Journal ArticleDOI

# Analyzing Origin of Multifractality of Surface Electromyography Signals in Dynamic Contractions

01 Aug 2015-Journal of Nanotechnology in Engineering and Medicine (American Society of Mechanical Engineers)-Vol. 6, Iss: 3, pp 031002

TL;DR: 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.

AbstractThe 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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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.
Abstract: Objective The objective of this research was to summarize and analyse the research findings regarding analysis of fatigue in the human triceps brachii (TB) muscle through surface electromyography (sEMG) observations. Methods We systematically searched through five major online scientific databases namely the PubMed, Science Direct, Wiley Online, Springer Link and SCOPUS databases, for articles written in the English language from the year 2001 to March 2017. We specifically searched for the words/phrases “surface electromyography” OR “sEMG” AND “muscle fatigue” AND “triceps” in the title, abstract and keywords to narrow our search and identified 291 articles, of which, 52 were found potentially the most relevant. Results Of 52 considered articles, 11 analysed fatigue in sports, 11 investigated rehabilitation, 15 considered exercises or trainings, 5 used TB as a co-activator or antagonist, and 5 contemplated elbow extension movements. In addition, 4 of the articles investigated both elbow flexors and extensors and 1 studied training effects in rehabilitation. Conclusion Although, many studies in this particular field have considered the TB, further investigations are required to explain some specific facts about fatigue in the TB. The compensation strategy that muscles use to overcome fatigue, the stabilization, overcoming of errors during fatigue along with effect of mental load on brachii muscles and the effect of sports drinks and other eatables on fatigue are a few potential zones that require further in-depth research. This study will guide and direct new researchers to areas that remain hidden.

17 citations

Journal ArticleDOI
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.
Abstract: Objectives: The objectives of this paper are to examine the source of multifractality in uterine electromyography (EMG) signals and to study the progression of pregnancy in the term (gestation period > 37 weeks) conditions using multifractal detrending moving average (MFDMA) algorithm. Methods: The signals for the study, considered from an online database, are obtained from the surface of abdomen during the second (T1) and third trimester (T2). The existence of multifractality is tested using Hurst and scaling exponents. With the intention of identifying the origin of multifractality, the preprocessed signals are converted to shuffle and surrogate data. The original and the transformed signals are subjected to MFDMA to extract multifractal spectrum features, namely strength of multifractality, maximum, minimum, and peak singularity exponents. Results: The Hurst and scaling exponents extracted from the signals indicate that uterine EMG signals are multifractal in nature. Further analysis shows that the source of multifractality is mainly owing to the presence of long-range correlation, which is computed as 79.98% in T1 and 82.43% in T2 groups. Among the extracted features, the peak singularity exponent and strength of multifractality show statistical significance in identifying the progression of pregnancy. The corresponding coefficients of variation are found to be low, which show that these features have low intersubject variability. Conclusion: It appears that the multifractal analysis can help in investigating the progressive changes in uterine muscle contractions during pregnancy.

12 citations

### Cites background from "Analyzing Origin of Multifractality..."

• ...Three possible scenarios namely, SOMsur ∼ SOMori , SOMsur < SOMori , and SOMsur << SOMori indicate strong, medium, and weak correlation respectively [36]....

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• ...sessed by shuffling the original series [31]–[36]....

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• ...The metric with a greater value is an indicator of major cause for multifractality [36]....

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Journal ArticleDOI
23 Jun 2016
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.
Abstract: Muscle contractions can be categorized into isometric, isotonic (concentric and eccentric) and isokinetic contractions. The eccentric contractions are very effective for promoting muscle hypertrophy and produce larger forces when compared to the concentric or isometric contractions. Surface electromyography signals are widely used for analyzing muscle activities. These signals are nonstationary, nonlinear and exhibit self-similar multifractal behavior. The research on surface electromyography signals using multifractal analysis is not well established for concentric and eccentric contractions. In this study, an attempt has been made to analyze the concentric and eccentric contractions associated with biceps brachii muscles using surface electromyography signals and multifractal detrended moving average algorithm. Surface electromyography signals were recorded from 20 healthy individuals while performing a single curl exercise. The preprocessed signals were divided into concentric and eccentric cycles and in turn divided into phases based on range of motion: lower (0°-90°) and upper (>90°). The segments of surface electromyography signal were subjected to multifractal detrended moving average algorithm, and multifractal features such as strength of multifractality, peak exponent value, maximum exponent and exponent index were extracted in addition to conventional linear features such as root mean square and median frequency. The results show that surface electromyography signals exhibit multifractal behavior in both concentric and eccentric cycles. The mean strength of multifractality increased by 15% in eccentric contraction compared to concentric contraction. The lowest and highest exponent index values are observed in the upper concentric and lower eccentric contractions, respectively. The multifractal features are observed to be helpful in differentiating surface electromyography signals along the range of motion as compared to root mean square and median frequency. 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.

