A Signal Processing Pipeline for Noninvasive Imaging of Ventricular Preexcitation
- 1 January 2005
- journal article
- research article
- Published by Georg Thieme Verlag KG in Methods of Information in Medicine
- Vol. 44 (04), 508-515
- https://doi.org/10.1055/s-0038-1634001
Abstract
Summary: Objectives: Noninvasive imaging of the cardiac activation sequence in humans could guide interventional curative treatment of cardiac arrhythmias by catheter ablation. Highly automated signal processing tools are desirable for clinical acceptance. The developed signal processing pipeline reduces user interactions to a minimum, which eases the operation by the staff in the catheter laboratory and increases the reproducibility of the results. Methods: A previously described R-peak detector was modified for automatic detection of all possible targets (beats) using the information of all leads in the ECG map. A direct method was applied for signal classification. The algorithm was tuned for distinguishing beats with an adenosine induced AV-nodal block from baseline morphology in Wolff-Parkinson-White (WPW) patients. Furthermore, an automatic identification of the QRS-interval borders was implemented. Results: The software was tested with data from eight patients having overt ventricular preexcitation. The R-peak detector captured all QRS-complexes with no false positive detection. The automatic classification was verified by demonstrating adenosine-induced prolongation of ventricular activation with statistical significance (p <0.001) in all patients. This also demonstrates the performance of the automatic detection of QRS-interval borders. Furthermore, all ectopic or paced beats were automatically separated from sinus rhythm. Computed activation maps are shown for one patient localizing the accessory pathway with an accuracy of 1 cm. Conclusions: The implemented signal processing pipeline is a powerful tool for selecting target beats for noninvasive activation imaging in WPW patients. It robustly identifies and classifies beats. The small beat to beat variations in the automatic QRS-interval detection indicate accurate identification of the time window of interest.Keywords
This publication has 15 references indexed in Scilit:
- Computationally Efficient Noninvasive Cardiac Activation Time ImagingMethods of Information in Medicine, 2005
- Atrial Noninvasive Activation Mapping of Paced Rhythm DataJournal of Cardiovascular Electrophysiology, 2003
- Noninvasive myocardial activation time imaging: a novel inverse algorithm applied to clinical ECG mapping dataIEEE Transactions on Biomedical Engineering, 2002
- Model-based imaging of cardiac electrical excitation in humansIEEE Transactions on Medical Imaging, 2002
- On modeling the Wilson terminal in the boundary and finite element methodIEEE Transactions on Biomedical Engineering, 2002
- Cardiac Electromagnetic Imaging as an Inverse ProblemElectromagnetics, 2001
- The use of the Hilbert transform in ECG signal analysisComputers in Biology and Medicine, 2001
- Application of high-order boundary elements to the electrocardiographic inverse problemComputer Methods and Programs in Biomedicine, 1999
- A new method for myocardial activation imagingIEEE Transactions on Biomedical Engineering, 1997
- Recommendations for standardization and specifications in automated electrocardiography: bandwidth and digital signal processing. A report for health professionals by an ad hoc writing group of the Committee on Electrocardiography and Cardiac Electrophysiology of the Council on Clinical Cardiology, American Heart Association.Circulation, 1990