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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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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