Distribution Analysis of Long-Term Heart Rate Variability Versus Blood Glucose

This research explores the class distributions of longterm heart rate variability (HRV) parameters compared to the distribution of glycated hemoglobin (HbAlc) which depicts the long-term blood glucose regulation ability. The goal is to find the optimal HRV parameter and time interval for which it is measured that correlates to the class distribution based on HbAlc the most. The class distribution separability will provide an answer if future highly accurate, precise, and sensitive machine learning classification can be constructed and if so, to aid their interaction with the input data. We found that removing a dataset sample in which at least one feature value is considered an outlier led to much better results. The strongest point-biserial correlations for the class distribution separation were found for 24-hour SDRMSSD-3
$(\mathrm{r}=-0.43)$
, 20-hour SDRMSSD-3
$(\mathrm{r}=-0.34)$
, and 24-hour ARMSSD-3
$(\mathrm{r}=-0.33)$
satisfying the significance p-value threshold (
$\mathrm{p}\leq 0.01$
). All correlations were negative, showcasing that lower HRV is associated with worse blood glucose regulation. We observed that the longer the measurement period, the better the point-biserial correlation. The best class distribution separation based on the univariate threshold is achieved for SDRMSSD-3 with
$\text{ACC}=86.52\%$
and a weighted F1 score of 86.71%, making it stand out as the single most valuable HRV parameter when it comes to distinguishing good from bad blood glucose regulation.