M. Sachs. Investigating the use of machine learning for classifying atrial diseases based on p-wave simulations on a biatrial shape model. Institute of Biomedical Engineering, Karlsruhe Institute of Technology (KIT). Masterarbeit. 2021
Atrial fibrosis is a disease with collagen deposited into the atrial tissue that can sustaine electrical rotors and can cause atrial fibrillation (AFib). AFib is a possible cause of stroke, heart failure and sudden cardiac death. The number of patients with AFib will double in the next decades and until today, only 50 % of patients with AFib can be treated effectively. The goal of this thesis is to investigate whether the degree of fibrosis on the atria can be detected by analyzing certain features of the electrocardiogram (ECG). The ECG is used as it is a non-invasive, cheap examination procedure. The 12-lead ECG that is used in this thesis can be found in every hospital. Based on a shape model , various shapes are created from a mean shape to determine whether the changes of features extracted from the ECG later can be linked to the change of the volume fraction of fibrosis or if they change because a difference of the atrial shapes causes them. Then, parts of the atrial volume of the shape model are replaced with fibrotic tissue in steps of 5 % up to 45 % of the total atrial tissue. Based on an atrial surface mesh and a torso mesh, a transfer matrix is calculated and the 12-lead ECGs are determined. For this thesis, 80 variations of shapes were used with ten degrees of fibrosis on each shape. The ECGs are then analyzed and certain features of the ECG, such as the P-wave terminal force of the V1 lead (PTFV1) value, the P-wave duration and the volume of the atria, are extracted. The results of this thesis show a correlation of the extracted features with the degree of fibrosis. Depending on the regression model that is used, the R-squared value varies between 0.49 and 0.61 (with 1 being a perfect match between the predicted values and the true results) with an error (root mean square error, RMSE) of 5.89 % to 6.73 %. The results imply the ECG features change due to adding fibrosis to the differnet shapes. The PTFV1 value and the P-wave duration both grow due to a slower excitation of the fibrotic tissue compared to the surrounding tissue.
M. Sachs. Untersuchung und Analyse der Morphologie des Pulssignals aus dem PPG und cbPPG. Institut für Biomedizinische Technik, Karlsruher Institut für Technologie (KIT). Bachelorarbeit. 2018
Die vorliegende Arbeit untersucht die Signalmorphologie eines Photoplethysmogramms (PPG) und eines kamerabasierten Photoplethysmogramms (cbPPG). Aus dem Signal des PPGs werden medizinisch relevante Parameter extrahiert, die mit denen des cbPPGs ver- glichen werden können. Aufgrund unzureichender und nicht auswertbarer Ergebnisse des cbPPGs wird eine Simulation zur Untersuchung der Ursachen durchgeführt. Die Simulation ergibt den Schluss, dass insbesondere das Rauschen reduziert werden muss, um mit Hilfe des cbPPGs eine auswertbare Signalmorphologie zu erhalten. The present thesis examines the signal morphology of the pulse signal of a photoplethysmo- gram (PPG) and a camera-based photoplethysmogram (cbPPG). Medical relevant parameters of the PPG signal get extracted to be compared to those of the cbPPG. Because of insufficient and not evaluable results regarding the cbPPG, a simulation of the cbPPG signal is realized to analyze the cause. The result of the simulation is that mainly noise has to be reduced in order to get an analyzable cbPPG signal.