Detection and Classification of Physiological Activation States Through Biosignal Analysis Using Machine Learning

Monitoring autonomic nervous system responses offers significant potential for personalized health applications and early intervention. This study presents a machine learning framework for binary classification of physiological activation states using electrocardiogram (ECG) and respiratory (RESP) signals from the WESAD dataset. Stress and amusement were grouped as Activation, whereas baseline and meditation were grouped as Non-Activation. The processing pipeline incorporates R-peak detection, temporal segmentation, handcrafted feature extraction, and subject-independent evaluation. Conventional machine learning classifiers and deep learning architectures were evaluated, together with a proposed Hybrid Multi-Scale Temporal Convolutional-Conformer (MST-Conformer) combining temporal representations learned from ECG and RESP waveforms with handcrafted physiological features. Performance was assessed using 15-fold Leave-One-Subject-Out (LOSO) cross-validation. Adding respiration-derived features to ECG-derived features improved conventional machine learning performance, increasing the highest balanced accuracy from 0.752 to 0.807 and AUC from 0.802 to 0.886. The complete Hybrid MST-Conformer incorporating 27 physiological features achieved a mean accuracy of 0.825, balanced accuracy of 0.820, F1-score of 0.763, and AUC of 0.904. Subject-level analysis also revealed substantial physiological heterogeneity. These results support the potential of combining learned temporal representations with handcrafted physiological information for subject-independent physiological activation classification.