ArticleScientific reports2022
A deep learning framework for epileptic seizure detection based on neonatal EEG signals.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.
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Who cites it
23 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Accuracy of Machine Learning in Detecting Pediatric Epileptic Seizures: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2024Pooled it
- Artificial Intelligence and Machine Learning in Pediatric Epilepsy: A Systematic Review.Neurology and therapy · 2026Review
- Hybrid fuzzy machine learning models optimized with meta-heuristics for accurate EEG-based neurological assessment.Scientific reports · 2026Article
- Hybrid quantum classical framework for electroencephalogram driven neurological processing in epileptic seizure taxonomy.Scientific reports · 2026Article
- Neonatal seizure detection from EEG using inception ResNetV2 feature extraction and XGBoost optimized with particle swarm optimization.Scientific reports · 2025Article
- Development of a deep learning-based prediction model for postoperative delirium using intraoperative electroencephalogram in adults.NPJ digital medicine · 2025Article
- Diagnosis of psoriasis and lichen planus in real-time using neural networks based on skin Biomechanical properties obtained from numerical simulation.Scientific reports · 2025Article
- Linear regressive weighted Gaussian kernel liquid neural network for brain tumor disease prediction using time series data.Scientific reports · 2025Article
- Epilepsy detection based on spatiotemporal feature interaction fusion of EEG signals.Frontiers in neurology · 2025Article
- Improved attention-based PCNN with GhostNet for epilepsy seizure detection using EEG and fMRI modalities: extractive pattern and histogram feature set.Frontiers in artificial intelligence · 2025Article
- Advancing epileptic seizure recognition through bidirectional LSTM networks.Frontiers in computational neuroscience · 2025Article
- Analysis of the impact of deep learning know-how and data in modelling neonatal EEG.Scientific reports · 2024Article
- Enhancing Epilepsy Seizure Detection Through Advanced EEG Preprocessing Techniques and Peak-to-Peak Amplitude Fluctuation Analysis.Diagnostics (Basel, Switzerland) · 2024Article
- TATPat based explainable EEG model for neonatal seizure detection.Scientific reports · 2024Article
- Seizure Onset Zone Detection Based on Convolutional Neural Networks and EEG Signals.Brain sciences · 2024Article
- Artificial intelligence for brain disease diagnosis using electroencephalogram signals.Journal of Zhejiang University. Science. B · 2024Review
- Article
- Deep Learning for Generalized EEG Seizure Detection after Hypoxia-Ischemia-Preclinical Validation.Bioengineering (Basel, Switzerland) · 2024Article
- Article
- Machine Learning and Artificial Intelligence Applications to Epilepsy: a Review for the Practicing Epileptologist.Current neurology and neuroscience reports · 2023Review
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Electroencephalogram (EEG) is one of the main diagnostic tests for epilepsy. The detection of epileptic activity is usually performed by a human expert and is based on finding specific patterns in the multi-channel electroencephalogram. This is a difficult and time-consuming task, therefore various attempts are made to automate it using both conventional and Deep Learning (DL) techniques. Unfortunately, authors do not often provide sufficiently detailed and complete information to be able to reproduce their results. Our work is intended to fill this gap. Using a carefully selected 79 neonatal EEG recordings we developed a complete framework for seizure detection using DL approch. We share a ready to use R and Python codes which allow: (a) read raw European Data Format files, (b) read data files containing the seizure annotations made by human experts, (c) extract train, validation and test data, (d) create an appropriate Convolutional Neural Network (CNN) model, (e) train the model, (f) check the quality of the neural classifier, (g) save all learning results.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.