Journal
KOREAN JOURNAL OF PHYSIOLOGY & PHARMACOLOGY
Volume 23, Issue 2, Pages 131-139Publisher
KOREAN JOURNAL OF PHYSIOLOGY & PHARMACOLOGY
DOI: 10.4196/kjpp.2019.23.2.131
Keywords
Deep learning; Epilepsy; Mice; Seizures; Spectral analysis
Categories
Funding
- National Research Foundation of Korea (NRF) - Korean government [NRF-2017R1D1A1B03029812]
- Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI) - Ministry of Health Welfare [HI15C2854]
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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.
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