Alina Momot

Methods of weighted averaging of ECG signals using Bayesian inference and criterion function minimization

Abstract Averaging signals in time domain is one of the main methods of noise attenuation in biomedical signal processing in case of systems producing repetitive patterns such as electrocardiographic (ECG) acquisition systems. This paper presents a comprehensive study of weighted averaging of ECG signal. Presented methods use criterion function minimization, partitioning of input set of data in the time domain as well as Bayesian and empirical Bayesian framework. The existing methods are described together with their extensions. Performance of all presented methods is experimentally evaluated and compared with the traditional averaging by using arithmetic mean and well-known weighted averaging methods based on criterion function minimization (WACFM).

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.

On Using Energy Signatures in Protein Structure Similarity Searching

The analysis of small molecular substructures (like enzyme active sites) in the whole protein structure can be supported by using methods of similarity searching. These methods allow to search the 3D structural patterns in a database of protein structures. However, the well-known methods of fold similarity searching like VAST or DALI are not appropriate for this task. Methods that benefit from a dependency between a spatial conformation and potential energy of protein structure seem to be more supportive. In the paper, we present a new version of the EAST (Energy Alignment Search Tool) algorithm that uses energy signatures in the process of similarity searching. This makes the algorithm not only more sensitive, but also eliminates disadvantages of previous implementations of our EAST method.

Fuzzy Weighted Averaging of Biomedical Signal Using Bayesian Inference

In many types of biomedical signals there is a need of noise attenuation which, in case of systems producing repetitive patterns such as ECG acquisition systems, may be made by means of averaging signals. Traditional arithmetic averaging technique assumes the constancy of the noise power cycle-wise, however the most types of noise are not stationary. In reality the variability of noise power from cycle to cycle is observed, which constitutes a motivation for using methods of weighted averaging. This paper proposes a new weighted method incorporating Bayesian and empirical Bayesian inference and its extension using fuzzy systems with fuzzy partitioning of input data in the time domain. Performance of the presented methods is experimentally evaluated and compared with the traditional averaging by using arithmetic mean and other well known weighted averaging methods.

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.

Weighted Averaging of ECG Signal Using Criterion Function Minimization

Averaging signals in time domain is one of the main methods of noise attenuation in biomedical signal processing. This paper proposes a new weighted averaging method using criterion function minimization and based on partitioning of input set in time domain, which is a generalization of an existing method. Performance of the new method is experimentally compared with the traditional averaging by using arithmetic mean and another weighted averaging method based on criterion function minimization.

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.

Application of Adaptive Weighed Averaging to Digital Filtering of 2D Images

In many biomedical fields there is a need of digital image analysis. The images often contain some disturbances in addition to the useful data. These disturbances should be reduces (or even eliminated, if it is possible) in order to improve the quality of the analysis. One of the possible methods of noise attenuation is low-pass filtering such as arithmetic mean and its generalization, namely weighted mean filtering where the weights are tuned by some adaptive algorithm.

On application of input data partitioning to Bayesian weighted averaging of biomedical signals

Averaging in the time domain may be used for noise attenuation in case of biomedical signals with a quasi-cyclical character. Traditional arithmetic averaging technique assumes the constancy of the noise power cycle-wise, however, most types of noise are not stationary and the variability of noise power is observed. It constitutes a motivation for using methods of weighted averaging, in particular Bayesian weighted averaging. This paper presents the computational study of Bayesian weighted averaging with traditional (sharp) and fuzzy partition of the input data in the presence of non-stationary noise. There is presented the known empirical Bayesian weighted averaging method (EBWA), with the parameter p describing the probabilistic model, and its modification NBWA which eliminates the parameter. Both methods can be extended by partitioning of the input data. The performance of presented methods is experimentally evaluated for an analytical signal as well as a real ECG signal and compared with traditional arithmetic averaging method. However, the methods can be applied to any signal with a quasi-cyclical character. The aim of the paper is to show the influence of the type of partition as well as the number of parts on the quality of the averaged signal.