**Part 2**

**Medical Applications** 

24 Will-be-set-by-IN-TECH

166 Fourier Transform Applications

Urpo, S.; Bakhareva, N.M.; Zaitsev, V.V.; Stepanov, A.V. (1994). Comparison of mm-wave

Verwichte, E.; Foullon, C.; Van Doorsselaere, T. (2010). Spatial Seismology of a Large Coronal

Wigner, E. (1932). On the Quantum Correction For Thermodynamic Equilibrium. *Phys. Rev.*,

Zaitsev, V.V. & Stepanov, A.V. (1992). Towards the circuit theory of solar flares. *Solar Physics*, Vol. 139, No. 2, 343–356, ISSN 0038-0938, (DOI:10.1007/BF00159158). Zaitsev, V.V.; Stepanov, A.V.; Urpo, S.; Pohjolainen, S. (1998). LRC-circuit analog of

Zaitsev, V.V.; Kislyakov, A.G.; Urpo, S.; Shkelev E.I. (2001a). Observational evidence for

Zaitsev, V.V.; Kislyakov, A.G.; Stepanov, A.V.; Urpo, S.; Shkelev E.I. (2001b).

Zaitsev, V.V.; Kislyakov, A.G.; Urpo, S.; Stepanov, A.V.; Shkelev E.I. (2003). Spectral-Temporal

Zaitsev, V. V.; Kislyakov, A. G.; Kislyakova, K. G. (2008). Parametric resonance in the solar corona. *Cosmic Research*, Vol. 46, No. 4, 301–308, (DOI:10.1134/S0010952508040035). Zaitsev, V. V. & Kislyakov, A. G. (2006). Parametric excitation of acoustic oscillations

*Astronomy Reports*, Vol. 47, No. 10, 873–882, (DOI:10.1134/1.1618999). Zaitsev, V.V.; Kislyakov, A.G.; Stepanov, A.V.; Kliem, B.; Fürst, E. (2004). Pulsating

Vol. 40, No. 5, 749–759, (DOI:10.1103/PhysRev.40.749).

0038-0938, (DOI:10.1007/BF00681102).

Ville, J. (1948). *Câbles et Transm.*, Vol. 2A, 61.

*Astrophysics*, Vol. 337, 887–896.

(DOI:0033-8443/01/4409-0697\$25.00).

(DOI:0033-8443/01/441-2-0036\$25.00).

(DOI:10.1134/1.1738154).

(DOI:10.1134/S1063772906100076).

and X-ray diagnostics of flare plasma. *Solar Physics*, Vol. 154, No. 2, 317–334, ISSN

Loop Arcade from TRACE and EIT Observations of its Transverse Oscillations. *The Astrophysical Journal*, Vol. 717, No. 1, 458–467, (DOI:10.1088/0004-637X/717/1/458).

current-carrying magnetic loop: diagnostics of electric parameters. *Astronomy and*

energy accumulation and dissipation in coronal magnetic loops. *Izv. Vyssh. Uchebn. Zaved., Ser. Radiofiz.* (transl. as *Radiophys. & Quant. Electronics*), Vol. 44, No. 9, 697–709,

Low-frequency pulsations of coronal magnetic loops. *Izv. Vyssh. Uchebn. Zaved., Ser. Radiofiz.* (transl. as *Radiophys. & Quant. Electronics*), Vol. 44, No. 1-2, 36–52,

Evolution of Low-Frequency Pulsations in the Microwave Radiation of Solar Flares.

Microwave Emission from the Star AD Leo. *Astronomy Letters*, Vol. 30, 319–324,

in closed coronal magnetic loops. *Astronomy Reports*, Vol. 50, No. 10, 823–833,

**6** 

*Brazil* 

**Spectral Analysis of** 

*3Pará State University, Belém, PA,* 

**Heart Rate Variability in Women** 

*1Federal University of São Carlos, São Carlos, SP,* 

Ester da Silva1,2, Ana Cristina S. Rebelo1, Nayara Y. Tamburús2,

*2College of Health Sciences, Methodist University of Piracicaba, Piracicaba, SP,* 

Mariana R. Salviati2, Marcio Clementino S. Santos3 and Roberta S. Zuttin2

This chapter discusses heart rate variability (HRV) to understand autonomic mechanisms and the use of linear analysis tools for frequency domain measures of HRV and spectral analysis by fast Fourier transform (FFT), and describes some results found in women.

Heart activity is largely modulated by the autonomic nervous system (ANS), which promotes rapid adjustments in the cardiovascular system during different stimuli (i.e., physical exercise, mental stress and postural change) (Hainsworth, 1998). HRV is a noninvasive measure used to analyze the influence of the autonomic nervous system on the heart, providing information about both sympathetic and parasympathetic contributions to consecutive heart rate (HR) oscillations. It has been proposed that a decrease in HRV is a powerful predictor of morbidity and mortality resulting from arrhythmic complications. HRV decreases with age (Catai et al., 2002; Melo et al., 2005) as a consequence of parasympathetic reduction and predominance of sympathetic modulation (Lipsitz et al.,

The tool most commonly used in the frequency domain is spectral analysis, which consists of decomposing the HR variation in a given period into its fundamental oscillatory components, defining them by their frequency and amplitude. One of the mathematical algorithms most commonly used to determine the number, frequency and amplitude of these components is the FFT. The sum of all the components constitutes the so-called total power spectral density. Spectral analysis involves three distinct spectral components: 1) very low-frequency (VLF) fluctuations related to the renin-angiotensin system and thermoregulation; 2) low-frequency (LF) fluctuations related to the sympathetic and parasympathetic nervous systems and to baroreflex activity; and 3) high-frequency (HF) fluctuations associated with vagal activity (Longo & Correia, 1995; Task Force, 1996). The sympathovagal balance can also be expressed by the LF/HF ratio. Based on this analysis, it is possible to observe the predominance of one component over the other and the relationship between them, reflecting the autonomic modulation of the heart in the control

**1. Introduction** 

of HR.

1990; Longo & Correia, 1995; Akselrod, 1995).
