Towards Interpretable Seizure Detection: An Excitation/Inhibition Dynamic Polynomial Network Framework for Electroencephalography.

Overview

1. Reading Academy, Nanjing University of Information Science and Technology, Nanjing 210044, China
2. School of Automation, Jiangsu Engineering Research Center on Meteorological Energy Using and Control (C-MEIC), Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET), and State Key Laboratory of Environment Characteristics and Effects for Near-Space, Nanjing University of Information Science and Technology, Nanjing 210044, China
3. School of Mechanical and Electrical Engineering, Anhui Jianzhu University, Hefei 230009, China
4. Department of Respiratory Medicine, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing 210029, China; (N.L.); (S.F.)
5. Department of Automation, Shanghai Jiao Tong University, Shanghai 200240, China
6. School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China
7. Waterford Institute, Nanjing University of Information Science and Technology, Nanjing 210044, China

Abstract

Epilepsy is a prevalent neurological disorder characterized by recurrent seizures, and electroencephalogram (EEG) signals provide a direct measure of brain activity for detection. Although deep learning achieves high accuracy, it often lacks physiological interpretability. We propose the Excitation/Inhibition Dynamic Polynomial Network (E/I-DynPolyNet), a biologically grounded framework for interpretable seizure detection. Specifically, E/I-DynPolyNet introduces a dual excitatory/inhibitory (E/I) pathway with sign-constrained synaptic weights, encouraging the learned activations to reflect latent E/I representations. Furthermore, a differentiable Wilson-Cowan (WC) module is embedded to govern the temporal evolution of E/I interactions, ensuring consistency with neurophysiological principles. A physics-informed optimization strategy integrates supervised learning with dynamical residual constraints and E/I balance regularization, guiding the model to learn physiologically consistent representations. Experimental results on the CHB-MIT and Bonn datasets demonstrate competitive accuracies of 95.81% and 98.5%, respectively. Crucially, E/I-DynPolyNet enables quantitative estimation of E/I imbalance, revealing that E/I ratios increase from 1.01 in the pre-ictal phase to 1.38 during seizures—a finding consistent with clinical observations of ictogenesis. These results indicate that E/I-DynPolyNet not only improves detection performance but also provides a mechanistic description of seizure dynamics, bridging the gap between data-driven learning and neurophysiological interpretation.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Data Availability Statement

The original CHB-MIT scalp EEG dataset and Bonn EEG dataset are publicly available from their respective data repositories. The processed data and experimental records used in this study will be made available by the corresponding author upon reasonable request. The source code, model implementation, preprocessing scripts, and training configuration are available from the corresponding author upon reasonable request.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 8 authors, 4 keywords, 6 MeSH terms, 5 funders, 30 references.

Cite

This paper

Sun, X., Yan, Y., Liu, N., Fang, S., Cai, J., Wu, E. Q., Song, A., & Xu, J. (2026). Towards Interpretable Seizure Detection: An Excitation/Inhibition Dynamic Polynomial Network Framework for Electroencephalography. Sensors (Basel, Switzerland), 26(11), 3488. https://doi.org/10.3390/s26113488

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