The effect of synchronisation of geomagnetic field variations and heartbeat parameters in humans: the possible role of the autonomic nervous system



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Abstract

BACKGROUND: … Variations in the geomagnetic field (GMF) represent a significant environmental factor with a considerable impact on human well-being and functional state, particularly in relation to the cardiovascular system. Concurrently, the biophysical mechanism underlying this influence, as well as its phenomenological manifestation across different spatial and temporal scales, remain poorly understood. In this study, we extend our investigation of the impact of synchronising human resting heart rate (HR) oscillations with GMF variations in the millihertz frequency range (periods of 3-40 min) ('biogeosynchronisation effect'), which we previously identified.

AIM: The objective of this study is to assess the potential involvement of the autonomic nervous system as a transmission link in the formation of the human body's response to fluctuations in GMF.

METHODS: During 2012-2024, 673 resting ECG R-to-R interval recording experiments were performed in eight practically healthy volunteers (Group 1, multiple recordings of each subject, lasting 100-120 min) and in a group of 39 subjects (Group 2, single recordings lasting 60 min each). The frequency of occurrence of the biogeosynchronisation effect of minute-by-minute time series of heart rate (HR) and statistical indices of heart rate variability (HRV) was compared. Cross-correlation and wavelet analysis methods were used

RESULTS: The distributions of the percentage of cases of synchronisation of SR parameters with the components of the GMF vector, obtained in general for the whole sample of experiments using the correlation analysis method, indicate a value of 32% for HR and 9-17% for HRV parameters. These values demonstrate a difference of twofold or greater. In accordance with the criterion of similarity of wavelet spectra, the effect of synchronisation was observed in 40% of cases for HR and in 24-28% for HRV parameters. The sample distributions obtained individually for each volunteer in Group 1 and cumulatively for all volunteers in Group 2 yielded analogous results. 

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Funding source. The work was carried out within the framework of the State Assignment of ITEB RAS No. 075-00224-24-03, State Assignment of IKI RAS, topic "Plasma", State Assignment of IBCP RAS (44.1. state topic number 0084-2019-004) and State Assignment of FGBUN FITC RAS Ural Branch No. 122011300469-7

Competing interests. The authors declare that they have no competing interests.

Author contribution. T.A. Zenchenko ‑ development of the research concept, data analysis, preparation and writing of the article; L.V. Poskotinova ‑ development of the research concept, data collection, editing of the article; N.I. Khorseva ‑ data collection, editing of the article; T.K. Breus ‑ literature review, collection and analysis of literary sources, writing of the text and editing of the article.

Acknowledgments. The results presented in this paper were obtained using geophysical data collected by Nurmijarvi and Borok observatories. The authors thank the Finnish Meteorological Institute and Borok Geophysical Observatory for providing the data and for their work within the INTERMAGNET project to disseminate high standards of geophysical observations. The authors also thank M.E. Diatroptov, A.A. Stankevich and A.E. Elfimova for their assistance in data collection.

 

TABLES

Table 1. List of volunteers of Group 1, anamnestic data and mean values of measured parameters. Mean values of indicators are given in the format Me (1 sq.; 3 sq.)

Номер

волонтера

Пол

Возраст

n

ЧСС

RMSSD

SDNN

AMO

SI

V1

ж

59

333

69.3

(65.2;

73.0)

21.9

(16.5;

27.9)

26.7

(20.9;

32.4)

59.8

(53.88;

66.0)

334.9

(239.6;

485.8)

V2

ж

45

165

61.6

(59.6;

63.6)

36.9

(29.4;

45.9)

31.7

(27.7;

35.5)

52.6

(47.3;

57.2)

197.1

(157.3;

234.8)

V3

ж

30

64

63.8

(60.2;

69.2)

45.3

(35.8;

52.9)

48.4

(41.3;

52.3)

38.7

(36.7;

44.0)

100.1

(83.4;

141.8)

V4

м

37

19

78.7

(75.6;

79.7)

18.8

(16.4;

21.6)

39.0

(36.0;

41.6)

46.6

(44.5;

48.3)

215.0

(185.5;

232.6)

V5

ж

53

10

80.1

(74.3;

80.3)

19.5

(17.5;

23.4)

28.0

(26.7;

30.4)

55.1

(51.8;

57.6)

308.8

(260.4;

358.7)

V6

м

59

10

62.5

(60.5;

63.1)

18.2

(16.5;

21.6)

25.0

(23.0

29.0)

60.9

(57.9;

63.6)

309.6

(249.0;

380.7)

V7

ж

42

11

71.9

(66.5;

73.5)

42.1

(36.2;

46.4)

45.8

(42.9;

49.9)

42.3

(38.5;

43.1)

128.6

(103.2;

146.6)

V8

ж

27

10

77.3

(73.5;

79.0)

40.6

(34.4;

47.4)

58.8

(53.8;

65.9)

33.8

(32.1;

35.8)

88.7

(73.9;

106.9)

 

Table 2. An example of the results of assessing the similarity of time series of physiological indicators and the GMF vector in the experiment is shown in Fig. 1 and 2.

ФП

|Ks|

Qx

ЧСС

6.53

0.522

RMSSD

3.65

0.574

SI

2.43

0.472

 

 

Fig. 1. Illustration of the correlation method for assessing the synchronization of physiological parameters HR, RMSSD, SI with variations in the X component of the GMF. (a) superposition of the original physiological parameters series (red) and the horizontal component of the GMF at the Borok geophysical station (BOXX, blue); (b) - superposition of the filtered time series; (c) - cross-correlation functions between the values of the physiological parameter and the GMF vector. Ks=−lg(p)*sign(r), where r is the value of the Spearman correlation coefficient, p is its level of statistical significance. The red dotted line corresponds to the boundary level of statistical significance p=0.0045 (|Ks|>2.35).

 

Fig. 2. Illustration of the method of comparing wavelet spectra. On the left are the wavelet spectra of the BOXX, HR, RMSSD, SI time series. On the right are the results of averaging the corresponding series on the ordinate axis.

 

Fig. 4. Sample distributions of the frequency of synchronisation of HR and HRV indices with the components of GMP for Group 1 volunteers according to the correlation method. The designations are as in Fig. 3.

 

Fig. 5. Sample distributions of the frequency of synchronisation  of HR and HRV indices with the components of HMP for Group 1 volunteers according to the wavelet spectra comparison method. The designations are as in Fig. 3.

Fig. 6. Sample distributions of the frequency of cases of synchronisation of HR and HRV indices with GMR components for volunteers of Group 2. (a) cross-correlation method of analysis was used; (b) the method of comparing wavelet spectra of time series was used. The designations are as in Fig. 3.

×

About the authors

T A Zenchenko

Space Research Institute, Russian Academy of Sciences

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

кандидат физикоматематических наук, ст. научный сотрудник

Russian Federation, Pushchino, Moscow Region

Lilia 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), Associate Professor, Chief Researcher

Russian Federation, Arkhangelsk

Nataliya Khorseva

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

Tamara Konstantinovna Breus

Email: breus36@mail.ru
ORCID iD: 0000-0003-4057-0844
Russian Federation

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