ECG Arrhythmia Classification and Interpretation using Convolutional Networks for Intelligent IoT Healthcare System

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dc.contributor.authorKovalchuk, Oleksii
dc.contributor.authorBarmak, Olexander
dc.contributor.authorRadiuk, Pavlo
dc.contributor.authorKrak, Iurii
dc.date.accessioned2024-08-07T09:38:10Z
dc.date.available2024-08-07T09:38:10Z
dc.date.issued2024-07-29
dc.descriptionKovalchuk O., Barmak O., Radiuk P., Krak Iu. ECG arrhythmia classification and interpretation using convolutional networks for intelligent IoT healthcare system. CEUR–WS, ISSN. 1613–0073. 2024. Vol. 3736. P. 47–62. (Scopus, Q4). URL: https://ceur-ws.org/Vol-3736/paper4.pdf
dc.description.abstractIn modern healthcare, timely and precise diagnosis of arrhythmias can significantly impact patient outcomes, as arrhythmias are indicative of various cardiac disorders that require immediate attention. The classification of these irregular heartbeats based on electrocardiogram (ECG) signals is essential for the development of intelligent healthcare systems that can provide real-time monitoring and diagnosis, integrating seamlessly into smart city infrastructures and IoT-enabled smart homes. In this paper, we propose novel methods to enhance the classification and interpretation of arrhythmia by ECG signals based on convolutional neural network (CNN). Leveraging the MIT-BIH Arrhythmia Database, which includes 48 recordings from 47 patients, the proposed approach involved preprocessing the ECG signals into fragments and enhancing the CNN architecture with Batch Normalization layers and an additional convolutional layer. The network was trained and validated using statistical metrics namely accuracy, precision, recall, and F1-scores. The results demonstrated an overall classification accuracy of 99.43%, with particularly high precision and recall for Normal beats, Right bundle branch block beats, and Left bundle branch block beats, achieving F1-scores close to 100%. The introduced CNN showed superior performance in distinguishing between nine types of arrhythmias. However, the study highlighted the limitation of relying on clinical features for decision justification, especially in cases of overlapping pathologies. Overall, the findings suggest that the proposed approach can serve as a reliable supporting tool for arrhythmia diagnosis, offering high accuracy and potential integration into real-time monitoring systems.
dc.identifier.citationECG arrhythmia classification and interpretation using convolutional networks for intelligent IoT healthcare system / O. Kovalchuk et al. 1st International Workshop on Intelligent & CyberPhysical Systems (ICyberPhyS-2024) : CEUR-Workshop Proceedings, Khmelnytskyi, Ukraine, 28 June 2024 / ed. by T. Hovorushchenko et al. Vol. 3736. CEUR-WS.org, Aachen, 2024. P. 47–62. URL: https://ceur-ws.org/Vol-3736/paper4.pdf
dc.identifier.issn1613–0073
dc.identifier.urihttps://elar.khmnu.edu.ua/handle/123456789/16632
dc.language.isoen
dc.publisherCEUR-WS.org
dc.subjectECG classification
dc.subjectECG interpretation
dc.subjectarrhythmia detection
dc.subjectconvolutional neural networks
dc.subjectIoT healthcare
dc.subjectintelligent systems
dc.titleECG Arrhythmia Classification and Interpretation using Convolutional Networks for Intelligent IoT Healthcare System
dc.typeТези доповідей
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