ArticleScientific reports2020
Automatic seizure detection based on imaged-EEG signals through fully convolutional networks.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- Review
- EEG glasses for real-time brain electrical activity monitoring.Scientific reports · 2025Article
- Detection and Severity Assessment of Parkinson's Disease Through Analyzing Wearable Sensor Data Using Gramian Angular Fields and Deep Convolutional Neural Networks.Sensors (Basel, Switzerland) · 2025Article
- Retraining and evaluation of machine learning and deep learning models for seizure classification from EEG data.Scientific reports · 2025Article
- Article
- Artificial intelligence in epilepsy - applications and pathways to the clinic.Nature reviews. Neurology · 2024Review
- Automated Seizure Detection Based on State-Space Model Identification.Sensors (Basel, Switzerland) · 2024Article
- Minimizing artifact-induced false-alarms for seizure detection in wearable EEG devices with gradient-boosted tree classifiers.Scientific reports · 2024Article
- Advanced framework for epilepsy detection through image-based EEG signal analysis.Frontiers in human neuroscience · 2024Article
- Estimating Patient-Level Uncertainty in Seizure Detection Using Group-Specific Out-of-Distribution Detection Technique.Sensors (Basel, Switzerland) · 2023Article
- SeizFt: Interpretable Machine Learning for Seizure Detection Using Wearables.Bioengineering (Basel, Switzerland) · 2023Article
- Characteristic analysis of epileptic brain network based on attention mechanism.Scientific reports · 2023Article
- Efficient and generalizable cross-patient epileptic seizure detection through a spiking neural network.Frontiers in neuroscience · 2023Article
- EEG biomarker candidates for the identification of epilepsy.Clinical neurophysiology practice · 2023Review
- PreEpiSeizures: description and outcomes of physiological data acquisition using wearable devices during video-EEG monitoring in people with epilepsy.Frontiers in physiology · 2023Article
- Graph-generative neural network for EEG-based epileptic seizure detection via discovery of dynamic brain functional connectivity.Scientific reports · 2022Article
- Nanopower Integrated Gaussian Mixture Model Classifier for Epileptic Seizure Prediction.Bioengineering (Basel, Switzerland) · 2022Article
- Engineering nonlinear epileptic biomarkers using deep learning and Benford's law.Scientific reports · 2022Article
- SASDL and RBATQ: Sparse Autoencoder With Swarm Based Deep Learning and Reinforcement Based Q-Learning for EEG Classification.IEEE open journal of engineering in medicine and biology · 2022Article
- Classification with a Deferral Option and Low-Trust Filtering for Automated Seizure Detection.Sensors (Basel, Switzerland) · 2021Article
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6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Seizure detection is a routine process in epilepsy units requiring manual intervention of well-trained specialists. This process could be extensive, inefficient and time-consuming, especially for long term recordings. We proposed an automatic method to detect epileptic seizures using an imaged-EEG representation of brain signals. To accomplish this, we analyzed EEG signals from two different datasets: the CHB-MIT Scalp EEG database and the EPILEPSIAE project that includes scalp and intracranial recordings. We used fully convolutional neural networks to automatically detect seizures. For our best model, we reached average accuracy and specificity values of 99.3% and 99.6%, respectively, for the CHB-MIT dataset, and corresponding values of 98.0% and 98.3% for the EPILEPSIAE patients. For these patients, the inclusion of intracranial electrodes together with scalp ones increased the average accuracy and specificity values to 99.6% and 58.3%, respectively. Regarding the other metrics, our best model reached average precision of 62.7%, recall of 58.3%, F-measure of 59.0% and AP of 54.5% on the CHB-MIT recordings, and comparatively lowers performances for the EPILEPSIAE dataset. For both databases, the number of false alarms per hour reached values less than 0.5/h for 92% of the CHB-MIT patients and less than 1.0/h for 80% of the EPILEPSIAE patients. Compared to recent studies, our lightweight approach does not need any estimation of pre-selected features and demonstrates high performances with promising possibilities for the introduction of such automatic methods in the clinical practice.
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