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graham fraser
automatic biosignal quality analysis for surface electromyography
CleanEMG is a collaborative research project between Carleton University and the University of New Brunswick. The objective of CleanEMG is to develop an open source user-friendly software tool to perform automatic noise quantification in surface electromyography (sEMG). During analysis, such a tool enables rejection of sEMG records that contain large amounts of interference, avoiding improper conclusions being drawn from poor data. Such a tool also allows for validation of an sEMG system setup, detecting issues in the acquired sEMG (e.g., power line interference, motion artifact) and potentially identifying sources of contamination. In addition to quantifying the level of contamination, methods may be extended to enable the judicial removal of contaminants through advanced signal processing. The CleanEMG tool would remove the requirement for a trained sEMG technician for proper and reliable sEMG signal acquisition. CleanEMG will simplify sEMG data acquisition, improve its reliability, and reduce associated time and costs.
Thesis supervisors Adrian D. C. Chan and James Green
biography
Graham Fraser is currently working on his Masters in Applied Science in Biomedical Engineering at Carleton University. His research interests include biosignal quality assessment and noise removal, digital signal processing, and neural signal processing. He received a Bachelors of Engineering in Software Engineering Co-op with High Distinction in 2010 from Carleton University. He has also received the Senate Medal for Outstanding Academic Achievement from Carleton University (2010), an NSERC USRA research grant award (2010), and the NSERC Alexander Graham Bell Canada Graduate Scholarship (2011). He has done co-op work placements at Entrust Ltd., IBM, and Rockwell Collins. He is currently treasurer for the Carleton University Biomedical Engineering graduate student club, CU@EMBS.
publications
- , "Automated Biosignal Quality Analysis for Electromyography using a One-Class Support Vector Machine", accepted to IEEE Transactions on Instrumentation and Measurement, 2014.
- , "Identification of Contaminant Type in Surface Electromyography (EMG) Signals", accepted to IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2014.
- , "Biosignal Quality Analysis of Surface EMG using a Correlation Coefficient Test for Normality", IEEE International Symposium on Medical Measurements and Applications, Ottawa, Canada, pp. 196-200, 2013.
- , "Detection of ADC clipping, quantization noise, and amplifier saturation in surface electromyography", IEEE International Symposium on Medical Measurements and Applications, pp. 162-166, Budapest, Hungary, 2012.
- , "CleanEMG: Comparing interpolation strategies for power line interference quantification in surface EMG signals", 35th Conference of the Canadian Medical & Biological Engineering Society , Halifax, Canada, 2012.
- , "Removal of electrocardiogram artifacts in surface electromyography using a moving average method", IEEE International Symposium on Medical Measurements and Applications, Budapest, Hungary, pp. 128-131, 2012.
- , "CleanEMG - Power line inteference estimation in sEMG using an adaptive least squares algorithm", 32nd Annual International Conference of the IEEE-EMBS, Boston MA, USA, pp. 7941-7944, 2011.
- , "CleanEMG: Quantifying power line interference in surface EMG signals", 34th Conference of the Canadian Medical & Biological Engineering Society and Festival of International Conferences on Caregiving, Disability, Aging and Technology, Toronto, Canada, 69825, pp. 1-4, 2011.
Last updated June 10, 2012