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

doi:10.1109/SKIMA.2014.7083561

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

Healthcare Innovation Centre

Research output: Chapter in Book/Report/Conference proceeding › Conference 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 language	English
Title of host publication	SKIMA 2014 - 8th International Conference on Software, Knowledge, Information Management and Applications
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781479963997
DOIs	https://doi.org/10.1109/SKIMA.2014.7083561
Publication status	Published - 8 Apr 2014
Event	8th International Conference on Software, Knowledge, Information Management and Applications - Dhaka, Bangladesh Duration: 18 Dec 2014 → 20 Dec 2014

Conference

Conference	8th International Conference on Software, Knowledge, Information Management and Applications
Abbreviated title	SKIMA 2014
Country/Territory	Bangladesh
City	Dhaka
Period	18/12/14 → 20/12/14

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10.1109/SKIMA.2014.7083561

Cite this

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 Article 7083561 Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SKIMA.2014.7083561

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title = "An efficient abnormal beat detection scheme from ECG signals using neural network and ensemble classifiers",

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.",

author = "Diptangshu Pandit and Li Zhang and Nauman Aslam and Chengyu Liu and Alamgir Hossain and Samiran Chattopadhyay",

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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., 8th International Conference on Software, Knowledge, Information Management and Applications, Dhaka, Bangladesh, 18/12/14. https://doi.org/10.1109/SKIMA.2014.7083561

An efficient abnormal beat detection scheme from ECG signals using neural network and ensemble classifiers. / Pandit, Diptangshu; Zhang, Li; Aslam, Nauman et al.
SKIMA 2014 - 8th International Conference on Software, Knowledge, Information Management and Applications. Institute of Electrical and Electronics Engineers Inc., 2014. 7083561.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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T1 - An efficient abnormal beat detection scheme from ECG signals using neural network and ensemble classifiers

AU - Pandit, Diptangshu

AU - Zhang, Li

AU - Aslam, Nauman

AU - Liu, Chengyu

AU - Hossain, Alamgir

AU - Chattopadhyay, Samiran

PY - 2014/4/8

Y1 - 2014/4/8

N2 - 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.

AB - 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.

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Pandit D, Zhang L, Aslam N, Liu C, Hossain A, Chattopadhyay S. 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. Institute of Electrical and Electronics Engineers Inc. 2014. 7083561 doi: 10.1109/SKIMA.2014.7083561

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

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