|
KMID : 0811720190230020131
|
|
Korean Journal of Physiology & Pharmacology
2019 Volume.23 No. 2 p.131 ~ p.139
|
|
|
Dual deep neural network-based classifiers to detect experimental seizures
|
|
Jang Hyun-Jong
Cho Kyung-Ok
|
|
|
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.
|
|
|
KEYWORD
|
|
|
Deep learning, Epilepsy, Mice, Seizures, Spectral analysis
|
|
|
FullTexts / Linksout information
|
|
 
|
|
|
Listed journal information
|
|
  
|
|