# HURSTS THE HEART 12TH EDITION PDF

Hurst's the Heart has become a standard reference textbook in cardiology. It is a comprehensive review by experts in their respective areas of cardiology. hursts the heart 11e vol 1 - institute-of-health-and - hursts the heart 11e vol 1 document hursts the heart 11e vol 1 is available in various formats such as pdf. Congenital Heart Disease · Volume 5, Issue 2 Hurst's the Heart Manual of Cardiology, 12th Edition, McGraw‐Hill Companies. Ramy Badawi.

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These specificities enable a more accurate fit of the shape of HRV time series. They result in some additional parameters, which can easily be estimated and interpreted. These specificities of the model constitute an innovation in the description and analysis of HRV.

This implies that the fBm is not well specified for HRV.

## Download Hurst's the Heart Manual of Cardiology Thirteenth Edition PDF Online

Multifractal dynamics are thus more accurate in this framework [24, 31]. But the fact that fBm is not well suited to HRV can be explained by many other model specifications than the 2 sole multifractality of the process, like a time-dependent Hurst exponent [17] or a Lamperti transform of a fBm [18], which generalizes the mean-reverting Ornstein-Uhlenbeck process.

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A rapid look at some healthy and at rest HRV time series shows besides a mean reversion [11, 23]. As a consequence, we add in our model a mean reversion to the fractional process.

In the fBm model, one can link the Hurst exponent H to the autocorrelation of the process. This is true for the fBm but not for all fractional processes. If the increments of the process are not Gaussian, another interpretation should be made [7].

Indeed, the Hurst exponent is a scaling parameter and the scaling rule is established by two causes: the non-conditional probability distribution of the increments of the process and the dependence between these increments. In this paper, we show that the sole Hurst exponent is not relevant for detecting stress in newborns.

In these cases, we can rather base our analysis on the parameter of our model which depicts mean reversion. Indeed, whatever the size of the dataset, we always observe a strong rise in this parameter and we can even define a threshold for this parameter, discriminating stress and rest. The rest of the paper is structured as follows. We first present the mean-reverting fLsm model and the method for estimating its parameters.

Then, we present the results of our analysis on 40 patients. Finally, we discuss these results and conclude. Afterwards, we will focus on quantifying the significance of each estimated value by the mean of a statistical bootstrap.

Therefore, in this i. This is a consequence of the observation of real HRV time series, in which, when the signal decreases below respectively increases above the mean, it progressively decreases resp. The mean reversion thus occurs after a certain time spent by Xt below resp.

On the contrary, in a more traditional fractional Ornstein-Uhlenbeck model, the mean-reversion effect is activated as soon as the time series crosses the mean. We found some papers introducing a mean-reversion in a model of HRV, but it corresponds to the traditional Ornstein-Uhlenbeck model [11, 23].

The main differences with the present paper are thus: the activation of the mean-reversion by an integral of Xt or by Xt ,4 the presence or not of a fractional aspect in the underlying stochastic process and therefore the presence or not of an autocorrelation in it.

First, it has to be noted that the observed signal is not in continuous time. Moreover, we assume that the mean-reversion mechanism works similarly in both directions, up and down.

To make the estimation more direct, we choose a specific u. However, an arbitrary choice of u may lead to numerical errors in equations 3 and 4. Two numerical errors are possible: if u is too large in absolute value, the cosines may have negative values and make the calculation of the logarithm impossible; when u goes to zero, the 5 4.

Finally, we estimate H by the method of absolute moments [37, 17]. More specifically, we consider two phases in the experiment: an at-rest phase followed by a stress phase. As there are less observations in the second phase than in the other, say T2 observations, the estimates in this phase are less accu- rate.

We want to determine if the variation of the estimated parameters between the two phases is significant.

We obtain the p-values by a statistical bootstrap. More precisely, for each set of estimated parameters, we simulate 1, mean-reverting fLsm with the base parameter estimated in phase 1 and with as many observations as in the second phase.

We then infer the parameters of interest for each simulated trajectory and we build a discrete distribution for each estimator. The lower this p-value, the more significant the variation of parameters. In this bootstrapping procedure, the simulation of fLsm is made possible by the simulation of independent standard symmetric stable random variables, which are then weighted and summed as in the integral definition of the fLsm presented in equation 2.

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In other words, we simulate first the increments of the fLsm. However, the simulation of stable random variables is not straightfor- ward. Nevertheless, for sharply leptokurtic distributions, like the Cauchy distribution, we can choose a larger u, based for example on the inverse of high quantiles of the increments instead of their maximum.

The study involved only full-term infants, without prenatal and perinatal risk factors, ready for discharge from the maternity ward. The subjects were naive for iatrogenic stress stimuli. After visual inspection and artifact correction, the raw data were further used for analysis.

A third-degree polynomial detrending was used to eliminate existing trends in the obtained signal, before the data analysis.

Infants were fed and placed in supine position before the procedure to diminish external artifacts. The research was conducted in the maternity ward, by ensuring no interruptions and excessive noise pollution. The protocol included three parts: a dummy stimulation phase, b the heel stick phase, c the treatment phase. Only phases a and b are used in this work, each consisting of two subphases. Phase a starts with the first baseline phase lasting 10 minutes phase 1 , followed by simulating the heel stick procedure phase 2 , by intermittently pressing the heel in a way the standard heel stick blood drawing is performed.

The duration of phase 2 was chosen to be 90 seconds, which is the average time to perform the actual blood drawing. At the end of the second subphase is the start of phase b. It contains two subphases as well, the first subphase being the second baseline phase 3 , followed by the heel stick blood sampling phase 4 , which ends as the beginning of phase c.

## Hurst’s the Heart, 13th Edition [PDF] (Textbook and Manual of Cardiology)

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No abstract is available for this article. Citing Literature Number of times cited according to CrossRef: Related Information. Email or Customer ID. Forgot password? Old Password. New Password. Your password has been changed. Returning user.Huttlera 3, Osijek, Croatia. In this bootstrapping procedure, the simulation of fLsm is made possible by the simulation of independent standard symmetric stable random variables, which are then weighted and summed as in the integral definition of the fLsm presented in equation 2.

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Existing literature on fractal scaling analysis of HRV only relies on the estimation of a Hurst exponent. To make the estimation more direct, we choose a specific u.