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.

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