Electrocardiogram Heartbeat Classification using Convolutional Neural Network-k Nearest Neighbor

Abdul, Zrar Kh. and Al‑Talabani, Abdulbasit K. and Rahman, Chnoor M. and Asaad, Safar M. (2024) Electrocardiogram Heartbeat Classification using Convolutional Neural Network-k Nearest Neighbor. ARO-THE SCIENTIFIC JOURNAL OF KOYA UNIVERSITY, 12 (1). pp. 61-67. ISSN 2410-9355

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Abstract

Electrocardiogram (ECG) analysis is widely used by cardiologists and medical practitioners for monitoring cardiac health. A high-performance automatic ECG classification system is challenging because there is difficulty in detecting and categorizing different waveforms in the signal, especially in manual analysis of ECG signals, which means, a better classification system is needed in terms of performance and accuracy. Hence, in this paper, the authors propose an accurate ECG classification and monitoring system called convolutional neural network-k nearest neighbor (CNN-kNN). The proposed method utilizes 1D-CNN and kNN. Unlike the existing techniques, the examined technique does not need training during classifying the ECG signals. The CNN-kNN is evaluated against the PhysioNet’s MIT-BIH and PTB diagnostics datasets. The CNN is fed using the ECG beat raw signal directly. In addition, the learned features are extracted from the 1D-CNN model and its dimensions are reduced using two fully connected layers and then fed to the k-NN classifier. The CNN-kNN model achieved average accuracies of 98% and 97.4% on arrhythmia and myocardial infarction classifications, respectively. These results are evidence of the great ability of the proposed model compared to the mentioned models in this article.

Item Type:	Article
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Uncontrolled Keywords:	Convolutional neural network, Electrocardiogram classification arrhythmia, K-nearest neighbor
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:	ARO-The Scientific Journal of Koya University > VOL 12, NO 1 (2024)
Depositing User:	Dr Salah Ismaeel Yahya
Date Deposited:	02 Sep 2024 06:57
Last Modified:	02 Sep 2024 06:57
URI:	http://eprints.koyauniversity.org/id/eprint/471

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