Classification of ECG arrhythmia using significant wavelet-based input features
by Shivani Saxena; Ritu Vijay; Pallavi Pahadiya; Kumod Kumar Gupta
International Journal of Medical Engineering and Informatics (IJMEI), Vol. 15, No. 1, 2023

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.

Online publication date: Wed, 30-Nov-2022

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Medical Engineering and Informatics (IJMEI):
Login with your Inderscience username and password:

    Username:        Password:         

Forgotten your password?


Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com