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<p>Objective: Cross-subject generalization in EEG-based seizure detection remains a significant challenge due to diverse physiological differences among patients, which can degrade model performance across individuals. This work aims to develop a robust, lightweight, and fast-deployable model for cross-subject seizure detection that achieves high accuracy while enabling real-time, low-latency inference on resource-constrained devices. Methods: We propose PSD-LW-DCN, a novel lightweight deep convolutional network that leverages power spectral density (PSD) features extracted via multitaper spectral estimation. Raw EEG signals are preprocessed through bandpass filtering, segmentation, and class balancing. PSD features are computed across multiple frequency bands (<InlineEquation ID="IEq100"> <EquationSource Format="TEX">\(\delta\)</EquationSource> </InlineEquation>,<InlineEquation ID="IEq602"> <EquationSource Format="TEX">\(\theta\)</EquationSource> </InlineEquation>,<InlineEquation ID="IEq603"> <EquationSource Format="TEX">\(\alpha\)</EquationSource> </InlineEquation>,<InlineEquation ID="IEq604"> <EquationSource Format="TEX">\(\beta\)</EquationSource> </InlineEquation>,<InlineEquation ID="IEq605"> <EquationSource Format="TEX">\(\gamma\)</EquationSource> </InlineEquation>), averaged across channels to enhance generalizability, and concatenated as input to the network. The model was trained and evaluated separately on the CHB-MIT and TUSZ datasets, with no data mixing between them. Results: With only 61,218 parameters, PSD-LW-DCN achieves state-of-the-art efficiency, reducing inference time to 1.9 ms/sample on CHB-MIT and 2.1 ms/sample on TUSZ, demonstrating its suitability for real-time applications. It attains 85.84% accuracy on CHB-MIT and 83.21% on TUSZ, representing improvements of 5% and 3%, respectively, over previous approaches. Notably, the model shows strong cross-subject generalization and robustness across diverse patient populations. In clinically meaningful event-based evaluation, it achieves a low false alarm rate of 0.33 false alarms per hour (FA/h) on CHB-MIT and 1.09 FA/h on TUSZ, indicating high operational reliability in long-term monitoring scenarios. Conclusions: PSD-LW-DCN offers an optimal balance between accuracy, speed, and model size, making it highly suitable for real-time, edge-based clinical diagnosis of epilepsy. Its efficiency and low false alarm burden confirm its potential for deployment in low-power wearable and point-of-care systems. Future work will focus on further reducing the false alarm rate, particularly in more heterogeneous datasets like TUSZ, to enhance clinical usability. </p>

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PSD-LW-DCN: a generalizable power spectral density based lightweight deep convolutional neural network for seizure detection

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月18日

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  • Peipei Gu,
  • Ming Zhang,
  • Meiyan Xu,
  • Duo Chen,
  • Jibin Shou,
  • Jiayang Guo,
  • Naian Xiao

摘要

Objective: Cross-subject generalization in EEG-based seizure detection remains a significant challenge due to diverse physiological differences among patients, which can degrade model performance across individuals. This work aims to develop a robust, lightweight, and fast-deployable model for cross-subject seizure detection that achieves high accuracy while enabling real-time, low-latency inference on resource-constrained devices. Methods: We propose PSD-LW-DCN, a novel lightweight deep convolutional network that leverages power spectral density (PSD) features extracted via multitaper spectral estimation. Raw EEG signals are preprocessed through bandpass filtering, segmentation, and class balancing. PSD features are computed across multiple frequency bands ( \(\delta\) , \(\theta\) , \(\alpha\) , \(\beta\) , \(\gamma\) ), averaged across channels to enhance generalizability, and concatenated as input to the network. The model was trained and evaluated separately on the CHB-MIT and TUSZ datasets, with no data mixing between them. Results: With only 61,218 parameters, PSD-LW-DCN achieves state-of-the-art efficiency, reducing inference time to 1.9 ms/sample on CHB-MIT and 2.1 ms/sample on TUSZ, demonstrating its suitability for real-time applications. It attains 85.84% accuracy on CHB-MIT and 83.21% on TUSZ, representing improvements of 5% and 3%, respectively, over previous approaches. Notably, the model shows strong cross-subject generalization and robustness across diverse patient populations. In clinically meaningful event-based evaluation, it achieves a low false alarm rate of 0.33 false alarms per hour (FA/h) on CHB-MIT and 1.09 FA/h on TUSZ, indicating high operational reliability in long-term monitoring scenarios. Conclusions: PSD-LW-DCN offers an optimal balance between accuracy, speed, and model size, making it highly suitable for real-time, edge-based clinical diagnosis of epilepsy. Its efficiency and low false alarm burden confirm its potential for deployment in low-power wearable and point-of-care systems. Future work will focus on further reducing the false alarm rate, particularly in more heterogeneous datasets like TUSZ, to enhance clinical usability.