De Ridder S, Neyt X, Pattyn N, Migeotte P-F
Signal and Image Centre of the Royal Military Academy, 1000 Brussels, Belgium.
Annu Int Conf IEEE Eng Med Biol Soc. 2011;2011:806-9. doi: 10.1109/IEMBS.2011.6090184.
During thoracic impedance signal acquisition, noise is inherently introduced and hence, denoising is required to allow for accurate event detection. This paper investigates the effectiveness of Ensemble Emperical Mode Decomposition to filter random noise. The performance of the EEMD method is compared with an optimal FIR filter and wavelet denoising. The IMF selection for signal reconstruction in the EEMD denoising method is optimized using a sequential search. Denoising performance was evaluated by the SNR and the accuracy in event detection after filtering. When all criteria are taken into account, wavelet seems to outperform both EEMD and FIR denoising.
在胸阻抗信号采集过程中,会不可避免地引入噪声,因此需要进行去噪处理,以便准确检测事件。本文研究了总体经验模态分解对随机噪声的滤波效果。将总体经验模态分解(EEMD)方法的性能与最优有限脉冲响应(FIR)滤波器和小波去噪进行了比较。在EEMD去噪方法中,使用顺序搜索对信号重构的固有模态函数(IMF)选择进行了优化。通过信噪比(SNR)以及滤波后事件检测的准确性来评估去噪性能。综合考虑所有标准时,小波去噪似乎优于EEMD和FIR去噪。