Key result
An automatic biventricular capture verification algorithm based on morphological analysis of the intracardiac electrogram achieved ≥99% classification accuracy in a canine model.
An automatic biventricular capture verification algorithm based on morphological analysis of intracardiac electrograms demonstrated ≥99% accuracy in a canine model.
AIMS: 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). METHODS AND RESULTS: 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. CONCLUSION: This study demonstrated the feasibility to perform automatic biventricular capture verification based on morphological analysis of the IEGM.
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Lian et al. (2013) studied Cardiac resynchronization therapy (n=6). Automatic biventricular capture verification algorithm (ASCI) vs. Visual examination of the IEGM was evaluated on Classification accuracy of capture vs non-capture paces. An automatic biventricular capture verification algorithm based on morphological analysis of the intracardiac electrogram achieved ≥99% classification accuracy in a canine model.
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