Epileptic Seizure Risk Assessment by Multi-Channel Imaging of the EEG

Autor:	Leal, Tiago, Lopes, Fabio, Teixeira, Cesar, Dourado, Antonio
Rok vydání:	2022
Předmět:	Electrical Engineering and Systems Science - Signal Processing Computer Science - Machine Learning I.2 J.3
Druh dokumentu:	Working Paper
Popis:	Refractory epileptic patients can suffer a seizure at any moment. Seizure prediction would substantially improve their lives. In this work, based on scalp EEG and its transformation into images, the likelihood of an epileptic seizure occurring at any moment is computed using an average of the softmax layer output (the likelihood) of a CNN, instead of the output of the classification layer. Results show that by analyzing the likelihood and thresholding it, prediction has higher sensitivity or a lower FPR/h. The best threshold for the likelihood was higher than 50% for 5 patients, and was lower for the remaining 36. However, more testing is needed, especially in new seizures, to better assess the real performance of this method. This work is a proof of concept with a positive outlook. Comment: 9 pages, 6 figures, one table
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2204.07034 Zobrazit plný text záznamu View this record from Arxiv