Why the study?
Patient seizure diaries are unreliable and standard full scalp-EEG setups have limited use outside the hospital, creating a need for discreet wearable devices using behind-the-ear channels to capture seizures at home.
Does visual seizure annotation and automated seizure detection using behind-the-ear EEG channels accurately detect seizures in patients with temporal lobe epilepsy?
Does visual seizure annotation and automated seizure detection using behind-the-ear EEG channels accurately detect seizures in patients with temporal lobe epilepsy?
A patient-specific seizure detection algorithm using only behind-the-ear EEG can automatically detect seizures with 69.1% sensitivity and low false-positive rates, offering a potential wearable solution for long-term monitoring.
May support wearable behind-the-ear monitoring in temporal lobe epilepsy; hypothesis-generating and requires prospective validation before practice change.
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.
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Vandecasteele et al. (2020) studied this question.
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