A novel approach to arrhythmia classification using RR interval and teager energy

Chandrakar Kamath

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

It is hypothesized that a key characteristic of electrocardiogram (ECG) signal is its nonlinear dynamic behaviour and that the nonlinear component changes more significantly between normal and arrhythmia conditions than the linear component. The usual statistical descriptors used in RR (R to R) interval analysis do not capture the nonlinear disposition of RR interval variability. In this paper we explore a novel approach to extract the features from nonlinear component of the RR interval signal using Teager energy operator (TEO). The key feature of Teager energy is that it models the energy of the source that generated the signal rather than the energy of the signal itself. Hence any deviations in regular rhythmic activity of the heart get reflected in the Teager energy function. The classification evaluated on MIT-BIH database, with RR interval and mean of Teager energy computed over RR interval as features, exhibits an average accuracy that exceeds 99.79%.

Original languageEnglish
Pages (from-to)744-755
Number of pages12
JournalJournal of Engineering Science and Technology
Volume7
Issue number6
Publication statusPublished - 01-12-2012
Externally publishedYes

Fingerprint

Electrocardiography

All Science Journal Classification (ASJC) codes

  • Engineering(all)

Cite this

@article{ae5dd967760d4681bfd64c7a2db89012,
title = "A novel approach to arrhythmia classification using RR interval and teager energy",
abstract = "It is hypothesized that a key characteristic of electrocardiogram (ECG) signal is its nonlinear dynamic behaviour and that the nonlinear component changes more significantly between normal and arrhythmia conditions than the linear component. The usual statistical descriptors used in RR (R to R) interval analysis do not capture the nonlinear disposition of RR interval variability. In this paper we explore a novel approach to extract the features from nonlinear component of the RR interval signal using Teager energy operator (TEO). The key feature of Teager energy is that it models the energy of the source that generated the signal rather than the energy of the signal itself. Hence any deviations in regular rhythmic activity of the heart get reflected in the Teager energy function. The classification evaluated on MIT-BIH database, with RR interval and mean of Teager energy computed over RR interval as features, exhibits an average accuracy that exceeds 99.79{\%}.",
author = "Chandrakar Kamath",
year = "2012",
month = "12",
day = "1",
language = "English",
volume = "7",
pages = "744--755",
journal = "Journal of Engineering Science and Technology",
issn = "1823-4690",
publisher = "Taylor's University College",
number = "6",

}

A novel approach to arrhythmia classification using RR interval and teager energy. / Kamath, Chandrakar.

In: Journal of Engineering Science and Technology, Vol. 7, No. 6, 01.12.2012, p. 744-755.

Research output: Contribution to journalArticle

TY - JOUR

T1 - A novel approach to arrhythmia classification using RR interval and teager energy

AU - Kamath, Chandrakar

PY - 2012/12/1

Y1 - 2012/12/1

N2 - It is hypothesized that a key characteristic of electrocardiogram (ECG) signal is its nonlinear dynamic behaviour and that the nonlinear component changes more significantly between normal and arrhythmia conditions than the linear component. The usual statistical descriptors used in RR (R to R) interval analysis do not capture the nonlinear disposition of RR interval variability. In this paper we explore a novel approach to extract the features from nonlinear component of the RR interval signal using Teager energy operator (TEO). The key feature of Teager energy is that it models the energy of the source that generated the signal rather than the energy of the signal itself. Hence any deviations in regular rhythmic activity of the heart get reflected in the Teager energy function. The classification evaluated on MIT-BIH database, with RR interval and mean of Teager energy computed over RR interval as features, exhibits an average accuracy that exceeds 99.79%.

AB - It is hypothesized that a key characteristic of electrocardiogram (ECG) signal is its nonlinear dynamic behaviour and that the nonlinear component changes more significantly between normal and arrhythmia conditions than the linear component. The usual statistical descriptors used in RR (R to R) interval analysis do not capture the nonlinear disposition of RR interval variability. In this paper we explore a novel approach to extract the features from nonlinear component of the RR interval signal using Teager energy operator (TEO). The key feature of Teager energy is that it models the energy of the source that generated the signal rather than the energy of the signal itself. Hence any deviations in regular rhythmic activity of the heart get reflected in the Teager energy function. The classification evaluated on MIT-BIH database, with RR interval and mean of Teager energy computed over RR interval as features, exhibits an average accuracy that exceeds 99.79%.

UR - http://www.scopus.com/inward/record.url?scp=84875995342&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84875995342&partnerID=8YFLogxK

M3 - Article

VL - 7

SP - 744

EP - 755

JO - Journal of Engineering Science and Technology

JF - Journal of Engineering Science and Technology

SN - 1823-4690

IS - 6

ER -