ArticleNeuroImage. Clinical2019
Seizure detection by convolutional neural network-based analysis of scalp electroencephalography plot images.
Article in NeuroImage. Clinical, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 1 of them a synthesis that pooled it.
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Who cites it
33 citing papers in PubMed, 1 synthesis or guideline pooled it, 160 citations in OpenAlex.
- Artificial Intelligence Technologies in Neurosurgery: a Systematic Literature Review Using Topic Modeling. Part II: Research Objectives and Perspectives.Sovremennye tekhnologii v meditsine · 2021Pooled it
- CMEpiNet: Complex-Valued Multimodal Epilepsy Detection Network Model.Sensors (Basel, Switzerland) · 2026Article
- A CNN-BiLSTM-GRU and attention-integrated hybrid network for epileptic seizure detection.Frontiers in neuroscience · 2026Article
- YOLOBT: a novel ERP bad trial detection network dynamically adjusting based on global signal quality.Frontiers in human neuroscience · 2026Article
- Automated Detection of Epileptic Seizures in EEG Signals via Micro-Capsule Networks.Brain sciences · 2025Article
- Assistive Artificial Intelligence in Epilepsy and Its Impact on Epilepsy Care in Low- and Middle-Income Countries.Brain sciences · 2025Review
- A novel dual-branch network for comprehensive spatiotemporal information integration for EEG-based epileptic seizure detection.PloS one · 2025Article
- Seizure Onset Zone Detection Based on Convolutional Neural Networks and EEG Signals.Brain sciences · 2024Article
- Epileptic seizure detection using CHB-MIT dataset: The overlooked perspectives.Royal Society open science · 2024Article
- Differentiating Epileptic and Psychogenic Non-Epileptic Seizures Using Machine Learning Analysis of EEG Plot Images.Sensors (Basel, Switzerland) · 2024Article
- Data leakage in deep learning studies of translational EEG.Frontiers in neuroscience · 2024Article
- Encoding temporal information in deep convolution neural network.Frontiers in neuroergonomics · 2024Article
- Automated recognition of epilepsy from EEG signals using a combining space-time algorithm of CNN-LSTM.Scientific reports · 2023Article
- Seizure detection with reduced electroencephalogram channels: research trends and outlook.Royal Society open science · 2023Review
- Automatic epileptic seizure detection based on EEG using a moth-flame optimization of one-dimensional convolutional neural networks.Frontiers in neuroscience · 2023Article
- Study on characteristic of epileptic multi-electroencephalograph base on Hilbert-Huang transform and brain network dynamics.Frontiers in neuroscience · 2023Article
- Ontology-based feature engineering in machine learning workflows for heterogeneous epilepsy patient records.Scientific reports · 2022Article
- Big data analysis and artificial intelligence in epilepsy - common data model analysis and machine learning-based seizure detection and forecasting.Clinical and experimental pediatrics · 2022Article
- Multi-Centroid Hyperdimensional Computing Approach for Epileptic Seizure Detection.Frontiers in neurology · 2022Article
- [Automatic epileptic seizure detection algorithm based on dual density dual tree complex wavelet transform].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2021Article
Corrections and comments
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Authors and funding
6 authors at 5 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We hypothesized that expert epileptologists can detect seizures directly by visually analyzing EEG plot images, unlike automated methods that analyze spectro-temporal features or complex, non-stationary features of EEG signals. If so, seizure detection could benefit from convolutional neural networks because their visual recognition ability is comparable to that of humans. We explored image-based seizure detection by applying convolutional neural networks to long-term EEG that included epileptic seizures. After filtering, EEG data were divided into short segments based on a given time window and converted into plot EEG images, each of which was classified by convolutional neural networks as 'seizure' or 'non-seizure'. These resultant labels were then used to design a clinically practical index for seizure detection. The best true positive rate was obtained using a 1-s time window. The median true positive rate of convolutional neural networks labelling by seconds was 74%, which was higher than that of commercially available seizure detection software (20% by BESA and 31% by Persyst). For practical use, the median of detected seizure rate by minutes was 100% by convolutional neural networks, which was higher than the 73.3% by BESA and 81.7% by Persyst. The false alarm of convolutional neural networks' seizure detection was issued at 0.2 per hour, which appears acceptable for clinical practice. Moreover, we demonstrated that seizure detection improved when training was performed using EEG patterns similar to those of testing data, suggesting that adding a variety of seizure patterns to the training dataset will improve our method. Thus, artificial visual recognition by convolutional neural networks allows for seizure detection, which otherwise currently relies on skillful visual inspection by expert epileptologists during clinical diagnosis.
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