8 citations

Book
28 Jan 2019
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.
Abstract: Disease of the central nervous system has been described in the literature as a group of neurological disorders for which the function of the brain or spinal cord is affected. This chapter outlines the general description of the diseases like epilepsy, Parkinson’s, Huntington’s, Alzheimer’s, and motor neuron diseases. Also a discussion on the diagnostic tools and the methodologies adapted is reviewed in detail. 1.1 Central Nervous System and Its Diseases The nervous system controls all activities of human beings. The nervous system consists of the brain and the spinal cord, as well as all the nerves throughout the body. Compared to other living organisms, humans are considered to be superior as the anatomy and physiology of the nervous system of humans are unique. The brain and spinal cord form the central nervous system (CNS), and all other nerves throughout the body are referred to as the peripheral nervous system (PNS). As the central nervous system (CNS) can determine the consciousness of us humans, it has been attributed to be the most complex organ in the human body. All aspects of our behavior from breathing to supporting our thoughts and feelings (Kandel and Squire 2000) are controlled by the nervous system. The human brain is the most sophisticated organ in the human body. The brain regulates vital body functions such as emotion, memory, cognition, motor activities, heart rate, respiration, and digestion. The human brain is a complex network of millions of neurons packed in a matrix of glial cells (Benson et al. 2017). Diseases of the brain may be caused either due to inherent dysfunction of the brain or due to complex interactions of the brain with the environment (Hyman et al. 2006). Brain diseases range widely from common neurological to psychiatric disorders. Throughout the life span, brain diseases affect a very significant portion of the population and are widely spread both across the developed and developing nations. Compared to other diseases, brain diseases account for the highest burden in terms of health, economy, and social capital globally (Nathan et al. 2001). More than 1.5 billion people are affected due to brain disorders worldwide, and with the passage of time, it is feared that this population will increase. Thus there is an urgent need of not © Springer Nature Singapore Pte Ltd. 2019 D. Ghosh et al., Multifractals and Chronic Diseases of the Central Nervous System, https://doi.org/10.1007/978-981-13-3552-5_1 1 only producing more drugs to treat CNS disorders but rigorous research so that early prognosis and diagnosis can be made and is the need of the day to help control this epidemic. Across the life span of human, the nature of the brain disorders changes. In young there is a high prevalence of psychiatric disorders, like depression, anxiety, schizophrenia, and substance abuse, whereas the elderly suffer markedly from neurodegenerative disorders such as dementia or stroke (Wittchen et al. 2011). More widely appreciated are the neurodegenerative disorders, namely, Parkinson’s (PD) and Alzheimer’s disease (AD), which are on the rise due to an older population (von Campenhausen et al. 2005). Huntington’s disease (HD) and amyotrophic lateral sclerosis (ALS) are other neurodegenerative diseases. The neurodegenerative diseases are characterized by inevitable gradual decline in cognitive ability and also the potential to self-sustain (Prince et al. 2013). The neurodegeneration produces a clinical syndrome called dementia, whose symptoms include inability to recollect, sudden and unexpected changes in one’s mood, and problems to communicate and reason (Devous 2002). Next to stroke, epilepsy is the second most common neurological disorder affecting approximately 50 million people worldwide. “Epilepsy” is derived from the Greek term epilambanein which means to seize, and it denotes the predisposition to have recurrent, unprovoked seizures (Quintero-Rincon et al. 2016). In epilepsy, the nerve cells of the brain transmit exorbitant electrical impulses that cause seizures. An epileptic seizure is defined as “a transient symptom of excessive or synchronous neuronal activity in the brain” (Fisher et al. 2005). Epilepsy is defined by two or more such unprovoked seizures. Seizures may be either focal or generalized. In focal seizures, only a specific segment of the brain is affected, while in generalized seizures the whole brain is affected (Acharya et al. 2012a). Epileptic seizures may lead to impairment or unconsciousness and psychic, autonomic, sensory, or motor problems (Lehnertz 2008). Electroencephalography (EEG) is an important clinical tool for monitoring and diagnosing neurological changes in epilepsy. Compared with other methods such as magnetoencephalography (MEG) and functional magnetic resonance imaging (fMRI), EEG is an affordable and safe technique for inspecting brain activity. Drugs and surgical treatment options are not sufficient to treat epilepsy. Among new therapies developed, implantable devices that deliver direct electrical stimulation to affected areas of the brain have shown promising results. The effectiveness of these treatments depends mainly on robust algorithms for seizure detection. As seizure onset cannot be predicted, a continuous recording of the EEG is essential to ascertain epilepsy. But since visual assessment of long EEG recordings is tedious and time-consuming (Song 2011), automated detection methods of epilepsy have gained importance. With a view to study the changes that occur in the brain in seizure and seizure-free status, we analyzed EEG signals using a latest state-of-theart methodology. The observations made are very interesting and are described in Chap. 2. Alzheimer’s disease another disease of the central nervous system is the major reason of dementia. This disease is described by an intensifying reduction in brain 2

