<article_id>JDP_V5_N1_RP6</article_id>
<title>ECG Feature Extraction and Parameter Evaluation for Detection of Heart Arrhythmias</title>
<author>Gandham Sreedevi</author>
<author>Bhuma Anuradha</author>
<journal>Journal on Digital Signal Processing</journal>
<issn>2322–0368</issn>
<volume>5</volume>
<issue>1</issue>
<first_page>29</first_page>
<last_page>38</last_page>
<keywords>ECG, Wavelet Transform, Feature Extraction, Artificial Neural Network, Classification, Bradycardia, Tachycardia</keywords>
<abstract>ECG analysis continues to play a vital role in the primary diagnosis and prognosis of cardiac ailments. This paper presents a new approach to classification of ECG signals based on feature extraction and Artificial Neural Network (ANN) using Discrete Wavelet Transform (DWT). Nineteen ECG signals from MIT-BIH database were used to test the performance of proposed method. A 97.12% of sensitivity and 94.37% of positive predictivity were reported in this test for QRS complex detection. Arrhythmias detected were bradycardia, tachycardia, premature ventricular contraction, supraventricular tachycardia, and myocardial infarction.</abstract>
<date>January - March 2017</date>
<copyright>Copyright &copy; 2017 i-manager publications. All rights reserved.</copyright>
<publisher>i-manager Publications</publisher>
<article_url>http://www.imanagerpublications.com/Article.aspx?ArticleId=13530</article_url>