RESUMEN
OBJECTIVE: To develop a novel synthetic multi-modal variable capable of capturing cardiovascular responses to acute mental stress and the stress-mitigating effect of transcutaneous median nerve stimulation (TMNS), as an initial step toward the overarching goal of enabling closed-loop controlled mitigation of the physiological response to acute mental stress. METHODS: Using data collected from 40 experiments in 20 participants involving acute mental stress and TMNS, we examined the ability of six plausibly explainable physio-markers to capture cardiovascular responses to acute mental stress and TMNS. Then, we developed a novel synthetic multi-modal variable by fusing the six physio-markers based on numerical optimization and compared its ability to capture cardiovascular responses to acute mental stress and TMNS against the six physio-markers in isolation. RESULTS: The synthetic multi-modal variable showed explainable responses to acute mental stress and TMNS in more experiments (24 vs ≤19). It also exhibited superior consistency, balanced sensitivity, and robustness compared to individual physio-markers. CONCLUSION: The results showed the promise of the synthetic multi-modal variable as a means to measure cardiovascular responses to acute mental stress and TMNS. However, the results also suggested the potential necessity to develop a personalized synthetic multi-modal variable. SIGNIFICANCE: The findings of this work may inform the realization of TMNS-enabled closed-loop control systems for the mitigation of sympathetic arousal to acute mental stress by leveraging physiological measurements that can readily be implemented in wearable form factors.
RESUMEN
This paper presents a novel computational algorithm to estimate blood volume decompensation state based on machine learning (ML) analysis of multi-modal wearable-compatible physiological signals. To the best of our knowledge, our algorithm may be the first of its kind which can not only discriminate normovolemia from hypovolemia but also classify hypovolemia into absolute hypovolemia and relative hypovolemia. We realized our blood volume classification algorithm by (i) extracting a multitude of features from multi-modal physiological signals including the electrocardiogram (ECG), the seismocardiogram (SCG), the ballistocardiogram (BCG), and the photoplethysmogram (PPG), (ii) constructing two ML classifiers using the features, one to classify normovolemia vs. hypovolemia and the other to classify hypovolemia into absolute hypovolemia and relative hypovolemia, and (iii) sequentially integrating the two to enable multi-class classification (normovolemia, absolute hypovolemia, and relative hypovolemia). We developed the blood volume decompensation state classification algorithm using the experimental data collected from six animals undergoing normovolemia, relative hypovolemia, and absolute hypovolemia challenges. Leave-one-subject-out analysis showed that our classification algorithm achieved an F1 score and accuracy of (i) 0.93 and 0.89 in classifying normovolemia vs. hypovolemia, (ii) 0.88 and 0.89 in classifying hypovolemia into absolute hypovolemia and relative hypovolemia, and (iii) 0.77 and 0.81 in classifying the overall blood volume decompensation state. The analysis of the features embedded in the ML classifiers indicated that many features are physiologically plausible, and that multi-modal SCG-BCG fusion may play an important role in achieving good blood volume classification efficacy. Our work may complement existing computational algorithms to estimate blood volume compensatory reserve as a potential decision-support tool to provide guidance on context-sensitive hypovolemia therapeutic strategy.