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Our work focuses on the development of a method that allows high-precision classification of ictal and preictal seizure activity in pediatric patients by analyzing EEG signals. In this study, different methods are analyzed using the CHB-MIT dataset, applying various preprocessing techniques and 1D-CNN model architectures. This paper compares two data acquisition methods identified during the experimentation process, namely training a 1D-CNN + LSTM model with single channel data (one channel per second) and multi-channel data (23 channels per second). The results showed that the multi-channel methodology outperformed its counterpart, achieving sensitivity, specificity, precision, accuracy and F1 score of 94.05%, 85.90%, 87.73%, 90.12% and 90.79%, respectively.

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Development of 1D-CNN Methods for Classifying Pediatric Epilepsy Through EEG Signals

  • 会议论文

  • 发表日期：

    2025年

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  • Oscar Flores-Palermo,
  • Christian Espiritu-Cueva,
  • Willy Ugarte

摘要

Our work focuses on the development of a method that allows high-precision classification of ictal and preictal seizure activity in pediatric patients by analyzing EEG signals. In this study, different methods are analyzed using the CHB-MIT dataset, applying various preprocessing techniques and 1D-CNN model architectures. This paper compares two data acquisition methods identified during the experimentation process, namely training a 1D-CNN + LSTM model with single channel data (one channel per second) and multi-channel data (23 channels per second). The results showed that the multi-channel methodology outperformed its counterpart, achieving sensitivity, specificity, precision, accuracy and F1 score of 94.05%, 85.90%, 87.73%, 90.12% and 90.79%, respectively.