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

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.

Keywords: automated algorithms; behind-the-ear EEG; epilepsy; reduced electrode montage; seizure detection; wearable sensors.

Conflict of interest statement

None of the authors has any conflict of interest to disclose. We confirm that we have read the Journal's position on issues involved in ethical publication and affirm that this report is consistent with those guidelines.

© 2020 The Authors. Epilepsia published by Wiley Periodicals, Inc. on behalf of International League Against Epilepsy.

Figures

FIGURE 1

Behind‐the‐ear electroencephalographic setup. Left panel shows extra behind‐the‐ear electrodes glued to the skin. Right panel shows bipolar channel derivations. Reproduced with permission from Gu et al14

FIGURE 2

Left temporal lobe seizure recorded with behind‐the‐ear electroencephalographic (EEG) setup. The four bipolar EEG channels are shown over a period of 10 seconds: (1) crosshead 1, (2) crosshead 2, (3) unilateral left, (4) unilateral right. The bipolar EEG channels were filtered with a bandpass filter (1‐25 Hz). The black horizontal line at 300 seconds depicts the seizure onset

FIGURE 3

Sensitivities for the different seizure types (A), localizations (B), lateralizations (C) and seizure durations in seconds (D) are plotted for the patient‐independent model with aim I and visual recognition study. The sensitivities were calculated as percentage recognized seizures in that category over whole the database. bi, bilateral; FA, focal aware; F‐BTC, focal to bilateral tonic‐clonic; FIA, focal impaired awareness; L, left; NC, not clear; par, parietal; R, right; temp, temporal