Time–frequency analysis of phonocardiogram signals using wavelet transform: a comparative study

Analysis of phonocardiogram (PCG) signals provides a non-invasive means to determine the abnormalities caused by cardiovascular system pathology. In general, time–frequency representation (TFR) methods are used to study the PCG signal because it is one of the non-stationary bio-signals. The continuous wavelet transform (CWT) is especially suitable for the analysis of non-stationary signals and to obtain the TFR, due to its high resolution, both in time and in frequency and has recently become a favourite tool. It decomposes a signal in terms of elementary contributions called wavelets, which are shifted and dilated copies of a fixed mother wavelet function, and yields a joint TFR. Although the basic characteristics of the wavelets are similar, each type of the wavelets produces a different TFR. In this study, eight real types of the most known wavelets are examined on typical PCG signals indicating heart abnormalities in order to determine the best wavelet to obtain a reliable TFR. For this purpose, the wavelet energy and frequency spectrum estimations based on the CWT and the spectra of the chosen wavelets were compared with the energy distribution and the autoregressive frequency spectra in order to determine the most suitable wavelet. The results show that Morlet wavelet is the most reliable wavelet for the time–frequency analysis of PCG signals.

[1]  R M Rangayyan,et al.  Phonocardiogram signal analysis: a review. , 1987, Critical reviews in biomedical engineering.

[3]  I Güler,et al.  Application of autoregressive and Fast Fourier Transform spectral analysis to tricuspid and mitral valve stenosis. , 1996, Computer methods and programs in biomedicine.

[4]  P S Reddy,et al.  Normal and abnormal heart sounds in cardiac diagnosis. Part I: Systolic sounds. , 1985, Current problems in cardiology.

[5]  J.T.E. McDonnell,et al.  New analysis-synthesis of first heart sounds using forward-backward overdetermined Prony's method , 1994, Proceedings of 16th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[6]  Metin Akay,et al.  Time frequency and wavelets in biomedical signal processing , 1998 .

[7]  C Longhini,et al.  Noninvasive estimation of the pulmonary systolic pressure from the spectral analysis of the second heart sound. , 1990, Acta cardiologica.

[8]  S. M. Debbal,et al.  Time-frequency analysis of the first and the second heartbeat sounds , 2007, Appl. Math. Comput..

[9]  Burhan Ergen,et al.  The analysis of heart sounds based on linear and high order statistical methods , 2001, 2001 Conference Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[10]  J.T.E. McDonnell,et al.  Spectral characterization and classification of Carpentier-Edwards heart valves implanted in the aortic position , 1996, IEEE Transactions on Biomedical Engineering.

[11]  J.T.E. McDonnell,et al.  Spectral composition of heart sounds before and after mechanical heart valve implantation using a modified forward-backward Prony's method , 1996, IEEE Transactions on Biomedical Engineering.

[12]  Z. Guo,et al.  Analysis of the first heart sound using the matching pursuit method , 2001, Medical and Biological Engineering and Computing.

[13]  Olivier Rioul,et al.  Time-scale energy distributions: a general class extending wavelet transforms , 1992, IEEE Trans. Signal Process..

[14]  Clifford M. Hurvich,et al.  A CORRECTED AKAIKE INFORMATION CRITERION FOR VECTOR AUTOREGRESSIVE MODEL SELECTION , 1993 .

[15]  A. Tilkian,et al.  Understanding heart sounds and murmurs : with an introduction to lung sounds , 1984 .

[16]  D. T. Barry,et al.  Time-frequency analysis of the first heart sound , 1995 .

[17]  Louis-Gilles Durand,et al.  Nonlinear transient chirp signal modeling of the aortic and pulmonary components of the second heart sound , 2000, IEEE Transactions on Biomedical Engineering.

[18]  Vikash Sethia.  Wavelet Applications in Medicine , 2003 .

[19]  T. Thayaparan.  Time-Frequency Signal Analysis , 2014 .

