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<p>Electroencephalography (EEG) is one of the most used techniques to perform diagnosis of epilepsy. However, manual annotation of seizures in EEG data is a major time-consuming step in the analysis process of EEGs. Different machine learning models have been developed to perform automated detection of seizures from EEGs. However, a large gap is observed between initial accuracies and those observed in clinical practice. In this work, we reproduced and assessed the accuracy of a large number of models, including deep learning networks, for detection of seizures from EEGs. Benchmarking included three different datasets for training and initial testing, and a manually annotated EEG from a local patient for further testing. Random forest and a convolutional neural network achieved the best results on public data, but a large reduction of accuracy was observed testing with the local data, especially for the neural network. We expect that the retrained models and the data available in this work will contribute to the integration of machine learning techniques as tools to improve the accuracy of diagnosis in clinical settings.</p>

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Retraining and evaluation of machine learning and deep learning models for seizure classification from EEG data

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年5月2日

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  • Juan Pablo Carvajal-Dossman¹,
  • Laura Guio,
  • Danilo García-Orjuela,
  • Jennifer J. Guzmán-Porras,
  • Kelly Garces,
  • Andres Naranjo,
  • Silvia Juliana Maradei-Anaya,
  • Jorge Duitama

摘要

Electroencephalography (EEG) is one of the most used techniques to perform diagnosis of epilepsy. However, manual annotation of seizures in EEG data is a major time-consuming step in the analysis process of EEGs. Different machine learning models have been developed to perform automated detection of seizures from EEGs. However, a large gap is observed between initial accuracies and those observed in clinical practice. In this work, we reproduced and assessed the accuracy of a large number of models, including deep learning networks, for detection of seizures from EEGs. Benchmarking included three different datasets for training and initial testing, and a manually annotated EEG from a local patient for further testing. Random forest and a convolutional neural network achieved the best results on public data, but a large reduction of accuracy was observed testing with the local data, especially for the neural network. We expect that the retrained models and the data available in this work will contribute to the integration of machine learning techniques as tools to improve the accuracy of diagnosis in clinical settings.