Dual deep neural network-based classifiers to detect experimental seizures.
10.4196/kjpp.2019.23.2.131
- Author:
Hyun Jong JANG
1
;
Kyung Ok CHO
Author Information
1. Department of Physiology, College of Medicine, The Catholic University of Korea, Seoul 06591, Korea.
- Publication Type:Original Article
- Keywords:
Deep learning;
Epilepsy;
Mice;
Seizures;
Spectral analysis
- MeSH:
Animals;
Dataset;
Electroencephalography;
Epilepsy;
Mice;
Microcomputers;
Seizures*
- From:The Korean Journal of Physiology and Pharmacology
2019;23(2):131-139
- CountryRepublic of Korea
- Language:English
-
Abstract:
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.
