Discrimination of endocardial electrogram disorganization using a signal regularity analysis
Language English Country United States Media print
Document type Journal Article, Research Support, Non-U.S. Gov't
- MeSH
- Algorithms MeSH
- Analysis of Variance MeSH
- Automation methods MeSH
- Electrocardiography methods MeSH
- Entropy MeSH
- Atrial Fibrillation physiopathology MeSH
- Fractals MeSH
- Humans MeSH
- Heart Atria physiopathology MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
Measures from the theory of nonlinear dynamics were applied on complex fractionated atrial electrograms (CFAEs) in order to characterize their physiological dynamic behavior. The results were obtained considering 113 short term atrial electrograms (A-EGMs) which were annotated by three experts into four classes of fractionation according to A-EGMs signal regularity. The following measures were applied on A-EGM signals: General Correlation Dimension, Approximate Entropy, Detrended Fluctuation Analysis, Lempel-Ziv Complexity, and Katz-Sevcik, Variance and Box Counting Fractal Dimension. Assessment of disorganization was evaluated by a Kruskal Wallis statistical test. Except Detrended Fluctuation Analysis and Variance Fractal Dimension, the CFAE disorganization was found statistically significant even for low significant level alpha = 0.001. Moreover, the increasing complexity of A-EGM signals was reflected by higher values of General Correlation Dimension of order 1 and Approximate Entropy.
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