WO2003055395A1 - Analysis of acoustic medical signals - Google Patents

Analysis of acoustic medical signals Download PDF

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Publication number
WO2003055395A1
WO2003055395A1 PCT/GB2002/005922 GB0205922W WO03055395A1 WO 2003055395 A1 WO2003055395 A1 WO 2003055395A1 GB 0205922 W GB0205922 W GB 0205922W WO 03055395 A1 WO03055395 A1 WO 03055395A1
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Prior art keywords
signal
wavelet
respiration
wavelet transform
pulse
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PCT/GB2002/005922
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French (fr)
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Paul Stanley Addison
James Nicholas Watson
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Cardiodigital Limited
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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B7/00—Instruments for auscultation
    • A61B7/02—Stethoscopes
    • A61B7/04—Electric stethoscopes
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B7/00—Instruments for auscultation
    • A61B7/003—Detecting lung or respiration noise

Definitions

  • the present invention relates to a method of analysis of medical signals, and in particular to a method of decomposition and analysis of signals used in phonocardiography. Specifically the invention relates to an improved method of denoising acoustic cardiac signals and in the extraction of clinically useful information from such signals including the location of relevant cardiac sounds in the signal and the monitoring and analysis of patient respiration including the determination of breathing rate.
  • the denoising of acoustic cardiac signals is essential in order to facilitate their clinical evaluation.
  • the removal of noise including movement artefact is the main goal of current signal analysis methods.
  • Our method allows for clinically relevant information to be extracted from acoustic cardiac waveforms even when a high level of noise is present in the signal .
  • Wavelet transforms allow a signal to be decomposed such that both the frequency characteristics and the temporal location of particular features in a time series may be highlighted simultaneously.
  • This feature localisation property overcomes the basic shortcoming of Fourier analysis, where the spectrum only contains globally averaged information which leads to location specific features in the signal being lost.
  • STFT short time Fourier transform
  • the wavelet-based method cannot resolve to an arbitrarily small scale due to the variable window width associated with the wavelet function.
  • the wavelet transform of a continuous time signal, x (t) is defined as:
  • a is the wavelet scale parameter
  • b is the wavelet location parameter
  • g (t) is the analysing wavelet function
  • g * (t) its complex conjugate.
  • the present invention provides a method of measuring physiological parameters, comprising processing a phonocardiogram signal using wavelet transform methods; and also provides a system suitable for carrying out the method.
  • the invention is a wavelet transform-based method for the elucidation of pertinent features from noisy acoustic cardiac waveforms associated with both cardiac and respiratory activity.
  • These techniques will be used within phonocardiographic signal acquisition and analysis devices (including remote devices used, for example, to monitor neonates) to monitor cardiac or respiratory activity, and preferably both, and provide information concerning these activities including pulse rate and respiration rate.
  • the methodology can facilitate the clinical interpretation of acoustic cardiac waveforms which suffer from excessive noise, including noise from movement artefact and poor coupling between microphone and patient. Poor coupling may arise from the lack of a rigid connection of the microphone and the patient skin or the placement of the microphone within the patient clothes. This document describes the methodology and its incorporation within a medical device.
  • Figure 2 (a) Wavelet Analysis of an Acoustic Cardiac Signal - Original Acoustic Cardiac Waveform.
  • Figure 2 (b) Wavelet Analysis of an Acoustic Cardiac Signal - the Wavelet Scalogram Corresponding to the signal in figure 2 (a)
  • Figure 2 (c) Wavelet Analysis of an Acoustic Cardiac Signal - The scalogram values from the selected band-pass centre frequency of the wavelet showing alternate large and small amplitude maxima (marked a and b in the figure) .
  • Figure 3 A detailed block diagram of a preferred embodiment of the wavelet-based pulse rate determination.
