Zpracování elektrokardiografického signálu a stanovení SAECG

Abstract

This thesis deals with electrocardiographic signal processing with a focus on the detection of ventricular late potentials using signal-averaged electrocardiography (SAECG). The main objective of the study is to compare selected transformation methods for the reconstruction of orthogonal leads and to evaluate their impact on the resulting SAECG parameters. As part of the work, an application was designed and implemented in the MATLAB environment, enabling complete ECG signal processing, including filtering, R-peak detection, segmentation, averaging, and the calculation of late potential parameters (fQRS, LAS40, RMS40). Five transformation methods and two averaging techniques were analyzed. The results showed that the inverse Dower transformation achieved the highest accuracy in both orthogonal lead reconstruction and SAECG parameter detection, with a detection accuracy of 80.1~\% and an F1 score of 51.9~\%. No statistically significant difference was found between mean and median averaging in terms of correlation; however, median averaging demonstrated greater robustness to outliers. The influence of experimental conditions on SAECG parameters was minimal. The main contribution of this thesis is the development of a functional tool for SAECG analysis and a comprehensive comparison of transformation methods in terms of their accuracy and robustness.