Key result
Age and gender correlate moderately with HRV to improve noninvasive glucose prediction in arrhythmia patients.
Why the study?
The authors sought to evaluate how age and gender relate to heart rate variability to adjust thresholds and improve performance in a noninvasive glucose estimator under development.
Does age and gender influence HRV parameters, and can they be used to adjust thresholds for noninvasive glucose estimation?
Observational (n=284)
Does age and gender influence HRV parameters, and can they be used to adjust thresholds for noninvasive glucose estimation?
Effect estimate: correlation coefficient of r=-0.58
p-value: p=0.0018
Adjusting for age and gender is critical when using HRV parameters for predictive modeling like noninvasive glucose monitoring, as these demographic factors significantly alter HRV baselines, especially differently in healthy patients versus those with arrhythmias or diabetes.
Supports demographic adjustment in HRV glucose models; leaves open prospective validation before clinical adoption.
Heart rate variability (HRV) parameters can reveal the performance of the autonomic nervous system and possibly estimate the type of its malfunction, such as that of detecting the blood glucose level. Therefore, we aim to find the impact of other factors on the proper calculation of HRV. In this paper, we research the relation between HRV and the age and gender of the patient to adjust the threshold correspondingly to the noninvasive glucose estimator that we are developing and improve its performance. While most of the literature research so far addresses healthy patients and only short- or long-term HRV, we apply a more holistic approach by including both healthy patients and patients with arrhythmia and different lengths of HRV measurements (short, middle, and long). The methods necessary to determine the correlation are (i) point biserial correlation, (ii) Pearson correlation, and (iii) Spearman rank correlation. We developed a mathematical model of a linear or monotonic dependence function and a machine learning and deep learning model, building a classification detector and level estimator. We used electrocardiogram (ECG) data from 4 different datasets consisting of 284 subjects. Age and gender influence HRV with a moderate correlation value of 0.58. This work elucidates the intricate interplay between individual input and output parameters compared with previous efforts, where correlations were found between HRV and blood glucose levels using deep learning techniques. It can successfully detect the influence of each input.
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Stojmenski et al. (2023) conducted an observational in Healthy subjects and patients with arrhythmia or diabetes (n=284). Age and gender was evaluated on Correlation between HRV parameters and age/gender (correlation coefficient of r=-0.58, p=0.0018). Age and gender significantly influence heart rate variability, with a moderate correlation value of -0.58 in arrhythmia patients, which helps improve HRV-based noninvasive glucose prediction.
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