  • Figure 4 (b) Wavelet Transform Phase Plot corresponding to the Signal in Figure 4 (a)
  • Figure 5 (a) Wavelet Transform Modulus Plot of the signal shown in Figure 4 (a) .
  • Figure 5 (b) Wavelet Transform Modulus Plot of the signal in Figure 4 (a) with possible routes of pulse and breathing ridges shown schematically.
  • Figure 6 A detailed block diagram of a preferred embodiment of the wavelet-based respiration and pulse rate determination.
  • Figure 1 shows an artefact free acoustic cardiac waveform.
  • the two adjacent wavetrain pulses corresponding to a single heartbeat (Si and S 2 sounds) are evident in the middle of the plot.
  • the time trace in Figure 2 (a) contains an acoustic cardiac waveform collected from a young baby where very poor coupling between the microphone and patient was present. As well as this poor coupling, the signal suffers from breathing and movement artefact, signal drop-outs and undersampling. (The difference in the quality from the trace in figure 1 is obvious.)
  • the wavelet scalogram derived from the signal in Figure 2(a) is shown in Figure 2b.
  • the bottom trace ( Figure 2 (c) ) contains the wavelet transform values across the scalogram corresponding to a selected band-pass frequency centre of the wavelet.
  • Figure 3 contains a block diagram of a preferred embodiment of the pulse detection and rate determination algorithm as incorporated within a medical device.
  • the digitised acoustic cardiac signal 10 is sent to a wavelet transformer 11.
  • a frequency level 12 is selected in wavelet space - either automatically or, as in this case, manually.
  • the wavelet transform values across this level are interrogated 13 and the occurrence of the alternate large and small peaks ( Figure 2(c)) used to determine the pulse frequency.
  • Figure 4 (a) contains a longer segment of the signal shown in Figure 2.
  • Figure 4(b) the wavelet transform phase is plotted, derived using a complex wavelet analysing function. At this resolution, new, regular structure can be observed at frequencies at, around, and below 1Hz . This corresponds to patient breathing.
  • Figure 5(a) contains a three dimensional, modulus plot of the (wavelet) transformed signal of Figure 4(a). Two ridges appear in the transform surface of Figure 5 (a) corresponding to the breathing and pulse signals. The pulse and breathing rate can be obtained directly from these ridges in the time frequency plane .
  • Figure 5 (b) shows the same scalogram as Figure 5 (a) .
  • a schematic of the loci of the breathing and respiration ridges is plotted as black broken lines above the wavelet transform surface.
  • the breathing and respiration ridges may be tracked across the wavelet transform surface using a suitable ridge following algorithm as is currently available. Using both phase and modulus information both breathing and pulse signals can be monitored to provide the clinically useful information (e.g. the breathing and/or pulse rate, significant changes in breathing and/or pulse pattern, etc.) hidden within signals containing high degrees of noise and artefact .
  • FIG. 6 contains a block diagram of a preferred embodiment of the pulse and respiration detection and rate determination algorithm.
  • the digitised acoustic cardiac signal 20 is sent to a wavelet transformer 21.
  • a representation of the transform is made in the time frequency domain as a surface 22. This can be, for example, the scalogram or rescaled scalogram as described above.
  • the dominant ridges associated with the manifestation of respiration in the signal are determined 23. This is used to provide a respiration frequency through time directly from the time-frequency representation 24.
  • the dominant ridges associated with the manifestation of pulse in the signal may also be determined 25. This is used to provide a pulse frequency through time directly from the time- frequency representation 26. This may also be used as a check of the pulse rate determination described above and in Figure 3.
  • the resulting breathing rate and pulse rate may then be displayed on the device.
  • the method may be similarly employed to analyse other acoustic medical signals and so determine pertinent clinical information.
  • the method may be further employed to detect and identify motion artefact within signals of medical relevance.

Abstract

An acoustic cardiac signal is decomposed by wavelet transform methods and the decomposed signal analysed to provide physiologically useful information. The information may be pulse and/or respiration rate, and abnormalities therein. The use of wavelet decomposition provides a high degree of removal of noise, artefact and transient signals.

