The focus of this work was to modify an existing algorithm originally designed for data mining software metrics and evaluate its usefulness as a quality assessment tool for surface electromyography (sEMG) signals. The pairwise attribute noise detection algorithm (PANDA) was configured to distinguish between clean and noisy sEMG signals. Multiple testing was performed to find the most effective configuration for contamination detection. Data contaminated with power line interference, motion artifact, saturation and combinations of the three were studied. Both simulated and recorded data were used in the configuration and testing stages of this work. PANDA was found to be able to detect low levels of contamination (SNRs of 3 – 17 dB, depending on the type of noise) with high sensitivity (100%). After verifying PANDA’s effectiveness, the algorithm was compared to a one-class support vector machine (SVM) designed for the same purpose. For all types of noise, PANDA was more sensitive than the SVM.
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