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Computational and Mathematical Methods in Medicine

Volume 2012 (2012), Article ID 291510, 5 pages

http://dx.doi.org/10.1155/2012/291510

## Heavy-Tailed Prediction Error: A Difficulty in Predicting Biomedical Signals of Noise Type

^{1}School of Information Science & Technology, East China Normal University, No. 500, Dong-Chuan Road, Shanghai 200241, China^{2}Department of Computer and Information Science, University of Macau, Padre Tomas Pereira Avenue, Taipa, Macau

Received 31 October 2012; Accepted 20 November 2012

Academic Editor: Carlo Cattani

Copyright © 2012 Ming Li et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### Abstract

A fractal signal in biomedical engineering may be characterized by noise, that is, the power spectrum density (PSD) divergences at . According the Taqqu’s law, noise has the properties of long-range dependence and heavy-tailed probability density function (PDF). The contribution of this paper is to exhibit that the prediction error of a biomedical signal of noise type is long-range dependent (LRD). Thus, it is heavy-tailed and of noise. Consequently, the variance of the prediction error is usually large or may not exist, making predicting biomedical signals of noise type difficult.

#### 1. Introduction

Signals of noise type are widely observed in biomedical engineering, ranging from heart rate to DNA and protein, see, for example, [1–37], just to cite a few. Predicting such a type of signals is desired in the field [38–43]. A fundamental issue in this regard is whether a biomedical signal of noise type to be predicted is predicable or not.

The predictability of signals of non- noise type is well studied [44–48]. However, the predictability of noise is rarely reported, to our best knowledge. Since many phenomena in biomedical engineering are characterized by noise [1–37], the predictability issue of noise is worth investigating.

Note that minimizing the mean square error (MSE) of prediction is a commonly used criterion in both theory and practical techniques of prediction, see, for example, [49–68]. Therefore, a sufficient condition for a biomedical signal to be predictable is that the variance of its predication error exists. If the variance of the predication error does not exist, on the contrary, it may be difficult to be predicted if not unpredictable. In the case of a signal being bandlimited, the variance of its predication error is generally finite. Consequently, it may be minimized and it is predictable. However, that is not always the case for biomedical signals of noise type.

Let be a biomedical signal in the class of noise. Then, its PDF is heavy-tailed, and it is LRD, see, for example, Adler et al. [69], Samorodnitsky and Taqqu [70], Mandelbrot [71], Li and Zhao [72]. Due to that, here and below, the terms, noise, LRD random function, and heavy-tailed random function are interchangeable.

Let be the PDF of a biomedical signal of noise type. Then, its variance is expressed by where is the mean of if it exists. The term of heavy tail in statistics implies that is large. Theoretically speaking, in general, we cannot assume that always exists [72]. In some cases, such as the Pareto distribution, the Cauchy distribution, -stable distributions [72], may be infinite. That does not exist is particularly true for signals in biomedical engineering and physiology, see Bassingthwaighte et al. [33] for the interpretation of this point of view.

Recall that a prediction error is a random function as we shall soon mention below. Therefore, whether the prediction error is of noise, or equivalently, heavy-tailed, turns to be a crucial issue we need studying. We aim at, in this research, exhibiting that prediction error of noise is heavy-tailed and accordingly is of noise. Thus, generally speaking, the variance of a prediction error of a biomedical signal of noise type may not exist or large. That is a reason why predicting biomedical signals of noise type is difficult.

The rest of this paper is organized as follows. Heavy-tailed prediction errors occurring in the prediction of biomedical signals of noise type are explained in Section 2. Discussions are in Section 3, which is followed by conclusions.

#### 2. Prediction Errors of Noise Type

We use to represent a biomedical signal in the discrete case for , where is the set of natural numbers. Let be a given sample of for . Denote by the predicted values of for . Then, the prediction error denoted by is given by

If one uses the given sample of for to obtain the predictions denoted by for , the error is usually different from (2), which implies that the error is a random variable. Denote by the PDF of . Then, its variance is expressed by where is the mean of .

Let be the operator of a predictor. Then, A natural requirement in terms of is that should be minimized. Thus, the premise that can be minimized is that it exists.

It is obviously seen that may be large if is heavy tailed. In a certain cases, may not exist. To explain the latter, we assume that follows a type of heavy-tailed distribution called the Pareto distribution.

Denote by the PDF of the Pareto distribution. Then [73], it is in the form where , , and . The mean and variance of are, respectively, expressed by The above exhibits that does not exist if or and if follows the Pareto distribution.

Note that the situation that does not exist may not occur if is light-tailed. Therefore, the question in this regard is whether is heavy-tailed if a biomedical signal is of noise. The answer to that question is affirmative. We explain it below.

Theorem 1. *Let be a biomedical signal of noise type to be predicted. Then, its prediction error is heavy-tailed. Consequently, it is of noise.*

*Proof. *Let be the autocorrelation function (ACF) of . Then,
where is lag and the mean operator. Let be the ACF of . Then,
Let be the ACF of . Then,

Note that
In the above expression, is the cross-correlation between and . On the other side, is the cross-correlation between and . Since , we have

Recall that is noise. Thus, it is heavy-tailed and hence LRD. Consequently, for a constant , we have
On the other hand, the predicted series is LRD. Thus, for a constant , the following holds:

In (11), if is summable, that is, it decays faster than or , it may be ignored for . In this case, is still non-summable. In fact, one has
On the other side, when is non-summable, is non-summable too. In any case, we may write by
Therefore, the prediction error is LRD. Its PDF is heavy-tailed according to the Taqqu’s law. Following [72], therefore, is a noise. This completes the proof.

#### 3. Discussions

The present result implies that cautions are needed for dealing with predication errors of biomedical signals of noise type. In fact, if specific biomedical signals are in the class of noise, the variances of their prediction errors may not exist or large [72]. Tucker and Garway-Heath used to state that their prediction errors with either prediction model they used are large [74]. The result in this paper may in a way provide their research with an explanation.

Due to the fact that a biomedical signal may be of noise, PDF estimation is suggested as a preparatory stage for prediction. As a matter of fact, if a PDF estimation of biomedical signal is light-tailed, its variance of prediction error exists. On the contrary, the variance of the prediction error may not exist. In the latter case, special techniques have to be considered [75–78]. For instance, weighting prediction error may be a technique necessarily to be taken into account, which is suggested in the domain of generalized functions over the Schwartz distributions [79].

#### 4. Conclusions

We have explained that the prediction error in predicting biomedical signals of noise type is usually LRD. This implies that its PDF is heavy-tailed and noise. Consequently, may in general be large. In some cases [72], may not exist, making the prediction of biomedical signals of noise type difficult with the way of minimizing .

#### Acknowledgments

This work was supported in part by the 973 plan under the Project Grant no. 2011CB302800 and by the National Natural Science Foundation of China under the Project Grant no. 61272402, 61070214, and 60873264.

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