A proof-of-concept investigation of multi-modal physiological signal responses to acute mental stress

In this paper, we investigated how cardiorespiratory parameters derived from multiple modalities of physiological signals respond to acute mental stress. The novelty of our study is that it derived physiology-inspired signatures of acute mental stress by exploiting (i) cross-integration of multi-modal physiological signals, (ii) cardio-mechanical signals (i.e., the seismocardiogram (SCG) and the ballistocardiogram (BCG)), and (iii) physiological signals compatible with wearable-based sensing platforms. Experimental results from 24 healthy volunteers showed that cross-integration of multi-modal wearable-compatible physiological signals may provide effective signatures that can elucidate the collective physiological changes in response to acute mental stress. In particular, (i) the R-R interval in the electrocar-diogram (ECG) and the pulse rate in the photoplethysmogram (PPG), (ii) PPG amplitude, (iii) pre-ejection period (PEP) and the ratio of PEP to left ventricular ejection time (LVET) (both of which are derived from ECG and SCG), (iv) the product of R-R interval and PEP as well as the ratio of pulse transit time (PTT) derived from SCG-PPG (or BCG-PPG) pairs to PPG amplitude, (v) pulse arrival time (PAT) derived from ECG-PPG pair and PTT, and (vi) respiratory rate may be interpreted as stress -induced acute changes in (i) heart rate, (ii) total peripheral resistance, (iii) cardiac contractility (i.e., stroke volume), (iv) cardiac output, (v) arterial blood pressure, and (vi) respiratory activity, respectively. In sum, this work illustrated the proof-of-concept of tracking acute mental stress via physiology-inspired signatures derived from multi-modal physiological signals, thereby paving the way for future efforts aimed at better quantifying acute stress responses in real-world settings.

Automated summary

In this study, we investigated how various cardiorespiratory parameters respond to acute mental stress by utilizing multi-modal physiological signals and wearable-based sensing platforms. The results showed that the cross-integration of these signals can provide effective signatures to elucidate the collective physiological changes during acute mental stress. This work demonstrates the proof-of-concept of tracking acute mental stress through physiology-inspired signatures derived from multi-modal physiological signals, paving the way for future research in quantifying stress responses in real-world settings.