A New Heartbeat-Respiration Coupling Metric for Relaxation/Stress Monitoring

Abstract

Objective: Identification and continuous monitoring of relaxation/stress level through easily measurable physiological signals has become an urgent need. Relaxation/stress is mainly mediated by autonomic nervous system (ANS) which can hardly be noninvasively-measured, but can be reflected in heartbeat and respiration because the human body is a strongly coupling system. However, existing metrics based on heart rate and respiration present low accuracy for relaxation assessment. Our main objective is to propose a reliable metric for relaxation/stress monitoring. Methods: By improving the quantification of Respiratory Sinus Arrhythmia (RSA) and introducing the phase difference between heart rate and respiration, we construct a new metric called Heart-Breath Coherence (HBC) based on heart rate and respiration collected synchronously. Firstly, its practical performance is examined by the multi-scenario experiment with 34 volunteers, which contains four scenarios: smelling odors, listening to sounds, emotional evocations, and watching videos. Then, the synthetic data are employed to test the precision of RSA quantification by HBC. Results: Comparing with 26 existing metrics, HBC shows the highest accuracy 91% (p-value $< $ 0.01 and effect size $>$ 0.8) in the multi-scenario experiment. The introduction of magnitude of phase difference is crucial for the success. Synthetic studies show that HBC improves the RSA quantification with the minimum error comparing to other metrics. Conclusion: HBC is a reliable metric for relaxation/stress monitoring and RSA quantification. Significance: HBC can provide the real-time monitoring of relaxation/stress level and reflect the ANS balance, which is meaningful for healthcare and may be used for psychology and clinical practice.