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Detrending knee joint vibration signals with a cascade moving average filter

Authors: Suxian Cai; Yunfeng Wu; Ning Xiang; Zhangting Zhong; Jia He 0012; Lei Shi; Fang Xu;

Detrending knee joint vibration signals with a cascade moving average filter

Abstract

Knee joint vibration signals are very useful for computer-aided analysis of the pathological conditions in the knee. In a vibration arthrometry test, the legs of patients with knee joint disorders may tremble due to the reaction of pain, which causes the baseline wander that may affect the diagnostic decision making in medical study. This paper presents a new type of cascade moving average filter with hierarchical layers to remove the baseline wander in the raw knee joint vibration signals. The first layer of the cascade filter contains two moving averaging operators with the same order. The five tail inputs of the first moving averaging operator are overlapping with the beginning inputs of the successive operator. The piecewise linear trends estimated by the moving average operators in the first layer were smoothed in the final cascade filter output. The simulation results showed that the cascade filter can effectively remove the baseline wander in the raw knee joint vibration signals.

Country
China (People's Republic of)
Related Organizations
Keywords

Adult, Male, Knee Joint, DISORDERS, VIBROARTHROGRAPHIC SIGNALS, Reproducibility of Results, Signal Processing, Computer-Assisted, DIAGNOSIS, Sensitivity and Specificity, Vibration, 796, RADIAL-BASIS FUNCTIONS, Humans, Female, Diagnosis, Computer-Assisted, Joint Diseases, SYSTEM

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Powered by OpenAIRE graph
Found an issue? Give us feedback
selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
14
Top 10%
Top 10%
Average
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