Description

Analysis of Acoustic Medical Signals
Field of Invention
The present invention relates to a method of analysis of medical signals, and in particular to a method of decomposition and analysis of signals used in phonocardiography. Specifically the invention relates to an improved method of denoising acoustic cardiac signals and in the extraction of clinically useful information from such signals including the location of relevant cardiac sounds in the signal and the monitoring and analysis of patient respiration including the determination of breathing rate.
Background The opening and closing of valves in a patient's heart causes high frequency vibrations in the adjacent heart wall and blood vessels. These vibrations can be heard as cardiac sounds. These can be detected using a transducer and converted into an electrical waveform for recording and display - the Phonocardiogram (PCG) . Phonocardiography is a standard study that is perhaps the most difficult and subjective of all non- invasive cardiac investigations.
The denoising of acoustic cardiac signals is essential in order to facilitate their clinical evaluation. The removal of noise including movement artefact is the main goal of current signal analysis methods. Our method allows for clinically relevant information to be extracted from acoustic cardiac waveforms even when a high level of noise is present in the signal .
Wavelet transforms allow a signal to be decomposed such that both the frequency characteristics and the temporal location of particular features in a time series may be highlighted simultaneously. This feature localisation property overcomes the basic shortcoming of Fourier analysis, where the spectrum only contains globally averaged information which leads to location specific features in the signal being lost. Even the short time Fourier transform (STFT) cannot resolve below a fixed scale on the signal due to the fixed window width associated with the method. The wavelet-based method, however, can resolve to an arbitrarily small scale due to the variable window width associated with the wavelet function. The wavelet transform of a continuous time signal, x (t) , is defined as:
Figure imgf000005_0001
where a is the wavelet scale parameter, b is the wavelet location parameter, g (t) is the analysing wavelet function and g* (t) its complex conjugate. By employing the appropriate wavelet function the signal can be decomposed so that specific information representing the noise, artefacts and underlying cardiac signal can be partitioned.
Both discrete and continuous wavelet transforms can be used to decompose the signal. However, the inherent redundancy in the continuous wavelet method increases clarity in the transform space and allows for arbitrarily high spectral and temporal resolution. For this reason we prefer a continuous wavelet transform-based methodology. In this application, the computed wavelet transform values T(a,b) , or their modulus | (α,b)| , or energy density
(the scalogram) | (α,b)| or rescaled scalogram
|j(a,b)| ja or other reasonable rescaling of the scalogram are plotted against a characteristic wavelet frequency for an instant in time. The wavelet representation is robust in that it can cope both with repeating features in time with shifting phase and isolated and/or transient features, making it ideal for applications such as this. In the preferred embodiment we use the complex Morlet wavelet as the analysing function including the complete Morlet wavelet as described in Addison et al, 2002. Those proficient in the art will realise that other wavelet functions may also be employed.
Invention Summary The present invention provides a method of measuring physiological parameters, comprising processing a phonocardiogram signal using wavelet transform methods; and also provides a system suitable for carrying out the method.
The invention is a wavelet transform-based method for the elucidation of pertinent features from noisy acoustic cardiac waveforms associated with both cardiac and respiratory activity. These techniques will be used within phonocardiographic signal acquisition and analysis devices (including remote devices used, for example, to monitor neonates) to monitor cardiac or respiratory activity, and preferably both, and provide information concerning these activities including pulse rate and respiration rate. The methodology can facilitate the clinical interpretation of acoustic cardiac waveforms which suffer from excessive noise, including noise from movement artefact and poor coupling between microphone and patient. Poor coupling may arise from the lack of a rigid connection of the microphone and the patient skin or the placement of the microphone within the patient clothes. This document describes the methodology and its incorporation within a medical device.
Brief Description of the Drawings Figure 1: Typical Acoustic Cardiac Waveform
Figure 2 (a) : Wavelet Analysis of an Acoustic Cardiac Signal - Original Acoustic Cardiac Waveform.
Figure 2 (b) : Wavelet Analysis of an Acoustic Cardiac Signal - the Wavelet Scalogram Corresponding to the signal in figure 2 (a)
Figure 2 (c) : Wavelet Analysis of an Acoustic Cardiac Signal - The scalogram values from the selected band-pass centre frequency of the wavelet showing alternate large and small amplitude maxima (marked a and b in the figure) .
Figure 3 : A detailed block diagram of a preferred embodiment of the wavelet-based pulse rate determination.
Figure 4 (a) : Acoustic Cardiac Time Series
Figure 4 (b) : Wavelet Transform Phase Plot corresponding to the Signal in Figure 4 (a)
Figure 5 (a) : Wavelet Transform Modulus Plot of the signal shown in Figure 4 (a) . Figure 5 (b) : Wavelet Transform Modulus Plot of the signal in Figure 4 (a) with possible routes of pulse and breathing ridges shown schematically.
