Open Access Open Access  Restricted Access Subscription or Fee Access

Cepstral Features Extraction for Heart Sounds Classification

Othmane El Badlaoui(1*), Abd-Errahim Maazouzi(2), Ahmed Hammouch(3)

(1) LRGE Lab, ENSET, Mohammed V University, Rabat, Morocco
(2) LRGE Lab, ENSET, Mohammed V University, Rabat, Morocco
(3) LRGE Lab, ENSET, Mohammed V University, Rabat, Morocco
(*) Corresponding author


DOI: https://doi.org/10.15866/iree.v13i5.14965

Abstract


A novel method for separation between normal and abnormal heart sounds based on Phonocardiogram (PCG) is presented in this article. For features extraction phase, Mel-Frequency Cepstral Coefficients (MFCC) algorithm is used to extract information from heart sound signals. In this step, changing the frames size, during framing process, shows it influence on the obtained result. In classification step, Support Vector Machines (SVM) is used with different kernels. Simulation results obtained from different databases are compared and discussed. The developed system gave good results when applied to different datasets with an accuracy of 96% and 98% for the first and the second dataset respectively. The developed system can be used for other applications in biomedical domains
Copyright © 2018 Praise Worthy Prize - All rights reserved.

Keywords


Phonocardiogram; Signal Processing; MFCC; SVM; Classification

Full Text:

PDF


References


rnceus.com/ekg/ekgnorm.html

A. K. Abbas, R. Bassam Phonocardiography Signal Processing 2009.
who.int/mediacentre/

D. Kumar, P. Carvalho, M. Antunes, J. Henriques, A. Sa e Melo, J. Habetha Heart Murmur Recognition and Segmentation by Complexity Signatures 30th Annual International IEEE EMBS Conference, Vancouver, British Columbia, Canada, August 20-24, 2008.
http://dx.doi.org/10.1109/iembs.2008.4649614

Z. Zhao, J. Wang Heart Sound Identification System International Conference on Electronics, Communications and Control (ICECC), 2011.
http://dx.doi.org/10.1109/icecc.2011.6067675

H. Wu, S. Kim, and K. Bae, Hidden Markov model with heart sound signals for identification of heart diseases, 20th International Congress on Acoustics, ICA 2010 23-27 August 2010, Sydney, Australia.

M. Singh, A. Cheema Heart sound classification using Feature Extraction of Phonocardiography signal International Journal of Computer Applications (0975-8887). Vol. 77, no 4, September 2013.
http://dx.doi.org/10.5120/13381-1001

M. abo-Zahhad, S. M. Ahmed, S. N. Abbas PCG Biometric Identification System Based on Feature Level Fusion using canonical Correlation analysis CCECE 2014, Toronto, Canada.
http://dx.doi.org/10.1109/ccece.2014.6901068

N. R. Sujit, C. Santhosh Kumar, C. B. Rajesh, Improving The Performance of Cardiac Abnormality Detection from PCG Signal American Institute of Physiscs Conference, 2016.
http://dx.doi.org/10.1063/1.4942735

S. Verma, T. Kashyp, Analysis of The Heart sound as Biometric Usinf MFCC and Liner SVM Classifier International Journal of Advenced Research in Electrical, Electronics and Instrumentation Engineering, 2007.

N. Dey, A. Das, S.Chaudhuri, Wavelet Based Normal and Abnormal Heart Sound Identification using Spectrogram Analysis, International Journal of Computer Science and Engineering Technologie (IJCSET). Vol. 3, no 6, June 2012.

N. Marques, R. Almeida, A.P Rocha, M. Coimbra, Exploring the Stationary Wavelet Transform Detail Coefficients for Detection and Identification of The S1 and S2 Heart Sounds, Computing in Cardiology,. Vol. 40, pp. 891-894, 2013.

