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

Annisa Humairani 1, 2*, Inung Wijayanto 1, 2, Sugondo Hadiyoso 3, 4, Suman Lata Tripathi 5, Achmad Rizal 1, 3

1 School of Electrical Engineering, Telkom University, Bandung, Indonesia
2 Center of Excellence for Biomedical and Healthcare Technology, Research Institute for Digital Health, Social and Wellness, Telkom University, Bandung, Indonesia
3 School of Applied Science, Telkom University, Bandung, Indonesia
4 Center of Green Technology, Research Institute for Intelligent Business and Sustainable Economy, Telkom University, Bandung, Indonesia
5 Department of Electronics and Telecommunications Engineering, Symbiosis International (Deemed University), Pune, India

 

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ABSTRACT

Fluctuation-based dispersion entropy (FDE) has emerged as a robust method for analysing biological signals. To improve its adaptability across multiple time scales, this study introduces a novel feature extraction technique, Intrinsic Mode Fluctuation-Based Dispersion Entropy (IMFDE), which combines empirical mode decomposition (EMD) and multiscale FDE. The electroencephalogram (EEG) signals were pre-processed, decomposed into intrinsic mode functions (IMFs), which were then used to compute IMFDE features. The proposed method was validated on a short-term dataset (Bonn University) and long-term dataset (Temple University Seizure Corpus – TUSZ). On the Bonn dataset, IMFDE achieved 99% accuracy, 100% sensitivity, and 98% specificity, and thus selected for subsequent testing for seizure prediction on TUSZ. On the TUSZ, IMFDE maintained a low false prediction rate (0.53 h-1) and 100% sensitivity for 2- and 4-min seizure prediction horizons. These results demonstrate that IMFDE outperforms existing entropy-based methods, offers an adaptive and reliable approach for epileptic seizure forecasting.


Keywords: Electroencephalogram, Empirical mode decomposition, Epilepsy, Fluctuation-based dispersion entropy, Intrinsic mode fluctuation-based dispersion entropy.


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REFERENCES

  1. Akor, P., Enemali, G., Muhammad, U., Singh, R.R., Larijani, H. 2025. Hierarchical deep learning for comprehensive epileptic seizure analysis: from detection to fine-grained classification. Information, 16, 532.

  2. Albaqami, H., Hassan, G.M., Datta, A. (2022). Wavelet-based multi-class seizure type classification system. Applied Sciences,12, 5702.

  3. Alickovic, E., Kevric, J., Subasi, A. 2018. Performance evaluation of empirical mode decomposition, discrete wavelet transforms, and wavelet packed decomposition for automated epileptic seizure detection and prediction. Biomedical Signal Processing and Control, 39, 94–102.

  4. Alotaiby, T.N., Alshebeili, S.A., Alotaibi, F.M., Alrshoud, S.R. 2017. Epileptic seizure prediction using CSP and LDA for scalp EEG signals. Computational Intelligence and Neuroscience, 2017, 1–11.

  5. Alturki, F.A., AlSharabi, K., Abdurraqeeb, A.M., Aljalal, M. 2020. EEG signal analysis for diagnosing neurological disorders using discrete wavelet transform and intelligent techniques. Sensors, 20, 2505.

  6. Andrzejak, R.G., Lehnertz, K., Mormann, F., Rieke, C., David, P., Elger, C.E. 2001. Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: Dependence on recording region and brain state. Physical Review E, 64, 061907.

  7. Assi, E.B, Nguyen, D. K., Rihana, S., Sawan, M. 2017. Towards accurate prediction of epileptic seizures: A review. Biomedical Signal Processing and Control, 34, 144–157.

  8. Azami, H., Arnold, S.E., Sanei, S., Chang, Z., Sapiro, G., Escudero, J., Gupta, A.S. 2019. Multiscale fluctuation-based dispersion entropy and its applications to neurological diseases. IEEE Access, 7, 68718–68733.

  9. Azami, H., Escudero, J. 2018a. Amplitude- and fluctuation-based dispersion entropy. Entropy, 20, 1–21.

  10. Azami, H., Escudero, J. 2018b. Coarse-graining approaches in univariate multiscale sample and dispersion entropy. Entropy, 20, 1–20.

  11. Bandt, C., Pompe, B. 2002. Permutation Entropy: A natural complexity measure for time series. Physical Review Letters, 88, 4.

  12. Berg, T., Ordentlich, O., Shayevitz, O. 2025. Memory complexity of estimating entropy and mutual information. IEEE Transactions on Information Theory, 71, 3334–3349.

  13. Boser, B.E., Guyon, I.M., Vapnik, V.N. 1992. A training algorithm for optimal margin classifiers. Proceedings of 5th Annual Workshop on Computational Learning Theory, 144–152.

