Classification of Heart Arrhythmia in ECG Signals using PCA and SVM
Sumanta Kuila1, Namrata Dhanda2, Subhankar Joardar3
1Sumanta Kuila, Department of Computer Science & Engineering, Haldia Institute of Technology, Haldia, (W.B), India.
2Namrata Dhanda, Department of Computer Science & Engineering ,Amity School of Engineering & Technology, Amity University, Lucknow, (Uttar Pradesh), India.
3Subhankar Joardar, Department of Computer Science & Engineering, Haldia Institute of Technology, Haldia, (W.B), India.
Manuscript received on July 12, 2020. | Revised Manuscript received on July 21, 2020. | Manuscript published on August 10, 2020. | PP: 193-198 | Volume-9 Issue-10, August 2020 | Retrieval Number: 100.1/ijitee.J74810891020 | DOI: 10.35940/ijitee.J7481.0891020
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Abstract: Electro cardiogram (ECG) signals records the vital information about the condition of heart of an individual. In this paper, we are aiming at preparing a model for classification of different types of heart arrhythmia. The MIT-BIH public database for heart arrhythmia has been used in the case of study. There are basically thirteen types of heart arrhythmia. The Principal Component Analysis (PCA) algorithm has been used to collect various important features of heart beats from an ECG signal. Then these features are trained and tested under Support Vector Machine (SVM) algorithm to classify the thirteen classes of heart arrhythmia. In the paper the proposed algorithm has been discussed and the outcome results have been validated. The result shows that the accuracy of our classifier in our research work is more than 91% in most of the cases.
Keywords: Arrhythmia, Electrocardiogram, MIT-BIH database, Principal Component Analysis, Support Vector Machine.
Scope of the Article: Classification