Biventricular capture verification by means of morphological analysis of intracardiac electrogram

• Published in: Europace (London, England) Vol. 15; no. 11; pp. 1677 - 1683
• Main Authors: Lian, Jie, Garner, Garth, Muessig, Dirk
• Format: Journal Article
• Language: English
• Published: England 01.11.2013
• Subjects: Algorithms; Animals; Cardiac Pacing, Artificial; Cardiac Resynchronization Therapy Devices; Dogs; Electrophysiologic Techniques, Cardiac - methods; Feasibility Studies; Follow-Up Studies; Models, Animal; Reproducibility of Results; Retrospective Studies; Ventricular Function, Left - physiology; Ventricular Function, Right - physiology; Capture verification; Biventricular pacing; Intracardiac electrogram; Adaptive signed correlation index; Morphological analysis
• ISSN: 1099-5129; 1532-2092
• DOI: 10.1093/europace/eut073

The aim of this study is to develop a new feature for automatic biventricular capture verification in cardiac resynchronization therapy (CRT) devices, by means of morphological analysis of the intracardiac electrogram (IEGM). The algorithm performs capture classification based on a novel adaptive signed correlation index (ASCI), which measures morphological similarity between the post-pace IEGM and a template waveform representing captured paces. To evaluate the performance of the algorithm, CRT pacemakers were implanted in six dogs. During a mean follow-up of 23 days, 175 biventricular threshold tests were conducted with various configurations of pace/sense polarities. Biventricular IEGMs were recorded and downloaded for offline analysis. Template signals for each pace/sense configuration in each chamber were created for individual dogs during the first follow-up. Each pace was annotated for capture or non-capture by visual examination of the IEGM. A total of 9991 capture paces and 4474 non-capture paces were included for morphological analysis. The calculated ASCI values were well separated for capture and non-capture paces irrespective of right/left pacing chambers, pace/sense configurations, pacing amplitude, individual dogs, and temporal proximity of the capture templates. Overall, the classification accuracy of the algorithm remained ≥99% for any ASCI cut-off value choosing between 0.18 and 0.52. This study demonstrated the feasibility to perform automatic biventricular capture verification based on morphological analysis of the IEGM.