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

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.

References

[1] Timmis A, Vardas P, Townsend N, et al. European society of cardiology: Cardiovascular disease statistics 2021. Eur Heart J. 2022; 43(8):716-799. doi: 10.1093/eurheartj/ehab892

[2] Chorin E, Hochstadt A, Schwartz AL, Matz G, Viskin S, Rosso R. Continuous heart rate monitoring for automatic detection of life-threatening arrhythmias with novel bio-sensing technology. Front Cardiovasc Med. 2021; 8:707621. doi: 10.3389/fcvm.2021.707621

[3] Zeid S, Buch G, Velmeden D, et al. Heart rate variability: Reference values and role for clinical profile and mortality in individuals with heart failure. Clin Res Cardiol. 2024; 113(9):1317-1330. doi: 10.1007/s00392-023-02248-7

[4] Materko W, Fernandes DF, Façanha CCR, et al. A machine learning approach to developing an accurate stratification of type 2 diabetes mellitus based on heart rate variability parameters using the K-means clustering technique in elderly women. Gazz Med Ital Arch Sci Med. 2024; 183:44-50. doi: 10.23736/S0393-3660.23.05096-9

[5] Materko W, Fernandes DF, Sadala MN, Pureza D, Alberto AAD, Pena FPS. Evaluation on heart rate variability parameters in elderly with type 2 diabetes mellitus using principal component analysis. Gazz Med Ital Arch Sci Med. 2022; 181:879-884. doi: 10.23736/S0393-3660.22.04782-9

[6] Gillinov S, Etiwy M, Wang R, et al. Variable accuracy of wearable heart rate monitors during aerobic exercise. Med Sci Sports Exerc. 2017; 49(8):1697-1703. doi: 10.1249/MSS.0000000000001284

[7] Materko W, Dos Reis Façanha CC, Guedes GC, et al. Temporal cross-correlation between Polar^®heart rate monitor interface board and ECG to measure RR interval at rest. Isokinet Exerc Sci. 2024; 32(1):59-64. doi: 10.3233/IES-230061

[8] Latino F, Tafuri F. Wearable sensors and the evaluation of physiological performance in elite field hockey players. Sports (Basel). 2024; 12(5):124. doi: 10.3390/sports12050124

[9] Trevizani GA, Nasario-Junior O, Benchimol-Barbosa PR, Silva LP, Nadal J. Cardiac autonomic changes in middle-aged women: Identification based on principal component analysis. Clin Physiol Funct Imaging. 2016; 36(4):269-273. doi: 10.1111/cpf.12222

[10] Perrone MA, Volterrani M, Manzi V, Barchiesi F, Iellamo F. Heart rate variability modifications in response to different types of exercise training in athletes. J Sports Med Phys Fitness. 2021; 61(10):1411-1415. doi: 10.23736/S0022-4707.21.12480-6

[11] Rafieisangari R, Shiri N. A multi-adaptive neuro-fuzzy inference system with variable thresholds for heartbeat classification. Artif Intell Health. 2024; 1(4):43-60. doi: 10.36922/aih.3367

[12] Nshimiyimana M, Niyogisubizo J, Ninteretse JD. Heartbeat classification using various machine learning models: A comparative study. Artif Intell Health. 2024; 1(4):61-72. doi: 10.36922/aih.3543

[13] Materko W, Miranda SAM, Bezerra THL, De Oliveira Figueira CAM. Heart rate variability in soccer players and the application of unsupervised machine learning. Explor Cardiol. 2025; 3:101241. doi: 10.37349/ec.2025.101241

[14] Materko W. Stratification fitness aerobic based on heart rate variability during rest by principal component analysis and K-means clustering. J Exerc Physiol. 2018; 21(1):91-101.

[15] Kaya IE, Pehlivanlı AÇ, Sekizkardeş EG, Ibrikci T. PCA based clustering for brain tumor segmentation of T1w MRI images. Comput Methods Programs Biomed. 2017; 140:19-28. doi: 10.1016/j.cmpb.2016.11.011

[16] Jolliffe IT, Cadima J. Principal component analysis: A review and recent developments. Philos Trans A Math Phys Eng Sci. 2016; 374(2065):20150202. doi: 10.1098/rsta.2015.0202

[17] Ikotun AM, Absalom E, Abualigah L, Abuhaija B, Jia H. K-means clustering algorithms: A comprehensive review, variants analysis, and advances in the era of big data. Inform Sci. 2023; 622:178-210. doi: 10.1016/j.ins.2022.11.139

[18] Sinaga KP, Yang MS. Unsupervised K-Means clustering algorithm. IEEE Access. 2020; 8:80716-80727. doi: 10.1109/access.2020.2992018

