Detecting signal quality by ensemble empirical mode decomposition and Monte Carlo verification. (July 2015) - Lee, Yun-Kai; Tang, Sung-Chun; Yeh, Jia-Rong; Shieh, Jiann-Shing
Highlights: Applied empirical mode decomposition and Monte Carlo verification algorithms to detect the signal quality. We simplify the signal quality into a simple distance index. Complicated noise level can be obtained via corresponding to a simple distance index. Abstract: The measurements of bio-signals are always subject to interference from noise, which would be able to affect the research results. In the present study, we introduce a technique to detect the signal quality by using ensemble empirical mode decomposition (EEMD) and Monte Carlo verification. We first decompose the original signals into several intrinsic mode functions (IMFs) and calculate the average distances between the signal IMFs and the negative (−1) slope line in Monte Carlo verification. Then, the approximate amount of white noise percent level in original signal could be obtained via corresponding to the created curve of distance and noise percent. This new proposed technique makes the approximate white noise percent level could be obtained much easier via a simple distance index through the EEMD and Monte Carlo verification methods.