Study of Arrhythmia Classification Algorithms on Electrocardiogram Using Deep Learning


  • Rezki Fauzan Arifin School of Computing, Telkom University, Indonesia
  • Satria Mandala Human Centric (HUMIC) Engineering, School of Computing, Telkom University, Indonesia




Arrhythmia; Classification; Deep Learning; Electrocardiogram (ECG); Machine Learning


Arrhythmia is a heart disease that occurs due to a disturbance in the heartbeat that causes the heart rhythm to become irregular. In some cases, arrhythmias can be life-threatening if not detected immediately. The method used to detect is electrocardiogram (ECG) signal analysis. To avoid misdiagnosis by cardiologists and to ease the workload, methods are proposed to detect and classify arrhythmias by utilizing Artificial Intelligence (AI). In recent years, there has been a lot of research on the detection of this disease. However, many of such studies are more likely to use machine learning algorithms in the classification process, and most of the accuracy results still do not reach optimal levels in general. Therefore, this study aims to classify arrhythmias using deep learning algorithms. There are several stages of performing arrhythmia detection, namely, preprocessing, feature extraction, and classification. The focus of this research is only on the classification stage, where the Long Short-Term Memory (LSTM) algorithm is proposed. After going through a series of experiments, the performance of the proposed algorithm is further analyzed to compare accuracy, specificity, and sensitivity with other machine learning algorithms based on previous research, with the aim of obtaining an optimal algorithm for arrhythmia detection. Based on the results of the study, the Long Short-Term Memory (LSTM) algorithm managed to outperform the performance of other machine learning algorithms with accuracy, specificity, and sensitivity results of 98.47%, 99.24%, and 97.67%, respectively.

GS Cited Analysis


Download data is not yet available.

Author Biographies

Rezki Fauzan Arifin, School of Computing, Telkom University, Indonesia



Satria Mandala, Human Centric (HUMIC) Engineering, School of Computing, Telkom University, Indonesia





Abdelhafid, E., Aymane, E., Benayad, N., Abdelalim, S., My Hachem, E. Y. A., Rachid, O. H. T., & Brahim, B. (2022). ECG Arrhythmia Classification Using Convolutional Neural Network. International Journal of Emerging Technology and Advanced Engineering, 12(7), 186–195.

Almazrouei, M., & Al-Rajab, M. (2022). A model to enhance the atrial fibrillations’ risk detection using deep learning. Periodicals of Engineering and Natural Sciences (PEN), 10(3), 122.

Chickaramanna, S. G., Veerabhadrappa, S. T., Shivakumaraswamy, P. M., Sheela, S. N., Keerthana, S. K., Likith, U., Swaroop, L., & Meghana, V. (2022). Classification of Arrhythmia Using Machine Learning Algorithm. Revue d’Intelligence Artificielle, 36(4), 529–534.

Dhyani, S., Kumar, A., & Choudhury, S. (2023). Analysis of ECG-based arrhythmia detection system using machine learning. MethodsX, 10, 102195.

Fki, Z., Ammar, B., & Ayed, M. Ben. (2023). Towards Automated Optimization of Residual Convolutional Neural Networks for Electrocardiogram Classification. Cognitive Computation.

Jahan, M. S., Mansourvar, M., Puthusserypady, S., Wiil, U. K., & Peimankar, A. (2022). Short-term atrial fibrillation detection using electrocardiograms: A comparison of machine learning approaches. International Journal of Medical Informatics, 163, 104790.

Kulkarni, A., Chong, D., & Batarseh, F. A. (2020). Foundations of data imbalance and solutions for a data democracy. In Data Democracy (pp. 83–106). Elsevier.

Li, J., Pang, S. peng, Xu, F., Ji, P., Zhou, S., & Shu, M. (2022). Two-dimensional ECG-based cardiac arrhythmia classification using DSE-ResNet. In Scientific Reports (Vol. 12, Issue 1).

Madan, P., Singh, V., Singh, D. P., Diwakar, M., Pant, B., & Kishor, A. (2022). A Hybrid Deep Learning Approach for ECG-Based Arrhythmia Classification. Bioengineering, 9(4), 1–26.

Merino-Monge, M., Castro-García, J. A., Lebrato-Vázquez, C., Gómez-González, I. M., & Molina-Cantero, A. J. (2023). Heartbeat detector from ECG and PPG signals based on wavelet transform and upper envelopes. Physical and Engineering Sciences in Medicine, 46(2), 597–608.

Mohanty, M., Dash, M., Biswal, P., & Sabut, S. (2021). Classification of ventricular arrhythmias using empirical mode decomposition and machine learning algorithms. Progress in Artificial Intelligence, 10(4), 489–504.

Montenegro, L., Abreu, M., Fred, A., & Machado, J. M. (2022). Human-Assisted vs. Deep Learning Feature Extraction: An Evaluation of ECG Features Extraction Methods for Arrhythmia Classification Using Machine Learning. Applied Sciences (Switzerland), 12(15), 1–15.

Obeidat, Y., & Alqudah, A. M. (2021). A Hybrid Lightweight 1D CNN-LSTM Architecture for Automated ECG Beat-Wise Classification. Traitement Du Signal, 38(5), 1281–1291.

Ozpolat, Z., & Karabatak, M. (2023). Performance Evaluation of Quantum-Based Machine Learning Algorithms for Cardiac Arrhythmia Classification. Diagnostics, 13(6), 1099.

Philip, A. M., & Hemalatha, S. (2022). Identifying Arrhythmias Based on ECG Classification Using Enhanced-PCA and Enhanced-SVM Methods. International Journal on Recent and Innovation Trends in Computing and Communication, 10(5), 1–12.

Rawi, A. A., Elbashir, M. K., & Ahmed, A. M. (2022). ECG Heartbeat Classification Using CONVXGB Model. Electronics (Switzerland), 11(15).

Ruan, H., Dai, X., Chen, S., & Qiu, X. (2022). Arrhythmia Classification and Diagnosis Based on ECG Signal: A Multi-Domain Collaborative Analysis and Decision Approach. Electronics (Switzerland), 11(19).

Sahoo, S., Dash, P., Mishra, B. S. P., & Sabut, S. K. (2022). Deep learning-based system to predict cardiac arrhythmia using hybrid features of transform techniques. Intelligent Systems with Applications, 16(July), 200127.


Toulni, Y., Nsiri, B., & Drissi, T. B. (2023). ECG Signal Classification Using DWT, MFCC and SVM Classifier. Traitement Du Signal, 40(1), 335–342.

Vijayarangan, S., R., V., Murugesan, B., S.P., P., Joseph, J., & Sivaprakasam, M. (2020). RPnet: A Deep Learning approach for robust R Peak detection in noisy ECG. 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 345–348.

Wu, M., Lu, Y., Yang, W., & Wong, S. Y. (2021). A Study on Arrhythmia via ECG Signal Classification Using the Convolutional Neural Network. Frontiers in Computational Neuroscience, 14.

Xie, T., Li, R., Shen, S., Zhang, X., Zhou, B., & Wang, Z. (2019). Intelligent Analysis of Premature Ventricular Contraction Based on Features and Random Forest. Journal of Healthcare Engineering, 2019, 1–10.


Crossmark Updates

How to Cite

Arifin, R. F., & Mandala, S. . (2023). Study of Arrhythmia Classification Algorithms on Electrocardiogram Using Deep Learning. Sinkron : Jurnal Dan Penelitian Teknik Informatika, 8(3), 1753-1760.