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A deep-SVM hybrid framework with enhanced EEG feature engineering and SHAP-based explainability for Alzheimer's

Frnaz Akbar1, Yazeed Alkhrijah2, Syed Muhammad Usman3

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This study presents a novel EEG analysis method for accurately differentiating Alzheimer's Disease (AD), Fronto Temporal Dementia (FTD), and Cognitively Normal (CN) adults. The hybrid approach combines spectral biomarkers and deep learning for explainable and robust classification.

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Machine Learning

Background:

  • Accurate differentiation of Alzheimer's Disease (AD), Fronto Temporal Dementia (FTD), and Cognitively Normal (CN) individuals using electroencephalography (EEG) is clinically significant yet challenging.
  • Existing EEG classification methods often lack interpretability or robustness across different datasets and conditions.

Purpose of the Study:

  • To develop and validate a novel, interpretable, and robust multi-class classification framework for distinguishing AD, FTD, and CN from EEG data.
  • To fuse spectral/connectivity biomarkers with deep learning temporal embeddings for enhanced classification accuracy.

Main Methods:

  • A hybrid classification approach combining interpretable EEG biomarkers (band power, spectral entropy, α-coherence) with temporal features from a lightweight 1D Convolutional Neural Network (1D-CNN).
  • Feature fusion followed by Principal Component Analysis (PCA) for dimensionality reduction and Support Vector Machine (SVM) for classification.
  • Rigorous methodology to prevent data leakage, including fitting data-dependent steps (SMOTE, z-scoring, PCA) strictly on training folds and employing inner-loop grid sweeps for hyperparameter optimization.

Main Results:

  • Achieved high classification accuracy (94.5%), macro-F1 score, and AUC on the OpenNeuro ds004504 dataset (AD/FTD/CN).
  • Demonstrated robustness through cross-condition testing on eyes-open recordings (ds006036) and zero-shot transfer to an independent dataset.
  • SHapley Additive exPlanations (SHAP) analysis provided clinically interpretable, subject-level attributions consistent with known electrophysiological changes in neurodegenerative diseases.

Conclusions:

  • The proposed transparent hybrid feature design offers an accurate, leakage-safe, and explainable method for EEG-based differentiation of AD, FTD, and CN.
  • The findings support the clinical utility of fusing interpretable biomarkers with deep learning for neurodegenerative disease classification.
  • Preliminary external validation suggests the generalizability of the developed classification framework.