https://scholars.lib.ntu.edu.tw/handle/123456789/523132
Title: | Discrete-wavelet-transform-based noise removal and feature extraction for ECG signals | Authors: | Lin H.-Y. Liang S.-Y. YI-LWUN HO YEN-HUNG LIN Ma H.-P. |
Issue Date: | 2014 | Publisher: | Elsevier Masson SAS | Journal Volume: | 35 | Journal Issue: | 6 | Start page/Pages: | 351-361 | Source: | IRBM | Abstract: | Nowadays, doctors use electrocardiogram (ECG) to diagnose heart diseases commonly. However, some nonideal effects are often distributed in ECG. Discrete wavelet transform (DWT) is efficient for nonstationary signal analysis. In this paper, the Symlets sym5 is chosen as the wavelet function to decompose recorded ECG signals for noise removal. Soft-thresholding method is then applied for feature detection. To detect ECG features, R peak of each heart beat is first detected, and the onset and offset of the QRS complex are then detected. Finally, the signal is recon-structed to remove high frequency interferences and applied with adaptive searching window and threshold to detect P and T waves. We use the MIT-BIH arrhythmia database for algorithm verification. For noise reduction, the SNR improvement is achieved at least 10dB at SNR 5dB, and most of the improvement SNR are better than other methods at least 1dB at different SNR. When applying to the real portable ECG device, all R peaks can be detected when patients walk, run, or move at the speed below 9km/h. The performance of delineation on database shows in our algorithm can achieve high sensitivity in detecting ECG features. The QRS detector attains a sensitivity over 99.94%, while detectors of P and T waves achieve 99.75% and 99.7%, respectively. ? 2014 Published by Elsevier Masson SAS. |
URI: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-84916604838&doi=10.1016%2fj.irbm.2014.10.004&partnerID=40&md5=04dd89856fc5e57cf5d1736ec9c43487 https://scholars.lib.ntu.edu.tw/handle/123456789/523132 |
ISSN: | 1959-0318 | DOI: | 10.1016/j.irbm.2014.10.004 | SDG/Keyword: | algorithm; Article; discrete wavelet transform based noise removal; electrocardiogram; electromyogram; exercise electrocardiography; Fourier transformation; heart beat; human; noise reduction; P wave; patient mobility; QRS complex; R wave; signal detection; signal noise ratio; T wave |
Appears in Collections: | 醫學系 |
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