Analysis of Sequential Visibility Motifs in Isometric Surface Electromyography Signals in Fatiguing Condition
01 Jul 2018-Vol. 2018, pp 2659-2662
TL;DR: An attempt is made to utilize graph signal processing methods such as Sequential Visibility motif for the analysis of muscle fatigue condition and the results show that the signals are unique for each subject.
Abstract: Muscle fatigue is the inability to exert the required force. Surface Electromyography (sEMG) is a technique used to study the muscle’s electrical property. These generated signals are complex and nonstationary in nature. In this work, an attempt is made to utilize graph signal processing methods such as Sequential Visibility motif for the analysis of muscle fatigue condition. The sEMG signals of 41 healthy adult volunteers are acquired from the biceps brachii muscle during isometric contraction with a 6 Kg load. The subjects are asked to perform the exercise until they are unable to continue. The signals are preprocessed, and the first and last 500 ms of the signal are considered for analysis. The segmented signals are subjected to sequential visibility graph algorithm. Further, the number of motifs for a subgraph of four is calculated. The results show that the signals are unique for each subject. The frequency of higher degree motif is more in the case of fatigue. The frequency of each unique motif is capable of differentiating nonfatigue and fatigue conditions. Nonparametric statistical test result indicates all features are significant with p<0.05. This method of analysis can be extended to other varied neuromuscular conditions.
Citations
More filters
01 Jul 2019
TL;DR: An attempt is made to characterize the variation in the complexity of the surface electromyography (sEMG) signals in fatigue by recording signals from 58 healthy volunteers from the biceps brachii muscle under well-defined dynamic contraction protocol.
Abstract: In this work, an attempt is made to characterize the variation in the complexity of the surface electromyography (sEMG) signals in fatigue. For this, sEMG signals from 58 healthy volunteers are recorded from the biceps brachii muscle under well-defined dynamic contraction protocol. The contractions are segmented, and the initial and final curls are extracted. These are considered as nonfatigue and fatigue respectively. Further, visibility graphs are constructed at multiple scales, and median degree centrality (MSMC) is calculated in them. To quantify the variations in the MSMC, two features namely, the average and standard deviation are calculated. The results reveal that the recorded signals are non-stationary. The constructed networks form distinct clusters in space. The MSMC feature shows a decreasing trend with scale in both nonfatigue and fatigue conditions. Additionally, the extracted features have higher values in fatigue. This may be due to the motor unit synchronization, which causes an increase in connectivity between nodes. All the extracted features showed statistical significance with p<0.005. This approach of analysis can be extended to characterize muscle in other neuromuscular conditions.
4 citations
Cites methods from "Analysis of Sequential Visibility M..."
...In the previous work, a sequential visibility graph-based method has been reported to characterize muscle fatigue [11]....
[...]
...Visibility graph is a method of transforming time series into graphs [9-11]....
[...]
01 Oct 2018
TL;DR: In this article, the degree distribution of visibility graphs was used to analyze surface electromyography (sEMG) signals in non-fatigue and fatigue conditions with a total of 58 subjects volunteered for the study.
Abstract: The reduction in muscle force is a common symptom of several neuromuscular diseases. This phenomenon is called muscle fatigue. In normal subjects, it is generally reversible. Surface electromyography (sEMG) signals are commonly used to analyze muscle fatigue. These signals are nonlinear and nonstationary in nature. In this work, an attempt is made to analyse sEMG signals in nonfatigue and fatigue conditions using the degree distribution of visibility graphs. The sEMG signals are recorded from the upper limb muscle namely the biceps brachii during dynamic contraction with a six-kilogram load. A total of 58 subjects volunteered for the study. The signals are preprocessed, and visibility graphs are constructed. The variation in the degree distribution is studied and characterized. The results indicate that the signals recorded are complex in nature. The degree distributions are distinct between nonfatigue and fatigue conditions. In fatigue, the percentage of higher degree nodes are more. Further, the decay rate of degree is larger in the case of nonfatigue indicating the signal is comparatively random. The statistical test indicates that the features extracted are significant with a $\mathbf{p} . It appears that this method of analysis would be useful for characterizing various neuromuscular conditions.
2 citations
Cites methods from "Analysis of Sequential Visibility M..."
...One such method of reconstruction is the visibility graph method [10-12]....
[...]
...sEMG signals from the biceps brachii muscle in isometric contraction were analyzed with the sequential visibility motif and concluded that the method is successful in differentiating nonfatigue and fatigue conditions [11]....
[...]
DOI•
TL;DR: In this article , the applicability of visibility graph motif features for the real-time monitoring of muscle fatigue is explored, and the network entropy features are able to characterize the changes in signal dynamics in nonfatigue and fatigue conditions.
Abstract: Monitoring of the physiological function during exercise can provide insights on the quality of the training and prevent injury. Specifically, the signals from the muscle sensors (surface electromyography) are difficult to interpret and limited attempts have been made to develop effective algorithms for the real-time monitoring of muscle fatigue. In this work, the applicability of visibility graph motif features for the real-time monitoring of muscle fatigue is explored. Experimental investigations have been conducted on 58 healthy adult volunteers. Results indicate that the network entropy features are able to characterize the changes in signal dynamics in nonfatigue and fatigue conditions. These metrics have the potential to be used as a marker to predict functional capabilities of humans in real-world scenarios.
