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<p>Automatic seizure detection based on electroencephalogram (EEG) signals is essential for monitoring and diagnosing epilepsy, as well as reducing the workload of neurologists who visually inspect long-term EEGs. In this work, a novel framework for automatic seizure detection is proposed by integrating the Stockwell transform (S-transform) with a lightweight Informer model. The S-transform is firstly used to convert EEG signals into multi-level time–frequency features. Subsequently, an Informer encoder is deployed to capture spatial and long-term dependencies of these EEG time–frequency features and perform classification for seizure detection. Both the segment-based evaluation and event-based evaluation were conducted on the CHB-MIT EEG database and the QH-SDU database in patient-specific scenarios. Due to the efficient multi-resolution time–frequency analysis capability of the S-transform and the Informer’s ability to measure spatio-temporal correlation with lower time complexity and memory usage, the proposed method achieved state-of-the-art outcomes over the two EEG databases. The experimental results substantiate the model's ability to generalize across different databases and potential for clinical application.</p>

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Efficient seizure detection by lightweight Informer combined with fusion of time–frequency–spatial features

  • 期刊论文

  • 发表日期：

    2025年4月9日

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  • Xiangwen Zhong,
  • Guijuan Jia,
  • Haozhou Cui,
  • Haotian Li,
  • Chuanyu Li,
  • Guoyang Liu,
  • Yi Li,
  • Weidong Zhou

摘要

Automatic seizure detection based on electroencephalogram (EEG) signals is essential for monitoring and diagnosing epilepsy, as well as reducing the workload of neurologists who visually inspect long-term EEGs. In this work, a novel framework for automatic seizure detection is proposed by integrating the Stockwell transform (S-transform) with a lightweight Informer model. The S-transform is firstly used to convert EEG signals into multi-level time–frequency features. Subsequently, an Informer encoder is deployed to capture spatial and long-term dependencies of these EEG time–frequency features and perform classification for seizure detection. Both the segment-based evaluation and event-based evaluation were conducted on the CHB-MIT EEG database and the QH-SDU database in patient-specific scenarios. Due to the efficient multi-resolution time–frequency analysis capability of the S-transform and the Informer’s ability to measure spatio-temporal correlation with lower time complexity and memory usage, the proposed method achieved state-of-the-art outcomes over the two EEG databases. The experimental results substantiate the model's ability to generalize across different databases and potential for clinical application.