Detecting and Classifying Life-threatening ECG Ventricular Arrhythmias using Wavelet Decomposition

Youngkyoo Jung; Willis J. Tompkins

Detecting and Classifying Life-threatening ECG Ventricular Arrhythmias using Wavelet Decomposition

Research output: Contribution to journal › Conference article › peer-review

Abstract

In this study, we developed a wavelet-based algorithm for detecting and classifying four types of ventricular arrhythmias. We implemented the algorithm using four different wavelets and compared each result. For extracted arrhythmia episodes from the MIT-BIH arrhythmia and malignant ventricular arrhythmia databases, a Daubechies wavelet of length four gave the best result of the four different wavelets studied. By using wavelet decomposition, we reduced the amount of data necessary to be processed by the algorithm to less than ten percent of the original data.

Original language	English (US)
Pages (from-to)	2390-2393
Number of pages	4
Journal	Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
Volume	3
State	Published - Dec 1 2003
Externally published	Yes
Event	A New Beginning for Human Health: Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society - Cancun, Mexico Duration: Sep 17 2003 → Sep 21 2003

Keywords

Supraventricular tachycardia
Ventricular fibrillation
Ventricular flutter
Ventricular tachycardia
Wavelet decomposition

ASJC Scopus subject areas

Signal Processing
Biomedical Engineering
Computer Vision and Pattern Recognition
Health Informatics

Access to Document

Fingerprint Dive into the research topics of 'Detecting and Classifying Life-threatening ECG Ventricular Arrhythmias using Wavelet Decomposition'. Together they form a unique fingerprint.

Cite this

@article{ada2bdc102c34ab6936104ce20502314,

title = "Detecting and Classifying Life-threatening ECG Ventricular Arrhythmias using Wavelet Decomposition",

abstract = "In this study, we developed a wavelet-based algorithm for detecting and classifying four types of ventricular arrhythmias. We implemented the algorithm using four different wavelets and compared each result. For extracted arrhythmia episodes from the MIT-BIH arrhythmia and malignant ventricular arrhythmia databases, a Daubechies wavelet of length four gave the best result of the four different wavelets studied. By using wavelet decomposition, we reduced the amount of data necessary to be processed by the algorithm to less than ten percent of the original data.",

keywords = "Supraventricular tachycardia, Ventricular fibrillation, Ventricular flutter, Ventricular tachycardia, Wavelet decomposition",

author = "Youngkyoo Jung and Tompkins, {Willis J.}",

year = "2003",

month = dec,

day = "1",

language = "English (US)",

volume = "3",

pages = "2390--2393",

journal = "Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference",

issn = "1557-170X",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

note = "A New Beginning for Human Health: Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society ; Conference date: 17-09-2003 Through 21-09-2003",

}

TY - JOUR

T1 - Detecting and Classifying Life-threatening ECG Ventricular Arrhythmias using Wavelet Decomposition

AU - Jung, Youngkyoo

AU - Tompkins, Willis J.

PY - 2003/12/1

Y1 - 2003/12/1

N2 - In this study, we developed a wavelet-based algorithm for detecting and classifying four types of ventricular arrhythmias. We implemented the algorithm using four different wavelets and compared each result. For extracted arrhythmia episodes from the MIT-BIH arrhythmia and malignant ventricular arrhythmia databases, a Daubechies wavelet of length four gave the best result of the four different wavelets studied. By using wavelet decomposition, we reduced the amount of data necessary to be processed by the algorithm to less than ten percent of the original data.

AB - In this study, we developed a wavelet-based algorithm for detecting and classifying four types of ventricular arrhythmias. We implemented the algorithm using four different wavelets and compared each result. For extracted arrhythmia episodes from the MIT-BIH arrhythmia and malignant ventricular arrhythmia databases, a Daubechies wavelet of length four gave the best result of the four different wavelets studied. By using wavelet decomposition, we reduced the amount of data necessary to be processed by the algorithm to less than ten percent of the original data.

KW - Supraventricular tachycardia

KW - Ventricular fibrillation

KW - Ventricular flutter

KW - Ventricular tachycardia

KW - Wavelet decomposition

UR - http://www.scopus.com/inward/record.url?scp=1542270973&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=1542270973&partnerID=8YFLogxK

M3 - Conference article

AN - SCOPUS:1542270973

VL - 3

SP - 2390

EP - 2393

JO - Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference

JF - Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference

SN - 1557-170X

T2 - A New Beginning for Human Health: Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

Y2 - 17 September 2003 through 21 September 2003

ER -