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Prediction of epileptic seizures from spectral features of intracranial eeg recordings using deep learning approach

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Abstract

Epilepsy is a prevalent neurological disorder, which disturbs the lives of millions of people worldwide owing to the onset of abrupt seizures. The forecasting of seizures could help in protecting their lives by alerts or in clinical operations during epilepsy surgeries. The present paper addresses this problem by proposing a deep learning framework for prediction of epileptic seizures using intracranial EEG (iEEG) recordings. This framework performs filtering and segmentation of iEEG signals into 10s, 20s, 30s, 40s, 50s and 60s duration segments. These segments are further resolved into eight distinct spectral bands corresponding to delta, theta, alpha, beta and gamma sub-bands with frequency-domain transformation. Then, mean amplitude and band power features are extracted from each band, which are provided to convolutional neural network (CNN) and long short-term memory network (LSTM) algorithms for classification. The simulation results of the proposed CNN model exhibit higher performance with average accuracy, sensitivity, specificity, AUC and F1 score of 94.74%, 95.8%, 94.46%, 95.13% and 94.75% respectively for iEEG segments of 40s duration. Thus, the performance analysis and comparison with existing literature unveil that the proposed CNN model is an optimal approach for accurate and real-time prediction of epileptic seizures.

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Authors and Affiliations

  1. Department of Electronics Technology, Guru Nanak Dev University, Amritsar, Punjab, India

    Kuldeep Singh

  2. Department of Engineering & Technology, Guru Nanak Dev University Regional Campus, Jalandhar, Punjab, India

    Jyoteesh Malhotra

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  1. Kuldeep Singh
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Correspondence to Kuldeep Singh.

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Ethical Approval

The present research work involve the use of publicly available Kaggle’s American Epilepsy Society Seizure Prediction Challenge dataset of intracranial EEG signals, which was jointly developed by University of Pennsylvania and the Mayo Clinic. This competition was sponsored by the National Institutes of Health (NINDS), the Epilepsy Foundation, and the American Epilepsy Society. All applicable international, national, and/or institutional guidelines for the care and use of animals were followed. All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

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Since the data used in this study is open access dataset. Therefore, informed consent was obtained by the developers of data from all individual participants included in the study.

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Singh, K., Malhotra, J. Prediction of epileptic seizures from spectral features of intracranial eeg recordings using deep learning approach. Multimed Tools Appl 81, 28875–28898 (2022). https://doi.org/10.1007/s11042-022-12611-x

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