International Journal of Applied Science and Engineering
Published by Chaoyang University of Technology

Mazaya Aqila 1, Annisa Humairani 1,2*,Tito Waluyo Purboyo 1,2, Dziban Naufal 1,2

1School of Electrical Engineering, Telkom University, Bandung, Indonesia

2Center of Excellence for Biomedical and Healthcare Technology, Research Institute for Digital Health, Social and Wellness, Telkom University, Bandung, Indonesia

 

Download Citation: |
{xpdfattach}


ABSTRACT

Arrhythmia detection continues to pose significant challenges because ECG signals are non-stationary and susceptible to noise. Signal decomposition, particularly variational mode decomposition (VMD) is advantageous for achieving frequency separation with minimal distortion and is often integrated with metaheuristic optimization to address diverse objectives. However, previous studies have only focused on a single integration workflow, necessitating a comparison of which integration method is more effective. This study proposes a comparison of VMD–PSO–integrated arrhythmia classification methods to assess how integration stages affect feature-extraction quality and ECG classification accuracy. The system was validated using an ECG dataset from the MIT-BIH Arrhythmia Database, which contains 40 patient recordings with five classes. This study focuses on two experiments: PSO for feature selection from VMD decomposition results and optimizing VMD parameters. The results show that differences in VMD parameters and the number of features used in both experiments affect classification quality. The highest accuracy, 97.42%, was achieved in experiment 1 where VMD used fixed parameters (K=5,α=adaptive) and the extracted features were selected using PSO to yield 39 out of 84 features. Building on these findings, the direct comparison of two PSO-based integration strategies provides a novel analysis of their effects on ECG arrhythmia classification, contrasting with prior work restricted to a single optimization aspect.


Keywords: Arrhythmia detection, ECG signal, Metaheuristic optimization, PSO, VMD.


Share this article with your colleagues

 


REFERENCES

  1. Ary, L.G., Amaral, L.A.N., Glass, L., Hausdorff, J.M., Ivanov, P.C., Mark, R.G., Mietus, J.E., Moody, G.B., Peng, C.K., Stanley, H.E. 2000. Components of a new research resource for complex physiologic signals. American College of Cardiology, 101, 661–670.

  2. Bansal, J.C., Bajpai, P., Rawat, A., Nagar, A.K. 2023. Sine cosine algorithm for multi-objective optimization. In: Sine Cosine algorithm for optimization. Springer Nature Singapore, 35–63.

  3. Bing, P., Liu, W., Zhai, Z., Li, J., Guo, Z., Xiang, Y., He, B., Zhu, L. 2024. A novel approach for denoising electrocardiogram signals to detect cardiovascular diseases using an efficient hybrid scheme. Frontiers in Cardiovascular Medicine, 11, 1277123.

  4. Chaubey, K., Saha, S. 2025. Electrocardiogram shockable arrhythmia detection using the fusion of Xception and VMD-based features and firefly optimized online sequential extreme learning machine. International Journal of Information Technology, 17, 1–13.

  5. Chen, S.S.M., Aday, A.W., Almarzooq, Z.I., Anderson, C.A., Arora, P., Avery, C.L., Baker-Smith, C.M., Barone Gibbs, B., Beaton, A.Z., Boehme, A.K. 2024. Heart disease and stroke statistics: A report of US and global data from the American Heart Association, 149, E347–E913.

  6. Dong, K., Sun, L., Xiong, H., Wang, W. 2020. ECG energy change study based on variational mode decomposition. American Journal of Biochemistry and Biotechnology, 16, 103–111.

  7. Dragomiretskiy, K., Zosso, D. 2014. Variational mode decomposition. IEEE Transactions on Signal Processing, 62, 531–544.

  8. Essa, E., Xie, X. 2021. An ensemble of deep learning-based multi-model for ECG heartbeats arrhythmia classification. IEEE Access, 9, 103452–103464.

  9. Fawcett, T. An introduction to ROC analysis. Pattern Recognition Letters, 27, 861–874.

  10. Hutasuhut, M., Tugiono, T., Nasyuha, A.H. 2021. Analisis aritmia (gangguan irama jantung) menerapkan metode certainty factor. Jurnal Media Informatika Budidarma, 5, 1386.

  11. Jabeur, T.B., Bashier, E., Sandhu, Q., Bwalya, K.J., Joshua, A. 2024. Comprehensive noise and artifact removal from ECG signals using wavelet, variational mode decomposition and nonlocal means algorithms. Mathematical Modelling of Engineering Problems, 11, 2109–2118.

