Abstract
Knee osteoarthritis (KOA) is one of the major causes of lower limb disability. This study aims to develop a computer-based approach to discriminate KOA individuals from controls by using entropy-based features, and therefore to provide an auxiliary, quantitative tool for KOA diagnosis. The surface EMG (sEMG) data were collected from the vastus lateralis, vastus medialis, biceps femoris, and semitendinosus when KOA participants and controls were walking barefoot on ground at a self-paced speed. We employed and compared three different entropy measures, including 1) approximate entropy, 2) sample entropy, 3) fuzzy entropy, for extracting KOA-related features from the sEMG signals for classification. The differences between the KOA group and healthy controls are primarily shown in the fuzzy entropy features extracted from the vastus medialis and biceps femoris muscle pair. Among all tested measures, the fuzzy entropy yielded the best performance in distinguishing KOA patients from controls, with 92% of accuracy, 91.43% of sensitivity and 93.33% of specificity. The results indicate that the fuzzy entropy method is applicable for extracting KOA-related features from sEMG, which can be developed as a sensitive metric for computer-assist diagnosis of knee osteoarthritis.
Highlights
Osteoarthritis (OA) is a progressive degenerative disorder characterized by the destruction of articular cartilage, resulting in joint pain, stiffness, limitation of movement, and even long-term disability, and it may require a joint replacement surgery [1], [2]
In the test of between-subjects effects, for approximate entropy (ApEn), there are no significance found (p = 0.855, 0.610, 0.018, 0.013 for vastus lateralis (VL), vastus medialis (VM), biceps femoris (BF) and ST respectively); for sample entropy (SampEn), significant differences are found in VL (p < 0.001), VM (p < 0.001) and ST (p < 0.001) while not in BF (p = 0.117); for fuzzy entropy (FuzzyEn), significant differences are found in all four muscles (p < 0.001)
(c), FuzzyEn values of all four muscle from knee OA (KOA) group are approximate two times greater than control groups. These statistical results indicate that the surface electromyogram (sEMG) signals of KOA patients contain more chaotic dynamics than the signals of control subjects, which are reflected with a larger entropy value
Summary
Osteoarthritis (OA) is a progressive degenerative disorder characterized by the destruction of articular cartilage, resulting in joint pain, stiffness, limitation of movement, and even long-term disability, and it may require a joint replacement surgery [1], [2]. It has been reported that the global prevalence rate of OA is around 15%, and the incidence rate for individuals aged 50 and above is as higher as 50%. The possibility of ending disabled is around 53% [3], [4]. Knees are joints primarily affected by OA, since they are bearing the weight of body. The knee OA (KOA) is a leading cause of lower limb functional disorder in elderly individuals, which greatly affects their ability to implement activities of daily living, such as walking.
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