ArticleThe Korean journal of physiology & pharmacology : official journal of the Korean Physiological Society and the Korean Society of Pharmacology2019
Dual deep neural network-based classifiers to detect experimental seizures.
Article in The Korean journal of physiology & pharmacology : official journal of the Korean Physiological Society and the Korean Society of Pharmacology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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11 citing papers in PubMed, 28 citations in OpenAlex.
- Evaluating the Severity of Autism Spectrum Disorder from EEG: A Multidisciplinary Approach Using Statistical and Artificial Intelligence Frameworks.Bioengineering (Basel, Switzerland) · 2025Article
- Article
- Assistive Artificial Intelligence in Epilepsy and Its Impact on Epilepsy Care in Low- and Middle-Income Countries.Brain sciences · 2025Review
- Barefoot walking improves cognitive ability in adolescents.The Korean journal of physiology & pharmacology : official journal of the Korean Physiological Society and the Korean Society of Pharmacology · 2024Article
- Semi-automated seizure detection using interpretable machine learning models.Research square · 2024Article
- A Recent Investigation on Detection and Classification of Epileptic Seizure Techniques Using EEG Signal.Brain sciences · 2021Review
- Deep Brain Stimulation Can Differentiate Subregions of the Human Subthalamic Nucleus Area by EEG Biomarkers.Frontiers in systems neuroscience · 2021Article
- Artificial Intelligence and Computational Approaches for Epilepsy.Journal of epilepsy research · 2020Review
- Deep Learning in Physiological Signal Data: A Survey.Sensors (Basel, Switzerland) · 2020Review
- Comparison of different input modalities and network structures for deep learning-based seizure detection.Scientific reports · 2020Article
- Feasibility of fully automated classification of whole slide images based on deep learning.The Korean journal of physiology & pharmacology : official journal of the Korean Physiological Society and the Korean Society of Pharmacology · 2020Article
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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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