23 Jan 2023
TL;DR: In this paper , the applicability of visibility graph motif features for the real-time monitoring of muscle fatigue is explored, and the network entropy feature is able to characterize the changes in signal dynamics in nonfatigue and fatigue condition.
Abstract: Monitoring of the physiological function during exercise can provide insights on the quality of the training and prevent injury. Specifically, the signals from the muscle sensors (Surface Electromyography) are difficult to interpret and limited attempts have been made to develop effective algorithms for the real-time monitoring of muscle fatigue. In this work, the applicability of visibility graph motif features for the real-time monitoring of muscle fatigue is explored. Experimental investigations have been conducted on 58 healthy adult volunteers. Results indicate that the network entropy feature are able characterize the changes in signal dynamics in nonfatigue and fatigue condition. These metrics have the potential to be used as a marker to predict functional capabilities of humans in real world scenarios.
References
More filters
TL;DR: A simple and fast computational method, the visibility algorithm, that converts a time series into a graph, which inherits several properties of the series in its structure, enhancing the fact that power law degree distributions are related to fractality.
Abstract: In this work we present a simple and fast computational method, the visibility algorithm, that converts a time series into a graph. The constructed graph inherits several properties of the series in its structure. Thereby, periodic series convert into regular graphs, and random series do so into random graphs. Moreover, fractal series convert into scale-free networks, enhancing the fact that power law degree distributions are related to fractality, something highly discussed recently. Some remarkable examples and analytical tools are outlined to test the method's reliability. Many different measures, recently developed in the complex network theory, could by means of this new approach characterize time series from a new point of view.
1,320 citations
"Analysis of Sequential Visibility M..." refers methods in this paper
...Sequential Visibility Graph Visibility algorithms are methods that map a timeseries into graphs [7, 9] as shown in fig....
[...]
...The visibility graph (VG) method is capable of mapping hidden structure of the series and the underlying dynamics into graph space [7]....
[...]
TL;DR: A new chaos–wavelet approach is presented for electroencephalogram (EEG)-based diagnosis of Alzheimer’s disease (AD) employing a recently developed concept in graph theory, visibility graph (VG), with a high diagnostic accuracy.
Abstract: A new chaos-wavelet approach is presented for electroencephalogram (EEG)-based diagnosis of Alzheimer's disease (AD) employing a recently developed concept in graph theory, visibility graph (VG). The approach is based on the research ideology that nonlinear features may not reveal differences between AD and control group in the band-limited EEG, but may represent noticeable differences in certain sub-bands. Hence, complexity of EEGs is computed using the VGs of EEGs and EEG sub-bands produced by wavelet decomposition. Two methods are employed for computation of complexity of the VGs: one based on the power of scale-freeness of a graph structure and the other based on the maximum eigenvalue of the adjacency matrix of a graph. Analysis of variation is used for feature selection. Two classifiers are applied to the selected features to distinguish AD and control EEGs: a Radial Basis Function Neural Network (RBFNN) and a two-stage classifier consisting of Principal Component Analysis (PCA) and the RBFNN. After comprehensive statistical studies, effective classification features and mathematical markers were discovered. Finally, using the discovered features and a two-stage classifier (PCA-RBFNN), a high diagnostic accuracy of 97.7% was obtained.
281 citations
"Analysis of Sequential Visibility M..." refers methods in this paper
...This method of analysis has been applied for the classification of Alzhimers disease using EEG signals [8]....
[...]
TL;DR: The reliability of the psychological and clinical neurophysiological assessment techniques available today allows a multidisciplinary approach to fatigue in neurological patients, which may contribute to the elucidation of the pathophysiological mechanisms of chronic fatigue, with the ultimate goal to develop tailored treatments for fatigue in Neurological patients.
Abstract: Fatigue is a multidimensional concept covering both physiological and psychological aspects. Chronic fatigue is a typical symptom of diseases such as cancer, multiple sclerosis (MS), Parkinson's disease (PD) and cerebrovascular disorders but is also presented by people in whom no defined somatic disease has been established. If certain criteria are met, chronic fatigue syndrome can be diagnosed. The 4-item Abbreviated Fatigue Questionnaire allows the extent of the experienced fatigue to be assessed with a high degree of reliability and validity. Physiological fatigue has been well defined and originates in both the peripheral and central nervous system. The condition can be assessed by combining force and surface-EMG measurements (including frequency analyses and muscle-fibre conduction estimations), twitch interpolation, magnetic stimulation of the motor cortex and analysis of changes in the readiness potential. Fatigue is a well-known phenomenon in both central and peripheral neurological disorders. Examples of the former conditions are multiple sclerosis, Parkinson's disease and stroke. Although it seems to be a universal symptom of many brain disorders, the unique characteristics of the concomitant fatigue also point to a specific relationship with several of these syndromes. As regards neuromuscular disorders, fatigue has been reported in patients with post-polio syndrome, myasthenia gravis, Guillain-Barre syndrome, facioscapulohumeral dystrophy, myotonic dystrophy and hereditary motor and sensory neuropathy type-I. More than 60% of all neuromuscular patients suffer from severe fatigue, a prevalence resembling that of patients with MS. Except for several rare myopathies with specific metabolic derangements leading to exercise-induced muscle fatigue, most studies have not identified a prominent peripheral cause for the fatigue in this population. In contrast, the central activation of the diseased neuromuscular system is generally found to be suboptimal. The reliability of the psychological and clinical neurophysiological assessment techniques available today allows a multidisciplinary approach to fatigue in neurological patients, which may contribute to the elucidation of the pathophysiological mechanisms of chronic fatigue, with the ultimate goal to develop tailored treatments for fatigue in neurological patients. The present report discusses the different manifestations of fatigue and the available tools to assess peripheral and central fatigue.
