Research ArticleOpen AccessGoogle Scholar indexed
Heart Diseases Diagnosis Using Intelligent Algorithm Based on PCG Signal Analysis
Faculty of Computer and Information Technology, Egyptian E-Learning University (EELU), Giza, Egypt
Faculty of Computer and Information Technology, Egyptian E-Learning University (EELU), Giza, Egypt
Physics Department, Faculty of Sciences, Ain Shams University, Cairo, Egypt
- 1 Faculty of Computer and Information Technology, Egyptian E-Learning University (EELU), Giza, Egypt
- 2 Faculty of Computer and Information Technology, Egyptian E-Learning University (EELU), Giza, Egypt
- 3 Physics Department, Faculty of Sciences, Ain Shams University, Cairo, Egypt
Copy link · social · email
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) has been used for classification stage with an overall accuracy 97%.
KeywordsHeart DiseasesPhonocardiogram (PCG)Feature ExtractionDiscrete Wavelet Transform (DWT)Artificial Neural Network (ANN)
- Salem, A.-B.M., Revett, K. and El-Dahshan, E.-S.A. (2009) Machine learning in electrocardiogram diagnosis. International Multiconference on Computer Science and Information Technology. IEEE, 429-433. https://doi.org/10.1109/imcsit.2009.5352689
- http://www.who.int/cardiovascular_diseases/en/
- Reed, T.R., Reed, N.E. and Fritzson, P. (2003) Heart Sound Analysis for Symptom Detection and Computer-Aided Diagnosis. Simulation: Modelling Practice and Theory, 12, 129-146. https://doi.org/10.1016/j.simpat.2003.11.005
- Avendaňo-Valencia, L.D., Ferrero, J.M. and Castellanos-Dominguez, G. (2007) Improved Parametric Estimation of Time-Frequency Representation for Cardiac Murmur Discrimination. Computers in Cardiology, 35, 157-160.
- Abbas, A.K., Bassam, R. and Kasim, R.M. (2008) Mitral Regurgitation PCG-Signal Classification Based on Adaptive Db-Wavelet. 4th Kuala Lumpur International Conference on Biomedical Engineering, Springer Berlin Heidelberg, 212-216.
- Roy, A.K., Misal, A. and Sinha, G.R. (2014) Classification of PCG Signals: A Survey.
- Mann, D.L., et al. (2014) Braunwald’s Heart Disease: A Textbook of Cardiovascular Medicine. Elsevier Health Sciences.
- Sivagowry, S., Durairaj, M. and Persia, A. (2013) An Empirical Study on Applying Data Mining Techniques for the Analysis and Prediction of Heart Disease. 2013 International Conference on Information Communication and Embedded Systems (ICICES), IEEE. https://doi.org/10.1109/icices.2013.6508204
- Dokur, Z. and Olmez. T. (2008) Heart Sound Classification Using Wavelet Transform and Incremental Self-Organizing Map. Digital Signal Processing, 18, 951-959. https://doi.org/10.1016/j.dsp.2008.06.001
- Kao, W.-C. and Wei, C.-C. (2011) Automatic Phonocardiograph Signal Analysis for Detecting Heart Valve Disorders. Expert Systems with Applications, 38, 6458-6468. https://doi.org/10.1016/j.eswa.2010.11.100
- http://www.peterjbentley.com/heartchallenge/
- Hanbay, D. (2009) An Expert System Based on Least Square Support Vector Machines for Diagnosis of the Valvular Heart Disease. Expert Systems with Applications, 36, 4232-4238.
- Haykin, S. (1999) Neural Networks: A Comprehensive Foundation. Prentice Hall, Upper Saddle River.
- Singh, M. and Amandeep, C. (2013) Heart Sounds Classification Using Feature Extraction of Phonocardiography Signal. International Journal of Computer Applications, 77, 13-17. https://doi.org/10.5120/13381-1001