The Korean Journal of Physiology and Pharmacology

The Korean Society of Pharmacology (대한약리학회)
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Dual deep neural network-based classifiers to detect experimental seizures

  • Jang, Hyun-Jong (Department of Physiology, College of Medicine, The Catholic University of Korea) ;
  • Cho, Kyung-Ok (Department of Biomedicine & Health Sciences, The Catholic University of Korea)
  • Received : 2018.12.02
  • Accepted : 2019.01.09
  • Published : 2019.03.01
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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.

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References

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