Soft Computing as a tool for Classification of Cardiovascular Abnormalities

Authors(3) :-Dilip Kumar S, Akshaya Yadhav, Archana Sankar

Classification of Electrocardiogram (ECG) for Cardio-Vascular Abnormalities (CVA) in the process of diagnosis is inevitable. In this paper, we propose a scheme to integrate Principal Component Analysis (PCA) with Neural Networks (NN) for classification of ECG Signals. A Neural Network (NN) with Back Propagation Algorithm is deployed as classifier. ECG samples consisting of Normal signals and three abnormal signals are taken from physionet arrhythmias database for our experiments. The PCA is used to minimize ECG signals into weighted sum of basic components that are statistically mutual independent. Thus, PCA is used for dimensionality reduction of data. Here a comparison of performance of Neural Network (NN) and Principal Component Analysis (PCA) with Neural Network (NN) are investigated. Principal Component Analysis (PCA) eliminates the least considerable data values, hence helps in improving the performance in classification of ECG signals. The results obtained suggest that Principal Component Analysis (PCA) with Neural Network (NN) performance is faster and better than Neural Network (NN) Classifier alone.

Authors and Affiliations

Dilip Kumar S
Assistant Professor, Instrumentation and Control Engineering, Sri Krishna College of Technology, Coimbatore, Tamilnadu, India
Akshaya Yadhav
Student, Instrumentation and Control Engineering, Sri Krishna College of Technology, Coimbatore, Tamilnadu, India
Archana Sankar
Student, Instrumentation and Control Engineering, Sri Krishna College of Technology, Coimbatore, Tamilnadu, India

Principal Component Analysis (PCA), Electrocardiogram (ECG), Neural Network (NN). Cardio-Vascular Abnormalities (CVA)

  1. T Acharya, R., Bhat, P. S., Iyengar, S.S., Roo, A. & Dua, S., “Classification of heart rate using aritificial neural network and fuzzy equivalence relation,” The Journal of the pattern Recognition Society, 2002.
  2. De Chazal, P., & Reilly, R. B., “Automatic classification of ECG beats using waveform shape and heart beat interval features,” In IEEE international conference on acoustic, speech and signal processing (ICASSP ‘03), vol. 2, pp. 269-272, Hong Kong, China, 2003
  3. Osowski, S & Linh, T. H., “ECG beat recognition using fuzzy hybrid neural network,” IEEE Transaction on Biomedical Engineering, vol. 48, no. 11, pp, 1265-1271, 2001.
  4. Dipti Patra, Manab Kumar Das,Smita Pradhan “Integration of FCM, PCM and Neural Networks for Classification of ECG Arrhythmias” IAENG International Journal of Computer Science, 36:3,IJCS_36_3_05.
  5. Dayong Gao, Micheal Madden, Micheal Schukat, Des Chambers, and Gerard Lyons “Arrhythmia Identification from ECG Signals with a Neural Network Classifier Based on a Bayesian Framework” In the Twenty-fourth SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, December 2004.
  6. Atena Sajedin, Shokoufeh Zakernejad, Soheil Faridi, Mehrdad Javadi and Reza Ebrahimpour “A Trainable Neural Network Ensemble for ECG Beat Classification” Proceedings of the International Conference on Neural Networks (ICNN2010), Amsterdam, Netherland, Publication year 2010, Page no 28-30
  7. Philip Langley, Emma J.Bowers, and Alan Murray “Principal Component Analysis as a Tool for Analyzing Beat-to-Beat Changes in ECG Features: Application to ECG Derived respiration” IEEE Trans Biomed Eng. 2010 Apr; 57(4):821-9. Epub 2009 Apr
  8. Wei Jiang, Seong G.Kong, and Gregory D.Peterson “ECG Signal Classification using Block-based Neural Networks” Neural Networks, 2005. IJCNN '05. Proceedings. 2005 IEEE International Joint Conference on 31 July-4 Aug. 2005, page (s) 326 - 331 vol. 1.

Publication Details

Published in : Volume 2 | Issue 5 | September-October 2017
Date of Publication : 2017-10-31
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 930-934
Manuscript Number : CSEIT1725214
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

Dilip Kumar S, Akshaya Yadhav, Archana Sankar , "Soft Computing as a tool for Classification of Cardiovascular Abnormalities ", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 2, Issue 5, pp.930-934, September-October-2017.
Journal URL : http://ijsrcseit.com/CSEIT1725214

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