Abstract
The mechanical action of the heart generates sounds which can provide diagnostic information about the functioning of the cardiovascular system. Cardiac auscultation is an important means to diagnose heart disorders by listening to the heart sounds using conventional stethoscope. The traditional cardiac auscultation techniques require sophisticated interpretive skills in diagnosis and it requires long time to expertise. The heart sounds often last for a short period of time and pathological splitting of the heart sound is difficult to discern using traditional auscultation because human ears lack desired sensitivity towards heart sounds and murmurs. Therefore, the automatic heart sound analysis using advanced signal processing techniques based on digital acquisition of these sounds can play an important role. The heart sounds can be captured and processed in the form of cardiac sound signals by placing an electronic stethoscope at the appropriate location on the subject’s chest. The cardiac sound signals can be used to extract valuable diagnostic features for detection and identification of the heart valve and other disorders. In this book chapter, a new method for segmentation and classification of cardiac sound signals using tunable-Q wavelet transform (TQWT) has been proposed. The proposed method uses constrained TQWT based segmentation of cardiac sound signals into heart beat cycles. The features obtained from heart beat cycles of separately reconstructed heart sounds and murmur can better represent the various types of cardiac sound signals than that of containing both. Even the parameters evolved during constrained TQWT based separation of heart sounds and murmur can serve as valuable diagnostic features. Therefore, various entropy measures namely time-domain based Shannon entropy, frequency-domain based spectral entropy, and non-linear method based approximate entropy and Lempel-Ziv complexity have been computed for each segmented heart beat cycles. Two features have been created by the parameters that have been optimized while constrained TQWT namely the redundancy and the number of levels of decomposition. These ten features form the final feature set for subsequent classification of cardiac sound signals using artificial neural network (ANN) based technique. In this study, the following classes of cardiac sound signals have been used: normal, aortic stenosis, aortic regurgitation, splitting of S2, mitral regurgitation and mitral stenosis. The performance of the proposed method has been validated with publicly available datasets. The proposed method has provided significant performance in segmentation and classification of cardiac sound signals.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Amit, G.,  Lessick, J.,​Gavriely, N.,, Intrator, N.: Acoustic indices of cardiac functionality. In: International Coriference on Bio-inspired Systems and Signal Processing (BIOSIGNALS), pp. 77–83. Setubal, Portugal (2008)
Amit, G., Gavriely, N., Intrator, N.: Cluster analysis and classification of heart sounds. Biomed. Sig. Process Cont. 4(1), 26–36 (2009)
Ari, S., Saha, G.: On a robust algorithm for heart sound segmentation. J. Mech. Med. Biol. 7, 129–150 (2007)
Ari, S., Saha, G.: Classification of heart sounds using empirical mode decomposition based features. Int. J. Med. Eng. Inform. 1(1), 91–108 (2008)
Ari, S., Saha, G.: In search of an optimization technique for artificial neural network to classify abnormal heart sounds. Appl. Soft. Comput. 9(1), 330–340 (2009)
Ari, S., Sensharma, K., Saha, G.: DSP implementation of heart valve disorder detection system from a phonocardiogram signal. J. Med. Eng. Technol. 32(2), 122–132 (2008)
Ari, S., Hembram, K., Saha, G.: Detection of cardiac abnormality from PCG signal using LMS based least square SVM cassifier. Expert Syst. Appl. 37, 8019–8026 (2010)
Barschdorff, D., Femmer, U., and Trowitzsch, E.: Automatic phonocardiogram signal analysis in infants based on wavelet transforms and artificial neural networks. In: Computers in Cardiology, pp. 753–756. Vienna, Austria (1995)
Cathers, I.: Neural network assisted cardiac auscultation. Art. Intell. Med. 7, 53–66 (1995)
Chauhan, S., Wang, P., Lim, C.S., Anantharaman, V.: A computer-aided MFCC-based HMM system for automatic auscultation. Comput. Biol. Med. 38(2), 221–233 (2008)
Choi, S.: Detection of valvular heart disorders using wavelet packet decomposition and support vector machine. Expert Syst. Appl. 35(4), 1679–1687 (2008)
Choi, S., Jiang, Z.: Comparison of envelope extraction algorithms for cardiac sound signal segmentation. Expert Syst. Appl. 34(2), 1056–1069 (2008)
Choi, S., Jiang, Z.: Cardiac sound murmurs classification with autoregressive spectral analysis and multi-support vector machine technique. Comput. Biol. Med. 40(1), 8–20 (2010)
Chung, Y.J. (2008). Using Kullback-Leibler distance in determining the classes for the heart sound signal classification. In: Intelligent Data Engineering and Automated Learning, pp. 49–56. Springer Heidelberg Berlin (2008)
