Research Area:  Machine Learning

Abstract:

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.

Keywords:  

Author(s) Name:  Abir Affes, Afef Mdhaffar, Chahnez Triki, Mohamed Jmaiel & Bernd Freisleben

Publisher name:  Springer

DOI:  10.1007/978-3-030-32785-9_8

Volume Information:  

Paper Link:   https://link.springer.com/chapter/10.1007/978-3-030-32785-9_8