Dual deep neural network-based classifiers to detect experimental seizures

• Published in: The Korean journal of physiology & pharmacology Vol. 23; no. 2; pp. 131 - 139
• Main Authors: Jang, Hyun-Jong, Cho, Kyung-Ok
• Format: Journal Article
• Language: Korean
• Published: 대한생리학회-대한약리학회 31.03.2019; The Korean Journal of Physiology & Pharmacology Editorial Office
• Subjects: Deep learning; Mice; Epilepsy; Spectral analysis; Seizures
• ISSN: 1226-4512; 2093-3827

Manually reviewing electroencephalograms (EEGs) is labor-intensive and demands automated seizure detection systems. To construct an efficient and robust event detector for experimental seizures from continuous EEG monitoring, we combined spectral analysis and deep neural networks. A deep neural network was trained to discriminate periodograms of 5-sec EEG segments from annotated convulsive seizures and the pre- and post-EEG segments. To use the entire EEG for training, a second network was trained with non-seizure EEGs that were misclassified as seizures by the first network. By sequentially applying the dual deep neural networks and simple pre- and post-processing, our autodetector identified all seizure events in 4,272 h of test EEG traces, with only 6 false positive events, corresponding to 100% sensitivity and 98% positive predictive value. Moreover, with pre-processing to reduce the computational burden, scanning and classifying 8,977 h of training and test EEG datasets took only 2.28 h with a personal computer. These results demonstrate that combining a basic feature extractor with dual deep neural networks and rule-based pre- and post-processing can detect convulsive seizures with great accuracy and low computational burden, highlighting the feasibility of our automated seizure detection algorithm.