Motivation: Treating Atrial Fibrillation
Atrial fibtrillation (AF) is the most common form of atrial arrythmia. AF causes a higher risk of stroke, heart failure, a higher mortality, and often leads to a reduced quality of life.
Catheter ablation of AF is an established and well-tried method in the treatment of symptomatic AF. Despite the broad clinical experience and knowledge collected in extensive studies of clinical data, the exact mechanisms of AF and its formation, and the process of its elimination through catheter ablation are still not completely understood. This causes controversial discussion in the field.
The automatic detection and analysis of complex fractionated atrial electrograms (CFAEs) is essential to support the electrophysiologist during catheter intervention therapies.
Methods: Feature Extraction and Data Analysis The aim of this research project is to establish an objective, feature-based classification for CFAEs. Based on the classification, the likelihood of a signal to comply with a discussed theory on the formation of AF can then be calculated. To this end, signals are recorded during catheter ablation of patients with AF, and they are then retrospectively analized mathematically with methods of signal processing and multivariate data analysis. Besides patients with persistent AF, which are in the focus of this study, the study will also include the analysis of signals from patients with paroxysmal AF.
The eligibility for our position is: university degree in biomedical engineering, computer science or physics. KIT is pursuing a gender equality policy. Women are therefore particularly encouraged to apply. If qualified, handicapped applicants will be preferred.
Scholarship Deadline: 20 Oct 2011
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