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Computational and Mathematical Methods in Medicine
Volume 2013, Article ID 373401, 11 pages
Research Article

Detection of Structural Changes in Tachogram Series for the Diagnosis of Atrial Fibrillation Events

Dipartimento di Matematica, Modellistica e Calcolo Scientifico (MOX), Politecnico di Milano, Via Bonardi 9, 20133 Milano, Italy

Received 25 October 2012; Accepted 25 March 2013

Academic Editor: Linamara Rizzo Battistella

Copyright © 2013 Francesca Ieva et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Atrial Fibrillation (AF) is the most common cardiac arrhythmia. It naturally tends to become a chronic condition, and chronic Atrial Fibrillation leads to an increase in the risk of death. The study of the electrocardiographic signal, and in particular of the tachogram series, is a usual and 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 the identification of Atrial Fibrillation events, based on the order identification of the ARIMA models used for describing the RR time series that characterize the different phases of AF (pre-, during, and post-AF). A simulation study is carried out in order to assess the performance of the proposed method. Moreover, an application to real data concerning patients affected by Atrial Fibrillation is presented and discussed. Since the proposed method looks at structural changes of ARIMA models fitted on the RR time series for the AF event with respect to the pre- and post-AF phases, it is able to identify starting and ending points of an AF event even when AF follows or comes before irregular heartbeat time slots.