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Visual seizure annotation and automated seizure detection using behind‐the‐ear electroencephalographic channels
Author(s) -
Vandecasteele Kaat,
De Cooman Thomas,
Dan Jonathan,
Cleeren Evy,
Van Huffel Sabine,
Hunyadi Borbála,
Van Paesschen Wim
Publication year - 2020
Publication title -
epilepsia
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.687
H-Index - 191
eISSN - 1528-1167
pISSN - 0013-9580
DOI - 10.1111/epi.16470
Subject(s) - electroencephalography , ictal , epilepsy , audiology , sensitivity (control systems) , scalp , computer science , wearable computer , temporal lobe , medicine , speech recognition , psychology , neuroscience , surgery , electronic engineering , engineering , embedded system
Objective Seizure diaries kept by patients are unreliable. Automated electroencephalography (EEG)‐based seizure detection systems are a useful support tool to objectively detect and register seizures during long‐term video‐EEG recording. However, this standard full scalp‐EEG recording setup is of limited use outside the hospital, and a discreet, wearable device is needed for capturing seizures in the home setting. We are developing a wearable device that records EEG with behind‐the‐ear electrodes. In this study, we determined whether the recognition of ictal patterns using only behind‐the‐ear EEG channels is possible. Second, an automated seizure detection algorithm was developed using only those behind‐the‐ear EEG channels. Methods Fifty‐four patients with a total of 182 seizures, mostly temporal lobe epilepsy (TLE), and 5284 hours of data, were recorded with a standard video‐EEG at University Hospital Leuven. In addition, extra behind‐the‐ear EEG channels were recorded. First, a neurologist was asked to annotate behind‐the‐ear EEG segments containing selected seizure and nonseizure fragments. Second, a data‐driven algorithm was developed using only behind‐the‐ear EEG. This algorithm was trained using data from other patients (patient‐independent model) or from the same patient (patient‐specific model). Results The visual recognition study resulted in 65.7% sensitivity and 94.4% specificity. By using those seizure annotations, the automated algorithm obtained 64.1% sensitivity and 2.8 false‐positive detections (FPs)/24 hours with the patient‐independent model. The patient‐specific model achieved 69.1% sensitivity and 0.49 FPs/24 hours. Significance Visual recognition of ictal EEG patterns using only behind‐the‐ear EEG is possible in a significant number of patients with TLE. A patient‐specific seizure detection algorithm using only behind‐the‐ear EEG was able to detect more seizures automatically than what patients typically report, with 0.49 FPs/24 hours. We conclude that a large number of refractory TLE patients can benefit from using this device.