On the Variability of Heart Rate Variability—Evidence from Prospective Study of Healthy Young College Students

Abstract

Heart rate variability (HRV) has been widely used as indices for autonomic regulation, including linear analyses, entropy and multi-scale entropy based nonlinear analyses, and however, it is strongly influenced by the conditions under which the signal is being recorded. To investigate the variability of healthy HRV under different settings, we recorded electrocardiograph (ECG) signals from 56 healthy young college students (20 h for each participant) at campus using wearable single-lead ECG device. Accurate R peak to R peak (RR) intervals were extracted by combing the advantages of five commonly used R-peak detection algorithms to eliminate data quality influence. Thorough and detailed linear and nonlinear HRV analyses were performed. Variability of HRV metrics were evaluated from five categories: (1) different states of daily activities; (2) different recording time period in the same day during free-running daily activities; (3) body postures of sitting and lying; (4) lying on the left, right and back; and (5) gender influence. For most of the analyzed HRV metrics, significant differences (p < 0.05) were found among different recording conditions within the five categories except lying on different positions. Results suggested that the standardization of ECG data collection and HRV analysis should be implemented in HRV related studies, especially for entropy and multi-scale entropy based analyses. Furthermore, this preliminary study provides reference values of HRV indices under various recording conditions of healthy young subjects that could be useful information for different applications (e.g., health monitoring and management).

A Multimodal Model of ECG and Heart Sound Signal by Considering Normal and Abnormal Heart

“…This method has strong advantages in computational performance and efficiency [15]. The first heart sound (S1) occurs at the beginning of ventricular contraction and corresponds to the QRS complex in cardiac signals [11], [16]- [18], and the S2 heart sound caused by semilunar valve closure occurs after the formation of a T wave in cardiac signals [19], [20]. S1 onset is traditionally thought to occur after R peak in previous ECGbased PCG segmentation studies [21]- [23].…”

Longitudinal Changes and Recovery in Heart Rate Variability of Young Healthy Subjects When Exposure to a Hypobaric Hypoxic Environment

“…It is easy to see that the subjects' complexity was significantly lower in the hypobaria hypoxia environment at all 20 different scales relative to the baseline level. Factors commonly trigger sympathetic-parasympathetic balance, such as postural changes, strenuous activity, or pathological changes (Porta et al, 2012(Porta et al, , 2015Cui et al, 2020). This decreases the SampEn of heart rate and thus significantly reduces the plateau MSE (Table 5), which may also result from sympathetic activation caused by plateau hypoxia.…”

Heart rate variability in different sleep stages is associated with metabolic function and glycemic control in type 2 diabetes mellitus

“…Dysfunction of ANS, caused by T2DM, happens in any part of ANS from early stages of diabetes, which damages the cardiovascular, gastrointestinal, genitourinary and neurovascular systems (Pfeifer et al, 1982;Singh et al, 2000;Goit et al, 2012). The function of healthy ANS has obvious circadian rhythm (Cui et al, 2020), and the dysfunction of ANS can result in the loss of circadian rhythm to varying degrees (Liu et al, 2020). Consequently, diabetes is often followed by sleep disorders (Barone and Menna-Barreto, 2011).…”