A patient-specific recurrent neural network algorithm using scalp EEG achieved preonset seizure detection in 14 of 25 patients with a median preonset time of 51 seconds and 0.06/hour false-positive rate.
Does a patient-specific recurrent neural network algorithm using scalp EEG features detect seizures before or immediately after clinical onset in patients with epilepsy?
A patient-specific recurrent neural network using scalp EEG can reliably detect seizures before clinical onset in a subset of patients with epilepsy without invasive electrodes.
The objective of this study is to develop a method for automatic detection of seizures before or immediately after clinical onset using features derived from scalp electroencephalogram. This detection method is patient specific. It uses recurrent neural networks and a variety of input features. For each patient, we trained and optimized the detection algorithm for two cases: (1) during the period immediately preceding seizure onset and (2) during the period immediately after seizure onset. Continuous scalp electroencephalogram recordings (duration 15-62 hours, median 25 hours) from 25 patients, including a total of 86 seizures, were used in this study. Preonset detection was successful in 14 of the 25 patients. For these 14 patients, all of the testing seizures were detected before seizure onset with a median preonset time of 51 seconds and false-positive (FP) rate was 0.06/hour. Postonset detection had 100% sensitivity, 0.023/hour FP rate, and median delay of 4 seconds after onset. The unique results of this study relate to preonset detection. Our results suggest that reliable preonset seizure detection may be achievable for a significant subset of patients with epilepsy without use of invasive electrodes.
Minasyan et al. (Tue,) conducted a other in Epilepsy (n=25). Patient-specific automatic seizure detection using recurrent neural networks was evaluated on Successful preonset and postonset seizure detection. A patient-specific recurrent neural network algorithm using scalp EEG achieved preonset seizure detection in 14 of 25 patients with a median preonset time of 51 seconds and 0.06/hour false-positive rate.