Abstract
Past decades witnessed the expansion of linear signal processing methods in numerous biomedical applications. However, the nonlinear behavior of biomedical signals revived the interest in nonlinear signal processing methods such as higher-order statistics, in particular higher-order cumulants (HOC). In this paper, HOC are utilized toward heart sound classification. Heart sounds are presented by wavelet packet decomposition trees. Information measures are then defined based on HOC of wavelet packet coefficients, and three basis selection methods are proposed to prune the trees and preserve the most informative nodes for feature extraction. In addition, an approach is introduced to reduce the dimensionality of the search space from the whole wavelet packet tree to a trapezoidal sub-tree of it. This approach can be recommended for signals with a short frequency range. HOC features are extracted from the coefficients of selected nodes and fed into support vector machine classifier. Experimental data is a set of 59 heart sounds from different categories: normal heart sounds, mitral regurgitation, aortic stenosis, and aortic regurgitation. The promising results achieved indicate the capabilities of HOC of wavelet packet coefficients to capture nonlinear characteristics of the heart sounds to be used for basis selection.
Similar content being viewed by others
References
Ahlstrom C, Hult P, Rask P, Karlsson J-E, Nylander E, Dahlström U, Ask P (2006) Feature extraction for systolic heart murmur classification. Ann Biomed Eng 34(11):1666–1677
Al-Naami B, Al-Nabulsi J, Amasha H, Torry J (2010) Utilizing wavelet transform and support vector machine for detection of the paradoxical splitting in the second heart sound. Med Biol Eng Comput 48(2):177–184
Cherif LH, Debbal SM, Bereksi-Reguig F (2010) Choice of the wavelet analyzing in the phonocardiogram signal analysis using the discrete and the packet wavelet transform. Expert Syst Appl 37(2):913–918
Choi S (2008) Detection of valvular heart disorders using wavelet packet decomposition and support vector machine. Expert Syst Appl 35(4):1679–1687
Choi S, Shin Y, Park H-K (2011) Selection of wavelet packet measures for insufficiency murmur identification. Expert Syst Appl 38(4):4264–4271
Chua KC, Chandran V, Acharya UR, Lim CM (2010) Application of higher order statistics/spectra in biomedical signals - a review. Med Eng Phys 32(7):679–689
Coifman RR, Wickerhauser MV (1992) Entropy-based algorithms for best basis selection. IEEE Trans Inf Theory 38(2):713–718
Dokur Z, Ölmez T (2008) Heart sound classification using wavelet transform and incremental self-organizing map. Digit Signal Proc 18(6):951–959
Ergen B, Tatar Y (2001) The analysis of heart sounds based on linear and high order statistical methods. In: Proceedings of the 23rd annual international conference of the IEEE engineering in medicine and biology society, vol 3, pp 2139–2141
Ergen B, Tatar Y (2005) Characterization of phonocardiogram signals using bispectral estimation. In: Proceedings of the eighth international symposium on signal processing and its applications, pp 203–206
Fatemian S, Hatzinakos D (2009) A new ECG feature extractor for biometric recognition. In: 16th international conference of the IEEE on digital signal processing, pp 1–6
Hadjileontiadis L, Panas S (1997) Discrimination of heart sounds using higher-order statistics. In: International conference of IEEE on engineering in medicine and biology society, pp 1138–1141
Haghighi-Mood A, Torry J (1995) Application of advanced signal processing techniques in analysis of heart sound. In: IEE colloquium on signal processing in cardiography
Keeton PIJ, Schlindwein FS (1997) Application of wavelets in Doppler ultrasound application of wavelets. Sens Rev 17(1):38–45
Kumar D, Carvalho P, Antunes M, Paiva RP, Henriques J (2010) Heart murmur classification with feature selection. In: 32nd annual international conference of the IEEE EMBS, vol 2010, pp 4566–4569
Liang H, Nartimo I (1998). A feature extraction algorithm based on wavelet packet decomposition for heart sound signals. In: Proceedings of the IEEE-SP international symposium on time-frequency and time-scale analysis, pp 93–96
Mendel J (1991) Tutorial on higher-order statistics (spectra) in signal processing and system theory: theoretical results and some applications. In: Proceedings of the IEEE, pp 278–300
Naseri H, Homaeinezhad MR (2013) Detection and boundary identification of phonocardiogram sounds using an expert frequency-energy based metric. Ann Biomed Eng 41(2):279–292
Safara F, Doraisamy S, Azman A, Jantan A (2012) Heart sounds clustering using a combination of temporal, spectral and geometric features. In: Computing in cardiology. IEEE, Poland, vol 39, pp 217–220
Safara F, Doraisamy S, Azman A, Jantan A, Abdullah Ramaiah AR (2013) Multi-level basis selection of wavelet packet decomposition tree for heart sound classification. Comput Biol Med 43(10):1407–1414
Saito N, Coifman RR (1995) Local discriminant bases and their applications. J Math Imaging Vis 5:337–358
Sanei S, Ghodsi M, Hassani H (2011) An adaptive singular spectrum analysis approach to murmur detection from heart sounds. Med Eng Phys 33(3):362–367
Shen M, Sun L (1997) The analysis and classification of phonocardiogram based on higher-order spectra. In: Proceedings of the IEEE signal processing workshop on higher-order statistics, pp 29–33
Shen M, Sun L (1997) Time-varying third-order cumulant spectra and its application to the analysis and diagnosis of phonocardiogram. In: Proceedings of the IEEE signal processing workshop on higher-order statistics, pp 24–28
Taplidou S, Hadjileontiadis L (2006) Nonlinear analysis of heart murmurs using wavelet-based higher-order spectral parameters. In: Proceedings of the 28th IEEE EMBS annual international conference, pp 4502–4505
Van Dijck G, Van Hulle MM (2011) Genetic algorithm for informative basis function selection from the wavelet packet decomposition with application to corrosion identification using acoustic emission. Chemometr Intell Lab Syst 107(2):318–332
Wang D, Miao D, **e C (2011) Best basis-based wavelet packet entropy feature extraction and hierarchical EEG classification for epileptic detection. Expert Syst Appl 38(11):14314–14320
Zhang D, He J, Yao J, Wu Y, Du M (2012) Noninvasive detection of mechanical prosthetic heart valve disorder. Comput Biol Med 42(8):785–792
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Safara, F. Cumulant-based trapezoidal basis selection for heart sound classification. Med Biol Eng Comput 53, 1153–1164 (2015). https://doi.org/10.1007/s11517-015-1394-4
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11517-015-1394-4