Chung, Y.J.: Classification of continuous heart sound signals using the ergodic hidden Markov model. In: Pattern Recognition and Image Analysis, pp. 563–570. Springer Heidelberg Berlin (2007)
Dokur, Z., Ölmez, T.: Feature determination for heart sounds based on divergence analysis. Digit. Signal Proc. 19(3), 521–531 (2009)
Dokur, Z., Ölmez, T.: Heart sound classification using wavelet transform and incremental self-organizing map. Digit. Signal Proc. 18(6), 951–959 (2008)
Durand, L.G., Pibarot, P.: Digital signal processing of the phonocardiogram: review of the most recent advancements. Crit. Rev. Biomed. Eng. 23, 163–219 (1995)
Feigen, L.P.: Physical characteristics of sound and hearing. Am. J. Cardiol. 28, 130–133 (1971)
Gamero, L.G., Watrous, R.: Detection of the first and second heart sound using probabilistic models. In: Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 2877–2880 (2003)
Groch, M.W., Domnanovich, J.R., Erwin, W.D.: A new heart-sounds gating device for medical imaging. IEEE Trans. Biomed. Eng. 39(3), 307–310 (1992)
Gupta, C.N., Palaniappan, R., Swaminathan, S., Krishnan, S.M.: Neural network classification of homomorphic segmented heart sounds. Appl. Soft Comput. 7(1), 286–297 (2007)
Hadi, H.M., Mashor, M.Y., Suboh, M.Z., and Mohamed, M.S.: Classification of heart sound based on S-transform and neural networks. In: Proceedings of International Conference on Information Sciences Signal Processing and their Applications, pp. 189–192. Kuala Lumpur, Malaysia (2010)
Haghighi-Mood, A., Torry, J.N.: A sub-band energy tracking algorithm for heart sound segmentation. In: Computers in Cardiology, pp. 501–504 (1995)
Hanna, I.R., Silverman, M.E.: A history of cardiac auscultation and some of its contributors. Am. J. Cardiol. 90, 259–267 (2002)
Huiying, L., Sakari, L., liro, H.: A heart sound segmentation algorithm using wavelet decomposition and reconstruction. In: Proceedings of 19th International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 1630–1633. Chicago, IL (1997)
Iwata, A., Ishii, A.N., Suzumura, N., Ikegaya, K.: Algorithm for detecting the first and the second heart sounds by spectral tracking. Med. Biol. Eng. Comput. 18, 19–26 (1980)
Jiang, Z., Choi, S.: A cardiac sound characteristic waveform method for in-home heart disorder monitoring with electric stethoscope. Expert Syst. Appl. 31(2), 286–298 (2006)
Kannathal, N., Choo, M.L., Acharya, U.R., Sadasivan, P.K.: Entropies for detection of epilepsy in EEG. Comput. Methods Programs Biomed. 80, 187–194 (2005)
Kao, W.C., Wei, C.C.: Automatic phonocardiograph signal analysis for detecting heart valve disorders. Expert Syst. Appl. 38(6), 6458–6468 (2011)
Kumar, D., Carvalho, P., Antunes, M., Henriques, J., Eugenio, L., Schmidt, R., Habetha, J.: Detection of S1 and S2 heart sounds by high frequency signatures. In: Procedings of 28th IEEE Engineering in Medicine and Biology Society Annual International Conference, pp. 1410–1416. New York , USA (2006)
Kumar, D., Carvalho, P., Antunes, M., Henriques, J., SaeMelo, A., Schmidt, R., and Habetha, J.: Third heart sound detection using wavelet transform-simplicity filter. In: Proceedings of Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 1277–1281. Lyon, France (2007)
Lehner, R.J., Rangayyan, R.M.: A three-channel microcomputer system for segmentation and characterization of the phonocardiogram. IEEE Trans. Biomed. Eng. 34(6), 485–489 (1987)
Lempel, A., Ziv, J.: On the complexity of finite sequences. IEEE Trans. Inf. Theory 22(1), 75–81 (1976)
Liang, H., Lukkarinen, S., Hartimo, I.: Heart sound segmentation algorithm based on heart sound envelogram. In: Computers in Cardiology, pp. 105–108 (1997)
Lippmann, R.P.: An introduction to computing with neural nets. IEEE ASSP Mag. 4(2), 4–22 (1987)
Livanos, G., Ranganathan, N., Jiang, J.: Heart sound analysis using the S-transform. In: Computers in Cardiology, pp. 587–590 (2000)
Lukkarinen, S., Noponen, A.L., Sikio, K., Angerla, A.: A new phonocardiographic recording system. In: Computers in Cardiology, pp. 117–120 (1997)
Maglogiannis, I., Loukis, E., Zafiropoulos, E., Stasis, A.: Support vectors machine-based identification of heart valve diseases using heart sounds. Comput. Methods Programs Biomed. 95, 47–61 (2009)
Malarvili, M.B., Kamarulafizam, I., Hussain, S., and Helmi, D.: Heart sound segmentation algorithm based on instantaneous energy of electrocardiogram. In: Computers in Cardiology, pp. 327–330 (2003)
Mangione, S., Nieman, L.Z.: Cardiac auscultatory skills of internal medicine and family practice trainees. J. Am. Med. Assoc. 278, 717–722 (1997)
Messer, S.R., Agzarian, J., Abbott, D.: Optimal wavelet denoising for phonocardiograms. J. Microelectron. 32, 931–941 (2001)
Moukadem, A., Dieterlen, A., Hueber, N., Brandt, C.: Comparative study of heart sounds localization. In: Proceedings of SPIE N8068-27, Bioelectronics, Biomedical, and Bioinspired Systems (2011).
