Abstract
Electromyogram (EMG) signal is a demonstration of muscular contraction. This being a non-stationary signal is distorted by Baseline Wander. Proper correction of Baseline Wander is a major issue while acquiring EMG signals as it may deteriorate the quality of the signal and make its diagnostic analysis difficult. This paper aims at proposing an effective method for Baseline Wander correction in the baseline-drifted EMG signals. Canonical correlation analysis (CCA) algorithm is first performed on the baseline-corrupted EMG signals to decompose them into various canonical components or variates. After that, morphological filtering deploying octagon-shaped structuring element is used to filter each canonical component. Finally, the results of the proposed technique are compared with the CCA-Gaussian- and CCA-thresholding-based techniques. Simulation results report that the Baseline Wander correction approach used in this work satisfyingly eliminates the Baseline Wander from EMG signals while distorting the original EMG signal to a minimum.
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Bhateja, V. et al. (2018). Baseline Correction in EMG Signals Using Mathematical Morphology and Canonical Correlation Analysis. In: Bhateja, V., Coello Coello, C., Satapathy, S., Pattnaik, P. (eds) Intelligent Engineering Informatics. Advances in Intelligent Systems and Computing, vol 695. Springer, Singapore. https://doi.org/10.1007/978-981-10-7566-7_58
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DOI: https://doi.org/10.1007/978-981-10-7566-7_58
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