Multiscale entropy (MSE) analysis was introduced in the 2002 to evaluate the complexity of a time series by quantifying its entropy over a range of temporal scales. The algorithm has been successfully applied in different research fields. Since its introduction, a number of modifications and refinements have been proposed, some aimed at increasing the accuracy of the entropy estimates, others a...

The mental status examination (MSE) is a component of all medical exams and may be viewed as the psychological equivalent of the physical exam. It is especially important in neurologic and psychiatric evaluations. The purpose is to evaluate, quantitatively and qualitatively, a range of mental functions and behaviors at a specific point in time. The MSE provides important information for diagnos...

This work generalizes the recently introduced univariate multiscale entropy (MSE) analysis to the multivariate case. This is achieved by introducing multivariate sample entropy (MSampEn) in a rigorous way, in order to account for both within- and cross-channel dependencies in multiple data channels, and by evaluating it over multiple temporal scales. The multivariate MSE (MMSE) method is shown ...

Mean Squared Error (MSE) has been the most widely used tool to solve the linear filter estimation or system identification problem. However, MSE gives biased results when the input signals are noisy. This paper presents a novel Error Whitening Criterion (EWC) to tackle the problem of linear system identification in the presence of additive white disturbances. We will motivate the theory behind ...

In this paper, a novel image denoising algorithm using M-band ridgelet transform is proposed for image denoising. The performance of the proposed method is tested on ultrasound images which are corrupted with Gaussian noise. The performance of the proposed method is compared with the existing ridgelet and curvelet transform in terms of peak-signal to noise ratio (PSNR) and mean square error (MS...

This paper proposes an affine projection algorithm (APA) with variable projection order using the mean-square error (MSE) criterion. The proposed algorithm determines the projection order at every iteration to use it in the update equation of the proposed APA. The MSE criterion is employed to decide the projection order reasonably. Moreover, this algorithm checks the steady-state condition to c...

In continuation to an earlier work, we further develop bounds on the mean-squared error (MSE) when estimating a deterministic parameter vector 0 in a given estimation problem, as well as estimators that achieve the optimal performance. The traditional Cramér–Rao (CR) type bounds provide benchmarks on the variance of any estimator of 0 under suitable regularity conditions, while requiring a prio...

About one-third of acute stroke patients may experience stroke-in-evolution, which is often associated with a worse outcome. Recently, we showed that multiscale entropy (MSE), a non-linear method for analysis of heart rate variability (HRV), is an early outcome predictor in non-atrial fibrillation (non-AF) stroke patients. We aimed to further investigate MSE as a predictor of SIE. We included 9...

One of the prime goals of statistical estimation theory is the development of performance bounds when estimating parameters of interest in a given model, as well as constructing estimators that achieve these limits. When the parameters to be estimated are deterministic, a popular approach is to bound the mean-squared error (MSE) achievable within the class of unbiased estimators. Although it is...