Removal of artifacts in knee joint vibroarthrographic signals using ensemble empirical mode decomposition and detrended fluctuation analysis

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	Removal of artifacts in knee joint vibroarthrographic signals using ensemble empirical mode decomposition and detrended fluctuation analysis
	Wu, Yunfeng ; Yang, Shanshan ; Zheng, Fang ; Cai, Suxian ; Lu, Meng ; Wu, Meihong ; Wu YF(吴云峰) ; Wu MH(吴梅红)
刊名	http://dx.doi.org/10.1088/0967-3334/35/3/429
	2014
关键词	LONG-RANGE CORRELATIONS RADIAL-BASIS FUNCTIONS CARTILAGE DEGENERATION HILBERT SPECTRUM TIME-SERIES CLASSIFICATION DIAGNOSIS NOISE
英文摘要	National Natural Science Foundation of China [81101115, 31200769]; Natural Science Foundation of Fujian Province of China [2011J01371]; Fundamental Research Funds for the Central Universities of China [2010121061]; Program for New Century Excellent Talents in Fujian Province University; High-resolution knee joint vibroarthrographic (VAG) signals can help physicians accurately evaluate the pathological condition of a degenerative knee joint, in order to prevent unnecessary exploratory surgery. Artifact cancellation is vital to preserve the quality of VAG signals prior to further computer-aided analysis. This paper describes a novel method that effectively utilizes ensemble empirical mode decomposition (EEMD) and detrended fluctuation analysis (DFA) algorithms for the removal of baseline wander and white noise in VAG signal processing. The EEMD method first successively decomposes the raw VAG signal into a set of intrinsic mode functions (IMFs) with fast and low oscillations, until the monotonic baseline wander remains in the last residue. Then, the DFA algorithm is applied to compute the fractal scaling index parameter for each IMF, in order to identify the anti-correlation and the long-range correlation components. Next, the DFA algorithm can be used to identify the anti-correlated and the long-range correlated IMFs, which assists in reconstructing the artifact-reduced VAG signals. Our experimental results showed that the combination of EEMD and DFA algorithms was able to provide averaged signal-to-noise ratio (SNR) values of 20.52 dB (standard deviation: 1.14 dB) and 20.87 dB (standard deviation: 1.89 dB) for 45 normal signals in healthy subjects and 20 pathological signals in symptomatic patients, respectively. The combination of EEMD and DFA algorithms can ameliorate the quality of VAG signals with great SNR improvements over the raw signal, and the results were also superior to those achieved by wavelet matching pursuit decomposition and time-delay neural filter.
语种	英语
出版者	IOP PUBLISHING LTD
内容类型	期刊论文
源URL	[http://dspace.xmu.edu.cn/handle/2288/92717]
专题	信息技术－已发表论文
推荐引用方式 GB/T 7714	Wu, Yunfeng,Yang, Shanshan,Zheng, Fang,et al. Removal of artifacts in knee joint vibroarthrographic signals using ensemble empirical mode decomposition and detrended fluctuation analysis[J]. http://dx.doi.org/10.1088/0967-3334/35/3/429,2014.
APA	Wu, Yunfeng.,Yang, Shanshan.,Zheng, Fang.,Cai, Suxian.,Lu, Meng.,...&吴梅红.(2014).Removal of artifacts in knee joint vibroarthrographic signals using ensemble empirical mode decomposition and detrended fluctuation analysis.http://dx.doi.org/10.1088/0967-3334/35/3/429.
MLA	Wu, Yunfeng,et al."Removal of artifacts in knee joint vibroarthrographic signals using ensemble empirical mode decomposition and detrended fluctuation analysis".http://dx.doi.org/10.1088/0967-3334/35/3/429 (2014).