Synchronization between geomagnetic field variations and human heart rate parameters: possible role of autonomic nervous system

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Abstract

BACKGROUND: Geomagnetic field variations are a significant environmental factor influencing human well-being and physiological state, particularly the cardiovascular system. However, both the biophysical mechanisms underlying this influence and its phenomenological patterns across various spatiotemporal scales remain poorly understood. This study continues the investigation of the previously identified effect of synchronization between resting heart rate oscillations and geomagnetic field variations within the millihertz frequency range (periods of 3–40 minutes), referred to as the “biogeosynchronization effect.”

AIM: To evaluate the possible role of the autonomic nervous system as a mediating pathway in the human body’s response to geomagnetic field variations.

METHODS: From 2012 to 2024, a total of 673 experiments involving resting-state electrocardiographic interval recordings were conducted in two groups: eight healthy volunteers (group 1), each undergoing multiple sessions lasting 100–120 minutes, and a cohort of 39 individuals (group 2), each with a single 60-minute session. The frequency of biogeosynchronization effects in minute-by-minute time series of heart rate and heart rate variability parameters was compared. Cross-correlation and wavelet analysis methods were employed.

RESULTS: Across the entire dataset, synchronization between heart rate parameters and components of the geomagnetic field vector occurred in 32% of cases, whereas heart rate variability parameters showed synchronization in only 9%–17%, according to correlation analysis, representing a two-fold or greater difference. Based on wavelet spectrum similarity, heart rate synchronization was observed in 40% of cases and heart rate variability parameters synchronization in 24%–28%. Individual distributions for each subject in group 1 and pooled results for group 2 revealed similar patterns.

CONCLUSION: The biogeosynchronization effect appears significantly more frequently in heart rate changes (p < 0.001) than in heart rate variability parameters, both in repeated individual recordings and in group-level analysis.

About the authors

Tatiana A. Zenchenko

Institute of Theoretical and Experimental Biophysics of the Russian Academy of Sciences; Space Research Institute of Russian Academy of Sciences

Author for correspondence.
Email: zench@mail.ru
ORCID iD: 0000-0002-0520-2029
SPIN-code: 8974-6685

Dr. Sci. (Biology), Cand. Sci. (Physics and Mathematics)

Russian Federation, 3 Institutskaya st, Pushchino, Moscow region, 142290; Moscow

Liliya V. Poskotinova

N. Laverov Federal Center for Integrated Arctic Research of the Ural Branch of the Russian Academy of Sciences

Email: liliya200572@mail.ru
ORCID iD: 0000-0002-7537-0837
SPIN-code: 3148-6180

Dr. Sci. (Biology) MD, Cand. Sci. (Medicine), Assistant Professor

Russian Federation, Arkhangelsk

Nataliya I. Khorseva

Institute of Biochemical Physics of the Russian Academy of Sciences

Email: sheridan1957@mail.ru
ORCID iD: 0000-0002-3444-0050

Cand. Sci. (Biology)

Russian Federation, Moscow

Tamara K. Breus

Space Research Institute of Russian Academy of Sciences

Email: breus36@mail.ru
ORCID iD: 0000-0003-4057-0844
SPIN-code: 1267-8561

Dr. Sci. (Physics and Mathematics)

Russian Federation, Moscow

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2. Fig. 1. Illustration of the correlation-based method for assessing synchronization of physiological parameters—heart rate (HR), RMSSD, and SI—with variations in the X component of the geomagnetic field (GMF): (a), superimposed raw time series of physiological parameters (red) and the horizontal GMF component from the Borok geophysical station (BOXX, blue); (b), superimposed filtered time series; (c), cross-correlation functions between values of each physiological parameter and the GMF component. Ks=–log₁₀(p)×sign(r), where r is the Spearman rank correlation coefficient and p is its statistical significance level. The red dashed line indicates the threshold of statistical significance at p=0.0045 (|Ks| >2.35).

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3. Fig. 2. Illustration of the wavelet spectrum comparison method. Left: wavelet spectra of BOXX geomagnetic field, heart rate (HR), RMSSD, and SI time series. Right: mean spectra of corresponding series along the ordinate axis.

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4. Fig. 3. Cumulative distribution of the frequency of biogeophysical synchronization between heart rate (HR) and heart rate variability (HRV) parameters with each horizontal component of the geomagnetic field (GMF) across all experiments: (a), cross-correlation analysis; (b), wavelet spectral similarity analysis. *p <0.05; **p <0.01; ***p <0.001. Asterisks next to the HRV parameter bars indicate the level of statistical significance for differences in synchronization frequency between HR and the respective HRV parameter with each GMF component.

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5. Fig. 4. Sample distributions of synchronization frequency between heart rate (HR) and heart rate variability (HRV) parameters with geomagnetic field components for group 1 volunteers using the correlation method. Legend is identical to that in Fig. 3.

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6. Fig. 5. Sample distributions of synchronization frequency between heart rate (HR) and heart rate variability (HRV) parameters with geomagnetic field components for group 1 volunteers using the wavelet spectrum comparison method. Legend is identical to that in Fig. 3.

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7. Fig. 6. Sample distributions of the frequency of synchronization events between heart rate (HR) and heart rate variability (HRV) parameters with components of the geomagnetic field for group 2 volunteers: (a), cross-correlation analysis; (b), wavelet spectrum comparison method. Legend is identical to that in Fig. 3.

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