  12. Jahangir, R., Islam, M.N., Islam, M.S., Islam, M.M. 2025. ECG-based heart arrhythmia classification using feature engineering and a hybrid stacked machine learning. BMC Cardiovascular Disorders, 25, 260.

  13. Kumar, A., Kumar, S., Dutt, V., Dubey, A.K., García Díaz, V. 2022. IoT-based ECG monitoring for arrhythmia classification using coyote grey wolf optimization-based deep learning CNN classifier. Biomedical Signal Processing and Control, 76, 103638.

  14. Laghari, A.A., Estrela, V.V., Yin, S. 2024. How to collect and interpret medical pictures captured in highly challenging environments that range from nanoscale to hyperspectral imaging. Current Medical Imaging, 20, 1.

  15. Laghari, A.A., Sun, Y., Alhussein, M., Aurangzeb, K., Anwar, M.S., Rashid, M. 2023. Deep residual-dense network based on bidirectional recurrent neural network for atrial fibrillation detection. Scientific Reports, 13, 15109.

  16. Li, Y., Qian, R., Li, K. 2022. Inter-patient arrhythmia classification with improved deep residual convolutional neural network. Computer Methods and Programs in Biomedicine, 214, 106582.

  17. Long, J., Wang, X., Dai, D., Tian, M., Zhu, G., Zhang, J. 2017. Denoising of UHF PD signals based on optimised VMD and wavelet transform. IET Science, Measurement and Technology, 11, 753–760.

  18. Luz, E., Menotti, D. 2011. An x-ray on methods aiming at arrhythmia classification in ECG signals. International Conference on Bioinformatics and Computational Biology (BIOCOMP’2011). The Steering Committee of The World Congress in Computer Science, Computer Engineering and Applied Computing (WorldComp).

  19. 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, 1–26.

  20. Mao, J., Li, Z., Li, S., Li, J. 2023. A novel ECG signal denoising algorithm based on sparrow search algorithm for optimal variational modal decomposition. Entropy, 25, 775.

  21. Markoulidakis, I., Rallis, I., Georgoulas, I., Kopsiaftis, G., Doulamis, A., Doulamis, N. 2021. Multiclass confusion matrix reduction method and its application on net promoter score classification problem. Technologies, 9, 412–419.

  22. Martin, S.S., Aday, A.W., Almarzooq, Z.I., Anderson, C.A.M., Arora, P., Avery, C.L., Baker-Smith, C.M., Barone Gibbs, B., Beaton, A.Z., Boehme, A.K. 2024. Heart disease and stroke statistics: A report of US and global data from the American Heart Association. Circulation, 149, E347–E913.

  23. Mert, A. 2016. ECG feature extraction based on the bandwidth properties of variational mode decomposition. Physiological Measurement, 37, 530–543.

  24. Mohanty, M., Biswal, P., Sabut, S. 2020. Machine learning approach to recognize ventricular arrhythmias using VMD based features. Multidimensional Syst. Signal Process, 31, 49–71.

  25. Mohanty, M., Dash, P., Sabut, S. 2024. Decision support system for predicting ventricular arrhythmias using non-linear features of ECG signals. SN Computer Science, 5, 357.

  26. Munir, M.A., Shah, R.A., Ali, M., Laghari, A.A., Almadhor, A., Gadekallu, T.R. 2024. Enhancing gene mutation prediction with sparse regularized autoencoders in lung cancer radiomics analysis. IEEE Access, 12, 1–12.

  27. Nugraha, A.F., Pramudita, B.A., Setiawan, N.A., Nugroho, H.A. 2017. R-peaks detection method for classifying arrhythmia disorder. Journal of the Medical Sciences (Berkala Ilmu Kedokteran), 49, 191–199.

  28. Ozpolat, Z., Karabatak, M. 2023. Performance evaluation of quantum-based machine learning algorithms for cardiac arrhythmia classification. Diagnostics, 13, 1099.

  29. Paraschakis, K., Castellani, A., Borboudakis, G., Tsamardinos, I. 2024. Confidence interval estimation of predictive performance in the context of AutoML. Proceedings of the Third International Conference on Automated Machine Learning. Proceedings of Machine Learning Research, 4, 1–14.

  30. Pramanik, P.K.D., Pal, S., Mukhopadhyay, M., Singh, S.P. 2021. Big data classification: techniques and tools. In Applications of Big Data in Healthcare. Academic Press, 1–43.

  31. Qaisar, S.M., I. Khan, S., Srinivasan, K., Krichen, M. 2023. Arrhythmia classification using multirate processing metaheuristic optimization and variational mode decomposition. Journal of King Saud University, Computer and Information Sciences, 35, 26–37.

