Title :
Detection of ventricular Arrhythmias using roots location in AR-modelling
Author :
Kafieh, Rahele ; Mehri, Alireza ; Amirfattahi, Rassoul
Author_Institution :
Isfahahan Univ. of Med., Isfahahan
Abstract :
This paper addresses the problem of automatic discrimination of rhythms in ECG signals. In performing the discrimination, fourth-order AR parameters of successive segments are estimated and the related roots are computed and used as inputs to the learning vector quantization (LVQ) classification algorithm. In discriminating normal (NSR) rhythm from arrhythmias, 98% of normal data and 88% of data with arrhythmia are classified correctly. Also in discriminating VF from VT, 72% of data with VF and 86% of data with VT are determined properly.
Keywords :
autoregressive processes; electrocardiography; learning (artificial intelligence); medical diagnostic computing; medical signal processing; patient diagnosis; pattern classification; vector quantisation; ECG signals; LVQ classification algorithm; autoregressive modelling; discriminating normal rhythm; fourth-order AR parameters; learning vector quantization; roots location; ventricular arrhythmias detection; Biomedical engineering; Electrocardiography; Fast Fourier transforms; Feature extraction; Fibrillation; Frequency estimation; Heart; Reliability engineering; Rhythm; Time domain analysis; Autoregressive modeling; Electrocardiogram (ECG); LVQ networks; ventricular fibrillation (VF); ventricular tachycardia (VT);
Conference_Titel :
Information, Communications & Signal Processing, 2007 6th International Conference on
Conference_Location :
Singapore
Print_ISBN :
978-1-4244-0982-2
Electronic_ISBN :
978-1-4244-0983-9
DOI :
10.1109/ICICS.2007.4449541
