AccScience Publishing / AIH / Online First / DOI: 10.36922/AIH025050006
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ORIGINAL RESEARCH ARTICLE

Stratifying autonomic nervous system regulation patterns in healthy men: A machine learning approach

Wollner Materko1,2*
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1 Department of Health, Faculty of of Health Sciences, Federal University of Amapá, Macapá, Amapá, Brazil
2 Department of Education, Faculty of Physical Education, Federal University of Amapá, Macapá, Amapá, Brazil
AIH, 025050006 https://doi.org/10.36922/AIH025050006
Received: 29 January 2025 | Revised: 24 June 2025 | Accepted: 30 June 2025 | Published online: 28 July 2025
© 2025 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )
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Abstract

Heart rate variability (HRV) is a critical non-invasive marker of autonomic nervous system regulation and plays an essential role in cardiovascular health. Individual differences in autonomic function necessitate the development of personalized health strategies. This study aimed to develop and validate a method that integrates principal component analysis (PCA) and K-means clustering to identify distinct patterns of autonomic regulation in healthy men using HRV data. A total of 80 young, healthy men (22.0 ± 2.8 years old, 65.2 ± 6.9 kg, and 171.0 ± 6.5 cm) were recruited, and their HRV data were analyzed using time-domain and frequency-domain parameters. PCA was applied to reduce the dimensionality of the HRV data, while K-means clustering was employed to identify distinct autonomic profiles. Silhouette index values were 0.397 for one cluster, 0.481 for two clusters, and 0.556 for three clusters, indicating that the three-cluster solution provided the best fit. Three statistically distinct and physiologically meaningful clusters were identified. Cluster 3 (n = 19) demonstrated significantly higher HRV parameters than cluster 1 (n = 33) and cluster 2 (n = 28) (p = 0.001). Post hoc analysis further confirms that cluster 1 differed significantly from both cluster 2 and cluster 3 (p = 0.001). Based on HRV characteristics, the clusters were characterized as “high vagal tone,” “intermediate vagal tone,” and “low vagal tone.” The “high vagal tone” cluster exhibited the strongest parasympathetic activity, while the “low vagal tone” cluster showed evidence of sympathetic predominance. This study demonstrates a robust approach for stratifying autonomic profiles, highlighting the potential of machine learning in advancing personalized cardiovascular health assessment.

Keywords
Heart rate variability
Autonomic nervous system
Machine learning
Principal component analysis
K-means clustering
Funding
This research was funded by the Amapá Research Support Foundation through its public call 003/2018, specifically within the “Research Program for the Unified Health System (SUS): management in Health-PPSUS.” The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Conflict of interest
The author declares no conflicts of interest.
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Artificial Intelligence in Health, Electronic ISSN: 3029-2387 Print ISSN: 3041-0894, Published by AccScience Publishing
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