  14. Cherian, R., Kanaga, E.G. 2022. Theoretical and methodological analysis of EEG based seizure detection and prediction: An exhaustive review. Journal of Neuroscience Methods, 369, 109483.

  15. Chiang, C.Y., Chang, N.F., Chen, T.C., Chen, H.H., Chen, L.G. 2011. Seizure prediction based on classification of EEG synchronization patterns with on-line retraining and post-processing scheme. Proceeding of 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 7564–7569.

  16. Das, S., Adhikary, A., Laghari, A.A., Mitra, S. 2023. Eldo-care: EEG with Kinect sensor based telehealthcare for the disabled and the elderly. Neuroscience Informatics, 3, 100130.

  17. Dong, Q., Zhang, H., Xiao, J., Sun, J. 2025. Multi-scale spatio-temporal attention network for epileptic seizure prediction. IEEE Journal of Biomedical and Health Informatics, 29, 4784–4795.

  18. Echegoyen, I., López-Sanz, D., Martínez, J.H., Maestú, F., Buldú, J.M. 2020. Permutation entropy and statistical complexity in mild cognitive impairment and Alzheimer’s Disease: An analysis based on frequency bands. Entropy, 22, 116.

  19. Gadhoumi, K., Lina, J.M., Mormann, F., Gotman, J. 2016. Seizure prediction for therapeutic devices: A review. Journal of Neuroscience Methods, 260, 270–282.

  20. Goncalves, P., Rilling, G., Flandrin, P. 2003. On empirical mode decomposition and its algorithms. IEEE-EURASIP Workshop on Nonlinear Signal and Image Processing, 3, 8–11.

  21. Hadiyoso, S., Irawati, I.D., Rizal, A. 2021. Epileptic electroencephalogram classification using relative wavelet sub-band energy and wavelet entropy. International Journal of Engineering, Transactions A Basics, 34, 75–81.

  22. Halim, H., Hakim, S., Boutkhoum, O., Hanine, M., El Moutaouakil, A. 2025. A novel 3D method based on region-growing and morphology for lung segmentation. International Journal of Online and Biomedical Engineering (IJOE), 21, 171–190.

  23. Huang, N.E., Shen, Z., Long, S.R., Wu, M.C., Snin, H.H., Zheng, Q., Yen, N.C., Tung, C.C., Liu, H.H. 1998. The empirical mode decomposition and the Hubert spectrum for nonlinear and non-stationary time series analysis. Proceedings of the royal society. Mathematical, Physical and Engineering Sciences, 454, 903–995.

  24. Humairani, A., Atmojo, B.S., Wijayanto, I., Hadiyoso, S. 2021. Fractal based feature extraction method for epileptic seizure detection in long-term EEG Recording. Journal of Physics: Conference Series, 1844, 012019.

  25. Kehri, V., Awale, R.N. 2020. A comparative analysis of wavelet-based FEMG signal denoising with threshold functions and facial expression classification using SVM and LSSVM. International Journal of Engineering, Transactions A Basics, 33, 1249–1256.

  26. Laghari, A.A., Estrela, V.V., Yin, S. 2022. How to collect and interpret medical pictures captured in highly challenging environments that range from nanoscale to hyperspectral imaging. Current Medical Imaging Formerly Current Medical Imaging Reviews, 20, e281222212228.

  27. 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, 131, 15109.

  28. Li, S., Zhou, W., Yuan, Q., Liu, Y. 2013. Seizure prediction using spike rate of intracranial EEG. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 21, 880–886.

  29. Li, Z., Li, Y., Zhang, K. 2019. A feature extraction method of ship-radiated noise based on fluctuation-based dispersion entropy and intrinsic time-scale decomposition. Entropy, 21, 693.

  30. Liu, W., Jiang, Y., Xu, Y.A., Liu, W., Jiang, Y., Xu, Y. 2022. A Super-fast algorithm for estimating sample entropy. Entropy, 24, 524.

  31. Lopes, F., Leal, A., Pinto, M.F., Dourado, A., Schulze-Bonhage, A., Dümpelmann, M., Teixeira, C. 2023. Removing artefacts and periodically retraining improve performance of neural network-based seizure prediction models. Scientific Reports, 13, 5918.

  32. McCallan, N., Davidson, S., Ng, K.Y., Biglarbeigi, P., Finlay, D., Lan, B.L., McLaughlin, J. 2023. Epileptic multi-seizure type classification using electroencephalogram signals from the Temple University Hospital Seizure Corpus: A review. Expert Systems with Applications, 234, 121040.

  33. Munir, M.A., Shah, R.A., Ali, M., Laghari, A.A., Almadhor, A., Gadekallu, T.R. 2025. Enhancing gene mutation prediction with sparse regularized autoencoders in lung cancer radiomics analysis. IEEE Access, 13, 7407–7425.

