journal article·2025-12-30Open access

Unveiling the Extremely Low Frequency Component of Heart Rate Variability

Krzysztof Adamczyk, Adam G. Polak

Applied Sciences· Volume 16· Issue 1

Abstract

Heart rate variability (HRV) comprises several components driven by various internal processes, the least understood of which is the ultra-low frequency (ULF) one. Recently published research has shown that the HRV frequency distribution in this range is bimodal. The main aims of this work were to verify this finding, to determine the basic characteristics of these two components and to analyze their potential physiological couplings. For this purpose, two components within the conventional ULF band (below 4 mHz) were extracted from HRVs of 25 patients with apnea using adaptive variational mode decomposition (AVMD) and continuous wavelet transform (CWT), and then analyzed with the Hilbert transform (HT), Savitzky–Golay filter, and empirical distributions of instantaneous amplitudes and frequencies. These studies have demonstrated the existence of both components in HRVs of all subjects and apnea groups: extremely low frequencies (ELFs) in the range of 0.01–0.4 mHz and narrowed ultra-low frequencies (nULFs) in the range of 0.1–4 mHz. The independence of both components is also shown. Concluding, heart rate variability is separately regulated by circadian rhythms (ELF bound) and ultradian fluctuations (nULF bound), which can be assessed by decomposing HRV, and the obtained components may be helpful to better understand the underlying homeostatic mechanisms, as well as in the long-term monitoring of patients.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Non-Invasive Vital Sign Monitoring · Heart Rate Variability and Autonomic Control · Healthcare Technology and Patient Monitoring

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Faculty of Electronics, Photonics and Microsystems, Wrocław University of Science and Technology, 50-372 Wrocław, Poland

Funding

National Science Centre, Poland

References (60)

