Biomedical subjects

Jacek M Leski

Publications and source records attributed to Jacek M Leski.

4 recordsLinked to original sources

PubMed2004

Computationally effective algorithm for robust weighted averaging.

One of the greatest disadvantages of the weighted signal averaging method is its sensitivity to the presence of noise and outliers in data and the need to estimate the noise variance in all signal cycles. The robust weighted averaging method based on the epsilon-insensitive loss function is free of these disadvantages, but has a very high computational burden and requires a choice of the insensitivity parameter epsilon. In this study, a new computationally effective algorithm for robust weighted averaging with automatic adjustment of the insensitivity parameter is introduced.

Algorithms↗

PubMed2004

Epsilon-insensitive fuzzy c-regression models: introduction to epsilon-insensitive fuzzy modeling.

This paper introduces a new epsilon-insensitive fuzzy c-regression models (epsilonFCRM), that can be used in fuzzy modeling. To fit these regression models to real data, a weighted epsilon-insensitive loss function is used. The proposed method make it possible to exclude an intrinsic inconsistency of fuzzy modeling, where crisp loss function (usually quadratic) is used to match real data and the fuzzy model. The epsilon-insensitive fuzzy modeling is based on human thinking and learning. This method allows easy control of generalization ability and outliers robustness. This approach leads to c simultaneous quadratic programming problems with bound constraints and one linear equality constraint. To solve this problem, computationally efficient numerical method, called incremental learning, is proposed. Finally, examples are given to demonstrate the validity of introduced approach to fuzzy modeling.

Journal Article↗

PubMed2004

An epsilon-margin nonlinear classifier based on fuzzy if-then rules.

This paper introduces a new classifier design methods that are based on a modification of the classical Ho-Kashyap procedure. First, it proposes a method to design a linear classifier using the absolute loss rather than the squared loss that results in a better approximation of the misclassification error and robustness of outliers. Additionally, easy control of the generalization ability is obtained by minimization of the Vapnik-Chervonenkis dimension. Next, an extension to a nonlinear classifier by an ensemble averaging technique is presented. Each classifier is represented by a fuzzy if-then rule in the Takagi-Sugeno-Kang form. Two approaches to the estimation of parameters value are used: local, where each of the if-then rule parameters are determined independently and global where all rules are obtained simultaneously. Finally, examples are given to demonstrate the validity of the introduced methods.

Journal Article↗

PubMed2002

Robust weighted averaging.

Signal averaging is often used to extract a useful signal embedded in noise. This method is especially useful for biomedical signals, where the spectra of the signal and noise significantly overlap. In this case, traditional filtering techniques introduce unacceptable signal distortion. In averaging methods, constancy of the noise power is usually assumed, but in reality noise features a variable power. In this case, it is more appropriate to use a weighted averaging. The main problem in this method is the estimation of the noise power in order to obtain the weight values. Additionally, biomedical signals often contain outliers. This requires robust averaging methods. This paper shows that signal averaging can be formulated as a problem of minimization of a criterion function. Based on this formulation new weighted averaging methods are introduced, including weighted averaging based on criterion function minimization (WACFM) and robust epsilon-insensitive WACFM. Performances of these new methods are experimentally compared with the traditional averaging and other weighted averaging methods using electrocardiographic signal with the muscle noise, impulsive noise, and time-misalignment of cycles. Finally, an application to the late potentials extraction is shown.

Algorithms↗