基于集合经验模态分解的心律失常心电图降噪。
Arrhythmia ECG noise reduction by ensemble empirical mode decomposition.
机构信息
Department of Photonics and Communication Engineering, Asia University, Wufeng, Taichung County, 500, Lioufeng Rd., Wufeng, Taichung County, 41354, Taiwan.
出版信息
Sensors (Basel). 2010;10(6):6063-80. doi: 10.3390/s100606063. Epub 2010 Jun 17.
Abstract
A novel noise filtering algorithm based on ensemble empirical mode decomposition (EEMD) is proposed to remove artifacts in electrocardiogram (ECG) traces. Three noise patterns with different power--50 Hz, EMG, and base line wander--were embedded into simulated and real ECG signals. Traditional IIR filter, Wiener filter, empirical mode decomposition (EMD) and EEMD were used to compare filtering performance. Mean square error between clean and filtered ECGs was used as filtering performance indexes. Results showed that high noise reduction is the major advantage of the EEMD based filter, especially on arrhythmia ECGs.
摘要
提出了一种基于集合经验模态分解（EEMD）的新型噪声滤波算法，用于去除心电图（ECG）迹线中的伪迹。将三种具有不同功率的噪声模式（50 Hz、肌电图和基线漂移）嵌入到模拟和真实的 ECG 信号中。传统的 IIR 滤波器、维纳滤波器、经验模态分解（EMD）和 EEMD 被用于比较滤波性能。干净和滤波后的 ECG 之间的均方误差被用作滤波性能指标。结果表明，基于 EEMD 的滤波器的主要优点是能够实现较高的降噪，特别是在心律失常的 ECG 上。
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