سال انتشار: ۱۳۹۱

محل انتشار: بیستمین کنفرانس مهندسی برق ایران

تعداد صفحات: ۴

نویسنده(ها):

Sara Abbaspour – Biomedical Engineering Faculty, Amirkabir University of Technology
Ali Fallah –
Ali Maleki – Electrical and Computer Engineering Faculty, Semnan University

چکیده:

Surface electromyograms (EMGs) are valuable in the pathophysiological study and clinical treatment. These recordings are critically often contaminated by cardiac artifact. The purposeof this article was to evaluate the performance of an adaptive filter and artificial neural network (ANN) in removingelectrocardiogram (ECG) contamination from surface EMGs recorded from the pectoralismajor muscles. Performance of thesemethods was quantified by power spectral density, coherence, signal to noise ratio, relative error and cross correlation in simulated noisy EMG signals. In between these two methods theANN has better results