Methodology Considerations in Phase Mapping of Human Cardiac Arrhythmias

• Published in: Circulation. Arrhythmia and electrophysiology Vol. 9; no. 11; p. e004409
• Main Authors: Vijayakumar, Ramya, Vasireddi, Sunil K, Cuculich, Phillip S, Faddis, Mitchell N, Rudy, Yoram
• Format: Journal Article
• Language: English
• Published: United States American Heart Association, Inc 01.11.2016
• Subjects: Algorithms; Atrial Fibrillation - physiopathology; Electrocardiography; Electrophysiologic Techniques, Cardiac - methods; Heart Conduction System - physiopathology; Humans; Signal Processing, Computer-Assisted; atrial fibrillation; phase mapping; cardiac arrhythmias; electrocardiography
• ISSN: 1941-3149; 1941-3084; 1941-3084
• DOI: 10.1161/CIRCEP.116.004409

BACKGROUND—Phase analysis of cardiac arrhythmias, particularly atrial fibrillation, has gained interest because of the ability to detect organized stable drivers (rotors) and target them for therapy. However, the lack of methodology details in publications on the topic has resulted in ongoing debate over the phase mapping technique. By comparing phase maps and activation maps, we examined advantages and limitations of phase mapping. METHODS AND RESULTS—Seven subjects were enrolled. We generated phase maps and activation maps from electrocardiographic imaging–reconstructed epicardial unipolar electrograms. For ventricular signals, phase was computed with (1) pseudoempirical mode decomposition detrending and (2) a novel Moving Average (MVG) detrending approach. For atrial fibrillation signals, MVG was modified to incorporate dynamic cycle length (DCL) changes (MVG-DCL). Phase maps were visually analyzed to study phase singularity points and rotors. Results show that phase is sensitive to cycle length choice, a limitation that was addressed by the MVG-DCL algorithm. MVG-DCL was optimal for atrial fibrillation analysis. Phase maps helped to highlight high-curvature wavefronts and rotors. However, for some activation patterns, phase generated nonrotational singularity points and false rotors. CONCLUSIONS—Phase mapping computes singularity points and visually highlights rotors. As such, it can help to provide a clearer picture of the spatiotemporal activation characteristics during atrial fibrillation. However, it is advisable to incorporate electrogram characteristics and the time-domain activation sequence in the analysis, to prevent misinterpretation and false rotor detection. Therefore, for mapping complex arrhythmias, a combined time-domain activation and phase mapping with variable cycle length seems to be the most reliable method.