Article: ECG beat classification using machine learning techniques Journal: International Journal of Biomedical Engineering and Technology (IJBET) 2018 Vol.26 No.1 pp.32 - 53 Abstract: An arrhythmia is an abnormality in the heart rhythm, or heartbeat pattern. ECG beats can be classified into six different arrhythmia beat types (left bundle branch block, right bundle branch block, paced beats, premature ventricular contradiction, atrial premature beats, and normal rhythm). Early and accurate detection of arrhythmia types is important in detecting heart diseases and choosing appropriate treatment for a patient. This paper proposes a combination of Particle Swarm Optimisation (PSO) and Feed Forward Neural Network (FFNN) for ECG beat classification. We have used MIT-BIH arrhythmia database for data collection and prepared three different datasets. Features, such as R peak sample number and QRS complex, are extracted using Pan-Tompkins algorithm. The extracted features are used as inputs to three different classifiers: Multi-Layer Perceptron Neural Network (MLPNN), Support Vector Machine (SVM), and PSO-FFNN. Results show high classification accuracy of over 97% with either of these three classifiers. The performance comparison of these classifiers is carried out using three measures: sensitivity, specificity, and accuracy. The results suggest that PSO-FFNN shows slightly better performance than MLPNN and SVM in terms of accuracy on all datasets. Inderscience Publishers - linking academia, business and industry through research

Title: ECG beat classification using machine learning techniques

Authors: Shweta H. Jambukia; Vipul K. Dabhi; Harshadkumar B. Prajapati

Addresses: Department of Information Technology, Dharmsinh Desai University, Nadiad, Gujarat, India ' Department of Information Technology, Dharmsinh Desai University, Nadiad, Gujarat, India ' Department of Information Technology, Dharmsinh Desai University, Nadiad, Gujarat, India

Abstract: An arrhythmia is an abnormality in the heart rhythm, or heartbeat pattern. ECG beats can be classified into six different arrhythmia beat types (left bundle branch block, right bundle branch block, paced beats, premature ventricular contradiction, atrial premature beats, and normal rhythm). Early and accurate detection of arrhythmia types is important in detecting heart diseases and choosing appropriate treatment for a patient. This paper proposes a combination of Particle Swarm Optimisation (PSO) and Feed Forward Neural Network (FFNN) for ECG beat classification. We have used MIT-BIH arrhythmia database for data collection and prepared three different datasets. Features, such as R peak sample number and QRS complex, are extracted using Pan-Tompkins algorithm. The extracted features are used as inputs to three different classifiers: Multi-Layer Perceptron Neural Network (MLPNN), Support Vector Machine (SVM), and PSO-FFNN. Results show high classification accuracy of over 97% with either of these three classifiers. The performance comparison of these classifiers is carried out using three measures: sensitivity, specificity, and accuracy. The results suggest that PSO-FFNN shows slightly better performance than MLPNN and SVM in terms of accuracy on all datasets.

Keywords: ECG beat classification; particle swarm optimisation; neural network; support vector machine; RR interval; QRS complex.

DOI: 10.1504/IJBET.2018.089255

International Journal of Biomedical Engineering and Technology, 2018 Vol.26 No.1, pp.32 - 53

Received: 23 Feb 2016
Accepted: 08 Jun 2016
Published online: 11 Jan 2018 *

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Title: ECG beat classification using machine learning techniques

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