[19] Ishaque S, Khan N, Krishnan S. Trends in heart-rate variability signal analysis. Front Digit Health. 2021; 3:639444. doi: 10.3389/fdgth.2021.639444

[20] Kleiger RE, Stein PK, Bigger JT Jr. Heart rate variability: Measurement and clinical utility. Ann Noninvasive Electrocardiol. 2005; 10(1):88-101. doi: 10.1111/j.1542-474X.2005.10101.x

[21] Akselrod S, Gordon D, Ubel FA, Shannon DC, Barger AC, Cohen RJ. Power spectrum analysis of heart rate fluctuations: A quantitative probe of beat-to-beat cardiovascular control. Science. 1981; 213(4504):220-222. doi: 10.1126/science.6166045

[22] Kleiger RE, Miller JP, Bigger JT Jr., Moss AJ, The Multicenter Post-Infarction Research Group. Decreased heart rate variability and its association with increased mortality after acute myocardial infarction. Am J Cardiol. 1987; 59(4):256-262. doi: 10.1016/0002-9149(87)90795-8

[23] Sammito S, Thielmann B, Böckelmann I. Update: Factors influencing heart rate variability-a narrative review. Front Physiol. 2024; 15:1430458. doi: 10.3389/fphys.2024.1430458

[24] Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology. Heart rate variability: Standards of measurement, physiological interpretation and clinical use. Circulation. 1996; 93(5):1043-1065. doi: 10.1161/01.cir.93.5.1043

[25] Clifford GD, Mcsharry PE, Tarassenko L. Characterizing artefact in the normal human 24-hour RR time series to aid identification and artificial replication of circadian variations in human beat to beat heart rate using a simple threshold. Comput Cardiol. 2002; 29:129-132. doi: 10.1109/CIC.2002.1166724

[26] Chemla D, Young J, Badilini F, et al. Comparison of fast Fourier transform and autoregressive spectral analysis for the study of heart rate variability in diabetic patients. Int J Cardiol. 2005; 104(3):307-313. doi: 10.1016/j.ijcard.2004.12.018

[27] Staffini A, Svensson T, Chung UI, Svensson AK. Heart rate modeling and prediction using autoregressive models and deep learning. Sensors (Basel). 2021; 22(1):34. doi: 10.3390/s22010034

[28] Hayano J, Yuda E. Assessment of autonomic function by long-term heart rate variability: Beyond the classical framework of LF and HF measurements. J Physiol Anthropol. 2021; 40(1):21. doi: 10.1186/s40101-021-00272-y

[29] Jarczok MN, Weimer K, Braun C, et al. Heart rate variability in the prediction of mortality: A systematic review and meta-analysis of healthy and patient populations. Neurosci Biobehav Rev. 2022; 143:104907. doi: 10.1016/j.neubiorev.2022.104907

[30] Choi A, Shin H. Quantitative analysis of the effect of an ectopic beat on the heart rate variability in the resting condition. Front Physiol. 2018; 9:922. doi: 10.3389/fphys.2018.00922

[31] Immanuel S, Teferra MN, Baumert M, Bidargaddi N. Heart rate variability for evaluating psychological stress changes in healthy adults: A scoping review. Neuropsychobiology. 2023; 82(4):187-202. doi: 10.1159/000530376

[32] Ardashev A, Loskutov A, Passman R, Zhelyakov E, Rytkin E, Efimov I. Theoretical and practical aspects of the nonlinear dynamics’ methods of heart rate variability analyses in tachyarrhythmia patients underwent radiofrequency catheter ablation. Cardiovasc Eng Technol. 2025; 16(2):190-201. doi: 10.1007/s13239-024-00766-7

[33] Bauer A, Kantelhardt JW, Bunde A, et al. Phase-rectified signal averaging detects quasi-periodicities in non-stationary data. Physica A. 2006; 364:423-434. doi: 10.1016/j.physa.2005.08.080

[34] Bauer A, Kantelhardt JW, Barthel P, et al. Deceleration capacity of heart rate as a predictor of mortality after myocardial infarction: Cohort study. Lancet. 2006; 367(9523):1674-1681. doi: 10.1016/S0140-6736(06)68735-7

[35] Alberto AC, Pedrosa RC, Zarzoso V, Nadal J. Association between circadian Holter ECG changes and sudden cardiac death in patients with Chagas heart disease. Physiol Meas. 2020; 41(2):025006. doi: 10.1088/1361-6579/ab6ebc

