Adaptive Komodo Mlipir–Optimized Spatiotemporal Graph Neural Network for Dataset-Specific Physiological-State Classification

Mon, 10 Aug 2026 00:00:00 +0000

Physiological-state classification requires models that can represent dependencies among sensor channels while preserving temporal variation. This study presents a dataset-specific evaluation of an Adaptive Komodo Mlipir Algorithm-optimized spatiotemporal graph neural network (AKMA-ST-GNN) using four public physiological datasets with different modalities and target definitions: STEW for cognitive workload, SEED for emotion, DEAP for affect, and WESAD for stress and amusement. The datasets were analyzed independently. No EEG signal from one dataset was combined with EMG or peripheral signals from another dataset, and workload, emotion, affect, and stress labels were not collapsed into a common target. The proposed workflow included signal filtering, training-set standardization, frequency-domain characterization where physiologically appropriate, within-dataset channel graph construction, spatiotemporal graph learning, and dataset-specific classification. AKMA was used as a hyperparameter-search wrapper rather than as an additional predictive layer. Under the preliminary 80% training and 20% testing protocol preserved in the source manuscript, the reported accuracies were 98.60% for STEW, 98.31% for WESAD, 98.06% for DEAP, and 98.12% for SEED. Precision, F1-score, specificity, and sensitivity were available only for selected datasets. Prediction-level outputs, class supports, confusion matrices, and decision scores were not retained, therefore, macro F1, weighted F1, balanced accuracy, Matthews correlation coefficient, Cohen's kappa, and ROC/AUC could not be reconstructed. Repeated-seed results and fold- or participant-level scores were also unavailable, preventing calculation of standard deviations, confidence intervals, and statistical significance tests. The reported values are consequently interpreted as preliminary evidence of technical feasibility rather than proof of stable or subject-independent generalization.

International Research Journal of Multidisciplinary Technovation

Adaptive Komodo Mlipir–Optimized Spatiotemporal Graph Neural Network for Dataset-Specific Physiological-State Classification