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Journal ArticleDOI

Analyzing Origin of Multifractality of Surface Electromyography Signals in Dynamic Contractions

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TLDR
In this paper, the origin of multifractality of surface electromyography (sEMG) signals during dynamic contraction in nonfatigue and fatigue conditions was analyzed and the results indicated that sEMG signals exhibit multifractal behavior.
Abstract
The aim of this study is 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 twenty two normal healthy subjects. The signals are divided into six equal segments on time scale for normalization. The first and sixth segments are considered as nonfatigue and fatigue condition 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 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 progressive changes in muscle contraction in varied neuromuscular studies.

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Citations
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Journal ArticleDOI

A systematic review on fatigue analysis in triceps brachii using surface electromyography

TL;DR: This study will guide and direct new researchers to areas that remain hidden in the human triceps brachii muscle through surface electromyography (sEMG) observations and identify areas that require further in-depth research.
Journal ArticleDOI

Multifractal Analysis of Uterine Electromyography Signals for the Assessment of Progression of Pregnancy in Term Conditions

TL;DR: The Hurst and scaling exponents extracted from the signals indicate that uterine EMG signals are multifractal in nature and can help in investigating the progressive changes in uterine muscle contractions during pregnancy.
Journal ArticleDOI

Analysis of concentric and eccentric contractions in biceps brachii muscles using surface electromyography signals and multifractal analysis

TL;DR: It appears that these multifractal features extracted from the concentric and eccentric contractions can be useful in the assessment of surface electromyography signals in sports medicine and training and also in rehabilitation programs.
Book

Multifractals and Chronic Diseases of the Central Nervous System

TL;DR: This chapter outlines the general description of the diseases like epilepsy, Parkinson’s, Huntington's, Alzheimer's, and motor neuron diseases, and a discussion on the diagnostic tools and the methodologies adapted is reviewed in detail.
Proceedings ArticleDOI

Analyzing the influence of curl speed in fatiguing biceps brachii muscles using sEMG signals and multifractal detrended moving average algorithm

TL;DR: Results show that multifractal features are able to differentiate sEMG signals in fatiguing conditions and the multifractality increased with faster curls as compared with slower curl speed by 12%.
References
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Journal ArticleDOI

Can muscle coordination be precisely studied by surface electromyography

TL;DR: The appropriateness of using EMG recordings for studying muscle coordination from EMG signals is discussed, and the main intrinsic drawbacks of the EMG technique are described and discussed.
Journal ArticleDOI

Electromyographic models to assess muscle fatigue.

TL;DR: This paper provides an overview of linear and non-linear sEMG models for estimating muscle fatigue, their ability to assess power loss and their limitations due to neuromuscular changes after a training period.
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Echolocation in Oilbirds and swiftlets

TL;DR: The advent of light-weight recording equipment and custom software for examining signals and reconstructing flight paths now provides the potential to study the echolocation behavior of birds in more detail and resolve ongoing discrepancies about several details.
Journal ArticleDOI

Analysis of intramuscular electromyogram signals

TL;DR: The current clinical use of intramuscular EMG signals relates to the diagnosis of myopathies, of diseases of the α-motor neuron and of the neuromuscular junction through the analysis of the interference signal or of the shape of some motor unit action potentials, usually without a full decomposition of the signal.
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