Automatic prediction of paroxysmal atrial fibrillation in patients with heart arrhythmia

Author

Arotaritei, Dragos ; Rotariu, Cristian

Author_Institution

Dept. of Biomed. Sci., Univ. of Med. & Pharmacy, Iasi, Romania

fYear

2014

fDate

16-18 Oct. 2014

Firstpage

549

Lastpage

552

Abstract

A new predictor that takes into accounts the randomness of RR interval before PAFib is proposed. Using data mining techniques, temporal patterns are identified based their presence in ECG that precedes paroxysmal atrial fibrillation (PAF) and not present in patients with normal ECG. The algorithm used the supposition that the premature atrial complexes (PAC) are responsible for most of PAF. Other statistical parameters that are related to randomness of signal are used to improve the accuracy of proposed algorithm.

Keywords

data mining; electrocardiography; medical disorders; medical signal processing; statistical analysis; ECG; RR interval; automatic prediction; data mining techniques; electrocardiogram; heart arrhythmia patients; paroxysmal atrial fibrillation; premature atrial complexes; statistical parameters; temporal pattern identification; Atrial fibrillation; Databases; Electrocardiography; Heart rate variability; Prediction algorithms; Rhythm; RR-tachogram; Teager-Kaiser operator; atrial fibrillation; heart arrhythmia; parosymal atrial fibrillation; pattern mining; premature atrial complexes;

fLanguage

English

Publisher

ieee

Conference_Titel

Electrical and Power Engineering (EPE), 2014 International Conference and Exposition on

Conference_Location

Iasi

Type

conf

DOI

10.1109/ICEPE.2014.6969969

Filename

6969969