifying embodiment of the invention, the software modules comprise at least one of the following: computer executable instructions for controlling a programmable processor to low-pass filter a signal indicative of the cardiovascular motion and to detect the AO-peaks from the low-pass filtered signal, and/or computer executable instructions for controlling a programmable processor to band-pass filter the signal indicative of the cardiovascular motion and to detect the AC-peaks from the band-pass filtered signal.

(61) A computer program product according to an exemplifying embodiment of the invention comprises a computer readable medium, e.g. a compact disc (CD), encoded with a computer program according to an embodiment of invention.

(62) A signal according to an exemplifying embodiment of the invention is encoded to carry information defining a computer program according to an embodiment of invention.

(63) Certain exemplifying new technologies for determining information indicative of cardiac malfunctions and abnormalities are based on the autocorrelation and/or the frequency spectrum of a signal indicative of cardiovascular motion.

(64) The autocorrelation, as a general concept, has the property that, when irregularity of the waveform of a signal under consideration increases, the autocorrelation gets more and more concentrated to a point that corresponds to zero time-shift between signal samples whose mutual correlation is expressed by the autocorrelation. The autocorrelation R.sub.e of an example signal S.sub.e(t) can be defined, for example, as:
R.sub.e()=E{(S.sub.e(t))(S.sub.e(t))}/.sup.2,
where E is the expected value operator, i.e. E{signal} is the expected value of the signal, t is time, is a time-shift between signal samples whose mutual correlation is expressed by R.sub.e(), and and .sup.2 are the mean and the variance of the example signal S.sub.e(t). For example, if the example signal S.sub.e(t) were ideal white noise IWN which has extremely irregular waveform, there would be zero correlation between any signal samples separated by a non-zero time-shift, and thus the autocorrelation R.sub.e() of the example signal would be only a single peak at the point =0.

(65) Such cardiac malfunctions and abnormalities, e.g. atrial fibrillation, which may be sometimes challenging to diagnose, may however cause irregularities on the waveform of the signal indicative of cardiovascular motion. These irregularities may be difficult to detect from waveforms of one or two heart-beat periods but they manifest themselves in longer time periods covering several successive heart-beat periods so that the autocorrelation is more concentrated on the point that corresponds to zero time-shift than normally. Therefore, a quantity that indicates the degree of concentration of the autocorrelation to the point that corresponds to the zero time-shift represents information indicative of cardiac malfunctions and abnormalities, e.g. atrial fibrillation.

(66) The obtaining the quantity indicating the degree of concentration of the autocorrelation can be based on computing an estimate of the autocorrelation or on computing an estimate of the frequency spectrum of the signal. The frequency spectrum is closely related to the autocorrelation because the power spectral density PSDF(f)F*(f) is the Fourier transformation of the autocorrelation, where F(f) and F*(f) are the frequency spectrum and its complex conjugate. The frequency spectrum, as a general concept, has the property that, when irregularity of the waveform of a signal under consideration increases, the frequency spectrum gets more and more evenly distributed. For example, if an example signal were ideal white noise IWN which has extremely irregular waveform, the frequency spectrum of the example signal would be totally flat. This property of the frequency spectrum can be used for obtaining the quantity indicating the degree of concentration of the autocorrelation.

(67) Certain exemplifying new apparatuses for determining information indicative of cardiac malfunctions and abnormalities are described below with the aid of numbered Clauses 1-6:

(68) Clause 1. An apparatus comprising: a signal interface for receiving a signal indicative of cardiovascular motion, and a processing device coupled to the signal interface and configured to: a) form an estimate of at least one of the following: the autocorrelation of the signal, the frequency spectrum of the signal, and b) form an indicator quantity that indicates the degree of concentration of the autocorrelation of the signal to a point that corresponds to zero time-shift between signal samples whose mutual correlation is expressed by the autocorrelation, the indicator quantity being indicative of cardiac malfunction and abnormality.

(69) Clause 2. An apparatus according to Clause 1, wherein the apparatus further comprises a sensor element for measuring the signal, the sensor element being connected to the signal interface via a data transfer link.

(70) Clause 3. An apparatus according to Clause 2, wherein the sensor element comprises one of the following: an accelerometer, a piezo-electronic sensor, an inclinometer, a microphone, a pressure sensor.

