Biventricular capture verification by means of morphological analysis of intracardiac electrogram.

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.

Risk assessment of R-on-T event based on modeled QT-RR relationship.

BACKGROUND: R-on-T event is a well-known trigger of ventricular tachycardia (VT) and ventricular fibrillation (VF). We propose a method to estimate the risk of R-on-T event from the inter-beat (RR) intervals based on modeled QT-RR relationship. METHODS: We retrospectively analyzed the Spontaneous Ventricular Tachyarrhythmia Database and the HAWAI Registry, which include a total of 397 RR interval recordings from 116 implantable cardioverter defibrillator patients. For each RR interval time series, QT intervals were estimated from the weighted average of preceding RR intervals using Bazett, Fridericia, and linear formulas. The risk score (RS) of each cycle was calculated to quantify the probability of R-on-T event based on the timing of R-wave relative to the estimated T-end. We identified 52,440 ectopic beats (EBs) episodes, 280 nonsustained VT (NSVT) episodes, and 352 sustained VT/VF episodes. The RS of episode onset and the prematurity index (PMI) of the initiating beat were compared. RESULTS: Using different QT-RR models, R-on-T events were respectively detected in 9% EB, 45% NSVT, 69% VT/VF (Bazett); in 6% EB, 41% NSVT, 65% VT/VF (Fridericia); and in 7% EB, 42% NSVT, 66% VT/VF (linear). No R-on-T event was found in normal beats. Consistent among three QT-RR models, the RS of episode onset rises sharply from EB to NSVT and to VT/VF episodes. In contrast, no trend in PMI is found. CONCLUSIONS: The risk of R-on-T can be estimated from RR intervals, based on modeled QT-RR relationship. An episode onset with higher RS has increased risk of developing into NSVT or VT/VF.

A simple method to detect atrial fibrillation using RR intervals.

Implantable loop recorders have been developed for long-term monitoring of cardiac arrhythmia, but their accuracy for atrial fibrillation (AF) detection is unsatisfactory. We sought to develop and evaluate a simple method for detecting AF using RR intervals. The new AF detection algorithm is based on a map that plots RR intervals versus change of RR intervals (RdR). The map is divided by a grid with 25-ms resolution in 2 axes and nonempty cells are counted to classify AF and non-AF episodes. We evaluated the performance of the method using 4 PhysioNet databases: MIT-BIH AF database, MIT-BIH arrhythmia database, MIT-BIH normal sinus rhythm (NSR) database, and NSR RR interval database (total 145 patients, 1,826 hours NSR, 96 hours AF, and 11 hours other rhythms). Each record is divided into consecutive windows containing 32, 64, or 128 RR intervals. AF detection is performed for each window and classification results are compared to annotations. A window is labeled true AF if >1/2 of cycles in the window are annotated as AF or non-AF otherwise. The RdR map shows signature patterns corresponding to various heart rhythms. Optimal nonempty cell cut-off threshold for AF detection was determined by receiver operating characteristic curve analysis, which yields excellent sensitivity and specificity for window sizes 32 (94.4% and 92.6%, respectively), 64 (95.8% and 94.3%), and 128 (95.9% and 95.4%). In conclusion, a single metric derived from the RdR map can achieve robust AF detection within as few as 32 heart beats.