Subscribe to RSS
DOI: 10.1055/s-0038-1634001
A Signal Processing Pipeline for Noninvasive Imaging of Ventricular Preexcitation
Publication History
Publication Date:
06 February 2018 (online)
![](https://www.thieme-connect.de/media/10.1055-s-00035037/200504/lookinside/thumbnails/10-1055-s-0038-1634001-1.jpg)
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.
-
References
- 1 Punske BB. Noninvasive electrical imaging: is it ready for clinical application?. Journal of Cardiovascular Electrophysiology 2003; 14: 720-1.
- 2 Schilling RJ. Which patient should be referred to an electrocardiologist: supraventricular tachycardia. Heart 2002; 36: 299-304.
- 3 Yee R, Klein GJ, Prystowsky E. The Wolff-Parkinson- White syndrome and related variants. In Zipes DP, Jalife J. editors Cardiac Electrophysiology – From Cell to Bedside. Philadelphia: W.B. Saunders; 1999: 845-61.
- 4 Tilg B, Fischer G, Modre R, Hanser F, Messnarz B, Schocke M. et al Model-based imaging of cardiac electrical excitation in humans. IEEE Transactions on Medical Imaging 2002; 21: 1031-9.
- 5 Fischer G, Pfeifer B, Seger M, Hintermüller Ch, Hanser F, Modre R. et al Computationally efficient noninvasive cardiac activation time imaging. Methods Inf Med; accepted for publication
- 6 Greensite F. Cardiac electromagnetic imaging as an inverse problem. Electromagnetics 2001; 21: 559-77.
- 7 Modre R, Tilg B, Fischer G, Wach P. Noninvasive myocardial activation time imaging: a novel inverse algorithm applied to clinical ECG mapping data. IEEE Transactions on Biomedical Engineering 2002; 49: 1153-61.
- 8 Fischer G, Tilg B, Wach P, Modre R, Leder U, Nowak H. Application of high-order boundary elements to the electrocardiographic inverse problem. Computer Methods and Programs in Biomedicine 1999; 58: 119-31.
- 9 Fischer G, Tilg B, Modre R, Hanser F, Messnarz B, Wach P. On modeling the Wilson terminal in the boundary and finite element method. IEEE Transactions on Biomedical Engineering 2002; 49: 217-24.
- 10 Huiskamp G, Greensite F. A new method for myocardial activation imaging. IEEE Transactions on Biomedical Engineering 1997; 44: 433-46.
- 11 Modre R, Tilg B, Fischer G, Hanser F, Messnarz B, Schocke M. et al Atrial noninvasive activation mapping of paced rhythm data. Journal of Cardiovascular Electrophysiology 2003; 14: 712-9.
- 12 Bailey JJ, Berson AS, Garson Jr A, Horan LG, Macfarlane PW, Mortara DW. et al 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; 81: 730-9.
- 13 Benitez D, Gaydecki PA, Zaidi A, Fitzpatrick AP. The use of the Hilbert transform in ECG signal analysis. Computers in Biology and Medicine 2001; 31: 399-406.
- 14 Dokur Z, Ölmez T. ECG beat classification by a novel hybrid neural network. Computer Methods and Programs in Biomedicine 2001; 66: 167-81.
- 15 Leder U, Unger R, Baier V, Haueisen J, Nowak H, Figulla HR. Effect of choice of baseline correction interval on localization of electrical heart activity (in German). Biomedizinische Technik 2000; 45: 114-8.
- 16 Fuller MS, Sándor G, Punske B, Taccardi B, MacLeod R, Ershler P. et al Estimates of repolarization dispersion from electrocardiographic measurements. Circulation 2000; 102: 685-91.
- 17 Gozolits S, Fischer G, Berger Th, Hanser F, Abou- Harb M, Tilg B. et al The global P-wave duration in the 65-lead ECG: single and dual site pacing in the structurally normal human atria. Journal of Cardiovascular Electrophysiology 2002; 13: 1240-5.
- 18 Simmons WN, Mackey S, He DS, Marcus FI. Comparison of gold versus platinum electrodes on myocardial lesion size using radiofrequency energy. Pacing and Clinical Electrophysiology 1996; 19: 398-402.
- 19 Sternickel K. Automatic pattern recognition in ECG time series. Computer Methods and Programs in Biomedicine 2002; 68: 109-15.
- 20 Ramanathan C, Ghanem RN, Jia P, Ryu K, Rudy Y. Noninvasive electrocardiographic imaging for cardiac electrophysiology and arrhythmia. Nature Medicine 2004; 10: 422-8.