Support Vector Machine For Classification of Heartbeat Time Series Data
A. Sasirekha1, P. Ganesh Kumar2

1A. Sasirekha, Assistant Professor, Department of Computer Science and Engineering, Info Institute Of Engineering, India.
2P. Ganesh Kumar, Assistant Professor, Department of Information Technology, Anna University of Technology, Coimbatore, India.

Manuscript received on August 11, 2013. | Revised Manuscript received on August 15, 2013. | Manuscript published on August 25, 2013. | PP: 38-41 | Volume-1, Issue-10, August 2013. | Retrieval Number: J04230811013/2013©BEIESP

Open Access | Ethics and Policies | Cite | Mendeley
© The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (

Abstract: Support vector machine (SVM) is a relatively new machine learning tool and has emerged as a powerful technique for learning from data and in particular, for solving binary classification problems. In the literature several statisticallearning paradigms have been proposed for developing a heart rate variability analysis. SVM classification decision which is based on the feature extraction of Heart rate variability (HRV) analysis. Results on a real-life long-term ECG recordings of young and elderly healthy dataset show that understandable SVMs provide a anticipating tool for the prediction of heart rate signals, where as a feature of heart have been generated. Feature extraction describes a pattern or relationships between input features and output class labels directly from the data. This paper proposes several different techniques for Feature extraction. The accuracy is obtained by using the comparison of HRV features.
Keywords: QRS detection algorithm, heart rate variability (HRV), support vector machine (SVM)