Sample entropy analysis of surface EMG for improved muscle activity onset detection against spurious background spikes |
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| Authors: | Xu Zhang Ping Zhou |
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| Institution: | 1. Sensory Motor Performance Program, Rehabilitation Institute of Chicago, IL, USA;2. Department of Physical Medicine & Rehabilitation, Northwestern University, Chicago, IL, USA;3. Institute of Biomedical Engineering, University of Science and Technology of China, Hefei, China;1. Hefei University of Technology (HFUT), Hefei, PR China;2. State Key Laboratory of Robotics and System, Harbin Institute of Technology (HIT), Harbin, PR China;1. School of Health Professions, Faculty of Health Education and Society, Plymouth University, UK;2. Stroke Rehabilitation Unit, Mount Gould Hospital Plymouth Teaching Primary Care Trust, UK;1. School of Electrical & Electronic Engineering, University College Dublin, Belfield, Dublin 4, Ireland;2. Department of Neurology, Odense University Hospital, University of Southern Denmark, Odense, Denmark;3. Integrated Physiology, Dept. of Nutrition, Exercise & Sports, University of Copenhagen, Copenhagen, Denmark;4. Insight Centre for Data Analytics, O’Brien Centre for Science, University College Dublin, Belfield, Dublin 4, Ireland;1. Department of Electronics and Communication Engineering, PDPM Indian Institute of Information Technology, Design and Manufacturing Jabalpur, India;2. Department of Electrical Engineering, Indian Institute Technology, Roorkee, India |
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| Abstract: | Voluntary surface electromyogram (EMG) signal is sometimes contaminated by spurious background spikes of both physiological and extrinsic or accidental origins. A novel method of muscle activity onset detection against such spurious spikes was proposed in this study based primarily on the sample entropy (SampEn) analysis of the surface EMG. The method takes advantage of the nonlinear properties of the SampEn analysis to distinguish voluntary surface EMG signals from spurious background spikes in the complexity domain. To facilitate muscle activity onset detection, the SampEn analysis of surface EMG was first performed to highlight voluntary EMG activity while suppressing spurious background spikes. Then, a SampEn threshold was optimized for muscle activity onset detection. The performance of the proposed method was examined using both semi-synthetic and experimental surface EMG signals. The SampEn based methods effectively reduced the detection error induced by spurious background spikes and achieved improved performance over the methods relying on conventional amplitude thresholding or its extended version in the Teager Kaiser Energy domain. |
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