Key result
Linear and non-linear parameters derived from intra-atrial signals successfully detected and quantified different dynamics during various types of atrial fibrillation episodes.
Why the study?
Can linear and non-linear parameters of intra-atrial signals discriminate among different atrial fibrillation episodes?
Can linear and non-linear parameters of intra-atrial signals discriminate among different atrial fibrillation episodes?
Linear and non-linear signal processing parameters can effectively characterize and classify different types of atrial fibrillation episodes from intra-atrial signals.
Supports AF subtype discrimination via intra-atrial signal analysis; hypothesis-generating and requires prospective clinical validation.
In this study linear and non-linear indexes for the characterization of the dynamics in both intra-atrial (IA) signals and atrial period (AP) series are presented and used to discriminate among different atrial fibrillation (AF) episodes. The level of predictability (LP) of AP series and its regularity (R) were computed through the identification of an autoregressive model of the AP series and through the estimation of corrected conditional entropy (CCE), respectively. In addition non-linear synchronization (NLS) patterns were analyzed through indexes based on the mutual corrected conditional entropy (MCCE). Results evidence the capability of the proposed parameters to detect and quantify the different dynamics of IA signals during different type of AF episodes.
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Mainard et al. (2003) studied Atrial fibrillation. Linear and non-linear parameters was evaluated on Classification of atrial fibrillation episodes. Linear and non-linear parameters derived from intra-atrial signals successfully detected and quantified different dynamics during various types of atrial fibrillation episodes.
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