  32. Rajendiran, D.K.J, Babu, C.G., Priyadharsini, K., Karthi, S.P. 2024. Certain investigation on hybrid neural network method for classification of ECG signal with a suitable FIR filter. Scientific Reports, 14, 1–27.

  33. Roonizi, A.K., Sassi, R. 2024. ECG signal decomposition using Fourier analysis. EURASIP Journal on Advances in Signal Processing, 1, 79.

  34. Sahoo, S., Subudhi, A., Dash, M., Sabut, S. 2020. Automatic classification of cardiac arrhythmias based on hybrid features and decision tree algorithm. Machine Intelligence Research, 17, 551–561.

  35. Sharma, R., Matharu, J.S., Parmar, K.S. 2026. A survey on Particle Swarm Optimization: Evolution, adaptations and practical implementations. Applied Soft Computing, 186, 114016.

  36. Singh, P., Pradhan, G. 2018. Variational mode decomposition-based ECG denoising using non-local means and wavelet domain filtering. Australasian Physical Engineering Sciences in Medicine, 41, 891–904.

  37. Thaler, M.S. 2019. The only EKG book you’ll ever need. Lippincott Williams & Wilkins, Philadelphia.

  38. Volkov, I.A., Priputin, V.S. 2024. Adaptive signal decomposition methods. Systems of signals generating and processing in the field of on-board communications (SOSG 2024). Conference Proceedings, IEEE, 1–5.

  39. Wang, H., Wang, Y., Liu, Y., Yao, Y. 2024. Electrocardiogram prediction based on variational mode decomposition and a convolutional gated recurrent unit. EURASIP Journal on Advances in Signal Processing, 1, 16.

  40. Xu, C., Yang, J., Zhang, T., Li, K., Zhang, K. 2023. Adaptive parameter selection variational mode decomposition based on a novel hybrid entropy and its applications in locomotive bearing diagnosis. Measurement, 217, 113110.

  41. Xu, H., Hassan, S.A., Haider, W., Sun, Y., Yu, X. 2025. A frequency-shifting variational mode decomposition-based approach to MI-EEG signal classification for brain–computer interfaces. Sensors, 25, 2134.

  42. Yıldırım, Ö., Pławiak, P., Tan, R.S., Acharya, U.R. 2018. Arrhythmia detection using deep convolutional neural network with long duration ECG signals. Computers in Biology and Medicine, 102, 411–420.

  43. Yin, S., Li, H., Teng, L., Laghari, A.A., Almadhor, A., Gregus, M., Sampedro, G.A. 2024. Brain CT image classification based on mask RCNN and attention mechanism. Scientific Reports, 14, 29300.

  44. Zakaria, H., Nurdiniyah, E.S.H., Kurniawati, A.M., Naufal, D., Sutisna, N. 2024. Morphological arrhythmia classification based on inter-patient and two leads ECG using machine learning. IEEE Access, 12, 147372–147386.

  45. Zeng, W., Yuan, C. 2021. ECG arrhythmia classification based on variational mode decomposition, Shannon energy envelope and deterministic learning. International Journal of Machine Learning and Cybernetics, 12, 2963–2988.

  46. Zhang, C., Chen, W., Chen, H. 2025. Denoising for ECG signals based on variational mode decomposition and recursive least squares. Journal of Measurements in Engineering, 13, 185–204.

  47. Zhang, Y., Zhao, H. 2025. Deep attention model for arrhythmia signal classification based on multi-objective crayfish optimization algorithmic variational mode decomposition. Scientific Reports, 15, 5080.

  48. Zhou, W., Feng, Z., Xu, Y.F., Wang, X., Lv, H. 2020. Empirical fourier decomposition: an accurate adaptive signal decomposition method. arXiv preprint arXiv:2009.08047.


ARTICLE INFORMATION

Received: 2025-10-31
Revised: 2026-01-04
Accepted: 2026-02-08
Available Online: 2026-04-15


Cite this article:

Aqila, M., Humairani, A., Purboyo, T.W., Naufal, D., 2026. Exploration of integration strategies of variational mode decomposition (VMD) and metaheuristic optimization approaches for arrhythmia detection in ECG signals. International Journal of Applied Science and Engineering, 23, 2025276. https://doi.org/10.6703/IJASE.202606_23(2).004

  Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

Other people also read ...

doaj logo

 

2.9
2023CiteScore
 
 
67th percentile
Powered by  Scopus
 
 

SCImago Journal & Country Rank

IJASE - Most Read Articles

IJASE - Most popular articles