By Giuseppe Nardulli, Sebastiano Stramaglia
Lawsuits of the convention held September 19-21, 2001 in Bari, Italy. This booklet, end result of the cross-disciplinary interplay between physicists biologists and physicians, covers numerous themes the place tools and methods rooted in physics are effectively utilized to research and to version biomedical facts.
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Additional info for Modelling Biomedical Signals
B) By studying the power spectrum of the increments we are able to circumvent the effects of the non-stationarity. Our results show that true scaling behavior is indeed observed for the power spectrum of the increments, both for the data and for the model (Fig. 2). (c) We calculate the probability density P(A) of the amplitudes A of the variations of interbeat intervals through the wavelet transform. It has been shown that the analysis of sequences of interbeat intervals with the wavelet transform 25 can reveal important scaling properties 26 for the distributions of the variations in complex nonstationary signals.
We further find that P(t) of the sleep state for the pooled data is consistent with an exponential distribution with a characteristic time r = 22 ± 1 min (Fig. 7b). In order to verify that P(t) of sleep state is better described by an exponential functional form rather than by a stretched exponential functional form, we fit these curves to the P(t) from pooled data. Using Levenberg-Marquardt method, we find that the stretched exponential form lead to worse fit. 7 x 10~ 2 , respectively. We also check the results by plotting log(| logP(t)\) versus logt (1) and find that the data are clearly more curved than when we plot logP(i) versus logt, indicating that an exponential form provides the best description of the data.
We find that the value of A does not change a or T. within statistical uncertainties. 4 Conclusions Our findings of a power-law distribution for wake periods and an exponential distribution for sleep periods are intriguing because the same sleep-control mechanisms give rise to two completely different types of dynamics—one without a characteristic scale and the other with. g. power law versus exponential) arises from the distinct number of microstates that can be explored by the sleep-wake system for these two states.