References

TSAS Version 3.01.01b

1

Yamamoto Y, Hughson RL. Coarse graining spectral analysis: new method for studying heart rate variability. J. Appl. Physiol. 71: 1143 - 1150, 1991.

2

Yamamoto Y, Hughson RL. Extracting fractal components from time series. Physica 68D: 250 - 264, 1993.

3

Yamamoto Y, Hughson RL, Peterson JC. Autonomic control of heart rate during exercise studied by heart rate variability spectral analysis. J. Appl. Physiol. 71: 1136 - 1142, 1991.

4

Yamamoto Y, Hughson RL, Nakamura Y. Autonomic nervous system responses to exercise in relation to ventilatory threshold. Chest 101: 206S - 210S, 1992.

5

Yamamoto Y, Hughson RL. On the fractal nature of heart rate variability in humans: effects of data length and beta-adrenergic blockade. Am. J. Physiol. 266: R40 - R49, 1994.

6

Grassberger P, Procaccia I. Measuring the strangeness of strange attractors. Physica 9D: 189 - 208, 1983.

7

King CC. Fractal and chaotic dynamics in nervous systems. Prog. Neurobiol. 36: 279 - 308, 1991.

8

Albano AM et al. Singular value decomposition and the Grassberger-Procaccia algorithm. Phys. Rev. A 38: 3017 - 3026, 1988.

9

Yamamoto Y et al. Operation Everest II: An indication of deterministic chaos in human heart rate variability at simulated extreme altitude. Biol. Cybern. 69: 205 - 212, 1993.

10

Holzfuss J, Mayer-Kress G. An approach to errorestimation in the application of dimension algorithms. In: Dimension and entropies in chaotic systems. Ed by Mayer-Kress G. Springer-Verlag, Berlin Heidelberg, 1986, pp114 - 122.

11

Barnsley MF et al. The science of fractal images. Springer-Verlag, New York, 1988.

12

Sugihara G, May RM. Nonlinear forecasting as a way of distinguishing chaos from measurement error in time series. Nature 344: 734 - 741, 1990.

13

Wales DJ. Calculating the rate of loss of information from chaotic time series by forecasting. Nature 350: 485 - 488, 1991.

14

Wolf A. Determining Lyapunov exponents from a time series. Physica 16D: 285 - 317, 1985.

15

Novak P, Novak V. Time/frequency mapping of the heart rate, blood pressure and respiratory signals. Med. & Biol. Eng. & Comput. 31: 103 - 110, 1993.

16

Blaber AP, Yamamoto Y, Hughson RL. Methodology of spontaneous baroreflex relationship assessed by surrogate data analysis. Am. J. Physiol. 268: H1682 - H1687, 1995.

17

For DOS users: As TSAS performs many floating point operations, the use of a math-coprocessor is recommended. Sometimes, running one of the sample batch files takes more than 10 min without a math-coprocessor, i.e., at least 10 times longer than with a math-coprocessor. The situation is manifested in the case of nonlinear analyses: the difference between 2 hours with and >20 hours without a coprocessor is substantial.