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中文
Yunfeng Wu

Associate professor

Supervisor of Master's Candidates


E-Mail:

Date of Employment:2009-12-14

School/Department:信息学院

Education Level:博士研究生毕业

Business Address:厦门大学翔安校区西部片区6号楼(睿信楼)202室

Gender:Male

Contact Information:yunfengwu@xmu.edu.cn

Degree:Doctor of Engineering (D.Eng.)

Status:在职

Alma Mater:北京邮电大学

Discipline:生物医学工程
信号与信息处理

Academic Honor:

2013   Outstanding talents in the new century

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

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DOI number:10.1088/0967-3334/35/3/429

Journal:Physiological Measurement

Abstract: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.

Co-author:Shanshan Yang,Fang Zheng,Suxian Cai,Meng Lu,Meihong Wu

First Author:Yunfeng Wu*

Indexed by:Article

Volume:35

Issue:3

Page Number:429-439

Translation or Not:no

Date of Publication:2014-02-12

Included Journals:SCI

Links to published journals:https://doi.org/10.1088/0967-3334/35/3/429