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Research Papers: Fractal Engineering and Biomedicine

Analyzing Origin of Multifractality of Surface Electromyography Signals in Dynamic Contractions

[+] Author and Article Information
Kiran Marri

NIID Lab,
Biomedical Engineering Group,
Department of Applied Mechanics,
Indian Institute of Technology Madras,
Chennai 600036, India
e-mail: kirankmr@gmail.com

Ramakrishnan Swaminathan

NIID Lab,
Biomedical Engineering Group,
Department of Applied Mechanics,
Indian Institute of Technology Madras,
Chennai 600036, India
e-mail: sramki@iitm.ac.in

1Corresponding author.

Manuscript received July 31, 2015; final manuscript received November 4, 2015; published online March 8, 2016. Assoc. Editor: Charalabos Doumanidis.

J. Nanotechnol. Eng. Med 6(3), 031002 (Mar 08, 2016) (9 pages) Paper No: NANO-15-1057; doi: 10.1115/1.4032005 History: Received July 31, 2015; Revised November 04, 2015

The aim of this study is to analyze the origin of multifractality of surface electromyography (sEMG) signals during dynamic contraction in nonfatigue and fatigue conditions. sEMG signals are recorded from triceps brachii muscles of 22 healthy subjects. The signals are divided into six equal segments on time scale for normalization. The first and sixth segments are considered as the nonfatigue and fatigue conditions, respectively. The source of multifractality can be due to correlation and probability distribution. The original sEMG series are transformed into shuffled and surrogate series. These three series namely, original, shuffled, and surrogate series in the nonfatigue and fatigue conditions are subjected to multifractal detrended fluctuation analysis (MFDFA) and features are extracted. The results indicate that sEMG signals exhibit multifractal behavior. Further investigation revealed that origin of multifractality is primarily due to correlation. The origin of multifractality due to correlation is quantified as 80% in nonfatigue and 86% in fatigue conditions. This method of multifractal analysis may be useful for analyzing the progressive changes in muscle contraction in varied neuromuscular studies.

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Figures

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Fig. 1

Overall methodology for determining origin of multifractality of sEMG signals

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Fig. 2

Representative sEMG signals with the nonfatigue and fatigue segments

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Fig. 3

Representative sEMG signal in (a) nonfatigue and (b) fatigue zones along with shuffled and surrogated series

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Fig. 4

Fluctuation function for selected orders in the (a) nonfatigue and (b) fatigue condition

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Fig. 5

Generalized Hurst exponent function for a representative signal in the (a) nonfatigue and (b) fatigue conditions using original, shuffled, and surrogate series

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Fig. 6

DOM in the (a) nonfatigue and (b) fatigue conditions for all subjects using original, shuffled, and surrogate series

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Fig. 7

Multifractal spectrum of the (a) nonfatigue and (b) fatigue conditions using three series for a representative subject

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Fig. 8

Origin of multifractality due to correlation (square) and PDF (circle) in the nonfatigue and fatigue conditions for all subjects

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Fig. 9

DOM due to small fluctuations in the nonfatigue and fatigue conditions

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