Long-term electroencephalography (EEG) monitoring is advised to patients with refractory epilepsy who have a failure of anti-seizure medication and therapy. However, its real-life application is limited mainly due to the use of multiple EEG channels. We proposed a patient-specific deep learning-based single-channel seizure detection approach using the long-term scalp EEG recordings of the Children’s Hospital Boston-Massachusetts Institute of Technology (CHB-MIT) dataset, in conjunction with neurologists’ confirmation of spatial seizure characteristics of individual patients.
We constructed 18-, 4-, and single-channel seizure detectors for 13 patients. Neurologists selected a specific channel among four channels, two close to the behind-the-ear and two at the forehead for each patient, after reviewing the patient’s distinctive seizure locations with seizure re-annotation.
Our multi- and single-channel detectors achieved an average sensitivity of 97.05–100%, false alarm rate of 0.22–0.40/h, and latency of 2.1–3.4 s for identification of seizures in continuous EEG recordings. The results demonstrated that seizure detection performance of our single-channel approach was comparable to that of our multi-channel ones.
We suggest that our single-channel approach in conjunction with clinical designation of the most prominent seizure locations has a high potential for wearable seizure detection on long-term EEG recordings for patients with refractory epilepsy.