Abstract
In spatiotemporal multi-channel surface electromyogram (EMG) images where the x-axis is time, the y-axis is EMG channels and the gray level is EMG amplitude, the motor unit action potential (MUAP) appears as a linear Gaussian structure. The appearance of this MUAP pattern in the spatiotemporal images is mostly distorted either by the destructive superposition of other MUAPs occurring in the conducting volume or by various noises such as a power line, bad electrode and skin contacts and movement artifacts. For accurate automatic detection of MUAP, EMG image enhancement is needed to suppress the background noises and enhance the line-like MUAP propagation patterns. This study presents several candidate filters to enhance the MUAPs propagation pattern in spatiotemporal EMG images. The filters, which can detect and enhance line-like structure in digital images, are used. Specifically, the Hermite shape filter is used for EMG image enhancement and compared with Gabor filter and steerable filters. The performance of the filters regarding accuracy, specificity, and sensitivity is evaluated with real sEMG signal measured from different muscles and computer-generated EMG signals. In the enhanced images the visibility of the MUAP region is improved. These results can help in better estimation of muscle characteristics from sEMG signals.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.References
Ambikapathy B, Krishnamurthy K (2018) Analysis of electromyograms recorded using invasive and noninvasive electrodes: a study based on entropy and Lyapunov exponents estimated using artificial neural networks. J Ambient Intell Hum Comput. https://doi.org/10.1007/s12652-018-0811-6
Amrutha N, Arul VH (2017) A review on noises in EMG signal and its removal. Int J Sci Res Publ 7:23–27
Bovik AC, Clark M, Geisler WS (1990) Multichannel texture analysis using localized spatial filters. IEEE Trans Pattern Anal Mach Intell 12(1):55–73. https://doi.org/10.1109/34.41384
Clausi DA, Jernigan EM (2000) Designing Gabor filters for optimal texture separability. Pattern Recognit 33(11):1835–1849. https://doi.org/10.1016/S0031-3203(99)00181-8
Dosaj A, Satapathy SC, Soundrapandiyan R, Kaur M, Hannoon N (2018) An efficient cloud based face recognition system for e-health secured login using steerable pyramid transform and local directional pattern. J Ambient Intell Hum Comput 1:1. https://doi.org/10.1007/s12652-018-1115-6
Farina D, Merletti R (2001) A novel approach for precise simulation of the EMG signal detected by surface electrodes. IEEE Trans Biomed Eng 48(6):637–646. https://doi.org/10.1109/10.923782
Freeman WT, Adelson EH (1991) The design and use of steerable filters. IEEE Trans Pattern Anal Mach Intell 13(9):891–906. https://doi.org/10.1109/34.93808
Huigen E, Peper A, Grimbergen CA (2002) Investigation into the origin of the noise of surface electrodes. Med Biol Eng Comput 40(3):332–338. https://doi.org/10.1007/BF02344216
Jacob M, Unser M (2004) Design of steerable filters for feature detection using canny-like criteria. IEEE Trans Pattern Anal Mach Intell 26(8):1007–1019. https://doi.org/10.1109/TPAMI.2004.44
Lo Conte LR, Merletti R, Sandri GV (1994) Hermite expansions of compact support waveforms: applications to myoelectric signals. IEEE Trans Biomed Eng 41(12):1147–1159. https://doi.org/10.1109/10.335863
Sato Y, Nakajima S, Shiraga N, Atsumi H, Yoshida S, Koller T et al (1998) Three-dimensional multi-scale line filter for segmentation and visualization of curvilinear structures in medical images. Med Image Anal 2(2):143–168. https://doi.org/10.1016/S1361-8415(98)80009-1
Soares FA, de Andrade MM, da Rocha AF, Merletti R (2010) Automatic tracking of innervation zones using image processing methods. In: ISSNIP Biosignals and Biorobotics Conference, pp 214–218
Staudenmann D, Kingma I, Daffertshofer A, Stegeman DF, VanDieen JH (2006) Improving EMG-based muscle force estimation by using a high-density EMG grid and principal component analysis. IEEE Trans Biomed Eng 53(4):712–719. https://doi.org/10.1109/TBME.2006.870246
Tan J-W, Walter S, Scheck A, Hrabal D, Hoffmann H, Kessler H, Traue HC (2012) Repeatability of facial electromyography (EMG) activity over corrugator supercilii and zygomaticus major on differentiating various emotions. J Ambient Intell Hum Comput 3(1):3–10. https://doi.org/10.1007/s12652-011-0084-9
Ullah K, Afsharipour B, Merletti R (2014a) EMG topographic image enhancement using multi scale filtering. In: Roa Romero LM (ed) XIII mediterranean conference on medical and biological engineering and computing 2013. Springer, Cham, pp 674–677
Ullah Khalil, Cescon C, Afsharipour B, Merletti R (2014b) Automatic detection of motor unit innervation zones of the external anal sphincter by multichannel surface EMG. J Electromyogr Kinesiol 24(6):860–867. https://doi.org/10.1016/J.JELEKIN.2014.05.003
van Dijk L, van der Sluis CK, van Dijk HW, Bongers RM (2016) Learning an EMG controlled game: task-specific adaptations and transfer. PLoS One 11(8):e0160817. https://doi.org/10.1371/journal.pone.0160817
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Islam, I.u., Ullah, K., Afaq, M. et al. Spatio-temporal sEMG image enhancement and motor unit action potential (MUAP) detection: algorithms and their analysis. J Ambient Intell Human Comput 10, 3809–3819 (2019). https://doi.org/10.1007/s12652-019-01411-1
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12652-019-01411-1