The ability to recognize various forms of contaminants in surface electromyography (EMG) signals and to ascertain the overall quality of such signals is important in many EMG-enabled rehabilitation systems. In this paper, new methods for the automatic identification of commonly occurring contaminant types in surface EMG signals are presented. Such methods are advantageous because the contaminant type is typically not known in advance. The presented approach uses support vector machines as the main classification systems. Both simulated and real EMG signals are used to assess the performance of the methods. The contaminants considered include: 1) electrocardiogram interference, 2) motion artifact, 3) power line interference, 4) amplifier saturation and 5) additive white Gaussian noise. Results show that the contaminants can readily be distinguished at lower signal to noise ratios, with a growing degree of confusion at higher signal to noise ratios, where their effects on signal quality are less significant.
|Number of pages||10|
|Journal||IEEE Transactions on Neural Systems and Rehabilitation Engineering|
|Early online date||21 Jan 2014|
|Publication status||Published - 31 Jul 2014|
- signal to noise ratio
- support vector machines