BACKGROUND: Atrial fibrillation catheter ablation is frequently guided by identification of fractionated electrograms, which are thought to be critical for maintenance of the arrhythmia. Objective automated means for identifying fractionation independent of physician interpretation have not been standardized or validated. OBJECTIVE: The purpose of this study was to standardize and validate an automated algorithm to rapidly identify fractionated electrograms for high-density atrial fibrillation fractionation mapping. METHODS: Left and right atrial fractionation maps were generated by EnSite NavX 6.0 software, using standardized ablation catheters in eight patients with atrial fibrillation. Two blinded electrophysiologists interpreted all electrograms as either fractionated or not fractionated. A stepwise approach was used to optimize automated settings to accurately identify fractionation. High-density fractionation maps were generated with a 20-pole mapping catheter in eight other patients. Two blinded electrophysiologists interpreted all electrograms as near field or far field. The algorithm was refined to optimize settings to exclude far-field signals and retain near-field signals. The sampling segment length was adjusted to optimize recording time to ensure reproducibility. RESULTS: Using 1,514 points, the automated software achieved sensitivity of 0.75 and specificity of 0.80 for identification of fractionated electrograms. Using 725 points collected via multipole catheters with optimal automated settings, 94% of near-field fractionated electrograms were accurately identified. A 6-second sampling length was needed for reproducible fractionation measurements. CONCLUSION: Standardized settings of EnSite NavX 6.0 software with 6-second data collection per point can rapidly and accurately generate high-density fractionation maps independent of physician electrogram interpretation. This may allow for an automated, standardized approach to atrial fibrillation fractionated ablation.
BACKGROUND:Atrial fibrillation catheter ablation is frequently guided by identification of fractionated electrograms, which are thought to be critical for maintenance of the arrhythmia. Objective automated means for identifying fractionation independent of physician interpretation have not been standardized or validated. OBJECTIVE: The purpose of this study was to standardize and validate an automated algorithm to rapidly identify fractionated electrograms for high-density atrial fibrillation fractionation mapping. METHODS: Left and right atrial fractionation maps were generated by EnSite NavX 6.0 software, using standardized ablation catheters in eight patients with atrial fibrillation. Two blinded electrophysiologists interpreted all electrograms as either fractionated or not fractionated. A stepwise approach was used to optimize automated settings to accurately identify fractionation. High-density fractionation maps were generated with a 20-pole mapping catheter in eight other patients. Two blinded electrophysiologists interpreted all electrograms as near field or far field. The algorithm was refined to optimize settings to exclude far-field signals and retain near-field signals. The sampling segment length was adjusted to optimize recording time to ensure reproducibility. RESULTS: Using 1,514 points, the automated software achieved sensitivity of 0.75 and specificity of 0.80 for identification of fractionated electrograms. Using 725 points collected via multipole catheters with optimal automated settings, 94% of near-field fractionated electrograms were accurately identified. A 6-second sampling length was needed for reproducible fractionation measurements. CONCLUSION: Standardized settings of EnSite NavX 6.0 software with 6-second data collection per point can rapidly and accurately generate high-density fractionation maps independent of physician electrogram interpretation. This may allow for an automated, standardized approach to atrial fibrillation fractionated ablation.
Authors: Amir S Jadidi; Edward Duncan; Shinsuke Miyazaki; Nicolas Lellouche; Ashok J Shah; Andrei Forclaz; Isabelle Nault; Matthew Wright; Lena Rivard; Xingpeng Liu; Daniel Scherr; Stephen B Wilton; Frédéric Sacher; Nicolas Derval; Sebastien Knecht; Steven J Kim; Mélèze Hocini; Sanjiv Narayan; Michel Haïssaguerre; Pierre Jaïs Journal: Circ Arrhythm Electrophysiol Date: 2012-01-03
Authors: David Gareth Jones; James W McCready; Riyaz A Kaba; Syed Y Ahsan; Jonathan C Lyne; Jack Wang; Oliver R Segal; Vias Markides; Pier D Lambiase; Tom Wong; Anthony W C Chow Journal: J Interv Card Electrophysiol Date: 2011-04-20 Impact factor: 1.900
Authors: Tiago P Almeida; Gavin S Chu; João L Salinet; Frederique J Vanheusden; Xin Li; Jiun H Tuan; Peter J Stafford; G André Ng; Fernando S Schlindwein Journal: Med Biol Eng Comput Date: 2016-02-25 Impact factor: 2.602