207 citations
"Analysis of Sequential Visibility M..." refers background in this paper
...Features that are commonly used include root mean square value, zero crossing rate, mean frequency and median frequency [1,3]....
[...]
...It also offers additional investigative and diagnostic information such as muscle fiber conduction velocity, and MU territory [2, 3]....
[...]
TL;DR: The proposed methods are found to be capable of handling the nonstationary and multicomponent variations of sEMG signals recorded in dynamic fatiguing contractions and the combination of EMBD- polynomial kernel based SVM could be used to detect the dynamic muscle fatigue conditions.
Abstract: Background and objective Surface electromyography (sEMG) based muscle fatigue research is widely preferred in sports science and occupational/rehabilitation studies due to its noninvasiveness. However, these signals are complex, multicomponent and highly nonstationary with large inter-subject variations, particularly during dynamic contractions. Hence, time-frequency based machine learning methodologies can improve the design of automated system for these signals. Methods In this work, the analysis based on high-resolution time-frequency methods, namely, Stockwell transform (S-transform), B-distribution (BD) and extended modified B-distribution (EMBD) are proposed to differentiate the dynamic muscle nonfatigue and fatigue conditions. The nonfatigue and fatigue segments of sEMG signals recorded from the biceps brachii of 52 healthy volunteers are preprocessed and subjected to S-transform, BD and EMBD. Twelve features are extracted from each method and prominent features are selected using genetic algorithm (GA) and binary particle swarm optimization (BPSO). Five machine learning algorithms, namely, naive Bayes, support vector machine (SVM) of polynomial and radial basis kernel, random forest and rotation forests are used for the classification. Results The results show that all the proposed time-frequency distributions (TFDs) are able to show the nonstationary variations of sEMG signals. Most of the features exhibit statistically significant difference in the muscle fatigue and nonfatigue conditions. The maximum number of features (66%) is reduced by GA and BPSO for EMBD and BD-TFD respectively. The combination of EMBD- polynomial kernel based SVM is found to be most accurate (91% accuracy) in classifying the conditions with the features selected using GA. Conclusions The proposed methods are found to be capable of handling the nonstationary and multicomponent variations of sEMG signals recorded in dynamic fatiguing contractions. Particularly, the combination of EMBD- polynomial kernel based SVM could be used to detect the dynamic muscle fatigue conditions.
103 citations
"Analysis of Sequential Visibility M..." refers methods in this paper
...The signals are down sampled to 1000Hz and preprocessed using a 10 - 400Hz bandpass filter and a 50Hz notch filter to remove motion artifact and power line interference respectively [11]....
[...]
TL;DR: This work develops a theory to compute in an exact way the motif profiles associated with general classes of deterministic and stochastic dynamics, and finds that this simple property is indeed a highly informative and computationally efficient feature capable of distinguishing among different dynamics and robust against noise contamination.
Abstract: Visibility algorithms transform time series into graphs and encode dynamical information in their topology, paving the way for graph-theoretical time series analysis as well as building a bridge between nonlinear dynamics and network science. In this work we introduce and study the concept of sequential visibility-graph motifs, smaller substructures of n consecutive nodes that appear with characteristic frequencies. We develop a theory to compute in an exact way the motif profiles associated with general classes of deterministic and stochastic dynamics. We find that this simple property is indeed a highly informative and computationally efficient feature capable of distinguishing among different dynamics and robust against noise contamination. We finally confirm that it can be used in practice to perform unsupervised learning, by extracting motif profiles from experimental heart-rate series and being able, accordingly, to disentangle meditative from other relaxation states. Applications of this general theory include the automatic classification and description of physical, biological, and financial time series.
43 citations
"Analysis of Sequential Visibility M..." refers background or methods in this paper
...Sequential Visibility Graph Visibility algorithms are methods that map a timeseries into graphs [7, 9] as shown in fig....
[...]
...Additionally, it is also used to quantify the changes in the dynamics of heart rate time series [9]....
[...]
...The number of possible subgraphs with 4 n is shown in Table 1 [9]....
[...]