Michał Momot

Improving performance of protein structure similarity searching by distributing computations in hierarchical multi-agent system

Since protein structure similarity searching is very complex and time-consuming, one of the possible acceleration methods is parallelization by distributing the calculation on multiple computers. In the paper, we present a theoretical model of the hierarchical multi-agent system dedicated to the task of protein structure similarity searching. We also show results of several numerical experiments confirming a suitability of such distribution for the similarity searching performed for the Muconate Lactonizing Enzyme (PDB ID = 1MUC) from the Protein Data Bank (PDB) against the database containing almost thousand randomly chosen molecules.

Scalable system for protein structure similarity searching

One of the advantages of using multi-agent systems in solving many problems is their high scalability adequately to the demand for computing power. This important feature underlies our agent-based system for protein structure similarity searching. In this paper, we present the general architecture of the system, implementation details, communication between agents, distribution of databases, and user interface. Moreover, presented results of numerical experiments show that distributing the computational procedure across multiple computers results in significant acceleration of the search process.

Empirical Bayesian Approach to Weighted Averaging of ECG Signal Using Cauchy Distribution

The analysis of the electrocardiographic signal recordings is greatly useful in the screening and diagnosing of cardiovascular diseases. However usually recording of the electrical activity of the heart is performed in the presence of noise. One of the commonly used techniques to extract a useful signal distorted by a noise is weighted averaging, since the nature of ECG signal is quasi-cyclic with level of noise power varying from cycle to cycle. This paper proposes a new weighted averaging method, which incorporates empirical Bayesian inference and the expectation-maximization technique. It is an extension of an existing method by introducing Cauchy distribution and the unknown parameter is estimated using interquartile range. Performance of the new method is experimentally compared with the traditional averaging by using arithmetic mean and other empirical Bayesian weighted averaging methods.

Robust estimation of respiratory rate based on linear regression

Among the many parameters of human life, which are subject to intensive monitoring, one can specify the frequency of of respiratory action. The measurement of such physical quantity can be performed directly by tracking the activity of the respiratory organs, as well as indirectly through the breathing frequency estimation based on the ECG signal. This paper presents a method to assess the respiratory rate based on robust estimation of the regression function for QRS electrocardiogram signal in conjunction with the estimation of the spectral density of the time series.

Fuzzy Weighted Averaging Using Criterion Function Minimization

In this paper there is presented the computational study of fuzzy weighted averaging of data in the presence of non-stationary noise. There is proposed a new method, which is an extension of Weighted Averaging using Criterion Function Minimization (WACFM). In the method the weighting coefficients are fuzzy numbers instead of classical real numbers. The determining of these coefficients requires the extending of WACFM method for certain types of fuzzy numbers. In the presented case there is made an assumption of the triangle membership function for fuzzy coefficients. The performance of presented method is experimentally evaluated and compared with the traditional arithmetic averaging as well as Weighted Averaging using Criterion Function Minimization (WACFM) for the ECG signal.

R-R interval prediction for adaptive sensing of ECG signal using robust regression

The paper presents the concept of selective transmission of the ECG signal between the recorder located on the body of the monitored person and the mobile device. This concept is based on the possibility of energy saving by transmitting only necessary parts of signal. For the ECG, the most important data consist of waves P-QRS-T waves, located only in a neighborhood of the central point on timeline. This suggests methods of predicting the position of the center points of subsequent cardiac cycles in order to select fragments to be transmitted. The paper proposes use of the regression function to determine the positions of these points on the basis of a finite number of values recorded in recent history. The parameters of the regression function are selected using an algorithm of robust estimation.

An Application of Fuzzy C-Regression Models to Characteristic Point Detection in Biomedical Signals

This work introduces a new fuzzy c-regression models with various loss functions. The algorithm consists in solving a sequence of weighted quadratic minimization problems where the weights used for the next iteration depend on values of models residuals for the current iteration. Simulations on real-life ECG signals are realized to evaluate the performance of the fuzzy clustering method.

Granular representation of temporal signals using differential quadratures

This article presents the general idea of granular representation of temporal data, particularly signal sampled with constant frequency. The core of presented method is based on using fuzzy numbers as information granules. Three types of fuzzy numbers are considered, as interval numbers, triangular numbers and Gaussian numbers. The input space contains values of first few derivatives of underlying signal, which are computed using certain numerical differentiation algorithms, including polynomial interpolation as well as polynomial approximation. Data granules are constructed using the optimization method according to objective function based on two criteria: high description ability and compactness of fuzzy numbers. The data granules are subject to the clustering process, namely fuzzy c-means. The centroids of created clusters form a granular vocabulary. Quality of description is quantitatively assessed by reconstruction criterion. Results of numerical experiments are presented, which incorporate exemplary biomedical signal, namely electrocardiographic signal.

Adaptive Time-Varying Frequency Characteristic Filtering of ECG Signal

The primary diagnostic tools allowing the detection of cardiovascular system diseases through the study of bioelectric activity of the heart are the electrocardiographic systems. The low amplitude ECG signal is usually disturbed by various types of noise, particularly by power line interference, which should be reduced without distortion of useful signal component. Since the amplitude (sometimes also frequency) of such noise varies in time, the adaptive approach may be applied to construct time-varying frequency characteristic filter.