scholarly journals VARIATION OF INSTANTANEOUS SPECTRAL CENTROID ACROSS BANDS OF SURFACE ELECTROMYOGRAPHIC SIGNALS

2021 ◽  
Vol 57 (2) ◽  
pp. 356-360
Author(s):  
Divya Bharathi Krishnamani ◽  
◽  
P. A. Karthick ◽  
Ramakrishnan Swaminathan ◽  
◽  
...  
Keyword(s):  
Muscle Fatigue ◽  
Frequency Band ◽  
Biceps Brachii ◽  
Shape Representation ◽  
Low Frequency ◽  
Geometric Feature ◽  
Frequency Bands ◽  
Spectral Centroid ◽  
Frequency Components ◽  
Semg Signals

Surface electromyography (sEMG) is a technique which noninvasively acquires the electrical activity of muscles and is widely used for muscle fatigue assessment. This study attempts to characterize the dynamic muscle fatiguing contractions with frequency bands of sEMG signals and a geometric feature namely the instantaneous spectral centroid (ISC). The sEMG signals are acquired from biceps brachii muscle of fifty-eight healthy volunteers. The frequency components of the signals are divided into low frequency band (10-45Hz), medium frequency band (55-95Hz) and high frequency band (95-400Hz). The signals associated with these bands are subjected to a Hilbert transform and analytical shape representation is obtained in the complex plane. The ISC feature is extracted from the resultant shape of the three frequency bands. The results show that this feature can differentiate the muscle nonfatigue and fatigue conditions (p<0.05). It is found the values of ISC is lower in fatigue conditions irrespective of frequency bands. It is also observed that the coefficient of variation of ISC in the low frequency band is less and it demonstrates the ability of handling inter-subject variations. Therefore, the proposed geometric feature from the low frequency band of sEMG signals could be considered for detecting muscle fatigue in various neuromuscular conditions.

scholarly journals Spillovers of Stock Markets among the BRICS: New Evidence in Time and Frequency Domains before the Outbreak of COVID-19 Pandemic

2021 ◽  
Vol 14 (3) ◽  
pp. 112
Author(s):  
Kai Shi
Keyword(s):  
Stock Market ◽  
Frequency Band ◽  
Stock Markets ◽  
Emerging Market ◽  
Low Frequency ◽  
Systematic Risk ◽  
Error Variance ◽  
Frequency Bands ◽  
Volatility Spillovers ◽  
Risk Sources

We attempted to comprehensively decode the connectedness among the abbreviation of five emerging market countries (BRICS) stock markets between 1 August 2002 and 31 December 2019 not only in time domain but also in frequency domain. A continuously varying spillover index based on forecasting error variance decomposition within a generalized abbreviation of vector-autoregression (VAR) framework was computed. With the help of spectral representation, heterogeneous frequency responses to shocks were separated into frequency-specific spillovers in five different frequency bands to reveal differentiated linkages among BRICS markets. Rolling sample analyses were introduced to allow for multiple changes during the sample period. It is found that return spillovers dominated by the high frequency band (within 1 week) part declined with the drop of frequencies, while volatility spillovers dominated by the low frequency band (above 1 quarter) part grew with the decline in frequencies; the dynamics of spillovers were influenced by crucial systematic risk events, and some similarities implied in the spillover dynamics in different frequency bands were found. From the perspective of identifying systematic risk sources, China’s stock market and Russia’s stock market, respectively, played an influential role for return spillover and volatility spillover across BRICS markets.

An Effective Brain Imaging Biomarker for AD and aMCI: ALFF in Slow-5 Frequency Band

2021 ◽  
Vol 18 ◽  
Author(s):  
Luoyu Wang ◽  
Qi Feng ◽  
Mei Wang ◽  
Tingting Zhu ◽  
Enyan Yu ◽  
...  
Keyword(s):  
Brain Imaging ◽  
Frequency Band ◽  
Low Frequency ◽  
Posterior Cingulate Cortex ◽  
Angular Gyrus ◽  
Imaging Biomarker ◽  
Support Vector ◽  
Frequency Bands ◽  
Local Functional ◽  
Left Angular Gyrus

