A deep learning framework for ECG Arrhythmia classification using GAM-ResNet18 and Namib beetle optimization-based domain adaptation
Lakshmi Naga Jayaprada Gavarraju1, Sandhya K2, Tejesh Reddy Singasani3, Dileep Pulugu1, Lokesh Sai Kiran Vatsavai4, Usha Rani Bajjuri5 and Ramesh Vatambeti6
Department of Computer Science and Engineering,Malla Reddy College of Engineering and Technology, Hyderabad 500100, Telangana,India1
School of Computing,Mohan Babu University, Tirupati 517102, Andhra Pradesh,India2
School of Computer and Information Sciences,University of the Cumberlands, Louisville, KY 40769,USA3
Department of Information Technology,SRKR Engineering College, Bhimavaram 534204, Andhra Pradesh,India4
Department of Computer Science and Engineering,Lakireddy Bali Reddy College of engineering, Mylavaram 521230, Andhra Pradesh,India5
School of Computer Science and Engineering,VIT-AP University, Vijayawada 522237, Andhra Pradesh,India6
School of Computing,Mohan Babu University, Tirupati 517102, Andhra Pradesh,India2
School of Computer and Information Sciences,University of the Cumberlands, Louisville, KY 40769,USA3
Department of Information Technology,SRKR Engineering College, Bhimavaram 534204, Andhra Pradesh,India4
Department of Computer Science and Engineering,Lakireddy Bali Reddy College of engineering, Mylavaram 521230, Andhra Pradesh,India5
School of Computer Science and Engineering,VIT-AP University, Vijayawada 522237, Andhra Pradesh,India6
Corresponding Author : Lakshmi Naga Jayaprada Gavarraju
Recieved : 12-April-2024; Revised : 13-May-2025; Accepted : 16-May-2025
Abstract
The diagnosis of cardiac disorders using electrocardiograms (ECGs) is critical, yet it remains susceptible to variability and often requires time-consuming manual interpretation by experts. To enhance efficiency and reduce dependency on expert analysis, the development of automated and accurate diagnostic tools is essential. This paper proposes an innovative domain adaptation network (DAN), inspired by the Wasserstein distance and generative adversarial networks (GANs). The network utilizes labeled data from multiple subjects (source domain) to improve classification performance for ECGs in a target domain. Feature extraction is conducted using a global attention module-ResNet18 (GAM-ResNet18) deep learning (DL) model with transfer learning, focusing on critical features such as the R axis, T axis, and QRS count. The proposed architecture consists of a classifier, domain discriminator, and feature extractor, and incorporates attention mechanisms along with a variance layer to enhance signal-to-feature discrimination. The model was trained and validated on both original and noise-attenuated ECG signals from the Chapman dataset. Evaluation metrics including accuracy, precision, recall, and F1-score demonstrate the superior performance of the proposed model, particularly in arrhythmia detection—a task often hindered by noise and irregular events. This model presents a promising solution for automated and efficient arrhythmia diagnosis, addressing the limitations of traditional ECG analysis. Its integration of advanced techniques such as the Wasserstein distance, GANs, attention mechanisms, and transfer learning highlights its potential to significantly improve the diagnosis of cardiac disorders in clinical settings.
Keywords
Electrocardiogram, Arrhythmia classification, Domain adaptation network, Generative adversarial network, Global attention module, Transfer learning.
Cite this article
Gavarraju LNJ, K S, Singasani TR, Pulugu D, Vatsavai LSK, Bajjuri UR, Vatambeti R. A deep learning framework for ECG Arrhythmia classification using GAM-ResNet18 and Namib beetle optimization-based domain adaptation. International Journal of Advanced Technology and Engineering Exploration. 2025;12(126):803-818. DOI : 10.19101/IJATEE.2024.111100597
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