Is Ultra-Short-Term Heart Rate Variability Valid in Non-static Conditions?

Abstract

In mobile healthcare, heart rate variability (HRV) is increasingly being used in dynamic patient states. In this situation, shortening of the measurement time is required. This study aimed to validate ultra-short-term HRV in non-static conditions. We conducted electrocardiogram (ECG) measurements at rest, during exercise, and in the post-exercise recovery period in 30 subjects and analyzed ultra-short-term HRV in time and frequency domains by ECG in 10, 30, 60, 120, 180, and 240-s intervals, and compared the values to the 5-min HRV. For statistical analysis, null hypothesis testing, Cohen’s d statistics, Pearson’s correlation coefficient, and Bland-Altman analysis were used, with a statistical significance level of P < 0.05. The feasibility of ultra-short-term HRV and the minimum time required for analysis showed differences in each condition and for each analysis method. If the strict criteria satisfying all the statistical methods were followed, the ultra-short-term HRV could be derived from a from 30 to 240-s length of ECG. However, at least 120 s was required in the post-exercise recovery or exercise conditions, and even ultra-short-term HRV was not measurable in some variables. In contrast, according to the lenient criteria needed to satisfy only one of the statistical criteria, the minimum time required for ultra-short-term HRV analysis was 10–60 s in the resting condition, 10–180 s in the exercise condition, and 10–120 s in the post-exercise recovery condition. In conclusion, the results of this study showed that a longer measurement time was required for ultra-short-term HRV analysis in dynamic conditions. This suggests that the existing ultra-short-term HRV research results derived from the static condition cannot applied to the non-static conditions of daily life and that a criterion specific to the non-static conditions are necessary.

Post-exercise heart rate recovery and parasympathetic reactivation are comparable between prepubertal boys and well-trained adult male endurance athletes

“…The power spectrum of HRV was not investigated due to inconsistencies in high frequency power (HF) compared to RMSSD, notably in the early phase of post-exercise recovery (Baek et al 2015 ; Hung et al 2020 ). Furthermore, Kim et al ( 2021 ) have recently shown that segments of at least 120 s were required to correctly quantify HF immediately after exercise completion, hence ruling out the possibility of obtaining segments of sufficient duration during the 5 min post-exercise recording in the present study. The relevance of analyzing RMSSD rather than HF immediately after exercise completion is also related to excellent agreement and accuracy of HRV in time domain analyses (Baek et al 2015 ; Hung et al 2020 ).…”

“…Ln RMSSD were calculated for each 30 s segment in recovery, i.e. Ln RMSSD 30 , as done previously (Goldberger et al 2006 ) and recently recommended by Kim et al ( 2021 ). The power spectrum of HRV was not investigated due to inconsistencies in high frequency power (HF) compared to RMSSD, notably in the early phase of post-exercise recovery (Baek et al 2015 ; Hung et al 2020 ).…”

Autonomic nervous activity analysis based on visibility graph complex networks and skin sympathetic nerve activity

“…These results were consistent with Castald's (Castaldo et al, 2019) investigation that NNmean and HF displayed consistency across all of the excerpt lengths (30 s, 1 min, 2 min, 3 min, and 5 min) for mental stress assessment. However, Jin Woong et al (Kim et al, 2021) studied UST HRV in non-static conditions by comparing UST HRV features (10, 30, 60, 120, 180, and 240-s) with those from 5-min HRV, the results showed that UST HRV variables derived from the static condition could not applied to the non-static conditions of daily life. Similarly, the CC, Trans, Dia and GE of VG features remained stable in both activated and inactivated segments across all data lengths, implying that these VG features could reveal the dynamical changes caused by the adjustment of autonomous neural system from UST segments.…”

Systemic application of the transient receptor potential vanilloid-type 4 antagonist GSK2193874 induces tail vasodilation in a mouse model of thermoregulation

“…HRV is a sensitive tool that assesses the time difference between consecutive heart beats to evaluate autonomic nervous system modulation [40,41]. Accumulating data suggest that ultra-short-range HRV can be successfully derived from as low as 30 s of human ECG [42,43] and pulse rate variability (estimation of variation in heart rate (HR) from photo plethysmography) has recently been successfully measured from the rat tail [44]. Potentially, measurement of HRV from tail-cuffs would be a useful reduction, refinement or replacement (3Rs) laboratory animal welfare advancement, since surgery is not required.…”