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Decision support system for arrhythmia prediction using convolutional neural network structure without preprocessing

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Abstract

Arrhythmia is a disease-influencing heart and is manifested by an irregular heartbeat. Atrial fibrillation (Afib), atrial flutter (Afl), and ventricular fibrillation (Vfib) are heart arrhythmias affecting predominantly senior citizens. An electrocardiogram (ECG) is a device serving to measure the ECG signal and diagnosis of an abnormal pattern which represents a heartbeat defects. Though it is possible to analyze these signals manually, in some cases it is a difficult task due to the often signal distortion by noise. Furthermore, manual analyzation of patterns is subjective and can lead to an inaccurate diagnosis. An automated computer-aided diagnosis (CAD) is a technique to eliminate these shortcomings. In this work, we present an 6-layer deep convolutional neural network (CNN) for automatic ECG pattern classification of the normal (Nr), Afib, Afl, and Vfib classes. This proposed CNN model requires simple feature extraction and no pre-processing of ECG signals. For two seconds ECG segments, the model obtained the accuracy of 97.78%, specificity and sensitivity of 98.82% and 99.76% respectively. This proposed system can be used as an assistant automatic tool in a clinical environment as a decision support system.

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Acknowledgements

Support from the Specific Research Project “Socio-economic models and autonomous systems 2” in Faculty of Informatics and Management, University of Hradec Kralove, is gratefully acknowledged.

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Authors and Affiliations

  1. Faculty of Information Technology, Ho Chi Minh City University of Technology (HUTECH), Ho Chi Minh City, Vietnam

    Hamido Fujita

  2. Faculty of Informatics and Management, University of Hradec Kralove, Hradec Kralove, Czech Republic

    Dalibor Cimr

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  1. Hamido Fujita
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  2. Dalibor Cimr
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Correspondence to Hamido Fujita.

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Fujita, H., Cimr, D. Decision support system for arrhythmia prediction using convolutional neural network structure without preprocessing. Appl Intell 49, 3383–3391 (2019). https://doi.org/10.1007/s10489-019-01461-0

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