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<p>Epilepsy affects over 50 million individuals worldwide, necessitating accurate and energy-efficient seizure detection systems. While Electroencephalography (EEG) is the clinical gold standard, automated classification faces challenges from high-dimensional, noisy, and non-stationary data. Traditional deep learning solutions often possess excessive computational demands, limiting their deployment in real-time, resource-constrained devices. This work proposes TemporalSNN, a lightweight spiking neural network designed for robust EEG seizure classification, optimized for neuromorphic deployment. The methodology utilizes a biologically inspired pipeline involving comprehensive feature extraction and Mutual Information (MI)-based feature selection to reduce input dimensionality. TemporalSNN employs Leaky Integrate-and-Fire dynamics to capture temporal dependencies, with performance evaluated on the Bonn, HAUZ, and Panwar benchmark datasets. Results demonstrate that MI-based feature selection significantly enhances model robustness and accuracy. On the challenging HAUZ dataset, binary accuracy improved dramatically from <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(77.2\%\)</EquationSource> </InlineEquation> to <InlineEquation ID="IEq2"> <EquationSource Format="TEX">\(92.5\%\)</EquationSource> </InlineEquation> (at 75% feature reduction), while on the Bonn dataset, binary accuracy increased from <InlineEquation ID="IEq3"> <EquationSource Format="TEX">\(97.1\%\)</EquationSource> </InlineEquation> to <InlineEquation ID="IEq4"> <EquationSource Format="TEX">\(98.2\%\)</EquationSource> </InlineEquation> with a substantial decrease in performance variance. These findings confirm that integrating MI-driven feature reduction with spiking computation yields efficient, low-complexity, and reliable seizure detection models suitable for practical, real-time clinical applications.</p>

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EEG seizure classification with temporal spiking neural networks and mutual information-based feature selection

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月23日

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  • Goldwyn Sudhakar Jebaraj,
  • Konguvel Elango

摘要

Epilepsy affects over 50 million individuals worldwide, necessitating accurate and energy-efficient seizure detection systems. While Electroencephalography (EEG) is the clinical gold standard, automated classification faces challenges from high-dimensional, noisy, and non-stationary data. Traditional deep learning solutions often possess excessive computational demands, limiting their deployment in real-time, resource-constrained devices. This work proposes TemporalSNN, a lightweight spiking neural network designed for robust EEG seizure classification, optimized for neuromorphic deployment. The methodology utilizes a biologically inspired pipeline involving comprehensive feature extraction and Mutual Information (MI)-based feature selection to reduce input dimensionality. TemporalSNN employs Leaky Integrate-and-Fire dynamics to capture temporal dependencies, with performance evaluated on the Bonn, HAUZ, and Panwar benchmark datasets. Results demonstrate that MI-based feature selection significantly enhances model robustness and accuracy. On the challenging HAUZ dataset, binary accuracy improved dramatically from \(77.2\%\) to \(92.5\%\) (at 75% feature reduction), while on the Bonn dataset, binary accuracy increased from \(97.1\%\) to \(98.2\%\) with a substantial decrease in performance variance. These findings confirm that integrating MI-driven feature reduction with spiking computation yields efficient, low-complexity, and reliable seizure detection models suitable for practical, real-time clinical applications.