Automated Heart Sound Classification from Unsegmented Phonocardiogram Signals Using Time Frequency Features
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Automated Heart Sound Classification from Unsegmented Phonocardiogram Signals Using Time Frequency Features

Authors: Nadia Masood Khan, Muhammad Salman Khan, Gul Muhammad Khan


Cardiologists perform cardiac auscultation to detect abnormalities in heart sounds. Since accurate auscultation is a crucial first step in screening patients with heart diseases, there is a need to develop computer-aided detection/diagnosis (CAD) systems to assist cardiologists in interpreting heart sounds and provide second opinions. In this paper different algorithms are implemented for automated heart sound classification using unsegmented phonocardiogram (PCG) signals. Support vector machine (SVM), artificial neural network (ANN) and cartesian genetic programming evolved artificial neural network (CGPANN) without the application of any segmentation algorithm has been explored in this study. The signals are first pre-processed to remove any unwanted frequencies. Both time and frequency domain features are then extracted for training the different models. The different algorithms are tested in multiple scenarios and their strengths and weaknesses are discussed. Results indicate that SVM outperforms the rest with an accuracy of 73.64%.

Keywords: Pattern recognition, machine learning, computer aided diagnosis, heart sound classification, and feature extraction.

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[1] B. M. Whitaker, P. B. Suresha, C. Liu, G. D. Clifford, and D. V. Anderson, “Combining sparse coding and time-domain features for heart sound classification,” Physiological measurement, vol. 38, no. 8, p. 1701, 2017.
[2] D. B. Springer, L. Tarassenko, and G. D. Clifford, “Logistic regression-hsmm-based heart sound segmentation,” IEEE Transactions on Biomedical Engineering, vol. 63, no. 4, pp. 822–832, 2016.
[3] I. Turkoglu, A. Arslan, and E. Ilkay, “An expert system for diagnosis of the heart valve diseases,” Expert systems with applications, vol. 23, no. 3, pp. 229–236, 2002.
[4] T. J. Hirschauer, H. Adeli, and J. A. Buford, “Computer-aided diagnosis of parkinsons disease using enhanced probabilistic neural network,” Journal of medical systems, vol. 39, no. 11, p. 179, 2015.
[5] J.-S. Chou and A.-D. Pham, “Smart artificial firefly colony algorithm-based support vector regression for enhanced forecasting in civil engineering,” Computer-Aided Civil and Infrastructure Engineering, vol. 30, no. 9, pp. 715–732, 2015.
[6] Z. Sankari and H. Adeli, “Probabilistic neural networks for diagnosis of alzheimer’s disease using conventional and wavelet coherence,” Journal of neuroscience methods, vol. 197, no. 1, pp. 165–170, 2011.
[7] J. J. G. Ortiz, C. P. Phoo, and J. Wiens, “Heart sound classification based on temporal alignment techniques,” in Computing in Cardiology Conference (CinC), 2016. IEEE, 2016, pp. 589–592.
[8] A. Ganguly and M. Sharma, “Detection of pathological heart murmurs by feature extraction of phonocardiogram signals,” Journal of Applied and Advanced Research, vol. 2, no. 4, pp. 200–205, 2017.
[9] C. Potes, S. Parvaneh, A. Rahman, and B. Conroy, “Ensemble of feature-based and deep learning-based classifiers for detection of abnormal heart sounds,” in Computing in Cardiology Conference (CinC), 2016. IEEE, 2016, pp. 621–624.
[10] M. Zabihi, A. B. Rad, S. Kiranyaz, M. Gabbouj, and A. K. Katsaggelos, “Heart sound anomaly and quality detection using ensemble of neural networks without segmentation,” in Computing in Cardiology Conference (CinC), 2016. IEEE, 2016, pp. 613–616.
[11] E. Kay and A. Agarwal, “Dropconnected neural network trained with diverse features for classifying heart sounds,” in Computing in Cardiology Conference (CinC), 2016. IEEE, 2016, pp. 617–620.
[12] G. Redlarski, D. Gradolewski, and A. Palkowski, “A system for heart sounds classification,” PloS one, vol. 9, no. 11, p. e112673, 2014.
[13] J.-b. Wu, S. Zhou, Z. Wu, and X.-m. Wu, “Research on the method of characteristic extraction and classification of phonocardiogram,” in Systems and Informatics (ICSAI), 2012 International Conference on. IEEE, 2012, pp. 1732–1735.
[14] M. Abdollahpur, A. Ghaffari, S. Ghiasi, and M. J. Mollakazemi, “Detection of pathological heart sounds,” Physiological measurement, vol. 38, no. 8, p. 1616, 2017.
[15] K. Ekˇstein and T. Pavelka, “Entropy and entropy-based features in signal processing.”
[16] M. M. Azmy, “Classification of normal and abnormal heart sounds using new mother wavelet and support vector machines,” in Electrical Engineering (ICEE), 2015 4th International Conference on. IEEE, 2015, pp. 1–3.
[17] J. F. Miller and P. Thomson, “Cartesian genetic programming,” in European Conference on Genetic Programming. Springer, 2000, pp. 121–132.
[18] M. M. Khan, A. M. Ahmad, G. M. Khan, and J. F. Miller, “Fast learning neural networks using cartesian genetic programming,” Neurocomputing, vol. 121, pp. 274–289, 2013.
[19] G. Khattak, M. Khan, G. Khan, F. Huenupan, and M. Curilem, “Automatic classification of seismic signals of the chilean llaima volcano using cartesian genetic programming based artificial neural network,” 2017.
[20] Z. Jiang and H. Wang, “A new approach on heart murmurs classification with svm technique,” in Information Technology Convergence, 2007. ISITC 2007. International Symposium on. IEEE, 2007, pp. 240–244.
[21] A. H. Salman, N. Ahmadi, R. Mengko, A. Z. Langi, and T. L. Mengko, “Empirical mode decomposition (emd) based denoising method for heart sound signal and its performance analysis,” International Journal of Electrical and Computer Engineering, vol. 6, no. 5, p. 2197, 2016.