Improved abnormality detection from raw ECG signals using feature enhancement

Pandit, Diptangshu, Zhang, Li, Aslam, Nauman, Liu, Changyu and Chattopadhyay, Samiran (2016) Improved abnormality detection from raw ECG signals using feature enhancement. In: 2016 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD). IEEE, Piscataway, pp. 1402-1406. ISBN 978-1-5090-4094-0

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This research presents an abnormal beat detection scheme from lead II Electrocardiogram (ECG) signals along with some improvements on feature extraction. A set of 16 features representing positions, durations, amplitudes and shapes of P, Q, R, S and T waves is proposed in this work for heart beat classification. These features carry important medical information for normal and abnormal beat detection. Diverse classifiers are employed for abnormality detection, including K-Nearest Neighbor, Decision Tree, Artificial Neural Network, Naive Bayesian Classifier, Random Forest, and Support Vector Machine along with some ensemble classifiers such as AdaBoostM1 and Bagging. We have evaluated the proposed system on raw one lead signals extracted from MIT-BIH Arrhythmia, QT and European ST-T databases in the Physionet databank. The experiments using this new set of 16 features achieve better performance for the three test databases than our previous system using a subset of these features.

Item Type: Book Section
Uncontrolled Keywords: abnormality detection, abnormal ECG, ECG processing, feature extraction, heart beat classification
Subjects: B800 Medical Technology
G400 Computer Science
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: Becky Skoyles
Date Deposited: 09 Dec 2016 14:14
Last Modified: 12 Oct 2019 12:46

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