Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Reseek-Arrhythmia: Empirical Evaluation of Resnet Architecture for Detection of Arrhythmia

Lip Yee Por
ORCID logo ,
Shams UI Haq
,
Sibghat Ullah Bazai
* ORCID logo ,
Ali Fatima
,
Shah Marjan
* ORCID logo ,
Jing Yang
,
Mohd Anjum
,
Sana Shahab
ORCID logo ,
Chin Soon Ku
*
Version 1 : Received: 7 August 2023 / Approved: 8 August 2023 / Online: 9 August 2023 (07:28:33 CEST)

How to cite: Por, L.Y.; UI Haq, S.; Ullah Bazai, S.; Fatima, A.; Marjan, S.; Yang, J.; Anjum, M.; Shahab, S.; Ku, C.S. Reseek-Arrhythmia: Empirical Evaluation of Resnet Architecture for Detection of Arrhythmia. Preprints 2023, 2023080683. https://doi.org/10.20944/preprints202308.0683.v1 Por, L.Y.; UI Haq, S.; Ullah Bazai, S.; Fatima, A.; Marjan, S.; Yang, J.; Anjum, M.; Shahab, S.; Ku, C.S. Reseek-Arrhythmia: Empirical Evaluation of Resnet Architecture for Detection of Arrhythmia. Preprints 2023, 2023080683. https://doi.org/10.20944/preprints202308.0683.v1

Abstract

Arrhythmia is a cardiac condition characterized by an irregular heart rhythm that hinders the proper circulation of blood, posing a severe risk to individuals’ lives. Globally, arrhythmias are recognized as a significant health concern, accounting for nearly 12 percent of all deaths. As a result, there has been a growing focus on utilizing artificial intelligence for the detection and classification of abnormal heartbeats. In recent years, self-operated heartbeat detection research has gained popularity due to its cost-effectiveness and potential for expediting therapy for individuals at risk of arrhythmias. However, building an efficient automatic heartbeat monitoring approach for arrhythmia identification and classification comes with several significant challenges. These challenges include addressing issues related to data quality, determining the range for heart rate segmentation, managing data imbalance difficulties, handling intra- and inter-patient variations, distinguishing supraventricular irregular heartbeats from regular heartbeats, and ensuring model interpretability. In this study, we propose the Reseek-Arrhythmia model, which leverages deep learning techniques to automatically detect and classify heart arrhythmia diseases. The model combines different convolutional blocks and identity blocks, along with essential components such as convolution layers, batch normalization layers, and activation layers. To train and evaluate the model, we utilized the MIT-BIH and PTB datasets. Remarkably, the proposed model achieves outstanding performance with an accuracy of 99.35% and 93.50% and an acceptable loss of 0.688 and 0.2564, respectively.

Keywords

heart disease; arrhythmia detection; ECG; convolutional neural network; deep learning; convolutional blocks; identity blocks

Subject

Public Health and Healthcare, Public Health and Health Services

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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