[36] Materko W, Bartels R, Pecanha T, Lima JRP, Carvalho ARS, Nadal J. Maximum oxygen uptake prediction model based on heart rate variability parameters for young healthy adult males at rest. Open Access Biostat Bioinform. 2018; 2(1):1-7. doi: 10.31031/oabb.2018.02.000536

[37] Nasario-Junior O, Benchimol-Barbosa PR, Pedrosa RC, Nadal J. Assessment of autonomic function by phase rectification of RR-interval histogram analysis in Chagas disease. Arq Bras Cardiol. 2015; 104(6):450-455. doi: 10.5935/abc.20150032

[38] Greenacre M, Groenen PJF, Hastie T, et al. Principal component analysis. Nat Rev Methods Primers. 2022; 2(1):100. doi: 10.1038/s43586-022-00184-w

[39] Peres-Neto PR, Jackson DA, Somers KM. How many principal components? Stopping rules for determining the number of non-trivial axes revisited. Comput Stat Data Anal. 2005; 49(4):974-997. doi: 10.1016/j.csda.2004.11.005

[40] Cadima J, Jolliffe IT. Loadings and correlations in the interpretation of principal components. J Appl Stat. 1995; 22(2):203-214. doi: 10.1080/02664769524627

[41] Dalmaijer ES, Nord CL, Astle DE. Statistical power for cluster analysis. BMC Bioinformatics. 2022; 23(1):205. doi: 10.1186/s12859-022-04675-1

[42] Lawton M, Ben-Shlomo Y, May MT, et al. Developing and validating Parkinson’s disease subtypes and their motor and cognitive progression. J Neurol Neurosurg Psychiatry. 2018; 89(12):1279-1287. doi: 10.1136/jnnp-2018-318337

[43] Scitovski R, Sabo K, Martínez-Álvarez F, Ungar Š. Cluster Analysis and Applications. Berlin: Springer; 2021.

[44] Sneath PHA. A method for testing the distinctness of clusters: A test of the disjunction of two clusters in Euclidean space as measured by their overlap. Math Geol. 1975; 7(2):123-143. doi: 10.1007/BF02068305

[45] Holtrop J, Bhatt DL, Ray KK, et al. Impact of the 2021 European society for cardiology prevention guideline’s stepwise approach for cardiovascular risk factor treatment in patients with established atherosclerotic cardiovascular disease. Eur J Prev Cardiol. 2024; 31(6):754-762. doi: 10.1093/eurjpc/zwae038

[46] World Medical Association. World Medical Association Declaration of Helsinki: Ethical principles for medical research involving human subjects. JAMA. 2013; 310(20):2191-2194. doi: 10.1001/jama.2013.281053

[47] Rousseeuw PJ. Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. J Comput Appl Math. 1987; 20:53-65. doi: 10.1016/0167-8396(87)90125-7

[48] Raju S, Woo H, Fawzy A, et al. Decreased cardiac autonomic function is associated with higher exacerbation risk and symptom burden in chronic obstructive pulmonary disease. J COPD Found. 2023; 10(3):328-334. doi: 10.15326/jcopdf.2023.0410

[49] Sundas A, Contreras I, Navarro-Otano J, Soler J, Beneyto A, Vehi J. Heart rate variability over the decades: A scoping review. PeerJ. 2025; 13:e19347. doi: 10.7717/peerj.19347

[50] Osborne MT, Shin LM, Mehta NN, Pitman RK, Fayad ZA, Tawakol A. Disentangling the links between psychosocial stress and cardiovascular disease. Circ Cardiovasc Imaging. 2020; 13(8):e010931. doi: 10.1161/circimaging.120.010931

[51] Vaccarino V, Bremner JD. Stress and cardiovascular disease: An update. Nat Rev Cardiol. 2024; 21(9):603-616. doi: 10.1038/s41569-024-01024-y

[52] Faust O, Hong W, Loh HW, et al. Heart rate variability for medical decision support systems: A review. Comput Biol Med. 2022; 145:105407. doi: 10.1016/j.compbiomed.2022.105407

[53] Lu L, Zhu T, Morelli D, et al. Uncertainties in the analysis of heart rate variability: A systematic review. IEEE Rev Biomed Eng. 2024; 17:180-196. doi: 10.1109/rbme.2023.3271595

[54] Pasquini L, Noohi F, Veziris CR, et al. Dynamic autonomic nervous system states arise during emotions and manifest in basal physiology. Psychophysiology. 2023; 60(4):e14218. doi: 10.1111/psyp.14218

[55] Turcu AM, Ilie AC, Ștefăniu R, et al. The impact of heart rate variability monitoring on preventing severe cardiovascular events. Diagnostics (Basel). 2023; 13(14):2382. doi: 10.3390/diagnostics13142382