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The main aim of our study was to investigate the possibility of applying machine learning techniques to the analysis of electromyographic patterns (EMG) collected from arthritic patients during gait. The EMG recordings were collected from the lower limbs of patients with arthritis and compared with those of healthy subjects (CO) with no musculoskeletal disorder. The study involved subjects suffering from two forms of arthritis, viz, rheumatoid arthritis (RA) and hip osteoarthritis (OA). The analysis of the data was plagued by two problems which frequently render the analysis of this type of data extremely difficult. One was the small number of human subjects that could be included in the investigation based on the terms specified in the inclusion and exclusion criteria for the study. The other was the high intra- and inter-subject variability present in EMG data. We identified some of the muscles differently employed by the arthritic patients by using machine learning techniques to classify the two groups and then identified the muscles that were critical for the classification. For the classification we employed least-squares kernel (LSK) algorithms, neural network algorithms like the Kohonen self organizing map, learning vector quantification and the multilayer perceptron. Finally we also tested the more classical technique of linear discriminant analysis (LDA). The performance of the different algorithms was compared. The LSK algorithm showed the highest capacity for classification. Our study demonstrates that the newly developed LSK algorithm is adept for the treatment of biological data. The muscles that were most important for distinguishing the RA from the CO subjects were the soleus and biceps femoris. For separating the OA and CO subjects however, it was the gluteus medialis muscle. Our study demonstrates how classification with EMG data can be used in the clinical setting. While such procedures are unnecessary for the diagnosis of the type of arthritis pre- - sent, an understanding of the muscles which are responsible for the classification can help to better identify targets for rehabilitative measures.
Neural Systems and Rehabilitation Engineering, IEEE Transactions on (Volume:18 , Issue: 2 )
Date of Publication: April 2010