Visual seizure annotation and automated seizure detection using behind‐the‐ear electroencephalographic channels

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 li...

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Veröffentlicht in:Epilepsia (Copenhagen) Jg. 61; H. 4; S. 766 - 775
Hauptverfasser: Vandecasteele, Kaat, De Cooman, Thomas, Dan, Jonathan, Cleeren, Evy, Van Huffel, Sabine, Hunyadi, Borbála, Van Paesschen, Wim
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States Wiley Subscription Services, Inc 01.04.2020
John Wiley and Sons Inc
Schlagworte:
Algorithms
automated algorithms
Automation
behind‐the‐ear EEG
Convulsions & seizures
Ear
EEG
Electroencephalography
Epilepsy
Full‐length Original Research
Patients
reduced electrode montage
seizure detection
Seizures
Temporal lobe
wearable sensors
epilepsy
wearable sensors
seizure detection
behind-the-ear EEG
reduced electrode montage
automated algorithms
ISSN:0013-9580, 1528-1167, 1528-1167
Online-Zugang:Volltext
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Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
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Borbála Hunyadi and Wim Van Paesschen share last authorship.
ISSN:0013-9580
1528-1167
1528-1167
DOI:10.1111/epi.16470