In this paper, we present a convolutional gated recurrent neural network (CGRNN) to predict epileptic seizures based on features extracted from EEG data that represent the temporal aspect and the frequency aspect of the signal. Using a dataset collected in the Children’s Hospital of Boston, CGRNN can predict epileptic seizures between 35 min and 5 min in advance. Our experimental results indicate that the performance of CGRNN varies between patients. We achieve an average sensitivity of 89% and a mean accuracy of 75.6% for the patients in the data set, with a mean False Positive Rate (FPR) of 1.6 per hour.
CITATION STYLE
Affes, A., Mdhaffar, A., Triki, C., Jmaiel, M., & Freisleben, B. (2019). A Convolutional Gated Recurrent Neural Network for Epileptic Seizure Prediction. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11862 LNCS, pp. 85–96). Springer. https://doi.org/10.1007/978-3-030-32785-9_8
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