Show source-provided references
1. Shaffer, F., and Ginsberg, J.P. (2017). An overview of heart rate variability metrics and norms. Front. Public Health, 5.10.3389/fpubh.2017.00258 ↗
2. Sieciński, S., Kostka, P.S., and Tkacz, E.J. (2020). Heart rate variability analysis on electrocardiograms, seismocardiograms and gyrocardiograms on healthy volunteers. Sensors, 20.10.3390/s20164522 ↗
3. Heart rate variability and cognitive performance in adults with cardiovascular risk
4. Mejía-Mejía, E., and Kyriacou, P.A. (2022). Photoplethysmography-Based Pulse Rate Variability and Haemodynamic Changes in the Absence of Heart Rate Variability: An In-Vitro Study. Appl. Sci., 12.10.3390/app12147238 ↗
5. Sieciński, S., Tkacz, E.J., and Kostka, P.S. (2023). Heart rate variability analysis on electrocardiograms, seismocardiograms and gyrocardiograms of healthy volunteers and patients with valvular heart diseases. Sensors, 23.10.3390/s23042152 ↗
6. Which indices of heart rate variability are the best predictors of mortality after acute myocardial infarction? Meta-analysis of observational studies10.1016/j.jelectrocard.2024.03.006 ↗
7. Georgieva-Tsaneva, G., Lebamovski, P., and Tsanev, Y.-A. (2025). Impact of Prolonged High-Intensity Training on Autonomic Regulation and Fatigue in Track and Field Athletes Assessed via Heart Rate Variability. Appl. Sci., 15.10.3390/app151910547 ↗
8. Nelushi, A.M., Manathunga, C.H., Gamage, N.G.S.S., and Nakayama, T. (2025). Assessing Cardiac Sympatho-Vagal Balance Through Wavelet Transform Analysis of Heart Rate Variability. Appl. Sci., 15.10.20944/preprints202501.0978.v1 ↗
9. Application of Taylor-Fourier Analysis to Photoplethysmography Signals for Instantaneous Heart Rate Measurement10.1109/TIM.2025.3621748 ↗
10. Impact of signal length and window size on heart rate variability and pulse rate variability metrics10.1088/1361-6579/adece2 ↗
11. Yuda, E., and Yoshida, Y. (2025). Influence of Posture and Sleep Duration on Heart Rate Variability in Older Subjects. Appl. Sci., 15.10.20944/preprints202502.0540.v1 ↗
12. Kiyono, K., Hayano, J., Kwak, S., Watanabe, E., and Yamamoto, Y. (2012). Non-Gaussianity of low frequency heart rate variability and sympathetic activation: Lack of increases in multiple system atrophy and Parkinson disease. Front. Physiol., 3.10.3389/fphys.2012.00034 ↗
13. Martino, P.F., Miller, D.P., Allen, M.T., Handy, J.D., and Servatius, R.J. (2020). Cardiorespiratory Response to Moderate Hypercapnia in Female College Students Expressing Behaviorally Inhibited Temperament. Front. Neurosci., 14.10.3389/fnins.2020.588813 ↗
14. Comparison of multiband filtering, empirical mode decomposition and short-time Fourier transform used to extract physiological components from long-term heart rate variability10.24425/mms.2021.137700 ↗
15. Adamczyk, K., and Polak, A.G. (2021, January 1–5). Derivation of frequency components from overnight heart rate variability using an adaptive variational mode decomposition. Proceedings of the 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Mexico City, Mexico.10.1109/EMBC46164.2021.9630414 ↗
16. Polak, A.G., Klich, B., Saganowski, S., Prucnal, M.A., and Kazienko, P. (2022). Processing photoplethysmograms recorded by smartwatches to improve the quality of derived pulse rate variability. Sensors, 22.10.20944/preprints202208.0479.v1 ↗
17. Adamczyk, K., and Polak, A.G. (2025). Online Algorithm for Deriving Heart Rate Variability Components and Their Time–Frequency Analysis. Appl. Sci., 15.10.3390/app15031210 ↗
18. Improving the quality of pulse rate variability derived from wearable devices using adaptive, spectrum and nonlinear filtering10.1016/j.bspc.2024.107336 ↗
19. Duration of photoplethysmographic signals for the extraction of Pulse Rate Variability Indices10.1016/j.bspc.2022.104214 ↗
20. Heart Rate Variability (HRV)
21. PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals10.1161/01.CIR.101.23.e215 ↗
22. Kropotov, J.D. (2022). The enigma of infra-slow fluctuations in the human EEG. Front. Hum. Neurosci., 16.10.3389/fnhum.2022.928410 ↗
23. Routine and sleep EEG: Minimum recording standards of the International Federation of Clinical Neurophysiology and the International League Against Epilepsy10.1111/epi.17448 ↗
24. On the Kolmogorov-Smirnov Test for Normality with Mean and Variance Unknown10.1080/01621459.1967.10482916 ↗
25. Automated diagnosis of coronary artery disease using scalogram-based tensor decomposition with heart rate signals10.1016/j.medengphy.2022.103811 ↗
26. Smoothing and differentiation of data by simplified least squares procedures10.1021/ac60214a047 ↗