A. Castro, Tiago T. V. Vinhoza, S. S. Mattos, M. T. Coimbra Heart Sound Segmentation of Pediatric Ascultations Using Wavelet Analysis, 35th Annual International Conference of the IEEE EMBS, Osaka, Japan, 3-7 July 2013.
http://dx.doi.org/10.1109/embc.2013.6610399

E. Delgado-Trejos, A. F. Quiceno-Manrique, J. I. Godino Llorente, M. Blanco-Velasco, G. Castellanos-Dominguez,Digital Ascultation analysis for Heart Murmur Detection, Annals of Biomedical Engineering. Vol. 37, no 2, pp. 337-353, February 2009.
http://dx.doi.org/10.1007/s10439-008-9611-z

I. Mglogiannis, E. Loukis, E. Zafiropoulos, S.A. Stasis Support Vectors Machine-based Identification of Heart Valve Diseases Using Heart Sounds Journal of Computer, Methods and Programs in Biomedicine, ELSEVIER. Vol. 95, pp. 47-61, 2009.
http://dx.doi.org/10.1016/j.cmpb.2009.01.003

J. Pedrosa, A. Castro, Tiago T. V. Vinhoza automatic Heart Sound Segmentation and Murmur Detection in Pediatric Phonocardiograms 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp.2294-2297, 2014.
http://dx.doi.org/10.1109/embc.2014.6944078

P. Sedighian, A. W. Subudhi, F. Scalzo, S. Asgari Pediatric Heart Sound Segmentation Using HMM 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 5490-5493, 2014.
http://dx.doi.org/10.1109/embc.2014.6944869

W. Fu, W. Yang Heart Sound Diagnosis Based on DTW and MFCC 3rd International Congress on Image and Signal Processing (CISP) 2010.
http://dx.doi.org/10.1109/cisp.2010.5646678

S. Young, G. Evermann, D. Kershaw, G. Moore, J. Odell, D. Ollason, D. Povey, V. Valtchev, P. Woodland The HTK Book (for HTK Version 3.2) Engineering Departement of Combridge University, 2006.

J. Martinez, H. Perez, E. Escamilla, M.M. Suzuki Speaker Recognition usin Mel Frequency Cepstral Coefficients (MFCC) and Vector Quantization (VQ) Techniques 22nd International Conference on electrical communications an Computers, pp. 248-251, 2012.
http://dx.doi.org/10.1109/conielecomp.2012.6189918

A. L. Coldberger Clinical Echocardiography: a Simplified Approach, Seventh Edition Elsevier 2006.
http://dx.doi.org/10.1177/0267659117732938

O. El Badlaoui, A. Hammouch Discrimination between normal and heart murmurs sound, based on statistical parameters extraction and classification Electric Electronics, Computer Science, Biomedical Engineerings' Meeting (EBBT), 2017. IEEE, 2017. p. 1-4, 20-21 April, Istanbul, Turkey.
http://dx.doi.org/10.1109/ebbt.2017.7956771

O. El Badlaoui, A. Hammouch Phonocardiogram classification based on MFCC extraction, International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA), 2017 IEEE IEEE, 2017. p. 217-221, 26-28 June, Annecy, France.
http://dx.doi.org/10.1109/civemsa.2017.7995329

V. N.Vapnik The Nature of Statistical Learning Theory 1995.
http://dx.doi.org/10.1007/978-1-4757-2440-0

Hendradi, R., Arifin, A., Shida, H., Gunawan, S., Purnomo, M., Hasegawa, H., Kanai, H., Signal Processing and Extensive Characterization Method of Heart Sounds Based on Wavelet Analysis, (2016) International Review of Electrical Engineering (IREE), 11 (1), pp. 55-68.
http://dx.doi.org/10.15866/iree.v11i1.8138

Bani Yassein, M., Hamdan, M., Shehadeh, H., Mrayan, L., A Novel Approach for Health Monitoring System Using Wireless Sensor Network, (2017) International Journal on Communications Antenna and Propagation (IRECAP), 7 (4), pp. 271-281.
http://dx.doi.org/10.15866/irecap.v7i4.11311

Aabid, M., Elakkary, A., Sefiani, N., Real-Time Cardiac Monitoring Through the Application of an Adaptive Controller to Human Heart, (2017) International Review of Automatic Control (IREACO), 10 (1), pp. 63-71.
http://dx.doi.org/10.15866/ireaco.v10i1.11014


Refbacks

  • There are currently no refbacks.



Please send any question about this web site to info@praiseworthyprize.com
Copyright © 2005-2022 Praise Worthy Prize