Using ultra-short-term Heart Rate Variability (HRV) analysis to track posture changes

Abstract

Body posture significantly influences heart rate variability (HRV) through the autonomic nervous system (ANS), which maintains hemodynamic stability by balancing sympathetic and parasympathetic activity. Postural changes affect blood distribution, consequently altering HRV. Previous studies indicated that a supine posture decreases sympathetic and increases parasympathetic activity while standing increases sympathetic and decreases parasympathetic activity. Sitting involves both systems’ activities. Recently, ultra-short-term HRV analysis has been used to track physiological changes for its practicality and real-time monitoring capabilities. This study recorded electrocardiogram (ECG) signals from 30 healthy adults in supine, sitting, and standing postures to monitor postural changes. After random extraction of the RR time series for each posture, 16 HRV metrics were calculated. Based on statistical analysis, the HRV metrics that showed the most significant changes in tracking posture were the mean RR, min RR, max RR, RMSDD, SD1, SD1/SD2, DFA a1 , and alpha ( a ). Nevertheless, several HRV indices were inconsistent, indicating that these values depended on the length of the recording time window. In addition, classification performance deteriorated if it was not specifically tailored or calibrated for each participant. The findings of this study reveal that mean RR, RMSDD, and SD1 provided the best posture classification performance using the ultra-short-term HRV analysis. Among these indices, the most sensitive index was RMSDD, showing an 82% change when comparing lying to standing postures. The consistency of these HRV indices across different time windows suggests that these indices are largely independent of the time window and exhibit changes within the same range as those reported in previous studies.