Name
Title | ||
|---|---|---|
Searching for “order” in atrial fibrillation using electrogram morphology recurrence plots |
Abstract | ||
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BackgroundBipolar electrograms recorded during atrial fibrillation (AF) can have an appearance of chaotic/random behavior. The aim of this study was to use a novel electrogram morphology recurrence (EMR) analysis to quantify the level of order in the morphology patterns in AF. MethodsRapid atrial pacing was performed in seven dogs at 600bpm for 3 weeks leading to sustained AF. Open chest high density electrical recordings were made in multiple atrial sites. EMR plots of bipolar electrograms at each site were created by cross-correlating morphologies of each detected activations with morphologies of every other activation. The following features of the EMR plots were quantified: recurrence rate (RR), determinism (DET), laminarity (LAM), average diagonal line length (L), trapping time (TT), divergence (DIV), and Shannon's entropy (ENTR). For each recording site, these measures were calculated for the normal sequence of morphologies and also after random shuffling of the electrogram orders. ResultsElectrograms recordings from a total of 3961 sites had average cycle lengths of 104¿22ms resulting in an average of 100¿19 activations detected per 10-s recording and an average RR of 0.38¿0.28 (range 0.02-1.00). Shuffling the order of the activation morphologies resulted in significant decreases in DET, LAM, L, TT, and ENTR and significant increases in DIV. ConclusionsEMR plots of AF electrograms show varying rates of recurrence with patterns that suggest an underlying deterministic structure to the activation sequences. A better understanding of AF dynamics could lead to improved methods in mapping and treating AF. Recurrence plots show patterns within atrial fibrillation electrograms.Recurrence plot diagonal and vertical lines indicate determinism and stationarity.Wide range of morphology recurrence rates seen in canine atrial fibrillation model.Shuffling electrogram order simulates random timing of electrogram recurrence. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1016/j.compbiomed.2015.07.018 | Computers in Biology and Medicine |
Keywords | Field | DocType |
Atrial fibrillation,Electrograms,Dynamics,Mapping,Non-linear analysis | Atrial fibrillation,Line length,Pattern recognition,Internal medicine,Cardiology,Morphology (linguistics),Algorithm,High density,Artificial intelligence,Electrocardiography,Mathematics | Journal |
Volume | Issue | ISSN |
65 | C | 0010-4825 |
Citations | PageRank | References |
1 | 0.41 | 1 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| David Gordon | 1 | 1 | 0.41 |
| Jeffrey J. Goldberger | 2 | 1 | 0.41 |
| Rishi Arora | 3 | 1 | 0.41 |
| Gary L. Aistrup | 4 | 1 | 0.41 |
| Jason Ng | 5 | 5 | 1.46 |