Classification of atrial ectopic origins into spatial segments based on the 12-lead ECG
The aim of this thesis is to study, whether the standard 12-lead ECG can be used to localize the origin of atrial ectopic beats.
A neural network shall be used to classify ECG signals into predefined spatial segments of the atria.
This work will involve the following steps:
*Literature research about:
-Atrial ectopic beats and their relation to changes in the ECG features
-Methods to split the atria into spatial segments
-Similar ML approaches to solve the inverse problem
-Design, training and validation of neural networks
*Splitting the atria into spatial segments
*Excitation simulations using Fast Marching
*Analysis of ECG variations and feature extraction
*Neural network design and training
*Application of to simulated data / validation
*(Application to measured data)