Seizure detection using ultra-long-term subcutaneous electroencephalography: A deep learning CNN-BiLSTM approach.

Abstract

Objective: This study reports development and validation of a deep learning seizure detection algorithm for two-channel subscalp electroencephalographic (EEG) recordings. Ultra-long-term monitoring of people with epilepsy may produce new insights into the timing and pattern of their seizures and may pave the way for novel therapeutic options. Although EEG is the accepted standard for epilepsy monitoring, ultra-long-term EEG recordings generate a massive quantity of data and are not reasonably reviewable in full by human readers.

Methods: The convolutional neural network-bidirectional long short-term memory (CNN-BiLSTM) hybrid algorithm uses nine layers, operating on channel spectrograms 5 min in length with 50% overlap. Retrospective subscalp EEG data from 16 patients recorded at three centers were available for algorithm development and testing. EEG was recorded for a median of 63 days (range = 9-508), and a median of 17 seizures (range = 0-96) were recorded. Training data were augmented with scalp EEG seizures, and performance was benchmarked against a conventional spectral power classifier algorithm. We also evaluated an intrapatient training-testing approach where the algorithm was trained on the first 45% of data.

Results: The CNN-BiLSTM trained on scalp EEG and subscalp EEG achieved an area under the receiver operating characteristic curve (AUROC) of .98 and an area under the precision-recall curve (AUPRC) of .50, which correspond to 94% sensitivity with 1.11 false detections per day. The same model trained on iEEG achieved only AUROC = .94 and AUPRC = .36. The conventional spectral band power detector achieved AUROC = .93 and AUPRC = .38. The CNN-BiLSTM detector trained on the earliest half of the subscalp EEG data achieved AUROC = .93 and AUPRC = .37, corresponding to 87% sensitivity and 5.9 false detections per day.

Significance: High sensitivity and specificity are possible in automated seizure detection in two-channel subscalp EEG data using a CNN-BiLSTM framework. Performance of the detector is superior using subcutaneous EEG data for training rather than intracranial EEG, but addition of scalp EEG seizures for training was helpful.