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2018 | OriginalPaper | Chapter

Ventricular Arrhythmia Classification Based on High-Order Statistical Features of ECG Signals

Authors : Sunghyun Moon, Jungjoon Kim

Published in: Advances in Computer Science and Ubiquitous Computing

Publisher: Springer Singapore

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Abstract

One class SVM classification model based on high-order statistical features of ECG signals is proposed. This utilizes distinct features of variance, skewness and kurtosis between normal signals and ventricular arrhythmia ECG signals. The model based on a few simple features motivates immediate treatment for sudden cardiac event and wearable technology in practice. The classification algorithm shows significantly improved performance of 98.9% accuracy in correct classification in the experiment using the MIT-BIH Malignant Ventricular Arrhythmia Database (VFDB). It is expected to be used in real-time electrocardiogram monitoring system in conjunction with ECG measurement part and application part.

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Literature
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Gawde, P.R., Bansal, A.K., Nielson, J.A.: Integrating markov model and morphology analysis for finer classification of ventricular arrhythmia in real time. In: IEEE EMBS International Conference on Biomedical and Health Informatics (BHI), pp. 409–412 (2017)
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Cheng, P., Dong, X.: Life-Threatening Ventricular Arrhythmia Detection with Personalized Features. IEEE Access, Vol. 5, 14195–14203 (2017)
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Li, Q.: Ventricular fibrillation and tachycardia classification using a machine learning approach. IEEE Trans. Biomed. Eng. 61(6), 1607–1613 (2014)CrossRef
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Chandra, B.S., Sastry, C.S., Jana, S.: Subject-specific detection of ventricular tachycardia using convolutional neural networks. In: Computing in Cardiology, pp. 53–56. IEEE Conference Publications (2016)
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Park, H.S., Woo, S.M., Kim, Y.S., Kang, B.J., Ban, S.W.: ECG pattern classification based on generic feature extraction. In: International Conference on Recent Advances in Circuits, Systems, Signal and Telecommunications, pp. 21–24 (2009)
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Metadata
Title
Ventricular Arrhythmia Classification Based on High-Order Statistical Features of ECG Signals
Authors
Sunghyun Moon
Jungjoon Kim
Copyright Year
2018
Publisher
Springer Singapore
Book
Advances in Computer Science and Ubiquitous Computing

Print ISBN: 978-981-10-7604-6

Electronic ISBN: 978-981-10-7605-3

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-981-10-7605-3_195