Catheter ablation of atrial fibrillation (AF) is still challenging and the sustaining mechanisms are discussed controversially. Basket mapping has emerged to a promising technique to detect temporary events like focal impulses fast changing fibrillation waves or meandering rotors.The aim of this study was to evaluate the atrial coverage of the basket catheter with respect to the distance of the electrodes to the endocardial surface and inter spline separation.
A. Luik, C. Schilling, M. Merkel, K. Schmidt, O. Dössel, and C. Schmitt. Impact of energy and CFAE classification in patients with persistent atrial fibrillation - analysis by a newly developed automatic algorithm (fuzzy decision tree). In Clinical Research in Cardiology, vol. 101(s1) , 2012
A. Luik, C. Schilling, M. Merkel, O. Dössel, and C. Schmitt. Effect of Pulmonary Vein Isolation on the mean Fractionation and the mean dominant Frequency of the left atrium in Patients with Persistent Atrial Fibrillation. In Heart Rhythm, vol. 6(5S) , pp. 153, 2009
Student Theses (1)
C. Merkel. Using Variational Autoencoders to Encode and Augment Vectorcardiographic Signals. Institute of Biomedical Engineering, Karlsruhe Institute of Technology (KIT). Masterarbeit. 2024
Abstract:
Cardiovascular diseases are responsible for an estimated 17.9 million deaths each year, according to WHO. The electrocardiogram (ECG) stands out as the most widely employed method for assessing the heart’s condition, given its non-invasive nature and ease of recording. While there are millions of signals recorded every day, ECG data available for research and development of new analyzation tools is rare due to patient protection and the limited time of healthcare professionals. Therefore, much of today’s software development relies on a few dozen publicly available data sets, offering only a limited representation of the ECG signals recorded daily in clinics.In an effort to address the challenge of limited ECG data for research, this thesis explores a method for encoding and augmenting ECG data using machine learning. An autoencoder is trained on existing ECG data to acquire a compressed representation of ECG signals and extract essential components from them. These extracted elements serve as a condensed yet informative summary of the original signals. Subsequently, the aim is to adjust and augment given ECG signals by modifying the extracted features. For simplification of the used data, the vectorcardiographic (VCG) representation of ECG signals is employed, reducing the required leads from twelve to three. The generated signals are heartbeats aligned at the R-peak with a fixed time window size. The intention is for these individual heartbeats to be stitched together to create a longer signal in a subsequent step.During this project it was firstly aimed to find a Variational Autoencoder (VAE) model of low complexity, which is able to achieve an acceptable reconstruction loss. Consequently, the VAE was trained on a large data set to learn a compressed representation in the latent space, capable of reconstructing the signal to high accuracy. Afterwards, transfer learning was employed to retrain the VAE on signals from a single patient, to incorporate learned morphologies from other patients into this specific patient’s signal.I achieved a latent space representation in which each variable represents specific morpholo- gies of the VCG, with little correlation between variables. The research has shown that this machine learning approach has improved capabilities compared the principal component analysis (PCA) regarding the ability to construct and augment unseen VCG signals. Transfer learning proved to be helpful for achieving better generalization and faster convergence of the model but the transfer of morphologies between patients did not meet the desired expec- tations. Lastly, a suggestion of potential improvements and further methods is presented, aiming to improve various aspects of the latent space.