Title
Kalman filter with augmented measurement model: an ECG imaging simulation study
Abstract
ECG imaging is a non-invasive technique of characterizing the electrical activity and the corresponding excitation conduction of the heart using body surface ECG. The method may provide great opportunities in the planning of cardiac interventions and in the diagnosis of cardiac diseases. This work introduces an algorithm for the imaging of transmembrane voltages that is based on a Kalman filter with an augmented measurement model. In the latter, a regularization term is integrated as additional "measurement". The filter is trained using a-priori-knowledge from a simulation model. Two effects are investigated: the influence of the training data on the reconstruction quality and the representation of a-priori knowledge in the trained covariance matrices. The proposed algorithm shows a promising quality of reconstruction and may be used in the future to introduce generic physiological knowledge in solutions of cardiac source imaging.
Year
DOI
Venue
2013
10.1007/978-3-642-38899-6_24
FIMH
Keywords
Field
DocType
promising quality,generic physiological knowledge,ecg imaging,ecg imaging simulation study,cardiac intervention,kalman filter,a-priori knowledge,body surface ecg,augmented measurement model,cardiac source imaging,cardiac disease
Training set,Simulation,Computer science,Matrix (mathematics),Voltage,Algorithm,Kalman filter,Regularization (mathematics),Ground truth,Covariance
Conference
Volume
ISSN
Citations 
7945
0302-9743
2
PageRank 
References 
Authors
0.38
7
6
Name
Order
Citations
PageRank
Walther H. W. Schulze1246.21
Francesc Elies Henar220.38
Danila Potyagaylo386.95
Axel Loewe41212.94
Matti Stenroos5687.12
Olaf Dössel626456.10