ECG + respiration activation-state classification on WESAD with a hybrid multi-scale TCN-Conformer, 15-fold LOSO (Leonelli … Sanchez-Galan; Sensors 2026)

Leonelli and colleagues classify binary Activation versus Non-Activation on WESAD using chest ECG and respiration only, with a hybrid multi-scale temporal convolutional Conformer fused to handcrafted physiological features. Under 15-fold leave-one-subject-out, the abstract reports accuracy 0.825, balanced accuracy 0.820, and AUC 0.904. The label grouping merges stress with amusement, so results are not directly comparable to standard WESAD boards.

• Form factor: chest (WESAD RespiBAN ECG + RESP)

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.

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• WESADWearable Stress and Affect Detection15 subjects · PPG · ECG · IMU