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

Accurate classification among Alzheimer's Disease (AD), Fronto Temporal Dementia (FTD), and Cognitively Normal (CN) adults from EEG remains challenging. We propose a multi-class classification method that fuses interpretable spectral/connectivity biomarkers (band power, spectral entropy, α-coherence) with compact temporal embeddings from a customized lightweight one Dimensional Convolutional Neural Network (1D-CNN). The fused features are reduced by Principal Component Analysis (PCA) and classified with Support Vector Machine (SVM). All data-dependent steps like Synthetic Minority Over Sampling Technique (SMOTE), z-scoring and PCA are fit strictly on training folds to prevent leakage. Hyperparameters including PCA dimensionality and SMOTE neighbors were selected via inner-loop grid sweeps that maximized macro-F1; full grids and class distributions before/after SMOTE (inner-train only) are reported in the Supplement. On OpenNeuro ds004504 (eyes-closed; [Formula: see text]; 36 AD/23 FTD/29 CN) the model achieved 94.5% accuracy, macro-F1 [Formula: see text], and AUC [Formula: see text]. Robustness was examined on two public datasets using the identical preprocessing/segmentation: (i) cross-condition testing on ds006036 (eyes-open recordings from the same participants) and (ii) zero-shot transfer to an independent OSF dataset with different instrumentation and demographics. SHapley Additive exPlanations (SHAP) analyses provided global, class-wise, and subject-level attributions aligned with known electrophysiology (e.g., reduced α-coherence, elevated θ), supporting clinical interpretability. These results indicate that a transparent hybrid feature design can deliver accurate, leakage-safe, and explainable EEG-based differentiation of AD, FTD, and CN with preliminary external checks.