ECG Feature Extraction and Parameter Evaluation for Detection of Heart Arrhythmias

Volume 5 Issue 1 January - March 2017

Research Paper

ECG Feature Extraction and Parameter Evaluation for Detection of Heart Arrhythmias

Gandham Sreedevi*, B. Anuradha**

* Research Scholar, Department of Electronics and Communication Engineering, Sri Venkateswara University, Tirupati, India.

** Professor, Department of Electronics and Communication Engineering, Sri Venkateswara University, Tirupati, India.

Sreedevi, G., Anuradha, B. (2017). ECG Feature Extraction and Parameter Evaluation for Detection of Heart Arrhythmias. i manager’s Journal on Digital Signal Processing, 5(1), 29-38. https://doi.org/10.26634/jdp.5.1.13530

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.