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Fractals and Electromyograms

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The Fractal Geometry of the Brain

Part of the book series: Springer Series in Computational Neuroscience ((NEUROSCI))

Abstract

The complexity nature of the physiological time series can be analysed using fractal theory. The nonlinearity of physiological systems have important relevance in modelling complicated surface electromyogram (sEMG) where the interactions and crosstalk occur over a wide range of temporal and spatial scales. Fractal theory-based analysis is one of the most promising new approaches for extracting such hidden interactions from physiological time series signal like sEMG, which can provide information regarding the characteristic temporal scales and the adaptability of muscle activity response. This chapter investigates the use of fractal theory for analysis of sEMG signal for applications in rehabilitation and age-related changes in the muscle properties and contraction.

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Authors and Affiliations

  1. School of Engineering, RMIT University, Melbourne, VIC, Australia

    Sridhar Poosapadi Arjunan & Dinesh Kant Kumar

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  1. Sridhar Poosapadi Arjunan
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  2. Dinesh Kant Kumar
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Correspondence to Sridhar Poosapadi Arjunan .

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Editors and Affiliations

  1. Department of Surgery Division of Neurosurgery, University of Toronto St. Michael's Hospital, Toronto, Ontario, Canada

    Antonio Di Ieva

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Arjunan, S.P., Kumar, D.K. (2016). Fractals and Electromyograms. In: Di Ieva, A. (eds) The Fractal Geometry of the Brain. Springer Series in Computational Neuroscience. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-3995-4_27

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