(71) Clause 4. An apparatus according to any of Clauses 1-3, wherein the processing device is configured to produce an indicator of atrial fibrillation in response to a situation in which the ratio P.sub.max/B.sub.R is at least 1.3, where P.sub.max is a value of the highest peak of the estimate of the autocorrelation and B.sub.R is an average of values of one or more next highest peaks of the estimate of the autocorrelation, the ratio P.sub.max/B.sub.R representing the indicator quantity.

(72) Clause 5. An apparatus according to any of Clauses 1-3, wherein the processing device is configured to produce an indicator of atrial fibrillation in response to a situation in which the ratio P.sub.max/B.sub.R is at least 1.4, where P.sub.max is a value of the highest peak of the estimate of the autocorrelation and B.sub.R is an average of values of one or more next highest peaks of the estimate of the autocorrelation, the ratio P.sub.max/B.sub.R representing the indicator quantity.

(73) Clause 6. An apparatus according to any of Clauses 1-3, wherein the processing device is configured to produce an indicator of atrial fibrillation in response to a situation in which the ratio P.sub.max/B.sub.R is at least 1.4, where P.sub.max is a value of the highest peak of the estimate of the autocorrelation and B.sub.R is an average of values of one or more next highest peaks of the estimate of the autocorrelation, the ratio P.sub.max/B.sub.R representing the indicator quantity.

(74) Certain exemplifying new methods for determining information indicative of cardiac malfunctions and abnormalities are described below with the aid of numbered Clauses 7-10:

(75) Clause 7. A method comprising: forming an estimate of at least one of the following: the autocorrelation of a signal indicative of cardiovascular motion, the frequency spectrum of the signal, and forming an indicator quantity that indicates the degree of concentration of the autocorrelation of the signal to a point that corresponds to zero time-shift between signal samples whose mutual correlation is expressed by the autocorrelation, the indicator quantity being indicative of cardiac malfunction and abnormality.

(76) Clause 8. A method according to Clause 7, wherein the method comprises producing an indicator of atrial fibrillation in response to a situation in which the ratio P.sub.max/B.sub.R is at least 1.3, where P.sub.max is a value of the highest peak of the estimate of the autocorrelation and B.sub.R is an average of values of one or more next highest peaks of the estimate of the autocorrelation, the ratio P.sub.max/B.sub.R representing the indicator quantity.

(77) Clause 9. A method according to Clause 7, wherein the method comprises producing an indicator of atrial fibrillation in response to a situation in which the ratio P.sub.max/B.sub.R is at least 1.4, where P.sub.max is a value of the highest peak of the estimate of the autocorrelation and B.sub.R is an average of values of one or more next highest peaks of the estimate of the autocorrelation, the ratio P.sub.max/B.sub.R representing the indicator quantity.

(78) Clause 10. A method according to Clause 7, wherein the method comprises producing an indicator of atrial fibrillation in response to a situation in which the ratio P.sub.max/B.sub.R is at least 1.4, where P.sub.max is a value of the highest peak of the estimate of the autocorrelation and B.sub.R is an average of values of one or more next highest peaks of the estimate of the autocorrelation, the ratio P.sub.max/B.sub.R representing the indicator quantity.

(79) A new computer program for determining information indicative of cardiac malfunctions and abnormalities is described below with the aid of a numbered Clause 11.

(80) Clause 11. A computer program comprising computer executable instructions for controlling a programmable processor to: form an estimate of at least one of the following: the autocorrelation of a signal indicative of cardiovascular motion, the frequency spectrum of the signal, and form an indicator quantity that indicates the degree of concentration of the autocorrelation of the signal to a point that corresponds to zero time-shift between signal samples whose mutual correlation is expressed by the autocorrelation, the indicator quantity being indicative of cardiac malfunction and abnormality.

(81) A new computer program product for determining information indicative of cardiac malfunctions and abnormalities is described below with the aid of a numbered Clause 12.

(82) Clause 12. A computer program product comprising a non-transitory computer readable medium encoded with a computer program according to Clause 11.

(83) The specific examples provided in the description given above should not be construed as limiting the scope and/or the applicability of the appended claims. Furthermore, it is also to be noted that, in many cases, the present invention can be used together with other techniques for detecting cardiac malfunctions and abnormalities.