Moukadem, A., Dieterlen, A., Hueber, N., and Brandt, C.: Localization of heart sounds based on S-transform and radial basis function neural network. In: IFMBE Proceedings of 15th Nordic-Baltic Conference on Biomedical Engineering and Medical Physics, pp. 168–171 (2011)
Moukadem, A., Dieterlen, A., Hueber, N., Brandt, C.: A robust heart sounds segmentation module based on S-transform. Biomed. Signal Process. Control 8(3), 273–281 (2013)
Myint, W.W., Dillard, B.: An electronic stethoscope with diagnosis capability. In: Proc.of the 33rd IEEE Southeastern Symposium on System Theory, pp. 133–137. Athens, OH (2001)
Naseri, H., Homaeinezhad, M.R.: Detection and boundary identification of phonocardiogram sounds using an expert frequency-energy based metric. Ann. Biomed. Eng. 41(2), 279–292 (2013)
Nieblas, C.I., Alonso, M.A., Conte, R., Villarreal. S.: High performance heart sound segmentation algorithm based on matching pursuit. In: IEEE Digital Signal Processing and Signal Processing Education Meeting (DSP/SPE), pp. 96–100. Napa, CA (2013)
Ölmez, T., Dokur, Z.: Classification of heart sounds using an artificial neural network. Pattern Recogn. Lett. 24, 617–629 (2003)
Pasterkamp, H., Kraman, S.S., Wodicka, G.R.: Respiratory sounds: advances beyond the stethoscope. Am. J. Respir. Crit. Care Med., 974–987 (1997)
Patidar, S., Pachori, R.B.: Constrained tunable-Q wavelet transform based analysis of cardiac sound signals. AASRI Procedia 4, 57–63 (2013)
Patidar, S., Pachori, R.B.: Segmentation of cardiac sound signals by removing murmurs using constrained tunable-Q wavelet transform. Biomed. Signal Process. Control 8(6), 559–567 (2013)
Patidar, S., Pachori, R.B.: A continuous wavelet transform based method for detecting heart valve disorders using phonocardiograph signals. In: International Conference on Convergence and Hybrid Information Technology, pp. 513–520. Daejeon, Korea (2012)
Pease A.: If the heart could speak. Pictures Future, pp. 60–61 (2001)
Pincus, S.M.: Approximate entropy as a measure of system complexity. Proc. Nat. Acad. Sci., 2297–2301 (1991)
Rajan, S., Doraiswami, R., Stevenson, R., and Watrous, R.: Wavelet based bank of correlators approach for phonocardiogram signal classification. In: Proceedings of the IEEE-SP International Symposium on Time-Frequency and Time-Scale Analysis, pp. 77–80. Pittsburgh, PA (1998)
Rangayyan, R.M., Lehner, R.J.: Phonocardiogram signal analysis: a review. Crit. Rev. Biomed. Eng. 15(3), 211–236 (1986)
Reed, T., Reed, N., and Fritzson, P.: Analysis of heart sounds for symptom detection and machine-aided diagnosis. In: 2nd Conference Modeling and Simulation in Biology, Medicine, and Biomedical Engineering, pp. 1–6. Delft, The Netherlands (2001)
Reed, T.R., Reed, N.E., Fritzson, P.: Heart sound analysis for symptom detection and computer-aided diagnosis. Simul. Model. Pract. Theory 12, 129–146 (2004)
Rumelhart, D.E., McClelland, J.L.: Parallel distributed processing: Explorations in the Microstructure of Cognition: Foundations. MIT Press, Cambridge, MA (1986)
Sabeti, M., Katebi, S., Boostani, R.: Entropy and complexity measures for EEG signal classification of schizophrenic and control participants. Artif. Intell. Med. 47, 263–274 (2009)
Sanei, S., Ghodsi, M., Hassani, H.: An adaptive singular spectrum analysis approach to murmur detection from heart sounds. Med. Eng. Phys. 33(3), 362–367 (2011)
Schmidt, S.E., Holst-Hansen, C., Graff, C., Toft, E., Struijk, J.J.: Segmentation of heart sound recordings by a duration-dependent hidden Markov model. Physiol. Meas. 31(4), 513–529 (2010)
Sejdic, E., Jiang, J.: Comparative study of three time-frequency representations with applications to a novel correlation method. In: Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), pp. 633–636 (2004)