[20]  S. M. Debbal,et al.  Automatic measure of the split in the second cardiac sound by using the wavelet transform technique , 2007, Comput. Biol. Medicine.

[21]  Juan Ignacio Godino-Llorente,et al.  Digital Auscultation Analysis for Heart Murmur Detection , 2009, Annals of Biomedical Engineering.

[22]  L.-G. Durand,et al.  Comparison of time-frequency distribution techniques for analysis of simulated Doppler ultrasound signals of the femoral artery , 1994, IEEE Transactions on Biomedical Engineering.

[23]  Side He,et al.  A comparison of the wavelet and short-time fourier transforms for Doppler spectral analysis. , 2003, Medical engineering & physics.

[24]  M. Obaidat,et al.  Phonocardiogram signal analysis: techniques and performance comparison. , 1993, Journal of medical engineering & technology.

[25]  W. Welkowitz,et al.  Detection of coronary occlusions using autoregressive modeling of diastolic heart sounds , 1990, IEEE Transactions on Biomedical Engineering.

[26]  L. Durand,et al.  Digital signal processing of the phonocardiogram: review of the most recent advancements. , 1995, Critical reviews in biomedical engineering.

[27]  J.L. Semmlow,et al.  Noninvasive acoustical detection of coronary artery disease: a comparative study of signal processing methods , 1993, IEEE Transactions on Biomedical Engineering.

[28]  G. Tognola,et al.  Wavelet analysis of click-evoked otoacoustic emissions , 1998, IEEE Transactions on Biomedical Engineering.

[29]  F S Schlindwein,et al.  Spectral broadening of clinical Doppler signals using FFT and autoregressive modelling. , 1998, European journal of ultrasound : official journal of the European Federation of Societies for Ultrasound in Medicine and Biology.

[30]  Rakesh Kumar Sinha,et al.  Backpropagation Artificial Neural Network Classifier to Detect Changes in Heart Sound due to Mitral Valve Regurgitation , 2007, Journal of Medical Systems.

[31]  Zhongwei Jiang,et al.  Cardiac sound murmurs classification with autoregressive spectral analysis and multi-support vector machine technique , 2010, Comput. Biol. Medicine.

[32]  Y. Tang,et al.  The synthesis of the aortic valve closure sound of the dog by the mean filter of forward and backward predictor , 1992, IEEE Transactions on Biomedical Engineering.

[33]  Douglas L. Jones,et al.  A resolution comparison of several time-frequency representations , 1992, IEEE Trans. Signal Process..

[34]  Andreas Voss,et al.  Diagnosing aortic valve stenosis by correlation analysis of wavelet filtered heart sounds , 2003, Medical and Biological Engineering and Computing.

[35]  A. Medl,et al.  Time Frequency and Wavelets in Biomedical Signal Processing , 1998, IEEE Engineering in Medicine and Biology Magazine.

[36]  Louis-Gilles Durand,et al.  Extraction of the aortic and pulmonary components of the second heart sound using a nonlinear transient chirp signal model , 2001, IEEE Transactions on Biomedical Engineering.

[37]  L. Senhadji,et al.  Analysis-synthesis of the phonocardiogram based on the matching pursuit method , 1998, IEEE Transactions on Biomedical Engineering.

[38]  Peter Hult,et al.  Feature Extraction for Systolic Heart Murmur Classification , 2006, Annals of Biomedical Engineering.

[39]  Wai Keung Li,et al.  A note on the corrected Akaike information criterion for threshold autoregressive models , 1998 .

[40]  Seung Hong Hong,et al.  Comparison between short time Fourier and wavelet transform for feature extraction of heart sound , 1999, Proceedings of IEEE. IEEE Region 10 Conference. TENCON 99. 'Multimedia Technology for Asia-Pacific Information Infrastructure' (Cat. No.99CH37030).

[41]  S. Khoor,et al.  Expert system for phonocardiographic monitoring of heart failure patients based onwavelet analysis , 2005, Computers in Cardiology, 2005.