Arrhythmia Classification with Single Beat ECG Evaluation and Support Vector Machine
Sundari Tribhuvanam1, H. C. Nagaraj2, V.P.S. Naidu3

1Sundari Tribhuvanam Department of Electronics, University of Mysore, Mysore, India.
2H. C. Nagaraj, Department of Electronics, Nitte Research and Education Academy, Bengaluru-India.
3V.P.S. Naidu, MSDF, FMCD, CSIR-NAL, Bengaluru-India. 

Manuscript received on September 15, 2019. | Revised Manuscript received on 22 September, 2019. | Manuscript published on October 10, 2019. | PP: 2814-2820 | Volume-8 Issue-12, October 2019. | Retrieval Number: L30251081219/2019©BEIESP | DOI: 10.35940/ijitee.L3025.1081219
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Abstract: Abnormal electrical activity of the human heart indicates cardiac dysfunction. The Electrocardiogram (ECG) is one of the non-invasive diagnostic techniques to detect cardiac abnormalities. Irregularity and non-stationarity in the ECG signal impose difficulties to clinicians for accurate diagnosis of heart diseases only by visual inspection. Automatic recognition of abnormal ECG beats aids in early detection of heart diseases. This paper explores the ECG single beat analysis to identify the cardiac abnormality. In this work, seven classes of arrhythmia are considered as recommended by AAMI(Association for the Advancement of Medical Instrumentation) standard. Beat feature database is generated from 44 recordings of the MIT-BIH arrhythmia database to support the arrhythmia classification. Classification is implemented with Multiclass Support Vector Machine (SVM) for non-linearly separable data effectively. Classification accuracy up to 93% is achieved for the selected input feature sets. This work assesses the suitability of the ECG input features for multi-class classification of arrhythmia.
Keywords: Feature Extraction, Multiclass Classification, Radial Basis Function, Single Beat Analysis, Support Vector Machine
Scope of the Article: Classification