Using hidden Markov toolkit for arrhythmia recognition

Author

Krimi, Samar ; Ouni, Kaïs ; Ellouze, Noureddine

Author_Institution

Signal, Image & Pattern Recognition Res. Unit, Univ. Tunis El Manar, Tunis, Tunisia

fYear

2012

fDate

2-4 May 2012

Firstpage

1

Lastpage

5

Abstract

This paper describes a recognition system based on diverse features combination for the automatic heartbeat recognition purpose. The method consists of three stages: at the first stage, we extract a set of features including the morphological ones, high order statistics and pitch synchronous decomposition from ECG data using QT database; at the second stage, we use the hidden Markov tree classifier, then the third stage is added as a tool on which we have implemented the hidden Markov tree. The classification accuracy of the proposed system is measured by sensitivity and specificity measures. These measures for average sensitivity and average specificity are 95,79%, 98,93% in case of separated features and 97,46%, 99,22% in case of combined features.

Keywords

electrocardiography; feature extraction; hidden Markov models; higher order statistics; medical signal processing; signal classification; source separation; trees (mathematics); ECG data; QT database; arrhythmia recognition system; automatic heartbeat recognition; diverse feature combination; feature extraction; hidden Markov toolkit; hidden Markov tree classifier; high order statistics; morphological feature; pitch synchronous decomposition; sensitivity measure; specificity measure; Databases; Electrocardiography; Feature extraction; Heart beat; Hidden Markov models; Training; Wavelet coefficients; Arrhythmia; classification; features; hidden Markov toolkit; hidden Markov tree;

fLanguage

English

Publisher

ieee

Conference_Titel

Communications Control and Signal Processing (ISCCSP), 2012 5th International Symposium on

Conference_Location

Rome

Print_ISBN

978-1-4673-0274-6

Type

conf

DOI

10.1109/ISCCSP.2012.6217805

Filename

6217805