Journal ArticleDOI
Analyzing Origin of Multifractality of Surface Electromyography Signals in Dynamic Contractions
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.read more
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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TL;DR: In this article, the authors developed a method for the multifractal characterization of nonstationary time series, which is based on a generalization of the detrended fluctuation analysis (DFA).
Journal ArticleDOI
Multifractality in human heartbeat dynamics
Plamen Ch. Ivanov,Plamen Ch. Ivanov,Luís A. Nunes Amaral,Luís A. Nunes Amaral,Ary L. Goldberger,Shlomo Havlin,Michael Rosenblum,Zbigniew R. Struzik,H. Eugene Stanley +8 more
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Electromyography. Physiology, engineering and non invasive applications
TL;DR: This work focuses on the development of models for Surface EMG Signal Generation based on the principles of Structure--Based SEMG models, which were developed in the context of motor control and Muscle Contraction.