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...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:IEEE access Ročník 13; s. 179568 - 179583
Hlavní autori: Mishra, Pratiti, Das, Himansu
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Piscataway IEEE 2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
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 prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí: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 learning (DL) techniques offer significant potential for fast and accurate medical decisions, especially when addressing imbalanced medical datasets. Therefore, this research proposes a novel artificial neural network architecture called the Variational Sparse Autoencoder (VSAE), which combines the strengths of a Sparse Autoencoder (SAE) and a Variational Autoencoder (VAE). The VSAE produces compact, sparse, and informative features for Random Forest (RF) classification, while Explainable AI techniques (XAI) methods like SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) enhance interpretability and transparency. LIME provides local interpretability whereas SHAP offers global interpretability by identifying the most influential EEG features contributing towards seizure detection. Additionally, 10-fold cross-validation (CV) is used to validate the proposed model. Compared to other conventional linear and non linear models, the proposed VSAE model demonstrates accuracy of 96.81%, precision 94.03%, recall 89.74% and F1 score 91.83% respectively.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2025.3620762