Background: As a potential brain imaging biomarker, amplitude of low frequency fluc-tuation (ALFF) has been used as a feature to distinguish patients with Alzheimer’s disease (AD) and amnestic mild cognitive impairment (aMCI) from normal controls (NC). However, it remains unclear whether the frequency-dependent pattern of ALFF alterations can effectively distinguish the different phases of the disease. Methods: In the present study, 52 AD and 50 aMCI patients were enrolled together with 43 NC in total. The ALFF values were calculated in the following three frequency bands: classical (0.01-0.08 Hz), slow-4 (0.027-0.073 Hz) and slow-5 (0.01-0.027 Hz) for the three different groups. Subsequently, the local functional abnormalities were employed as features to examine the effect of classification among AD, aMCI and NC using a support vector machine (SVM). Results: We found that the among-group differences of ALFF in the different frequency bands were mainly located in the left hippocampus (HP), right HP, bilateral posterior cingulate cortex (PCC) and bilateral precuneus (PCu), left angular gyrus (AG) and left medial prefrontal cortex (mPFC). When the local functional abnormalities were employed as features, we identified that the ALFF in the slow-5 frequency band showed the highest accuracy to distinguish among the three groups. Conclusion: These findings may deepen our understanding of the pathogenesis of AD and suggest that slow-5 frequency band may be helpful to explore the pathogenesis and distinguish the phases of this disease.

scholarly journals Effects of Muscle Fatigue and Recovery on Complexity of Surface Electromyography of Biceps Brachii

Entropy ◽  
2021 ◽  
Vol 23 (8) ◽  
pp. 1036
Author(s):  
Fuyuan Liao ◽  
Xueyan Zhang ◽  
Chunmei Cao ◽  
Isabella Yu-Ju Hung ◽  
Yanni Chen ◽  
...  
Keyword(s):  
Muscle Fatigue ◽  
Surface Electromyography ◽  
Biceps Brachii ◽  
Cupping Therapy ◽  
Nonlinear Method ◽  
Muscle Recovery ◽  
Sham Control ◽  
Semg Signal ◽  
Relative Change ◽  
Semg Signals

This study aimed to investigate the degree of regularity of surface electromyography (sEMG) signals during muscle fatigue during dynamic contractions and muscle recovery after cupping therapy. To the best of our knowledge, this is the first study assessing both muscle fatigue and muscle recovery using a nonlinear method. Twelve healthy participants were recruited to perform biceps curls at 75% of the 10 repetitions maximum under four conditions: immediately and 24 h after cupping therapy (−300 mmHg pressure), as well as after sham control (no negative pressure). Cupping therapy or sham control was assigned to each participant according to a pre-determined counter-balanced order and applied to the participant’s biceps brachii for 5 min. The degree of regularity of the sEMG signal during the first, second, and last 10 repetitions (Reps) of biceps curls was quantified using a modified sample entropy (Ems) algorithm. When exercise was performed immediately or 24 h after sham control, Ems of the sEMG signal showed a significant decrease from the first to second 10 Reps; when exercise was performed immediately after cupping therapy, Ems also showed a significant decrease from the first to second 10 Reps but its relative change was significantly smaller compared to the condition of exercise immediately after sham control. When exercise was performed 24 h after cupping therapy, Ems did not show a significant decrease, while its relative change was significantly smaller compared to the condition of exercise 24 h after sham control. These results indicated that the degree of regularity of sEMG signals quantified by Ems is capable of assessing muscle fatigue and the effect of cupping therapy. Moreover, this measure seems to be more sensitive to muscle fatigue and could yield more consistent results compared to the traditional linear measures.

Determining patterns of motor recruitment during locomotion

2002 ◽  
Vol 205 (3) ◽  
pp. 359-369 ◽  
Author(s):  
James M. Wakeling ◽  
Motoshi Kaya ◽  
Genevieve K. Temple ◽  
Ian A. Johnston ◽  
Walter Herzog
Keyword(s):  
Motor Unit ◽  
Frequency Band ◽  
Fibre Type ◽  
Low Frequency ◽  
Motor Units ◽  
Motor Unit Recruitment ◽  
Medial Gastrocnemius ◽  
Fibre Types ◽  
Myoelectric Signals ◽  
Frequency Bands

SUMMARY Motor units are the functional units of muscle contraction in vertebrates. Each motor unit comprises muscle fibres of a particular fibre type and can be considered as fast or slow depending on its fibre-type composition. Motor units are typically recruited in a set order, from slow to fast, in response to the force requirements from the muscle. The anatomical separation of fast and slow muscle in fish permits direct recordings from these two fibre types. The frequency spectra from different slow and fast myotomal muscles were measured in the rainbow trout Oncorhynchus mykiss. These two muscle fibre types generated distinct low and high myoelectric frequency bands. The cat paw-shake is an activity that recruits mainly fast muscle. This study showed that the myoelectric signal from the medial gastrocnemius of the cat was concentrated in a high frequency band during paw-shake behaviour. During slow walking, the slow motor units of the medial gastrocnemius are also recruited, and this appeared as increased muscle activity within a low frequency band. Therefore, high and low frequency bands could be distinguished in the myoelectric signals from the cat medial gastrocnemius and probably corresponded, respectively, to fast and slow motor unit recruitment. Myoelectric signals are resolved into time/frequency space using wavelets to demonstrate how patterns of motor unit recruitment can be determined for a range of locomotor activities.

