Age and Gender Impact on Heart Rate Variability towards Noninvasive Glucose Measurement

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 pap...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2023-10, Vol.23 (21), p.8697
Main Authors: Stojmenski, Aleksandar, Gusev, Marjan, Chorbev, Ivan, Tudjarski, Stojancho, Poposka, Lidija, Vavlukis, Marija
Format: Article
Language:English
Subjects:
Age groups
Aging
Analysis
Arrhythmia
Blood sugar
Cardiac arrhythmia
Cardiac patients
Electrocardiogram
Electrocardiography
Exercise
Gender differences
Glucose
glucose levels
Heart beat
Heart rate
heart rate variability
Hypotheses
Influence
Machine learning
Measurement
Medical research
Medicine, Experimental
Women
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Summary: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.
ISSN:1424-8220
1424-8220
DOI:10.3390/s23218697