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

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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.",

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AU - Tompkins, Willis J.

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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.

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KW - Ventricular flutter

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

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

Abstract

Keywords

ASJC Scopus subject areas

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