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.
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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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.
26 citations
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.
21 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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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.
11 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
11 citations
01 Aug 2016
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%.
Abstract: In this work, an attempt has been made to analyze surface electromyography (sEMG) signals of fatiguing biceps brachii muscles at different curl speeds using multifractal detrended moving average (MFDMA) algorithm. For this purpose, signals are recorded from fifty eight healthy subjects while performing curl exercise at their comfortable speed until fatigue. The signals of first and last curls are considered as nonfatigue and fatigue conditions, respectively. Further, the number of curls performed by each subject and the endurance time is used for computing the normalized curl speed. The signals are grouped into fast, medium and slow using curl speeds. The curl segments are subjected to MFDMA to derive degree of multifractality (DOM), maximum singularity exponent (MXE) and exponent length multifractality index (EMX). The results show that multifractal features are able to differentiate sEMG signals in fatiguing conditions. The multifractality increased with faster curls as compared with slower curl speed by 12%. High statistical significance is observed using EMX and DOM values between curl speed and fatigue conditions. It appears that this method of analyzing sEMG signals with curl speed can be useful in understanding muscle dynamics in varied neuromuscular conditions and sports medicine.
7 citations
Cites background from "Analyzing Origin of Multifractality..."
...The multifractal theory has been reported to investigate dynamical properties of complex multicomponent sEMG signals in isometric and dynamic fatigue conditions [8], [9]....
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References
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TL;DR: Non-linear parameters of surface electromyogram (sEMG) and tremor acceleration and accelerometer signal proved to be more reliable for the shaking form ofPD, while diagnostics of the rigid form of PD using EMG remains an open question.
Abstract: The pre-clinical diagnostics is essential for management of Parkinson’s disease (PD). Although PD has been studied intensively in the last decades, the pre-clinical indicators of that motor disorder have yet to be established. Several approaches were proposed but the definitive method is still lacking. Here we report on the non-linear characteristics of surface electromyogram (sEMG) and tremor acceleration as a possible diagnostic tool, and, in prospective, as a predictor for PD. Following this approach we calculated such non-linear parameters of sEMG and accelerometer signal as correlation dimension, entropy, and determinism. We found that the non-linear parameters allowed discriminating some 85% of healthy controls from PD patients. Thus, this approach offers considerable potential for developing sEMG-based method for pre-clinical diagnostics of PD. However, non-linear parameters proved to be more reliable for the shaking form of PD, while diagnostics of the rigid form of PD using EMG remains an open question.
37 citations
TL;DR: In this article, the authors present the results of multifractal testing of two sets of financial data: daily data of the Dow Jones Industrial Average (DJIA) index and minutely data of Euro Stoxx 50 index.
Abstract: This paper presents the results of multifractal testing of two sets of financial data: daily data of the Dow Jones Industrial Average (DJIA) index and minutely data of the Euro Stoxx 50 index. Where multifractal scaling is found, the spectrum of scaling exponents is calculated via Multifractal Detrended Fluctuation Analysis. In both cases, further investigations reveal that the temporal correlations in the data are a more significant source of the multifractal scaling than are the distributions of the returns. It is also shown that the extreme events which make up the heavy tails of the distribution of the Euro Stoxx 50 log returns distort the scaling in the data set. The most extreme events are inimical to the scaling regime. This result is in contrast to previous findings that extreme events contribute to multifractality.
36 citations
TL;DR: Experimental results show that the proposed TVAR-model-based PSD estimators can achieve more stable and precise instantaneous parameter estimation than conventional methods.
35 citations
TL;DR: In this article, the authors used multifractal detrended fluctuation analysis (MF-DFA) to study the electrical discharge current fluctuations in plasma and show that it behaves as a weak anti-correlated process.
Abstract: We use multifractal detrended fluctuation analysis (MF-DFA) to study the electrical discharge current fluctuations in plasma and show that it has multifractal properties and behaves as a weak anti-correlated process. Comparison of the MF-DFA results for the original series with those for the shuffled and surrogate series shows that correlation of the fluctuations is responsible for the multifractal nature of the electrical discharge current.
28 citations
TL;DR: In this paper, a multifractal detrended fluctuation analysis (MF-DFA) is used to verify whether or not the returns of time series of prices paid to farmers in original markets can be described by the multifractual approach.
Abstract: The multifractal detrended fluctuation analysis (MF-DFA) is used to verify whether or not the returns of time series of prices paid to farmers in original markets can be described by the multifractal approach. By way of example, 5 weekly time series of prices of different breeds, slaughter weight and market differentiation from 2000 to 2012 are analyzed. Results obtained from the multifractal parameters and multifractal spectra show that the price series of livestock products are of a multifractal nature. The Hurst exponent shows that these time series are stationary signals, some of which exhibit long memory (Merino milk-fed in Seville and Segurena paschal in Jaen), short memory (Merino paschal in Cordoba and Segurena milk-fed in Jaen) or even are close to an uncorrelated signals (Merino paschal in Seville). MF-DFA is able to discern the different underlying dynamics that play an important role in different types of sheep livestock markets, such as degree and source of multifractality. In addition, the main source of multifractality of these time series is due to the broadness of the probability function, instead of the long-range correlation properties between small and large fluctuations, which play a clearly secondary role.
26 citations