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.
Periodicity:January - March'2017
DOI : 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.

Keywords

ECG, Wavelet Transform, Feature Extraction, Artificial Neural Network, Classification, Bradycardia, Tachycardia.

How to Cite this Article?

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

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