| Author Name | Affiliation | | LI Yu-rong | College of Electrical Engineering and Automation,Fuzhou University,Fuzhou 350108,China
Fujian Key Laboratory of Medical Instrumentation & Pharmaceutical Technology,Fuzhou 350002,China | | LIAO Zhi-wei | College of Electrical Engineering and Automation,Fuzhou University,Fuzhou 350108,China
Fujian Key Laboratory of Medical Instrumentation & Pharmaceutical Technology,Fuzhou 350002,China | | DU Min | Fujian Key Laboratory of Medical Instrumentation & Pharmaceutical Technology,Fuzhou 350002,China |
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| Abstract: |
| The sEMG signals are collected from the vastus lateralis,vastus medialis,biceps femoris,and semitendinosus of lower extremity during level walking among control subjects and knee osteoarthritis (OA) patients,the latter including mild,moderate and severe degree.The 5-fold cross-validation is used to measure the accuracy of the proposed analysis algorithm on collected sEMG recordings.For comparison,the more classical feature vectors of form factor,degree of skewness,kurtosis,and wavelet entropy are also tested.In experiment,the normalized energy ratio and marginal spectrum ratio achieve larger accuracy than the other features for all the four muscular groups.Moreover the accuracy of vastus medialis and biceps femoris are larger than that of vastus lateralis and semitendinosus.These results suggest that the normalized energy ratio and marginal spectrum ratio via the analysis of knee sEMG signals by HHT can server as characteristic parameters to easily classify osteoarthritis with noninvasive method.The more important muscular groups for maintaining the knee joint function are medialis and biceps femoris;as a result of that they should be exercise especially for rehabilitation. |
| Key words: osteoarthritis (OA) noninvasive diagnosis surface electromyography (sEMG) Hilbert-Huang Transform (HHT) neural network classifier |
| DOI：10.11916/j.issn.1005-9113.2011.06.022 |
| Clc Number:R318 |
| Fund: |
