Application of a New Method of Spectral Analysis for Detecting Synchronous Processes in Heliobiology
- 5 Downloads
Until now, the sum of periodic variations was the main carrier of information in biological and heliogeophysical signals. However, not all of the time series of interest contain dominant periodic components. The relaxation analysis used in this paper makes it possible to generalize the “spectral” formalism to signals that cannot be represented as a sum of a limited number of quasiperiodic components. An algorithm for filtering noise and long-period trends is developed based on the separation of the original signal into rapidly and slowly relaxing components. The main theorem that guarantees the operability of the algorithm is proven. A method for constructing an orthonormal basis whose components have a strictly defined relaxation time is described. The result of the signal expansion over this basis is called the relaxation spectrum. It can be used to divide the time series into signal-to-noise or oscillation-trend if there are no adequate Fourier or stochastic models. The hypothesis that geomagnetic rhythms with periods of about 7 and 9 days have the most significant influence on physiological indicators of biological objects (including those of the population as a whole), as was stated earlier by the heliobiologists, is confirmed based on an automatic algorithm for extracting reliable spectral peaks of population and heliogeomagnetic time series and detecting similar peaks using the Sørensen measure.
Keywords:spectral formalism generalization signal filtering orthonormal basis generation heliobiophysics
This work was supported by the Russian Foundation for Basic Research, project no. 15-04-02945.
- 1.Breus, T.K., Zenchenko, T.A., Stoilova, I., and Dimitrova, S., Heliogeomagnetic rhythms are indeed synchronizers of biological “clocks”, in UN/ESA/ NASA/JAXA/BAS Workshop on “The first results from the International Heliophysical Year 2007, Sozopol, Bulgaria, June 2–6, 2008, Sozopol, 2008, p. 70.Google Scholar
- 6.Halberg, F., Cornélissen, G., Bingham, Ch., et al., Chronomics: Imaging in time by phase synchronization reveals wide spectral–biospheric resonances beyond short rhythms, Neuroendocrinol. Lett., 2003, vol. 24, no. 5, pp. 355–380.Google Scholar
- 7.Halberg, F., Cornélissen, G., and Schwartzkopff, O., Quo vadis chronomics 2008: Measuring variability in us, among us and around us, in Proc. of the Conference “Noninvasive Methods in Cardiology”, Brno, Czech Republic, October 4–7, 2008, Halberg, F., Kenner, T., Fiser, B., and Siegelova, J., Eds., Brno, 2008a, pp. 16–25. http://web.fnusa.cz/files/kfdr2008/sbornik_ 2008.pdf.Google Scholar
- 9.Halberg, F., Cornélissen, G., Beaty, L.A., Otsuka, K., Watanabe, E., Sotern, R.B., Katinas, G.S., Chaplitski, D., Sanchez de la Pena, S., Ulmer V., Revilla, M., Zeeman, M., Schwartzkopff, O., and Singh, R.B., Phoenix research team, BIOKOS project team: Advances in chronomics in 2006–2008. Part 1: Concordance of rhythms of biosphere and heliogeophysical processes, Geofiz. Protsessy Biosfera, 2009, vol. 8, no. 2, pp. 43–74.Google Scholar
- 10.Vladimirskii, B.M., Kosmicheskaya pogoda i biosfera. Istoriya issledovanii i sovremennost' (Space Weather and the Biosphere. History of Research and Modern Times), Moscow: URSS, 2016.Google Scholar
- 11.Vladimirskii, B.M., Narmanskii, V.Ya., and Temuriants, N.A., Global rhythmics of the solar system in the terrestrial habitat, Biophysics, 1995, vol. 40, no. 4, pp. 731–736.Google Scholar