Abstract:

The analysis of the heart rate turbulence (HRT) can be used to evaluate the risk of sudden cardiac death. For that purpose ectopic beats have to be classified. A method for automatic ectopic beat detection and classification based on a Support-Vector-Machine (SVM) was developed before. In this work an analysis similar to the HRT was carried out on the morphological features of the QRS complex and the T wave of the ECG following a ventricular premature contractions (VPC). A study of 56 subjects, suffering from a various number of ventricular premature contractions was conducted.

Abstract:

Die Analyse der Herz Raten Turbulenz (HRT) dient der Vorhersage eines ploetzlichen Herztodes. Hierzu muessen die ektopen Schlaege im EKG ausgewertet werden. Zur automatischen Detektion und Klassifikation ektoper Schlaege wurde 2010 eine Methode entwickelt, welche auf Basis einer Support-Vector-Machine (SVM) die Schlaege klassifiziert. Artefakte im EKG-Signal fuehren nicht selten zur Fehlklassifizierung, da sie nicht vollstaendig von ektopen Schlaegen unterschieden werden koennen. Um die Genauigkeit die HRT Analyse zu verbessern, wurde ein Algorithmus zur automatischen Unterscheidung von Artefakten und ektopen Schlaegen entwickelt.

Abstract:

Various types of heart disease are associated with structural remodeling of cardiac cells. In this work, we present a software framework for automated analyses of structures and protein distributions involved in excitation-contraction coupling in cardiac muscle cells (myocytes). The software framework was designed for processing sets of three-dimensional image stacks, which were created by fluorescent labeling and scanning confocal microscopy of ventricular myocytes from a rabbit infarction model. Design of the software framework reflected the large data volume of image stacks and their large number by selection of efficient and automated methods of digital image processing. Specifically, we selected methods with small user interaction and automated parameter identification by analysis of image stacks. We applied the software framework to exemplary data yielding quantitative information on the arrangement of cell membrane (sarcolemma), the density of ryanodine receptor clusters and their distance to the sarcolemma. We suggest that the presented software framework can be used to automatically quantify various aspects of cellular remodeling, which will provide insights in basic mechanisms of heart diseases and their modeling using computational approaches. Further applications of the developed approaches include clinical cardiological diagnosis and therapy planning.