8 citations

Journal Article
TL;DR: The investigated parameters reveal that the three heads of TB act independently before fatigue onset and appear to work in union after fatigue, and spectral parameters to be more specific predictors of fatigue.
Abstract: OBJECTIVE The objective of this study was to investigate fatigue in the three heads of the triceps brachii (TB) muscle using surface electromyography (sEMG) obtained at 30%, 45% and 60% of maximal voluntary contraction (MVC). METHODS Twenty-five subjects performed isometric elbow extension until failure, and the rate of fatigue (ROF), time to fatigue (TTF) and normalized TTF (NTTF) were statistically analysed. Subsequently, the behaviour of root-mean-square (RMS), mean-power frequency (MPF) and median-power frequency (MDF) under pre-, onset- and post-fatigue conditions were compared. RESULTS The findings indicated that, among the heads, ROF was statistically significant at 30% and 45% MVC (P 0.05). For every head, only TTF was statistically significant (P<0.05) at different intensities. MPF and MDF under pre-, onset- and post-fatigue conditions were statistically significant (P<0.05) among the heads at all intensities, whereas RMS showed no such behaviour. CONCLUSION The investigated parameters reveal that the three heads of TB act independently before fatigue onset and appear to work in union after fatigue. Synergist head pairs exhibit similar spectral and temporal behaviour in contrast to the non-synergist TB head pair. We find spectral parameters to be more specific predictors of fatigue.

4 citations

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TL;DR: A blend of erudition (fascinating and sometimes obscure historical minutiae abound), popularization (mathematical rigor is relegated to appendices) and exposition (the reader need have little knowledge of the fields involved) is presented in this article.
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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).
Abstract: We develop a method for the multifractal characterization of nonstationary time series, which is based on a generalization of the detrended fluctuation analysis (DFA). We relate our multifractal DFA method to the standard partition function-based multifractal formalism, and prove that both approaches are equivalent for stationary signals with compact support. By analyzing several examples we show that the new method can reliably determine the multifractal scaling behavior of time series. By comparing the multifractal DFA results for original series with those for shuffled series we can distinguish multifractality due to long-range correlations from multifractality due to a broad probability density function. We also compare our results with the wavelet transform modulus maxima method, and show that the results are equivalent.

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Journal ArticleDOI

TL;DR: A description of normalized distributions (measures) lying upon possibly fractal sets; for example those arising in dynamical systems theory, focusing upon the scaling properties of such measures, which are characterized by two indices: \ensuremath{\alpha}, which determines the strength of their singularities; and f, which describes how densely they are distributed.
Abstract: We propose a description of normalized distributions (measures) lying upon possibly fractal sets; for example those arising in dynamical systems theory. We focus upon the scaling properties of such measures, by considering their singularities, which are characterized by two indices: \ensuremath{\alpha}, which determines the strength of their singularities; and f, which describes how densely they are distributed. The spectrum of singularities is described by giving the possible range of \ensuremath{\alpha} values and the function f(\ensuremath{\alpha}). We apply this formalism to the ${2}^{\ensuremath{\infty}}$ cycle of period doubling, to the devil's staircase of mode locking, and to trajectories on 2-tori with golden-mean winding numbers. In all cases the new formalism allows an introduction of smooth functions to characterize the measures. We believe that this formalism is readily applicable to experiments and should result in new tests of global universality.

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Journal ArticleDOI
Thomas C. Halsey

2,510 citations