Advanced ECG Signal Classification for Early Arrhythmia Detection Using Deep Learning and Comparative Algorithms

Naveen Kumar Penjerla; Sudha Ellison Mathe

doi:10.48084/etasr.17512

Authors

Naveen Kumar Penjerla School of Electronics Engineering, VIT-AP University, Amaravathi, Andhra Pradesh, India
Sudha Ellison Mathe School of Electronics Engineering, VIT-AP University, Amaravathi, Andhra Pradesh, India

Volume: 16 | Issue: 3 | Pages: 36877-36885 | June 2026 | https://doi.org/10.48084/etasr.17512

Received: 13 January 2026 | Revised: 24 February 2026, 2 April 2026, and 10 April 2026 | Accepted: 17 April 2026 | Online: 6 June 2026

Corresponding author: Sudha Ellison Mathe

Abstract

This study proposes a robust framework that combines contour-based feature extraction with Deep Neural Networks (DNNs) for reliable arrhythmia detection. The approach was evaluated on the MIT-BIH arrhythmia database, where preprocessing techniques, such as wavelet transform, empirical mode decomposition, and adaptive digital filtering, were applied to suppress baseline wander, power line interference, muscle artifacts, and electrode noise. Comparative experiments were conducted using Support Vector Machines (SVMs) and k-Nearest Neighbors (KNNs) classifiers. The results demonstrated that while SVM achieved an accuracy of 97% and KNN reached 73.9%, the proposed DNN model outperformed both, with a classification accuracy of 99.3%. These findings revealed the potential of the proposed system to enhance the accuracy and robustness of ECG-based diagnostics, offering a reliable tool for early-stage arrhythmia detection and supporting timely clinical decision-making.

Keywords:

ECG, Arrhythmia, MIT-BIH, Deep Neural Network, Feature Extraction, SVM, KNN

References

A. E. M. Atwa, E.-S. Atlam, A. Ahmed, M. A. Atwa, E. M. Abdelrahim, and A. I. Siam, "Interpretable Deep Learning Models for Arrhythmia Classification Based on ECG Signals Using PTB-X Dataset," Diagnostics, vol. 15, no. 15, Aug. 2025, Art. no. 1950.

A. Shaheen, L. Ye, C. Karunaratne, and T. Seppänen, "Fully-Gated Denoising Auto-Encoder for Artifact Reduction in ECG Signals," Sensors, vol. 25, no. 3, Jan. 2025, Art. no. 801.

S. Sattar et al., "Cardiac Arrhythmia Classification Using Advanced Deep Learning Techniques on Digitized ECG Datasets," Sensors, vol. 24, no. 8, Apr. 2024, Art. no. 2484.

K. Hanbay, "Deep neural network based approach for ECG classification using hybrid differential features and active learning," IET Signal Processing, vol. 13, no. 2, pp. 165–175, 2019.

F. Li, J. Wu, M. Jia, Z. Chen, and Y. Pu, "Automated Heartbeat Classification Exploiting Convolutional Neural Network With Channel-Wise Attention," IEEE Access, vol. 7, pp. 122955–122963, Aug. 2019.

M. M. Eduardo Vasconcellos et al., "Siamese Convolutional Neural Network for Heartbeat Classification Using Limited 12-Lead ECG Datasets," IEEE Access, vol. 11, pp. 5365–5376, Jan. 2023.

U. Satija, B. Ramkumar, and M. S. Manikandan, "A New Automated Signal Quality-Aware ECG Beat Classification Method for Unsupervised ECG Diagnosis Environments," IEEE Sensors Journal, vol. 19, no. 1, pp. 277–286, Jan. 2019.

U. Satija, B. Ramkumar, and M. S. Manikandan, "Automated ECG Noise Detection and Classification System for Unsupervised Healthcare Monitoring," IEEE Journal of Biomedical and Health Informatics, vol. 22, no. 3, pp. 722–732, May 2018.

J. Huang, B. Chen, B. Yao, and W. He, "ECG Arrhythmia Classification Using STFT-Based Spectrogram and Convolutional Neural Network," IEEE Access, vol. 7, pp. 92871–92880, July 2019.

S. S. Xu, M.-W. Mak, and C.-C. Cheung, "Towards End-to-End ECG Classification With Raw Signal Extraction and Deep Neural Networks," IEEE Journal of Biomedical and Health Informatics, vol. 23, no. 4, pp. 1574–1584, July 2019.

N. Feng, S. Xu, Y. Liang, and K. Liu, "A Probabilistic Process Neural Network and Its Application in ECG Classification," IEEE Access, vol. 7, pp. 50431–50439, Apr. 2019.