Figure 6: A detailed block diagram of a preferred embodiment of the wavelet-based respiration and pulse rate determination.
Details of the Method and Preliminary Results Figure 1 shows an artefact free acoustic cardiac waveform. The two adjacent wavetrain pulses corresponding to a single heartbeat (Si and S2 sounds) are evident in the middle of the plot.
The time trace in Figure 2 (a) contains an acoustic cardiac waveform collected from a young baby where very poor coupling between the microphone and patient was present. As well as this poor coupling, the signal suffers from breathing and movement artefact, signal drop-outs and undersampling. (The difference in the quality from the trace in figure 1 is obvious.) The wavelet scalogram derived from the signal in Figure 2(a) is shown in Figure 2b. The bottom trace (Figure 2 (c) ) contains the wavelet transform values across the scalogram corresponding to a selected band-pass frequency centre of the wavelet. Alternate large and small peaks are evident in this figure (denoted a and b in the plot) while other features related to motion artefacts and other noise have been suppressed. These alternate large and small peaks correspond to the dominant double pulsing signature of the acoustic cardiac waveform observed in Figure 1. We can see very clearly from the figure that the beat frequency is approximately 140 beats per minute which is as we would expect from a young baby (from which the original signal was collected) . An additional strength of this technique is its intrinsic ability cope with the under-sampled trace. At the scales of interest the wavelet functions correlate with the wavetrain envelopes of the signal allowing the identification and location of the individual cardiac sounds.
Figure 3 contains a block diagram of a preferred embodiment of the pulse detection and rate determination algorithm as incorporated within a medical device. In the present invention the digitised acoustic cardiac signal 10 is sent to a wavelet transformer 11. A frequency level 12 is selected in wavelet space - either automatically or, as in this case, manually. The wavelet transform values across this level are interrogated 13 and the occurrence of the alternate large and small peaks (Figure 2(c)) used to determine the pulse frequency.
Figure 4 (a) contains a longer segment of the signal shown in Figure 2. Below this in Figure 4(b) the wavelet transform phase is plotted, derived using a complex wavelet analysing function. At this resolution, new, regular structure can be observed at frequencies at, around, and below 1Hz . This corresponds to patient breathing. Figure 5(a) contains a three dimensional, modulus plot of the (wavelet) transformed signal of Figure 4(a). Two ridges appear in the transform surface of Figure 5 (a) corresponding to the breathing and pulse signals. The pulse and breathing rate can be obtained directly from these ridges in the time frequency plane .
One commonly used definition of a wavelet ridge is:
Figure imgf000010_0001
However, we further discard inflection points, looking only for local maxima in the modulus of the rescaled scalogram. Note that other, more general definitions of ridges may be used in the algorithm. Other forms of rescaling of the scalogram may also be used as appropriate (including the original unsealed scalogram). In the method 'scalogram' may be taken to mean rescaled scalogram and 'ridge' may be taken to mean any reasonable definition of a ridge on the scalogram surface including the ridges derived from time-frequency reassignment methods.
Figure 5 (b) shows the same scalogram as Figure 5 (a) . A schematic of the loci of the breathing and respiration ridges is plotted as black broken lines above the wavelet transform surface.
The breathing and respiration ridges may be tracked across the wavelet transform surface using a suitable ridge following algorithm as is currently available. Using both phase and modulus information both breathing and pulse signals can be monitored to provide the clinically useful information (e.g. the breathing and/or pulse rate, significant changes in breathing and/or pulse pattern, etc.) hidden within signals containing high degrees of noise and artefact .
By analysing the signal in the ways described above, useful clinical information can be obtained from the acoustic heart signal so aiding diagnosis.
The methods described above are to be incorporated within a device for the measurement and analysis of acoustic cardiac signals. Figure 6 contains a block diagram of a preferred embodiment of the pulse and respiration detection and rate determination algorithm. In the present invention the digitised acoustic cardiac signal 20 is sent to a wavelet transformer 21. A representation of the transform is made in the time frequency domain as a surface 22. This can be, for example, the scalogram or rescaled scalogram as described above. The dominant ridges associated with the manifestation of respiration in the signal are determined 23. This is used to provide a respiration frequency through time directly from the time-frequency representation 24. The dominant ridges associated with the manifestation of pulse in the signal may also be determined 25. This is used to provide a pulse frequency through time directly from the time- frequency representation 26. This may also be used as a check of the pulse rate determination described above and in Figure 3. The resulting breathing rate and pulse rate may then be displayed on the device.
Modifications to the Method The method may be similarly employed to analyse other acoustic medical signals and so determine pertinent clinical information.
The method may be further employed to detect and identify motion artefact within signals of medical relevance.
References P.S. Addison, J.N. Watson and T. Feng, λLow- Oscillation Complex Wavelets', Journal of Sound and Vibration, 2002, Vol.254 (4), 733-762.

