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

Authors: Kaat Vandecasteele; Thomas De Cooman; Jonathan Dan; Evy Cleeren; Sabine Van Huffel; Borbála Hunyadi; Wim Van Paesschen;

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

Abstract

AbstractObjectiveSeizure 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.MethodsFifty‐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).ResultsThe 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.SignificanceVisual 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.

Keywords

Male, automated algorithms, wearable sensors, reduced electrode montage, seizure detection, Electroencephalography, Signal Processing, Computer-Assisted, Sensitivity and Specificity, Wearable Electronic Devices, Epilepsy, Temporal Lobe, Seizures, 616, Full‐length Original Research, epilepsy, Humans, Female, behind-the-ear EEG, Electrodes, Algorithms

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selected citations
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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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