H. Ma and L. Xia, "Atrial Fibrillation Detection Algorithm Based on Graph Convolution Network," IEEE Access, vol. 11, pp. 67191–67200, June 2023.

X. Zhai and C. Tin, "Automated ECG Classification Using Dual Heartbeat Coupling Based on Convolutional Neural Network," IEEE Access, vol. 6, pp. 27465–27472, May 2018.

C. Venkatesan, P. Karthigaikumar, A. Paul, S. Satheeskumaran, and R. Kumar, "ECG Signal Preprocessing and SVM Classifier-Based Abnormality Detection in Remote Healthcare Applications," IEEE Access, vol. 6, pp. 9767–9773, Jan. 2018.

J. Mao, Z. Li, S. Li, and J. Li, "A Novel ECG Signal Denoising Algorithm Based on Sparrow Search Algorithm for Optimal Variational Modal Decomposition," Entropy, vol. 25, no. 5, May 2023, Art. no. 775.

W. Sun, N. Zeng, and Y. He, "Morphological Arrhythmia Automated Diagnosis Method Using Gray-Level Co-Occurrence Matrix Enhanced Convolutional Neural Network," IEEE Access, vol. 7, pp. 67123–67129, May 2019.

P. Archana and S. V. Shashikala, "A Hybrid Deep Learning Heart Disease Prediction Framework Utilizing Multi-Modal Medical Imaging and Novel Feature Fusion Techniques," Engineering, Technology & Applied Science Research, vol. 15, no. 6, pp. 28596–28602, Dec. 2025.

X. An, S. Shi, Q. Wang, Y. Yu, and Q. Liu, "Research on a Lightweight Arrhythmia Classification Model Based on Knowledge Distillation for Wearable Single-Lead ECG Monitoring Systems," Sensors, vol. 24, no. 24, Dec. 2024, Art. no. 7896.

B.-T. Pham, P. T. Le, T.-C. Tai, Y.-C. Hsu, Y.-H. Li, and J.-C. Wang, "Electrocardiogram Heartbeat Classification for Arrhythmias and Myocardial Infarction," Sensors, vol. 23, no. 6, Mar. 2023, Art. no. 2993.

S. Ikram et al., "Transformer-based ECG classification for early detection of cardiac arrhythmias," Frontiers in Medicine, vol. 12, Aug. 2025.

M. Lee, J. Lim, and J. Kim, "ECG-GraphNet: Advanced arrhythmia classification based on graph convolutional networks," Heart Rhythm O2, vol. 6, no. 8, pp. 1199–1211, Aug. 2025.

H. M. Lynn, S. B. Pan, and P. Kim, "A Deep Bidirectional GRU Network Model for Biometric Electrocardiogram Classification Based on Recurrent Neural Networks," IEEE Access, vol. 7, pp. 145395–145405, Sept. 2019.

G. B. Moody and R. G. Mark, "The impact of the MIT-BIH Arrhythmia Database," IEEE Engineering in Medicine and Biology Magazine, vol. 20, no. 3, pp. 45–50, June 2001.

G. B. Moody and R. G. Mark, "MIT-BIH Arrhythmia Database." PhysioNet, 1992.

H. Wang, T. Shen, S. Jiang, J. Wang, Y. Ma, and Y. Zhang, "Visualized Lead Selection for Arrhythmia Classification Based on a Lead Activation Heatmap Using Multi-Lead ECGs," Bioengineering, vol. 11, no. 6, June 2024, Art. no. 578.

M. Scarpiniti, "Arrhythmia Detection by Data Fusion of ECG Scalograms and Phasograms," Sensors, vol. 24, no. 24, Dec. 2024, Art. no. 8043.

A. E. Curtin, K. V. Burns, A. J. Bank, and T. I. Netoff, "QRS Complex Detection and Measurement Algorithms for Multichannel ECGs in Cardiac Resynchronization Therapy Patients," IEEE Journal of Translational Engineering in Health and Medicine, vol. 6, pp. 1–11, June 2018.

R. A. Félix, A. Ochoa-Brust, W. Mata-López, R. Martínez-Peláez, L. J. Mena, and L. L. Valdez-Velázquez, "Fast Parabolic Fitting: An R-Peak Detection Algorithm for Wearable ECG Devices," Sensors, vol. 23, no. 21, Oct. 2023, Art. no. 8796.

W. Zhu, X. Chen, Y. Wang, and L. Wang, "Arrhythmia Recognition and Classification Using ECG Morphology and Segment Feature Analysis," IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 16, no. 1, pp. 131–138, Jan. 2019.

Q. Xiao et al., "Deep Learning-Based ECG Arrhythmia Classification: A Systematic Review," Applied Sciences, vol. 13, no. 8, Apr. 2023, Art. no. 4964.