oalogo2  

AUTHOR(S):

Mohammed Nabih-Ali, EL-Sayed A. El-Dahshan, Ashraf S. Yahia

 

TITLE

Heart Diseases diagnosis using intelligent algorithm based on PCG signal analysis

pdf PDF

ABSTRACT

This paper presents an intelligent algorithm for heart diseases diagnosis using phonocardiogram (PCG). The proposed technique consists of four stages; data acquisition, pre-processing, feature extraction and classification. PASCAL heart sound database is used in this research. The second stage concerns with removing noise and artifacts from the PCG signals. Feature extraction stage is carried out using discrete wavelet transform (DWT). Finally, artificial neural network (ANN) have been used for classification stage with an overall accuracy 97%.

KEYWORDS

Heart Diseases – Phonocardiogram (PCG) – Feature Extraction – Discrete Wavelet Transform (DWT) – Artificial Neural Network (ANN)

REFERENCES

[1] Salem, Abdel-Badeeh M., Kenneth Revett, and El-Sayed A. El-Dahshan. "Machine learning in electrocardiogram diagnosis." International Multiconference on Computer Science and Information Technology. IEEE, 2009, pp 429- 433.

[2] http://www.who.int/cardiovascular_diseases/en/

[3] T.R. Reed, N.E. Reed, P. Fritzson, Heart sound analysis for symptom detection and computeraided diagnosis, Simulation: Modelling Practice and Theory Vol. 12, 2003, 129–146.

[4] L.D. Avendaňo-Valencia, J.M. Ferrero, G. Castellanos-Domınguez; Improved parametric estimation of time-frequency representation for cardiac murmur discrimination; Computers in Cardiology Vol. 35, 2007, pp. 157-160.

[5] Abbas, A.K., Bassam, R. and Kasim, R.M., 2008. Mitral regurgitation PCG-signal classification based on adaptive Db-wavelet. In 4th Kuala Lumpur International Conference on Biomedical Engineering 2008, pp. 212-216. Springer Berlin Heidelberg.

[6] Roy, Ajay Kumar, Abhishek Misal, and G. R. Sinha. Classification of PCG Signals: A Survey, 2014.

[7] Mann, Douglas L., et al. Braunwald's heart disease: a textbook of cardiovascular medicine. Elsevier Health Sciences, 2014.

[8] http://www.peterjbentley.com/heartchallenge/

[9] Hanbay, Davut. "An expert system based on least square support vector machines for diagnosis of the valvular heart disease." Expert Systems with Applications Vol. 36, 2009, pp 4232-4238.

[10] S. Haykin, Neural Networks: A comprehensive Foundation, Prentice Hall, 1999.

[11] Gupta, Cota Navin, et al. "Neural network classification of homomorphic segmented heart sounds." Applied Soft Computing vol. 7, no. 1, 2007, pp. 286-297.

[12] Dokur, Zümray, and Tamer Ölmez. "Heart sound classification using wavelet transform and incremental self-organizing map." Digital Signal Processing vol. 18, no. 6, 2008, pp. 951-959.

[13] R. Das, I. Turkoglu, A. Sengur; Diagnosis of valvular heart diseases through neural networks ensembles; Computer methods and Programs in Biomedicine vol. 93, 2009, pp. 185-191.

[14] Wen-Chung Kao, Chih-Chao Wei; Automatic phonocardiograph signal analysis for detecting heart valve disorders; Expert Systems with Applications vol. 38, 2011, pp. 6458-6468.

[15] Sivagowry, S., M. Durairaj, and A. Persia. "An empirical study on applying data mining techniques for the analysis and prediction of heart disease." Information Communication and Embedded Systems (ICICES), 2013 International Conference on. IEEE, 2013.

[16] J. E. Guillermo, E. N. Sanchez, L. J. Ricalde et al.; Intelligent Classification of Real Heart Diseases Based on Radial Wavelet Neural Network, 7th Cairo International Biomedical Engineering Conference, Cairo, Egypt, December 2014, pp. 162-165.

[17] Gavrovska, Ana, et al. "Classification of prolapsed mitral valve versus healthy heart from phonocardiograms by multifractal analysis." Computational and mathematical methods in medicine 2013.

[18] Yao, Hao-Dong, et al. "A novel murmur-based heart sound feature extraction technique using envelope-morphological analysis." Seventh International Conference on Digital Image Processing (ICDIP15). International Society for Optics and Photonics, 2015.

[19] E. J. Harfash. “Diagnostic the Heart Valve Diseases using Eigen Vectors.” International Journal of Computer Science and Mobile Computing. Vol. 5, no. 3, 2016, pp. 273 – 278.

[20] Imani, Maryam, and Hassan Ghassemian. "Curve fitting, filter bank and wavelet feature fusion for classification of PCG signals." Electrical Engineering (ICEE), 2016 24th Iranian Conference on. IEEE, (2016).

Cite this paper

Mohammed Nabih-Ali, EL-Sayed A. El-Dahshan, Ashraf S. Yahia. (2017) Heart Diseases diagnosis using intelligent algorithm based on PCG signal analysis. International Journal of Biology and Biomedicine, 2, 81-85

 

cc.png
Copyright © 2017 Author(s) retain the copyright of this article.
This article is published under the terms of the Creative Commons Attribution License 4.0