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 |
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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
Detecting and Classifying Life-threatening ECG Ventricular Arrhythmias using Wavelet Decomposition. / Jung, Youngkyoo; Tompkins, Willis J.
In: Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings, Vol. 3, 01.12.2003, p. 2390-2393.Research output: Contribution to journal › Conference article
}
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
ER -