Classification of Muscle Fatigue in Dynamic Contraction Using Surface Electromyography Signals and Multifractal Singularity Spectral Analysis

2016 ◽  
Vol 138 (11) ◽  
Author(s):  
Kiran Marri ◽  
Ramakrishnan Swaminathan
Keyword(s):  
Muscle Fatigue ◽  
Surface Electromyography ◽  
Nearest Neighbor ◽  
Biceps Brachii ◽  
Information Gain ◽  
Multifractal Spectrum ◽  
Singularity Spectrum ◽  
K Nearest Neighbor ◽  
Dynamic Contraction ◽  
Semg Signals

Muscle fatigue is a neuromuscular condition experienced during daily activities. This phenomenon is generally characterized using surface electromyography (sEMG) signals and has gained a lot of interest in the fields of clinical rehabilitation, prosthetics control, and sports medicine. sEMG signals are complex, nonstationary and also exhibit self-similarity fractal characteristics. In this work, an attempt has been made to differentiate sEMG signals in nonfatigue and fatigue conditions during dynamic contraction using multifractal analysis. sEMG signals are recorded from biceps brachii muscles of 42 healthy adult volunteers while performing curl exercise. The signals are preprocessed and segmented into nonfatigue and fatigue conditions using the first and last curls, respectively. The multifractal detrended moving average algorithm (MFDMA) is applied to both segments, and multifractal singularity spectrum (SSM) function is derived. Five conventional features are extracted from the singularity spectrum. Twenty-five new features are proposed for analyzing muscle fatigue from the multifractal spectrum. These proposed features are adopted from analysis of sEMG signals and muscle fatigue studies performed in time and frequency domain. These proposed 25 feature sets are compared with conventional five features using feature selection methods such as Wilcoxon rank sum, information gain (IG) and genetic algorithm (GA) techniques. Two classification algorithms, namely, k-nearest neighbor (k-NN) and logistic regression (LR), are explored for differentiating muscle fatigue. The results show that about 60% of the proposed features are statistically highly significant and suitable for muscle fatigue analysis. The results also show that eight proposed features ranked among the top 10 features. The classification accuracy with conventional features in dynamic contraction is 75%. This accuracy improved to 88% with k-NN-GA combination with proposed new feature set. Based on the results, it appears that the multifractal spectrum analysis with new singularity features can be used for clinical evaluation in varied neuromuscular conditions, and the proposed features can also be useful in analyzing other physiological time series.

A Novel Accelerometer Mounting Method for Sensing Performance Improvement in Acoustic Measurements From the Knee

2020 ◽  
Vol 143 (3) ◽  
Author(s):  
Goktug C. Ozmen ◽  
Mohsen Safaei ◽  
Lan Lan ◽  
Omer T. Inan
Keyword(s):  
Frequency Band ◽  
High Frequency ◽  
Low Frequency ◽  
Adhesive Tape ◽  
Spectral Centroid ◽  
Wearable Systems ◽  
Electrodynamic Shaker ◽  
3D Printed ◽  
The Mean ◽  
Sensing Performance

Abstract In this study, we propose a new mounting method to improve accelerometer sensing performance in the 50 Hz–10 kHz frequency band for knee sound measurement. The proposed method includes a thin double-sided adhesive tape for mounting and a 3D-printed custom-designed backing prototype. In our mechanical setup with an electrodynamic shaker, the measurements showed a 13 dB increase in the accelerometer's sensing performance in the 1–10 kHz frequency band when it is mounted with the craft tape under 2 N backing force applied through low-friction tape. As a proof-of-concept study, knee sounds of healthy subjects (n = 10) were recorded. When the backing force was applied, we observed statistically significant (p &lt; 0.01) incremental changes in spectral centroid, spectral roll-off frequencies, and high-frequency (1–10 kHz) root-mean-square (RMS) acceleration, while low-frequency (50 Hz–1 kHz) RMS acceleration remained unchanged. The mean spectral centroid and spectral roll-off frequencies increased from 0.8 kHz and 4.15 kHz to 1.35 kHz and 5.9 kHz, respectively. The mean high-frequency acceleration increased from 0.45 mgRMS to 0.9 mgRMS with backing. We showed that the backing force improves the sensing performance of the accelerometer when mounted with the craft tape and the proposed backing prototype. This new method has the potential to be implemented in today's wearable systems to improve the sensing performance of accelerometers in knee sound measurements.

scholarly journals Comparison of Frequency Bands Using Spectral Entropy for Epileptic Seizure Prediction

