Automatic seizure detection based on imaged-EEG signals through fully convolutional networks

Gómez, Catalina, Arbeláez, Pablo, Navarrete, Miguel, Alvarado-Rojas, Catalina, Le Van Quyen, Michel and Valderrama, Mario 2020. Automatic seizure detection based on imaged-EEG signals through fully convolutional networks. Scientific Reports 10 , 21833. 10.1038/s41598-020-78784-3

PDF - Published Version Available under License Creative Commons Attribution. Download (2MB)

Abstract

Seizure detection is a routine process in epilepsy units requiring manual intervention of well-trained specialists. This process could be extensive, inefficient and time-consuming, especially for long term recordings. We proposed an automatic method to detect epileptic seizures using an imaged-EEG representation of brain signals. To accomplish this, we analyzed EEG signals from two different datasets: the CHB-MIT Scalp EEG database and the EPILEPSIAE project that includes scalp and intracranial recordings. We used fully convolutional neural networks to automatically detect seizures. For our best model, we reached average accuracy and specificity values of 99.3% and 99.6%, respectively, for the CHB-MIT dataset, and corresponding values of 98.0% and 98.3% for the EPILEPSIAE patients. For these patients, the inclusion of intracranial electrodes together with scalp ones increased the average accuracy and specificity values to 99.6% and 58.3%, respectively. Regarding the other metrics, our best model reached average precision of 62.7%, recall of 58.3%, F-measure of 59.0% and AP of 54.5% on the CHB-MIT recordings, and comparatively lowers performances for the EPILEPSIAE dataset. For both databases, the number of false alarms per hour reached values less than 0.5/h for 92% of the CHB-MIT patients and less than 1.0/h for 80% of the EPILEPSIAE patients. Compared to recent studies, our lightweight approach does not need any estimation of pre-selected features and demonstrates high performances with promising possibilities for the introduction of such automatic methods in the clinical practice.

Item Type:	Article
Date Type:	Published Online
Status:	Published
Schools:	Psychology
Additional Information:	This article is licensed under a Creative Commons Attribution 4.0 International License
Publisher:	Nature Research
ISSN:	2045-2322
Date of First Compliant Deposit:	24 August 2021
Date of Acceptance:	26 November 2020
Last Modified:	10 May 2023 15:44
URI:	https://orca.cardiff.ac.uk/id/eprint/143637

Citation Data

Cited 34 times in Scopus. View in Scopus. Powered By Scopus® Data

Actions (repository staff only)

Edit Item