ArticleIEEE journal of translational engineering in health and medicine2021
A Deep Convolutional Neural Network Method to Detect Seizures and Characteristic Frequencies Using Epileptic Electroencephalogram (EEG) Data.
Article in IEEE journal of translational engineering in health and medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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31 citing papers in PubMed.
- Multi-domain feature fusion with variational mode decomposition and hybrid LightGBM-Logistic Regression for multi-class seizure classification.BioData mining · 2026Article
- Operational Transformer: An investigation of epilepsy detection.Brain topography · 2026Article
- Hybrid quantum classical framework for electroencephalogram driven neurological processing in epileptic seizure taxonomy.Scientific reports · 2026Article
- AI-driven European Retrospective Database Study to predict disease onset in patients with epilepsy and depression.BMJ neurology open · 2026Article
- A Method for Explainable Epileptic Seizure Detection Through Wavelet Transforms Obtained by Electroencephalogram-Based Audio Recordings.Sensors (Basel, Switzerland) · 2025Article
- Classically studied coherent structures only paint a partial picture of wall-bounded turbulence.Nature communications · 2025Article
- An algorithm for seizure detection in rodents.Epilepsia open · 2025Article
- Revolutionizing sleep disorder diagnosis: A Multi-Task learning approach optimized with genetic and Q-Learning techniques.Scientific reports · 2025Article
- Predicting Epileptic Seizures Using EfficientNet-B0 and SVMs: A Deep Learning Methodology for EEG Analysis.Bioengineering (Basel, Switzerland) · 2025Article
- EPIC-NET: EEG-based epilepsy classification and brain localization using Optuna wave-gated recurrent unit network.Frontiers in computational neuroscience · 2025Article
- Using vision transformers for electrographic seizure classification to aid physician review of intracranial electroencephalography recordings.Frontiers in human neuroscience · 2025Article
- Adaptive weighted dual MAML: Proposing a novel method for the automated diagnosis of partial sleep deprivation.PloS one · 2025Article
- Latent Prototype-Based Clustering: A Novel Exploratory Electroencephalography Analysis Approach.Sensors (Basel, Switzerland) · 2024Article
- Evaluation of the Relation between Ictal EEG Features and XAI Explanations.Brain sciences · 2024Article
- Identifying EEG Biomarkers of Depression with Novel Explainable Deep Learning Architectures.bioRxiv : the preprint server for biology · 2024Article
- Article
- Simultaneous EEG-fNIRS Data Classification Through Selective Channel Representation and Spectrogram Imaging.IEEE journal of translational engineering in health and medicine · 2024Article
- Data leakage in deep learning studies of translational EEG.Frontiers in neuroscience · 2024Article
- Neural activity during inhibitory control predicts suicidal ideation with machine learning.NPP - digital psychiatry and neuroscience · 2024Article
- Risk of data leakage in estimating the diagnostic performance of a deep-learning-based computer-aided system for psychiatric disorders.Scientific reports · 2023Article
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6 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundDiagnosing epileptic seizures using electroencephalogram (EEG) in combination with deep learning computational methods has received much attention in recent years. However, to date, deep learning techniques in seizure detection have not been effectively harnessed due to sub-optimal classifier design and improper representation of the time-domain signal.
methodsIn this study, we focused on designing and evaluating deep convolutional neural network-based classifiers for seizure detection. Signal-to-image conversion methods are proposed to convert time-domain EEG signal to a time-frequency represented image to prepare the input data for classification. We proposed and evaluated three classification methods comprising of five classifiers to determine which is more accurate for seizure detection. Accuracy data were then compared to previous studies of the same dataset.
resultsWe found our proposed model and signal-to-image conversion method outperformed all previous studies in the most cases. The proposed FT-VGG16 classifier achieved the highest average classification accuracy of 99.21%. In addition, the Shapley Additive exPlanations (SHAP) analysis approach was employed to uncover the feature frequencies in the EEG that contribute most to improved classification accuracy. To the best of our knowledge, this is the first study to compute the contribution of frequency components to target seizure classification; thus allowing the identification of distinct seizure-related EEG frequency components compared to normal EEG measures.
conclusionThus our developed deep convolutional neural network models are useful to detect seizures and characteristic frequencies using EEG data collected from the patients and this model could be clinically applicable for the automated seizures detection.
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