Entropy-driven deep learning framework for epilepsy detection using electro encephalogram signals

[Display omitted] •Novel entropy-driven deep learning detects epilepsy with 94% accuracy from EEG signals.•Multivariate entropy features capture complex brain activity for robust seizure detection.•UMAP enhances feature discrimination for better epilepsy classification.•ResNet and Bi-LSTM model capt...

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Veröffentlicht in:Neuroscience Jg. 577; S. 12 - 24
Hauptverfasser: Singh Sikarwar, Sandeep, Rana, Arun Kumar, Sengar, Sandeep Singh
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States Elsevier Inc 21.06.2025
Schlagworte:
Deep Learning
EEG signals
Electroencephalography - methods
Entropy
Epilepsy - diagnosis
Epilepsy - physiopathology
Epilepsy detection
Humans
Multiple variable entropy
Neural Networks, Computer
Neurological disorder
Signal Processing, Computer-Assisted
Temporal dynamics
EEG signals
Epilepsy detection
Temporal dynamics
Multiple variable entropy
Neurological disorder
Deep Learning
ISSN:0306-4522, 1873-7544, 1873-7544
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Zusammenfassung:[Display omitted] •Novel entropy-driven deep learning detects epilepsy with 94% accuracy from EEG signals.•Multivariate entropy features capture complex brain activity for robust seizure detection.•UMAP enhances feature discrimination for better epilepsy classification.•ResNet and Bi-LSTM model captures both spatial and temporal EEG patterns effectively.•Proposed method outperforms traditional models in epilepsy detection using EEG data. Epilepsy is one of the most frequently occurring neurological disorders that require early and accurate detection. This paper introduces a novel approach for the automatic identification of epilepsy in EEG signals by incorporating advanced entropy-based measures with modern pre-processing techniques. The objective is to develop a robust and effective epilepsy detection method. EEG data were pre-processed using adaptive wavelet denoising models to suppress noise while preserving signal integrity. Multivariate entropy features, including Multiple Variable Permutation Entropy (mvMPE) and Multiple Variable Multi-Scale Fuzzy Entropy (mvMFE), were extracted to capture both complexity and frequency-specific variations. Additionally, Uniform Manifold Approximation and Projection (UMAP) was applied for non-linear dimensionality reduction, enhancing the discriminative power of features. A Residual Convolutional Neural Network (ResNet) integrated with Bi-Directional Long Short-Term Memory (Bi-LSTM) was employed to capture both temporal dynamics and spatial features. The proposed model demonstrated superior classification accuracy compared to traditional approaches. Implemented using Python, the model achieved an accuracy of 94%, F1-Score of 96%, recall of 93%, specificity of 87.70%, and precision of 82.21%. This study highlights the synergy between advanced entropy measures and cutting-edge deep learning architectures for robust and accurate epilepsy detection.
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ISSN:0306-4522
1873-7544
1873-7544
DOI:10.1016/j.neuroscience.2025.05.003