27. Why and How Savitzky–Golay Filters Should Be Replaced10.1021/acsmeasuresciau.1c00054 ↗
28. Overcoming the negative frequencies-instantaneous frequency and amplitude estimation using osculating circle method10.51400/2709-6998.2165 ↗
29. Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing10.1111/j.2517-6161.1995.tb02031.x ↗
30. Evans, J.D. (1996). Straightforward Statistics for the Behavioral Sciences, Brooks/Cole Publishing.
31. Clinical guideline for the evaluation, management and long-term care of obstructive sleep apnea in adults10.5664/jcsm.27497 ↗
32. Generation of circadian rhythms in the suprachiasmatic nucleus10.1038/s41583-018-0026-z ↗
33. Dijk, D.J., and Landolt, H.P. (2019). Sleep physiology, circadian rhythms, waking performance and the development of sleep-wake therapeutics. Sleep-Wake Neurobiology and Pharmacology, Springer.10.1007/978-3-030-11272-1 ↗
34. Core body temperature changes in school-age children with circadian rhythm sleep–wake disorder10.1016/j.sleep.2021.08.026 ↗
35. Circadian rhythmicity of body temperature and metabolism10.1080/23328940.2020.1743605 ↗
36. Circadian rhythms and the gastrointestinal tract: Relationship to metabolism and gut hormones10.1210/endocr/bqaa167 ↗
37. Endocrine regulation of circadian rhythms10.1038/s44323-025-00024-6 ↗
38. Gerkema, M.P. (2002). Ultradian rhythms. Biological Rhythms, Springer.10.1007/978-3-662-06085-8_17 ↗
39. Evidence for a coupled oscillator model of endocrine ultradian rhythms10.1177/0748730418791423 ↗
40. MECHANISMS IN ENDOCRINOLOGY: Does circadian and ultradian glucocorticoid exposure affect the brain?10.1530/EJE-18-0853 ↗
41. Circadian and ultradian rhythms: Clinical implications10.1111/joim.13795 ↗
42. Ultradian rhythms in heart rate variability and distal body temperature anticipate onset of the luteinizing hormone surge10.1038/s41598-020-76236-6 ↗
43. Ultradian Hemodynamics and Autonomic-Central Nervous System Activity During Sleep: A Pilot Study with Insights for Hypertension10.1089/imr.2021.0031 ↗
44. Thermoregulation and the ultradian basic rest–activity cycle10.1016/B978-0-444-63912-7.00022-9 ↗
45. Heart rate and body temperature relationship in children admitted to PICU-A machine learning approach10.1109/TBME.2025.3541978 ↗
46. Blood pressure variability and cardiovascular disease: Systematic review and meta-analysis10.1136/bmj.i4098 ↗
47. Short-to long-term blood pressure variability: Current evidence and new evaluations10.1038/s41440-023-01199-w ↗
48. Soni, A., and Rawal, K. (2019, January 12–13). A review on physiological signals: Heart rate variability and skin conductance. Proceedings of the First International Conference on Computing, Communications, and Cyber-Security (IC4S 2019), Gdańsk, Poland.
49. Nechyporenko, A., Frohme, M., Strelchuk, Y., Omelchenko, V., Gargin, V., Ishchenko, L., and Alekseeva, V. (2024). Galvanic Skin Response and Photoplethysmography for Stress Recognition Using Machine Learning and Wearable Sensors. Appl. Sci., 14.10.3390/app142411997 ↗
50. Heart rate variability as a potential non-invasive marker of blood glucose level10.1134/S0362119721020031 ↗
51. Chemoreflexes, sleep apnea, and sympathetic dysregulation10.1007/s11906-014-0476-2 ↗
52. Chronic intermittent hypoxia in obstructive sleep apnea: A narrative review from pathophysiological pathways to a precision clinical approach10.1007/s11325-019-01967-4 ↗
53. Sleep and autonomic nervous system10.1016/j.cophys.2020.01.002 ↗
54. Reproducibility in computational sleep research: A call for action10.1093/sleep/zsad143 ↗
55. Heart rate variability in adults with obstructive sleep apnea: A systematic review10.5935/1984-0063.20190082 ↗
56. Cardiac autonomic activity in obstructive sleep apnea: Time-dependent and spectral analysis of heart rate variability using 24-hour Holter electrocardiograms
57. Clinical implication of heart rate variability in obstructive sleep apnea syndrome patients10.1097/SCS.0000000000001782 ↗
58. Very low frequency component of heart rate variability as a marker for therapeutic efficacy in patients with obstructive sleep apnea: Preliminary study10.4103/jrms.JRMS_62_18 ↗
59. Attar, E.T. (2025). Detailed evaluation of sleep apnea using heart rate variability: A machine learning and statistical method using ECG data. Front. Neurol., 16.10.3389/fneur.2025.1636983 ↗
60. Polak, A.G., Prucnal, M.A., and Adamczyk, K. (2024, January 15–17). Analysis of the Relationship Between Electrodermal Activity and Blood Glucose Level in Diabetics. Proceedings of the International Work-Conference on Bioinformatics and Biomedical Engineering, Cham, Switzerland.10.1007/978-3-031-64629-4_21 ↗