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

Youngkyoo Jung, Willis J. Tompkins

Research output: Contribution to journalConference article

9 Citations (Scopus)

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 languageEnglish (US)
Pages (from-to)2390-2393
Number of pages4
JournalAnnual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
Volume3
StatePublished - Dec 1 2003
Externally publishedYes
EventA 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 2003Sep 21 2003

Fingerprint

Wavelet decomposition
Electrocardiography
Cardiac Arrhythmias
Databases

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

Cite this

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