Statistical models for detecting Atrial Fibrillation events


Statistical learning
Health Analytics
Statistical models for detecting Atrial Fibrillation events
Tuesday 24th April 2012
Ieva, F.; Paganoni, A. M.; Zanini, P.
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Atrial Fibrillation is the most common cardiac arrhythmia that naturally tends to become a chronic condition and chronic Atrial Fibrillation leads to an increase in the risk of death. The study of time series of time intervals between an R peak in the electrocardiogram and the following one is an effective way to investigate the presence of Atrial Fibrillation and to detect when a single event starts and ends. This work presents a new statistical method to deal with identification of Atrial Fibrillation events. Some simulations in order to assess the performances of the proposed method are detailed and the results obtained applying this method to real data concerning patients affected by Atrial Fibrillation are discussed.
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Computational and Mathematical Methods in Medicine, Vol. 2013, Article ID 373401, 11 pages. (doi:10.1155/2013/373401)