Abstract
Predicting seizures before they happen can help prevent them through medication. In this research, first, a total of 22 features were extracted from 5-s segmented EEG signals. Second, tensors were developed as inputs for different deep transfer learning models to find the best model for predicting epileptic seizures. The effect of Pre-ictal state duration was also investigated by selecting four different intervals of 10, 20, 30, and 40 min. Then, nine models were created by combining three ImageNet convolutional networks with three classifiers and were examined for predicting seizures patient-dependently. The Xception convolutional network with a Fully Connected (FC) classifier achieved an average sensitivity of 98.47% and a False Prediction Rate (FPR) of 0.031 h−1 in a 40-min Pre-ictal state for ten patients from the European database. The most promising result of this study was the patient-independent prediction of epileptic seizures; the MobileNet-V2 model with an FC classifier was trained with one patient’s data and tested on six other patients, achieving a sensitivity rate of 98.39% and an FPR of 0.029 h−1 for a 40-min Pre-ictal scheme.
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Acknowledgements
This study was carried out following a previous project in the Babol Noshirvani University of Technology, and used the same limited dataset involving EEG features collected under FP7 Epilepsia Project, and made available to Dr. Rasekhi through a researcher visit opportunity to the Department of Informatics in the University of Coimbra, Portugal. Again, we thank Professor António Dourado and Dr. C.A. Teixeira from UC for their essential support and priceless contribution. Thanks also go to Google to provide GPU access at ‘colab.research.google.com’ that made it possible to run the related programs.
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Sarvi Zargar, B., Karami Mollaei, M.R., Ebrahimi, F. et al. Generalizable epileptic seizures prediction based on deep transfer learning. Cogn Neurodyn 17, 119–131 (2023). https://doi.org/10.1007/s11571-022-09809-y
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DOI: https://doi.org/10.1007/s11571-022-09809-y