  34. Nicolaou, N., Georgiou, J. 2012. Detection of epileptic electroencephalogram based on permutation entropy and support vector machines. Expert Systems with Applications, 39, 202–209.

  35. Ouyang, G., Dang, C., Li, X. 2009. Multiscale entropy analysis of EEG recordings in epileptic rats. Biomedical Engineering: Applications, Basis and Communications, 21, 169–176.

  36. Popov, A., Faes, L., Kotiuchyi, I., Pernice, R., Kharytonov, V. 2020. Entropy characteristics of heart rate wavelet multiscale components in epileptic children before and after seizures. Proceeding of 11th Conference of the European Study Group on Cardiovascular Oscillations (ESGCO), 1–2.

  37. Qin, G. 2026. Behavior Analysis of students in preschool mathematics teaching based on deep learning. Journal of Applied Science and Engineering, 29, 585–593.

  38. Rasheed, K., Qayyum, A., Qadir, J., Sivathamboo, S., Kwan, P., Kuhlmann, L., O’Brien, T., Razi, A. 2020. Machine learning for predicting epileptic seizures using EEG signals: A review, 14, 139–155.

  39. Rayatnia, A., Khanbabaie, R. 2019. Common spatial patterns feature extraction and support vector machine classification for motor imagery with the secondbrain. International Journal of Engineering Transactions, 32, 1284–1289.

  40. Savadkoohi, M., Oladunni, T., Thompson, L. 2020. A machine learning approach to epileptic seizure prediction using Electroencephalogram (EEG) signal. Biocybernetics and Biomedical Engineering, 40, 1328–1341.

  41. Shah, V., Weltin, E., Lopez, S., McHugh, J.R., Veloso, L., Golmohammadi, M., Obeid, I., Picone, J. 2018. The Temple University Hospital Seizure Detection Corpus. Frontiers in Neuroinformatics, 12, 1–6.

  42. Shannon, C.E. 2001. A mathematical theory of communication. ACM SIGMOBILE Mobile Computing and Communications Review, 5, 3–55.

  43. Sharma, M., Pachori, R.B., Rajendra Acharya, U. 2017. A new approach to characterize epileptic seizures using analytic time-frequency flexible wavelet transform and fractal dimension. Pattern Recognition Letters, 94, 172–179.

  44. Singh, G., Kaur, M., Singh, B. 2021. Detection of epileptic seizure EEG signal using multiscale entropies and complete ensemble empirical mode decomposition. Wireless Personal Communications, 116, 845–864.

  45. Siuly, S., Alcin, O.F., Kabir, E., Sengur, A., Wang, H., Zhang, Y., Whittaker, F. 2020. A new framework for automatic detection of patients with mild cognitive impairment using resting-state EEG signals. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 28, 1966–1976.

  46. Solaija, M.S.J., Saleem, S., Khurshid, K., Hassan, S.A., Kamboh, A.M. 2018. Dynamic mode decomposition based epileptic seizure detection from scalp EEG. IEEE Access, 6, 38683–38692.

  47. Tzallas, A.T., Tsipouras, M.G., Fotiadis, D.I. 2009. Epileptic seizure detection in EEGS using time–frequency analysis. IEEE Transactions on Information Technology in Biomedicine, 13, 703–710.
  48. Wang, Q., Huang, J., Lu, S., Lin, Y., Xu, K., Yang, L., Lin, H. 2024. IPEval: A bilingual intellectual property agency consultation evaluation benchmark for large language models. Journal of Artificial Intelligence Research, 2, 9–27.

  49. Wijayanto, I., Hartanto, R., Nugroho, H.A. 2021. Multi-distance fluctuation-based dispersion fractal for epileptic seizure detection in EEG signal. Biomedical Signal Processing and Control, 69, 102938.

  50. Wijayanto, I., Rizal, A., Humairani, A. 2019. Seizure detection based on EEG signals using katz fractal and SVM classifiers. Proceeding of 5th International Conference on Science in Information Technology (ICSITech), Yogyakarta, Indonesia, 78–82.

  51. World Health Organization (WHO). 2023. Epilepsy. World Health Organization. Available at: https://www.who.int/news-room/fact-sheets/detail/epilepsy. Accessed July 29, 2025.

  52. 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.


ARTICLE INFORMATION

Received: 2025-05-15
Revised: 2025-08-03
Accepted: 2025-08-29
Available Online: 2025-12-10


Cite this article:

Annisa, H., Inung, W., Sugondo, H., Suman, L.T., Achmad, R., 2025. Epileptic seizure prediction on EEG signal using multiscale intrinsic mode fluctuation-based dispersion entropy. International Journal of Applied Science and Engineering, 22, 2025186. https://doi.org/10.6703/IJASE.202512_22(4).006

  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.

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