Automated Heart Sound Classification from Unsegmented Phonocardiogram Signals Using Time Frequency Features
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%.
[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.
[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.
@article{"International Journal of Information, Control and Computer Sciences:77716", author = "Nadia Masood Khan and Muhammad Salman Khan and Gul Muhammad Khan", title = "Automated Heart Sound Classification from Unsegmented Phonocardiogram Signals Using Time Frequency Features", abstract = "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.", volume = "12", number = "8", pages = "598-6", }
