An Explainable AI Framework Integrating Variational Sparse Autoencoder and Random Forest for EEG-Based Epilepsy Detection

Epilepsy is a medical condition characterized by sudden and frequent sensory disruptions which is commonly detected by electroencephalogram (EEG) analysis. However, analyzing these signals is challenging for traditional classifiers due to their non-stationary nature and high dimensionality. Deep lea...

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Vydáno v:	IEEE access Ročník 13; s. 179568 - 179583
Hlavní autoři:	Mishra, Pratiti, Das, Himansu
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Piscataway IEEE 2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Accuracy Artificial neural networks Autoencoders Brain modeling Classification algorithms Computational efficiency Decision trees Dimensionality reduction EEG Electroencephalography Epilepsy Explainable artificial intelligence Feature extraction LIME Machine learning Radio frequency SAE SHAP VAE VSAE
ISSN:	2169-3536, 2169-3536
On-line přístup:	Získat plný text
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