Abstract
In a substantial number of patients atrial fibrillation (AF) recurs after successful electrical cardioversion, but at present there are no reliable clinical markers for confidently identifying the patients in which recurrence will occur within a short period of time. This study evaluates the predictive classification performance of Sample Entropy (SampEn) in the discrimination between recurrent and non-recurrent AF episodes. A validated database of 35 ECG recordings acquired from AF subjects undergoing cardioversion was used throughout the study, together with their known recurrence status at one month. SampEn was applied to these QRST-reduced electrocardiograms, to atrial activity (AA), and also to heart rate (R-R intervals). The sample entropy of R–R intervals was significantly reduced (p=0.043) in the recurrent AF episodes compared with maintenance sinus rhythm episodes. SampEn applied to the AA signal showed a opposite results, it was reduced with a significant increasing trend in the maintenance sinus rhythm episodes (p=0.017). There is a need for welldefined studies with larger patient groups in order to assess the entropy changes further and to look for possible changes, which might predict AF recurrence.
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Abad, R. et al. (2007). Sample Entropy Analysis of Electrocardiograms to Characterize Recurrent Atrial Fibrillation. In: Jarm, T., Kramar, P., Zupanic, A. (eds) 11th Mediterranean Conference on Medical and Biomedical Engineering and Computing 2007. IFMBE Proceedings, vol 16. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73044-6_15
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DOI: https://doi.org/10.1007/978-3-540-73044-6_15
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