Claims

1. A method of measuring physiological parameters, comprising processing a phonocardiogram signal using wavelet transform methods.
2. A method according to claim 1, in which the processing is effective to remove one or more of noise, artefact, and transient features.
3. A method according to claim 1 or claim 2, for use in the identification and communication of measurable features corresponding to a subject's pulse rate.
4. A method according to claim 3, in which wavelet transform values are inspected at a selected frequency across the transform surface.
5. A method according to claim 3, in which pulse rate is continuously derived using ridge following methods.
6. A method according to claim 3, including the further step of identifying abnormal pulse patterns including the cessation of pulse.
7. A method according to claim 1 or claim 2, for use in the identification and communication of measurable features corresponding to a subject's respiration.
8. A method according to claim 7, in which respiration is continuously derived using ridge following methods.
9. A method according to claim 7 or claim 8, including the further step of identifying abnormal respiration patterns including the cessation of breathing.
10. A method according to any preceding claim, including the preliminary step of obtaining said phonocardiogram signal by applying an acoustic probe to a subject's trunk to obtain an analogue signal containing cardiac and respiratory information, and digitising said analogue signal to provide said phonocardiogram signal.
11. A method according to any preceding claim, in which said processing comprises deriving a surface representation of the wavelet transform values and plotting said surface representation against a location parameter and a scale parameter.
12. A method according to claim 11, in which said surface representation includes complex wavelet transform values.
13. A method according to claim 11, in which said surface representation includes rescaled wavelet transform values.
14. A method according to claim 11, in which said surface representation includes an energy scalogram.
15. A method according to any of claims 11 to 14, in which said scale parameter is wavelet dilation.
16. A method according to any of claims 11 to 14, in which said scale parameter is a characteristic feature of the wavelet function used in the wavelet decomposition.
17. A method according to any of claims 11 to 16, including the feature of isolating and identifying the component features of the temporal trace of the phonocardiogram waveform in wavelet space.
18. A physiological measurement system comprising: an acoustic probe attachable to the trunk of a subject to obtain an analogue signal containing cardiac and respiratory information; analogue to digital converter means arranged to convert said analogue signal into a digital phonocardiogram signal; and signal processing means receiving said phonocardiogram and arranged to process that signal by wavelet transform techniques.
19. The system of claim 18, in which the signal processing means is arranged to carry out the processing defined in any of claims 11 to 17.
20. The system of claim 18 or claim 19, further including a display device for displaying derived respiration and/or pulse rate.
21. The system of any of claims 18 to 20, further including alarm means triggered by abnormal patterns of pulse and/or respiration.
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