Knee Joint Vibroarthrographic Signal Processing and Analysis

93 Pages·2015·2.92 MB·English

by  Yunfeng Wu (auth.)

SPRINGER BRIEFS IN BIOENGINEERING
Yunfeng Wu
Knee Joint
Vibroarthrographic
Signal Processing
and Analysis
123
SpringerBriefs in Bioengineering
Moreinformationaboutthisseriesathttp://www.springer.com/series/10280
Yunfeng Wu
Knee Joint
Vibroarthrographic Signal
Processing and Analysis
123
YunfengWu
SchoolofInformationScienceandTechnology
XiamenUniversity
Xiamen,Fujian,China
ISSN2193-097X ISSN2193-0988 (electronic)
SpringerBriefsinBioengineering
ISBN978-3-662-44283-8 ISBN978-3-662-44284-5 (eBook)
DOI10.1007/978-3-662-44284-5
LibraryofCongressControlNumber:2014959278
SpringerHeidelbergNewYorkDordrechtLondon
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Tomygrandparents:WeiqingWu,MeihuiSu;
ShaowenXiong,XiuyingRuan
YunfengWu

Preface
The knee plays an important role in human locomotion activities and daily
performance. However, the knee joint often suffers from different inflammations
andimpacttraumasuchasosteoarthritis,tearsofmeniscus,andcartilagedisorders.
Vibration arthrometry is a noninvasive technique which has high potential for
effectivedetectionofkneepathologyinroutineexaminations.Thisbookprovidesa
systematicaldescriptiononthevibroarthrograpymethodology,alongwiththerecent
advances in vibroarthrographic signal preprocessing, feature analysis, and pattern
classification.Theoverallcontextofthebookiscomposedoffivechapters.
Chapter 1 presents knee anatomy, together with the descriptions of joint struc-
tures in detail. The text introduces knee biomechanics and different types of knee
joint disorders. An overview of knee joint pathology detection methods, such as
X-rayimaging,computedtomography,magneticresonanceimaging,ultrasonogra-
phy,opticalcoherencetomography,arthroscopy,andvibroarthrography,isgivenin
thechapteraswell.
Chapter 2provides theflowchart thatshows the entireprocedures of knee joint
vibroarthrographic signal analysis. The chapter concentrates on the instrument
settings and experiment protocol for signal acquisition, as well as the artifact
removal in the signal preprocessing. The text describes several signal processing
methods to eliminate the baseline wander, random noise, and muscle contraction
interference.
Chapter 3 discusses the vibroarthrographic signal processing and analysis
approachesintimeandfrequencydomains.Thespatiotemporalprocessingmethods
contain the temporal waveform analysis, adaptive segmentations, and time-variant
signal fluctuation or complexity analysis. The frequency and time-frequency
analysis based on Fourier transform and matching pursuit decomposition are also
provided with the detailed mathematical representations. The chapter also reviews
the recent development of statistical analysis for vibroarthrographic signal feature
extraction.
Chapter 4 first presents the advantages of feature selection and dimensionality
reduction for signal pattern analysis. Then, the chapter introduces a few machine
learning paradigms for vibroarthrographic signal classifications, including the
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Fisher’s linear discriminant analysis, radial basis function network, support vector
machines, Bayesian decision rule, and multiple classifier fusion systems. The
chapter also summarizes and compares the diagnostic results and key findings of
severalpreviousstudiesonvibroarthrographicsignalclassifications.
Chapter5concludesthebookwithashortreviewofthecutting-edgetechnolo-
giesforkneepathologydiagnosis,andthensummarizesthestate-of-the-artmethods
forvibroarthrographicsignalanalysis.Thechapterendswithadiscussiononsome
interestingtopicsandchallengesforfutureresearch.
Xiamen,Fujian,China YunfengWu
Acknowledgements
I would like to take this opportunity to express my gratitude to Prof. Rangaraj
M. Rangayyan, who supervised my PhD dissertation on this research topic, and
Prof.SridharKrishnan,whocollaboratedwithmeinthefieldofbiomedicalsignal
analysis. I also thank the members of my research group: Ms. Suxian Cai, Ms.
Shanshan Yang, Ms. Xin Luo, Mr. Kaizhi Liu, Mr. Lei Shi, Ms. Fang Zheng, Mr.
Meng Lu, Ms. Pinnan Chen, and Prof. Meihong Wu, for their diligent work and
hearty contributions on the research projects. Finally, I acknowledge the research
grants supported by the National Natural Science Foundation of China (grant no.
81101115),theFundamentalResearchFundsfortheCentralUniversitiesofChina
(grant no. 2010121061), and the Program for New Century Excellent Talents in
FujianProvinceUniversity.
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