Abstract:
:This study aimed at developing a method for automated electrocardiography (ECG) artifact detection and removal from trunk electromyography signals. Independent Component Analysis (ICA) method was applied to the simulated data set of ECG-corrupted surface electromyography (SEMG) signals. Independent Components (ICs) correspond to ECG artifact were then identified by an automated detection algorithm and subsequently removed. The detection performance of the algorithm was compared to that by visual inspection, while the artifact elimination performance was compared with Butterworth high pass filter at 30 Hz cutoff (BW HPF 30). The automated ECG-artifact detection algorithm successfully recognized the ECG source components in all data sets with a sensitivity of 100% and specificity of 99%. Better performance indicated by a significantly higher correlation coefficient (p<0.001) with the original EMG recordings was found in the SEMG data cleaned by the ICA-based method, than that by BW HPF 30. The automated ECG-artifact removal method for trunk SEMG recordings proposed in this study was demonstrated to produce a very good detection rate and preserved essential EMG components while keeping its distortion to minimum. The automatic nature of our method has solved the problem of visual inspection by standard ICA methods and brings great clinical benefits.
journal_name
Med Eng Physjournal_title
Medical engineering & physicsauthors
Mak JN,Hu Y,Luk KDdoi
10.1016/j.medengphy.2010.05.007subject
Has Abstractpub_date
2010-10-01 00:00:00pages
840-8issue
8eissn
1350-4533issn
1873-4030pii
S1350-4533(10)00112-8journal_volume
32pub_type
杂志文章abstract::In order to assess the actual load on front teeth and tooth supportive tissue during brass instrument playing, a measuring system was developed permitting simultaneous recordings of mouthpiece forces and incisor deflections. By the application of strain gauges, the mouthpiece itself was converted into an extra-oral fo...
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