Quantification of electrocardiogram rhythmicity to detect life threatening cardiac arrhythmias using spectral entropy

Chandrakar Kamath

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

Changes in the normal rhythmicity of the heart may result in different cardiac arrhythmias, which may be fatal or cause serious damage to the heart if sustained over long periods of time. Ventricular tachycardia or fibrillation (VTVF) as fatal cardiac arrhythmia is the major cause leading to sudden cardiac death. It is crucial for the patient to receive immediate medical intervention when either VT or VF occurs. In this study, we present a novel, and computationally fast method to quantify the rhythmicity of the short-term electrocardiogram (ECG) signals based on spectral entropy feature and there by discriminate between normal sinus rhythm (NSR) and life threatening arrhythmias like, ventricular tachycardia/fibrillation (VT/VF). The receiver operating characteristic curve (ROC) analysis confirms the robustness of this new approach for a window length of 2 s and exhibits an average sensitivity = 99.4% (99.4%), specificity = 98.7% (99.0%), positive predictivity = 98.7% (99.6%), and accuracy = 98.9% (99.2%), to distinguish between normal and VT (VF) subjects. The presented method is simple, highly accurate, computationally efficient, and well suited for real time implementation in automated external defibrillators (AEDs).

Original languageEnglish
Pages (from-to)588-602
Number of pages15
JournalJournal of Engineering Science and Technology
Volume8
Issue number5
Publication statusPublished - 01-10-2013
Externally publishedYes

Fingerprint

Electrocardiography
Entropy
Defibrillators

All Science Journal Classification (ASJC) codes

  • Engineering(all)

Cite this

@article{f12bbf5ba2a94f6fa63b09608e6f453a,
title = "Quantification of electrocardiogram rhythmicity to detect life threatening cardiac arrhythmias using spectral entropy",
abstract = "Changes in the normal rhythmicity of the heart may result in different cardiac arrhythmias, which may be fatal or cause serious damage to the heart if sustained over long periods of time. Ventricular tachycardia or fibrillation (VTVF) as fatal cardiac arrhythmia is the major cause leading to sudden cardiac death. It is crucial for the patient to receive immediate medical intervention when either VT or VF occurs. In this study, we present a novel, and computationally fast method to quantify the rhythmicity of the short-term electrocardiogram (ECG) signals based on spectral entropy feature and there by discriminate between normal sinus rhythm (NSR) and life threatening arrhythmias like, ventricular tachycardia/fibrillation (VT/VF). The receiver operating characteristic curve (ROC) analysis confirms the robustness of this new approach for a window length of 2 s and exhibits an average sensitivity = 99.4{\%} (99.4{\%}), specificity = 98.7{\%} (99.0{\%}), positive predictivity = 98.7{\%} (99.6{\%}), and accuracy = 98.9{\%} (99.2{\%}), to distinguish between normal and VT (VF) subjects. The presented method is simple, highly accurate, computationally efficient, and well suited for real time implementation in automated external defibrillators (AEDs).",
author = "Chandrakar Kamath",
year = "2013",
month = "10",
day = "1",
language = "English",
volume = "8",
pages = "588--602",
journal = "Journal of Engineering Science and Technology",
issn = "1823-4690",
publisher = "Taylor's University College",
number = "5",

}

Quantification of electrocardiogram rhythmicity to detect life threatening cardiac arrhythmias using spectral entropy. / Kamath, Chandrakar.

In: Journal of Engineering Science and Technology, Vol. 8, No. 5, 01.10.2013, p. 588-602.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Quantification of electrocardiogram rhythmicity to detect life threatening cardiac arrhythmias using spectral entropy

AU - Kamath, Chandrakar

PY - 2013/10/1

Y1 - 2013/10/1

N2 - Changes in the normal rhythmicity of the heart may result in different cardiac arrhythmias, which may be fatal or cause serious damage to the heart if sustained over long periods of time. Ventricular tachycardia or fibrillation (VTVF) as fatal cardiac arrhythmia is the major cause leading to sudden cardiac death. It is crucial for the patient to receive immediate medical intervention when either VT or VF occurs. In this study, we present a novel, and computationally fast method to quantify the rhythmicity of the short-term electrocardiogram (ECG) signals based on spectral entropy feature and there by discriminate between normal sinus rhythm (NSR) and life threatening arrhythmias like, ventricular tachycardia/fibrillation (VT/VF). The receiver operating characteristic curve (ROC) analysis confirms the robustness of this new approach for a window length of 2 s and exhibits an average sensitivity = 99.4% (99.4%), specificity = 98.7% (99.0%), positive predictivity = 98.7% (99.6%), and accuracy = 98.9% (99.2%), to distinguish between normal and VT (VF) subjects. The presented method is simple, highly accurate, computationally efficient, and well suited for real time implementation in automated external defibrillators (AEDs).

AB - Changes in the normal rhythmicity of the heart may result in different cardiac arrhythmias, which may be fatal or cause serious damage to the heart if sustained over long periods of time. Ventricular tachycardia or fibrillation (VTVF) as fatal cardiac arrhythmia is the major cause leading to sudden cardiac death. It is crucial for the patient to receive immediate medical intervention when either VT or VF occurs. In this study, we present a novel, and computationally fast method to quantify the rhythmicity of the short-term electrocardiogram (ECG) signals based on spectral entropy feature and there by discriminate between normal sinus rhythm (NSR) and life threatening arrhythmias like, ventricular tachycardia/fibrillation (VT/VF). The receiver operating characteristic curve (ROC) analysis confirms the robustness of this new approach for a window length of 2 s and exhibits an average sensitivity = 99.4% (99.4%), specificity = 98.7% (99.0%), positive predictivity = 98.7% (99.6%), and accuracy = 98.9% (99.2%), to distinguish between normal and VT (VF) subjects. The presented method is simple, highly accurate, computationally efficient, and well suited for real time implementation in automated external defibrillators (AEDs).

UR - http://www.scopus.com/inward/record.url?scp=84888874669&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84888874669&partnerID=8YFLogxK

M3 - Article

AN - SCOPUS:84888874669

VL - 8

SP - 588

EP - 602

JO - Journal of Engineering Science and Technology

JF - Journal of Engineering Science and Technology

SN - 1823-4690

IS - 5

ER -