2013 ◽  
Vol 2013 ◽  
pp. 1-5 ◽  
Author(s):  
Susana Blanco ◽  
Arturo Garay ◽  
Diego Coulombie
Keyword(s):  
Frequency Band ◽  
Epileptic Seizure ◽  
Signal Analysis ◽  
Fourier Spectrum ◽  
Low Frequency ◽  
Eeg Signals ◽  
High Frequencies ◽  
Frequency Bands ◽  
Low Correlation ◽  
Rate Of Increase

Introduction. Under the hypothesis that the uncontrolled neuronal synchronization propagates recruiting more and more neurons, the aim is to detect its onset as early as possible by signal analysis. This synchronization is not noticeable just by looking at the EEG, so mathematical tools are needed for its identification. Objective. The aim of this study is to compare the results of spectral entropies calculated in different frequency bands of the EEG signals to decide which band may be a better tool to predict an epileptic seizure. Materials and Methods. Invasive ictal records were used. We measured the Fourier spectrum entropy of the electroencephalographic signals 4 to 32 minutes before the attack in low, medium and high frequencies. Results. The high-frequency band shows a markedly rate of increase of the entropy, with positive slopes and low correlation coefficient. The entropy rate of growth in the low-frequency band is practically zero, with a correlation around 0.2 and mostly positive slopes. The mid-frequency band showed both positive and negative slopes with low correlation. Conclusions. The entropy in the high frequencies could be predictor, because it shows changes in the previous moments of the attack. Its main problem is the variability, which makes it difficult to set the threshold that ensures an adequate prediction.

scholarly journals Modulation of Brain Electroencephalography Oscillations by Electroacupuncture in a Rat Model of Postincisional Pain

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Jing Wang ◽  
Jing Wang ◽  
Xuezhu Li ◽  
Duan Li ◽  
Xiao-Li Li ◽  
...  
Keyword(s):  
Frequency Band ◽  
Low Frequency ◽  
Spontaneous Pain ◽  
Brain Oscillations ◽  
Beta Band ◽  
Pain Model ◽  
Frequency Bands ◽  
Analgesic Effects ◽  
Before And After ◽  
Beta Frequency

The present study aimed to investigate how ongoing brain rhythmical oscillations changed during the postoperative pain and whether electroacupuncture (EA) regulated these brain oscillations when it relieved pain. We established a postincisional pain model of rats with plantar incision to mimic the clinical pathological pain state, tested the analgesic effects of EA, and recorded electroencephalography (EEG) activities before and after the EA application. By analysis of power spectrum and bicoherence of EEG, we found that in rats with postincisional pain, ongoing activities at the delta-frequency band decreased, while activities at theta-, alpha-, and beta-frequency bands increased. EA treatment on these postincisional pain rats decreased the power at high-frequency bands especially at the beta-frequency band and reversed the enhancement of the cross-frequency coupling strength between the beta band and low-frequency bands. After searching for the PubMed, our study is the first time to describe that brain oscillations are correlated with the processing of spontaneous pain information in postincisional pain model of rats, and EA could regulate these brain rhythmical frequency oscillations, including the power and cross-frequency couplings.

Effect of Spindle Speeds on 3D Topography of Ball-End Milled Surfaces Using Wavelet Analysis Method

2016 ◽  
Vol 836-837 ◽  
pp. 13-19 ◽  
Author(s):  
Shuai Liu ◽  
Jun Zhao ◽  
Wen Zhen Qin ◽  
Ji Ming Pang
Keyword(s):  
Wavelet Analysis ◽  
Frequency Band ◽  
Low Frequency ◽  
Spindle Speed ◽  
Median Frequency ◽  
Frequency Bands ◽  
Multi Scale ◽  
3D Topography ◽  
Changing Trend ◽  
Height Data

Optical profiler is employed to acquire topography height data of ball-end milled die steel surface under different spindle speeds ranging from 2000rpm to 12000rpm with lead angle of 20° and tilt angle of-10°. By multi-scale wavelet analysis, measured height data are decomposed and then been reconstructed, meanwhile 3D topography and 3D roughness in different frequency bands are obtained. The results show that the changing trend of roughness with frequency band under different spindle speeds is not the same. In the high frequency bands, roughness has a tendency to increase with the increasing spindle speed. In the median frequency band, the roughness of the surface machined under low spindle speed 2000 rpm is the largest and the roughness of the surface machined under high spindle speed 12000 rpm is the lowest. In the low frequency bands, the roughness of the surface machined under low spindle speed 2000rpm is much larger than those obtained under other spindle speeds, and with the increasing spindle speed, the changing trend of roughness increases firstly then decreases.

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