An efficient abnormal beat detection scheme from ECG signals using neural network and ensemble classifiers

Diptangshu Pandit, Li Zhang, Nauman Aslam, Chengyu Liu, Alamgir Hossain, Samiran Chattopadhyay

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)

Abstract

This paper presents an investigation into the development of an efficient scheme to detect abnormal beat from lead II Electro Cardio Gram (ECG) signals. Firstly, a fast ECG feature extraction algorithm was proposed which could extract the locations, amplitudes waves and interval from lead II ECG signal. We then created 11 customized features based on the outputs of the feature extraction algorithm. Then, we used these 11 features to train an artificial neural network and an ensemble classifier respectively for detecting the abnormal ECG beats. Three manually annotated databases were used for training and testing our system: MIT-BIH Arrhythmia, QT and European ST-T database availed from Physionet databank. The results showed that for an abnormal beat detection, the neural network classifier had an overall accuracy of 98.73% and the ensemble classifier with AdaBoost had 99.40%. Using time domain processing approach, the proposed scheme reduced overall computational complexity as compared to the existing methods with an aim to deploy on the mobile devices in the future to promote early and instant abnormal ECG beat detection.

Original languageEnglish
Title of host publicationSKIMA 2014 - 8th International Conference on Software, Knowledge, Information Management and Applications
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781479963997
DOIs
Publication statusPublished - 8 Apr 2014
Event8th International Conference on Software, Knowledge, Information Management and Applications - Dhaka, Bangladesh
Duration: 18 Dec 201420 Dec 2014

Conference

Conference8th International Conference on Software, Knowledge, Information Management and Applications
Abbreviated titleSKIMA 2014
CountryBangladesh
CityDhaka
Period18/12/1420/12/14

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    Pandit, D., Zhang, L., Aslam, N., Liu, C., Hossain, A., & Chattopadhyay, S. (2014). An efficient abnormal beat detection scheme from ECG signals using neural network and ensemble classifiers. In SKIMA 2014 - 8th International Conference on Software, Knowledge, Information Management and Applications [7083561] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SKIMA.2014.7083561