Selesnick, I.W.: Wavelet transform with tunable Q-factor. IEEE Trans. Signal Process. 59(8), 3560–3575 (2011)
Sepehri, A.A., Gharehbaghi, A., Dutoit, T., Kocharian, A., Kiani, A.: A novel method for pediatric heart sound segmentation without using the ECG. Comput. Methods Programs Biomed. 99, 43–48 (2010)
Shannon, C.E., Weaver, W.: The mathematical theory of communication. University of Illinois Press, Champaign (1963)
Shino, H., Yoshida, H., Yana, K., Harada, K. Sudoh, J., Harasewa, E.: Detection and classification of systolic murmur for phonocardiogram screening. In: Proceedings of 18th International Conference of the IEEE Engineering in Medical and Biology Society, pp. 123–124 (1996)
Sun, S., Jiang, Z., Wang, H., Fang, Y.: Automatic moment segmentation and peak detection analysis of heart sound pattern via short-time modified Hilbert transform. Comput. Methods Programs Biomed. 114 (3), 219-230 (2014)
Sun, S., Wang, H., Jiang, Z., Fang, Y., Tao, T.: Segmentation-based heart sound feature extraction combined with classifier models for a VSD diagnosis system. Expert Syst. Appl. 41 (4), 1769–1780 (2014)
Syed, Z., Leeds, D., Curtis, D., Nesta, F., Levine, R.A., Guttag, J.: A framework for the analysis of acoustical cardiac signals. IEEE Trans. Biomed. Eng. 54(4), 651–662 (2007)
Tang, H., Li, T., Qiu, T., Park, Y.: Segmentation of heart sounds based on dynamic clustering. Biomed. Signal Process. Control 7(5), 509–516 (2012)
Thompson, W.R., Hayek, C.S., Tuchinda, C., Telford, J.K., Lombardo, J.S.: Automated cardiac auscultation for detection of pathologic heart murmurs, Pediatr. Cardiol, 373–379 (2001)
Tseng, Y.L., Ko, P.Y., Jaw, F.S.: Detection of the third and fourth heart sounds using Hilbert-Huang transform. BioMed. Eng. OnLine 11(8), 1–13 (2012)
Vepa, J., Tolay, P., Jain, A.: Segmentation of heart sounds using simplicity features and timing information. In: IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 469–472 (2008)
Wang, P., Lim, C.S., Chauhan, S., Foo, J.Y.A., Anantharaman, V.: Phonocardiographic signal analysis method using a modified hidden Markov model. Ann. Biomed. Eng. 35(3), 367–374 (2007)
Watrous, R.L.: Computer-Aided auscultation of the heart: From anatomy and physiology to diagnostic decision support. In: Proceedings of the 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 140–143. New York, USA (2006)
Yan, Z., Jiang, Z., Miyamoto, A., Wei, Y.: The moment segmentation analysis of heart sound pattern. Comput. Methods Programs Biomed. 98, 140–150 (2010)
Yuan, J., He, Z., Zi, Y.: Gear fault detection using customized multiwavelet lifting schemes. Mech. Syst. Signal Process. 24(5), 1509–1528 (2010)
Yuenyong, S., Nishihara, A., Kongprawechnon, W., Tungpimolrut, K.: A framework for automatic heart sound analysis without segmentation. BioMed. Eng. Online 10, 01-23 (2011)
Acknowledgments
The authors would like to thank Dr. Niranjan Garg, Cardiologist, Department of Cardiology, RD Gardi Medical College, Ujjain, India for his valuable clinical suggestions and discussions to improve the manuscript.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Patidar, S., Pachori, R.B. (2015). Classification of Heart Disorders Based on Tunable-Q Wavelet Transform of Cardiac Sound Signals. In: Azar, A., Vaidyanathan, S. (eds) Chaos Modeling and Control Systems Design. Studies in Computational Intelligence, vol 581. Springer, Cham. https://doi.org/10.1007/978-3-319-13132-0_10
Download citation
DOI: https://doi.org/10.1007/978-3-319-13132-0_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-13131-3
Online ISBN: 978-3-319-13132-0
eBook Packages: EngineeringEngineering (R0)