ArticleBMC medical informatics and decision making2023
An automated detection of epileptic seizures EEG using CNN classifier based on feature fusion with high accuracy.
Article in BMC medical informatics and decision making, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 2 of them syntheses that pooled it.
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Who cites it
24 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Artificial intelligence in electroencephalography analysis for epilepsy diagnosis and management.Frontiers in neurology · 2025Pooled it
- Machine learning applied to epilepsy: bibliometric and visual analysis from 2004 to 2023.Frontiers in neurology · 2024Pooled it
- CosFNet: A Lightweight Epileptic EEG Detection Model Based on Cosine Convolution and FNet.Bioengineering (Basel, Switzerland) · 2026Article
- Multiscale Cosine Convolution Neural Network for Robust and Interpretable Epileptic EEG Detection.Biosensors · 2026Article
- PSD-LW-DCN: a generalizable power spectral density based lightweight deep convolutional neural network for seizure detection.Scientific reports · 2026Article
- Automatic EEG artifact detection using a local-global feature fusion network in time and time-frequency domains.Acta epileptologica · 2026Article
- Enhancing seizure prediction using a DC-SA-EBiLSTM framework with self-attention mechanism.Frontiers in neuroscience · 2026Article
- Research on epilepsy detection methods based on interpretable features and machine learning.PloS one · 2026Article
- Comprehensive review of machine learning and deep learning techniques for epileptic seizure detection and prediction based on neuroimaging modalities.Visual computing for industry, biomedicine, and art · 2025Review
- Neonatal seizure detection from EEG using inception ResNetV2 feature extraction and XGBoost optimized with particle swarm optimization.Scientific reports · 2025Article
- Epileptic seizure detection from electroencephalogram signals based on 1D CNN-LSTM deep learning model using discrete wavelet transform.Scientific reports · 2025Article
- A Novel Co-Designed Multi-Domain Entropy and Its Dynamic Synapse Classification Approach for EEG Seizure Detection.Entropy (Basel, Switzerland) · 2025Article
- Automated Detection of Epileptic Seizures in EEG Signals via Micro-Capsule Networks.Brain sciences · 2025Article
- A hybrid CNN-Bi-LSTM model with feature fusion for accurate epilepsy seizure detection.BMC medical informatics and decision making · 2025Article
- A Real-Time Epilepsy Detection Method Using Embedded Zero Tree Wavelet Approach and Support Vector Machine.Behavioural neurology · 2025Article
- Advancing epileptic seizure recognition through bidirectional LSTM networks.Frontiers in computational neuroscience · 2025Article
- CMTS-GNN: a cross-modal temporal-spectral graph neural network with cognitive network explainability.Frontiers in neurology · 2025Article
- Enhanced visibility graph for EEG classification.Frontiers in neuroscience · 2025Article
- Detection of Anxiety-Based Epileptic Seizures in EEG Signals Using Fuzzy Features and Parrot Optimization-Tuned LSTM.Brain sciences · 2024Article
- A scheme combining feature fusion and hybrid deep learning models for epileptic seizure detection and prediction.Scientific reports · 2024Article
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7 authors.
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Abstract
backgroundEpilepsy is a neurological disorder that is usually detected by electroencephalogram (EEG) signals. Since manual examination of epilepsy seizures is a laborious and time-consuming process, lots of automatic epilepsy detection algorithms have been proposed. However, most of the available classification algorithms for epilepsy EEG signals adopted a single feature extraction, in turn to result in low classification accuracy. Although a small account of studies have carried out feature fusion, the computational efficiency is reduced due to too many features, because there are also some poor features that interfere with the classification results.
methodsIn order to solve the above problems, an automatic recognition method of epilepsy EEG signals based on feature fusion and selection is proposed in this paper. Firstly, the Approximate Entropy (ApEn), Fuzzy Entropy (FuzzyEn), Sample Entropy (SampEn), and Standard Deviation (STD) mixed features of the subband obtained by the Discrete Wavelet Transform (DWT) decomposition of EEG signals are extracted. Secondly, the random forest algorithm is used for feature selection. Finally, the Convolutional Neural Network (CNN) is used to classify epilepsy EEG signals.
resultsThe empirical evaluation of the presented algorithm is performed on the benchmark Bonn EEG datasets and New Delhi datasets. In the interictal and ictal classification tasks of Bonn datasets, the proposed model achieves an accuracy of 99.9%, a sensitivity of 100%, a precision of 99.81%, and a specificity of 99.8%. For the interictal-ictal case of New Delhi datasets, the proposed model achieves a classification accuracy of 100%, a sensitivity of 100%, a specificity of 100%, and a precision of 100%.
conclusionThe proposed model can effectively realize the high-precision automatic detection and classification of epilepsy EEG signals. This model can provide high-precision automatic detection capability for clinical epilepsy EEG detection. We hope to provide positive implications for the prediction of seizure EEG.
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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.