Tobias Georg Oesterlein1, Jochen Schmid2, Silvio Bauer3, Amir Jadidi4, Claus Schmitt5, Olaf Dössel6, Armin Luik7. 1. Institute of Biomedical Engineering, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany. Electronic address: tobias.oesterlein@kit.edu. 2. Institute of Biomedical Engineering, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany. Electronic address: jochen.schmid@kit.edu. 3. Institute of Biomedical Engineering, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany. Electronic address: silvio.bauer@student.kit.edu. 4. Universitäts-Herzzentrum Freiburg-Bad Krozingen, Germany. Electronic address: amir.jadidi@universitaets-herzzentrum.de. 5. Städtisches Klinikum Karlsruhe, Karlsruhe, Germany. Electronic address: Claus.Schmitt@klinikum-karlsruhe.de. 6. Institute of Biomedical Engineering, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany. Electronic address: olaf.doessel@kit.edu. 7. Städtisches Klinikum Karlsruhe, Karlsruhe, Germany. Electronic address: armin.luik@klinikum-karlsruhe.de.
Abstract
BACKGROUND AND OBJECTIVE: Progress in biomedical engineering has improved the hardware available for diagnosis and treatment of cardiac arrhythmias. But although huge amounts of intracardiac electrograms (EGMs) can be acquired during electrophysiological examinations, there is still a lack of software aiding diagnosis. The development of novel algorithms for the automated analysis of EGMs has proven difficult, due to the highly interdisciplinary nature of this task and hampered data access in clinical systems. Thus we developed a software platform, which allows rapid implementation of new algorithms, verification of their functionality and suitable visualization for discussion in the clinical environment. METHODS: A software for visualization was developed in Qt5 and C++ utilizing the class library of VTK. The algorithms for signal analysis were implemented in MATLAB. Clinical data for analysis was exported from electroanatomical mapping systems. RESULTS: The visualization software KaPAVIE (Karlsruhe Platform for Analysis and Visualization of Intracardiac Electrograms) was implemented and tested on several clinical datasets. Both common and novel algorithms were implemented which address important clinical questions in diagnosis of different arrhythmias. It proved useful in discussions with clinicians due to its interactive and user-friendly design. Time after export from the clinical mapping system to visualization is below 5min. CONCLUSION: KaPAVIE(2) is a powerful platform for the development of novel algorithms in the clinical environment. Simultaneous and interactive visualization of measured EGM data and the results of analysis will aid diagnosis and help understanding the underlying mechanisms of complex arrhythmias like atrial fibrillation.
BACKGROUND AND OBJECTIVE: Progress in biomedical engineering has improved the hardware available for diagnosis and treatment of cardiac arrhythmias. But although huge amounts of intracardiac electrograms (EGMs) can be acquired during electrophysiological examinations, there is still a lack of software aiding diagnosis. The development of novel algorithms for the automated analysis of EGMs has proven difficult, due to the highly interdisciplinary nature of this task and hampered data access in clinical systems. Thus we developed a software platform, which allows rapid implementation of new algorithms, verification of their functionality and suitable visualization for discussion in the clinical environment. METHODS: A software for visualization was developed in Qt5 and C++ utilizing the class library of VTK. The algorithms for signal analysis were implemented in MATLAB. Clinical data for analysis was exported from electroanatomical mapping systems. RESULTS: The visualization software KaPAVIE (Karlsruhe Platform for Analysis and Visualization of Intracardiac Electrograms) was implemented and tested on several clinical datasets. Both common and novel algorithms were implemented which address important clinical questions in diagnosis of different arrhythmias. It proved useful in discussions with clinicians due to its interactive and user-friendly design. Time after export from the clinical mapping system to visualization is below 5min. CONCLUSION: KaPAVIE(2) is a powerful platform for the development of novel algorithms in the clinical environment. Simultaneous and interactive visualization of measured EGM data and the results of analysis will aid diagnosis and help understanding the underlying mechanisms of complex arrhythmias like atrial fibrillation.
Authors: Tobias Oesterlein; Daniel Frisch; Axel Loewe; Gunnar Seemann; Claus Schmitt; Olaf Dössel; Armin Luik Journal: Biomed Res Int Date: 2016-12-13 Impact factor: 3.411
Authors: Gordon A Begg; Rashed Karim; Tobias Oesterlein; Lee N Graham; Andrew J Hogarth; Stephen P Page; Christopher B Pepper; Kawal Rhode; Gregory Y H Lip; Arun V Holden; Sven Plein; Muzahir H Tayebjee Journal: PLoS One Date: 2018-01-02 Impact factor: 3.240
Authors: Gordon A Begg; Peter P Swoboda; Rashed Karim; Tobias Oesterlein; Kawal Rhode; Arun V Holden; John P Greenwood; Eduard Shantsila; Gregory Y H Lip; Sven Plein; Muzahir H Tayebjee Journal: J Cardiovasc Magn Reson Date: 2020-02-10 Impact factor: 5.364