Title: Classification of ECG arrhythmia using significant wavelet-based input features

Authors: Shivani Saxena; Ritu Vijay; Pallavi Pahadiya; Kumod Kumar Gupta

Addresses: Department of Physical Sciences, Banasthali Vidyapith, Rajasthan, India ' Department of Physical Sciences, Banasthali Vidyapith, Rajasthan, India ' Banasthali Vidyapith, Rajasthan 304022, India; SAGE University, Indore, 452020, India ' Banasthali Vidyapith, Rajasthan 304022, India; Delhi Technical Campus, 201306, India

Abstract: This paper proposes an automated approach to classify ECG arrhythmia using wavelet transform and neural network. Wavelet-based optimal ECG feature sets are prepared followed by regression plots in curve fitting. These feature sets are further used for pattern recognition to distinguish in between normal or abnormal arrhythmia classes using multi-layer perceptron neural network (MLP NN). To evaluate performances of the designed classifier accuracy, selectivity and sensitivity parameters are measured. The average accuracy of the classifier is 99.05% which is comparatively higher than the existing methods with dependence on less input features.

Keywords: ECG arrhythmia; MLP NN; performance indices; regression plot; wavelet transform.

DOI: 10.1504/IJMEI.2023.127252

International Journal of Medical Engineering and Informatics, 2023 Vol.15 No.1, pp.23 - 32

Received: 30 Sep 2020
Accepted: 31 Jan 2021
Published online: 30 Nov 2022 *