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

Journal Article (2020)
Author(s)

Kaat Vandecasteele (Katholieke Universiteit Leuven)

Thomas De Cooman (Katholieke Universiteit Leuven)

Jonathan Dan (Katholieke Universiteit Leuven, Byteflies)

Evy Cleeren (University Hospital Leuven)

Sabine Van Huffel (Katholieke Universiteit Leuven)

Borbala Hunyadi (TU Delft - Signal Processing Systems)

Wim Van Paesschen (Katholieke Universiteit Leuven, University Hospital Leuven)

Research Group
Signal Processing Systems
Copyright
© 2020 Kaat Vandecasteele, Thomas De Cooman, Jonathan Dan, Evy Cleeren, Sabine Van Huffel, Borbala Hunyadi, Wim Van Paesschen
DOI related publication
https://doi.org/10.1111/epi.16470
More Info
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Publication Year
2020
Language
English
Copyright
© 2020 Kaat Vandecasteele, Thomas De Cooman, Jonathan Dan, Evy Cleeren, Sabine Van Huffel, Borbala Hunyadi, Wim Van Paesschen
Research Group
Signal Processing Systems
Issue number
4
Volume number
61
Pages (from-to)
766-775
Reuse Rights

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Abstract

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.