Multiwave: A Wavelet-Based ECG Data Compression Algorithm
Summary :
MultiWave data compression algorithm is based on the multiresolution wavelet techniqu for decomposing Electrocardiogram (ECG) signals into their coarse and successively more detailed components. At each successive resolution, or scale, the data are convolved with appropriate filters and then the alternate samples are discarded. This procedure results in a data compression rate that increased on a dyadic scale with successive wavelet resolutions. ECG signals recorded from patients with normal sinus rhythm, supraventricular tachycardia, and ventriular tachycardia are analyzed. The data compression rates and the percentage distortion levels at each resolution are obtained. The performance of the MultiWave data compression algorithm is shown to be superior to another algorithm (the Turning Point algorithm) that also carries out data reduction on a dyadic scale.
- Publication
- IEICE TRANSACTIONS on Information Vol.E76-D No.12 pp.1462-1469
- Publication Date
- 1993/12/25
- Publicized
- Online ISSN
- DOI
- Type of Manuscript
- Special Section PAPER (Special Section on ECG Data Compression)
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Nitish V. THAKOR, Yi-chun SUN, Hervé RIX, Pere CAMINAL, "Multiwave: A Wavelet-Based ECG Data Compression Algorithm" in IEICE TRANSACTIONS on Information,
vol. E76-D, no. 12, pp. 1462-1469, December 1993, doi: .
Abstract: MultiWave data compression algorithm is based on the multiresolution wavelet techniqu for decomposing Electrocardiogram (ECG) signals into their coarse and successively more detailed components. At each successive resolution, or scale, the data are convolved with appropriate filters and then the alternate samples are discarded. This procedure results in a data compression rate that increased on a dyadic scale with successive wavelet resolutions. ECG signals recorded from patients with normal sinus rhythm, supraventricular tachycardia, and ventriular tachycardia are analyzed. The data compression rates and the percentage distortion levels at each resolution are obtained. The performance of the MultiWave data compression algorithm is shown to be superior to another algorithm (the Turning Point algorithm) that also carries out data reduction on a dyadic scale.
URL: https://global.ieice.org/en_transactions/information/10.1587/e76-d_12_1462/_p
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@ARTICLE{e76-d_12_1462,
author={Nitish V. THAKOR, Yi-chun SUN, Hervé RIX, Pere CAMINAL, },
journal={IEICE TRANSACTIONS on Information},
title={Multiwave: A Wavelet-Based ECG Data Compression Algorithm},
year={1993},
volume={E76-D},
number={12},
pages={1462-1469},
abstract={MultiWave data compression algorithm is based on the multiresolution wavelet techniqu for decomposing Electrocardiogram (ECG) signals into their coarse and successively more detailed components. At each successive resolution, or scale, the data are convolved with appropriate filters and then the alternate samples are discarded. This procedure results in a data compression rate that increased on a dyadic scale with successive wavelet resolutions. ECG signals recorded from patients with normal sinus rhythm, supraventricular tachycardia, and ventriular tachycardia are analyzed. The data compression rates and the percentage distortion levels at each resolution are obtained. The performance of the MultiWave data compression algorithm is shown to be superior to another algorithm (the Turning Point algorithm) that also carries out data reduction on a dyadic scale.},
keywords={},
doi={},
ISSN={},
month={December},}
Copy
TY - JOUR
TI - Multiwave: A Wavelet-Based ECG Data Compression Algorithm
T2 - IEICE TRANSACTIONS on Information
SP - 1462
EP - 1469
AU - Nitish V. THAKOR
AU - Yi-chun SUN
AU - Hervé RIX
AU - Pere CAMINAL
PY - 1993
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E76-D
IS - 12
JA - IEICE TRANSACTIONS on Information
Y1 - December 1993
AB - MultiWave data compression algorithm is based on the multiresolution wavelet techniqu for decomposing Electrocardiogram (ECG) signals into their coarse and successively more detailed components. At each successive resolution, or scale, the data are convolved with appropriate filters and then the alternate samples are discarded. This procedure results in a data compression rate that increased on a dyadic scale with successive wavelet resolutions. ECG signals recorded from patients with normal sinus rhythm, supraventricular tachycardia, and ventriular tachycardia are analyzed. The data compression rates and the percentage distortion levels at each resolution are obtained. The performance of the MultiWave data compression algorithm is shown to be superior to another algorithm (the Turning Point algorithm) that also carries out data reduction on a dyadic scale.
ER -
English
