title: Detection of structural changes in tachogram series for the diagnosis of atrial fibrillation events.
abstract: 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.
authors.affiliation: Dipartimento di Matematica, Modellistica e Calcolo Scientifico (MOX), Politecnico di Milano, Via Bonardi 9, 20133 Milano, Italy.
authors.forename: Francesca
authors.initials: F
authors.lastname: Ieva
authors.forename: Anna Maria
authors.initials: AM
authors.lastname: Paganoni
authors.forename: Paolo
authors.initials: P
authors.lastname: Zanini
mesh_terms: D001281:Atrial Fibrillation / Q000175:diagnosis* / Q000503:physiopathology; D019295:Computational Biology; D003198:Computer Simulation; D003936:Diagnosis, Computer-Assisted / Q000379:methods* / Q000706:statistics & numerical data; D004562:Electrocardiography / Q000706:statistics & numerical data*; D006339:Heart Rate; D006801:Humans; D008955:Models, Cardiovascular; D013997:Time Factors