Discrimination of physiological tremor from pathological tremor using accelerometer and surface EMG signals

2020 ◽  
Vol 28 (5) ◽  
pp. 461-476
Author(s):  
A. Hossen ◽  
G. Deuschl ◽  
S. Groppa ◽  
U. Heute ◽  
M. Muthuraman
Keyword(s):  
Surface Emg ◽  
Biomedical Signal Processing ◽  
Physiological Tremor ◽  
Soft Decision ◽  
Accelerometer Signal ◽  
Power Spectral ◽  
Careful Clinical Examination ◽  
Physiological Analysis ◽  
Pathological Tremor ◽  
Semg Signals

BACKGROUND AND OBJECTIVE: Although careful clinical examination and medical history are the most important steps towards a diagnostic separation between different tremors, the electro-physiological analysis of the tremor using accelerometry and electromyography (EMG) of the affected limbs are promising tools. METHODS: A soft-decision wavelet-based decomposition technique is applied with 8 decomposition stages to estimate the power spectral density of accelerometer and surface EMG signals (sEMG) sampled at 800 Hz. A discrimination factor between physiological tremor (PH) and pathological tremor, namely, essential tremor (ET) and the tremor caused by Parkinson’s disease (PD), is obtained by summing the power entropy in band 6 (B6: 7.8125–9.375 Hz) and band 11 (B11: 15.625–17.1875 Hz). RESULTS: A discrimination accuracy of 93.87% is obtained between the PH group and the ET & PD group using a voting between three results obtained from the accelerometer signal and two sEMG signals. CONCLUSION: Biomedical signal processing techniques based on high resolution wavelet spectral analysis of accelerometer and sEMG signals are implemented to efficiently perform classification between physiological tremor and pathological tremor.

scholarly journals Power spectral analysis of surface EMG in stroke: A preliminary study

2013 ◽  
Author(s):  
Sudarsan Srivatsan ◽  
Xiaogang Hu ◽  
Brian Jeon ◽  
Aneesha K. Suresh ◽  
William Zev Rymer ◽  
...  
Keyword(s):  
Spectral Analysis ◽  
Power Spectral Analysis ◽  
Surface Emg ◽  
Power Spectral ◽  
Preliminary Study

Development of a Surface EMG-Based Control System for Controlling Assistive Devices

2020 ◽  
pp. 765-785
Author(s):  
Uvanesh K. ◽  
Suraj Kumar Nayak ◽  
Biswajeet Champaty ◽  
Goutam Thakur ◽  
Biswajit Mohapatra ◽  
...  
Keyword(s):  
Control System ◽  
Wavelet Transforms ◽  
High Efficiency ◽  
Surface Emg ◽  
Assistive Device ◽  
Discrete Wavelet ◽  
Motor Disability ◽  
Robotic Vehicle ◽  
The Time Domain ◽  
Semg Signals

The current study discusses about the development of an EMG based wireless control system for the patients suffering from high-level motor disability. Surface EMG (sEMG) signals were processed in the time domain and using discrete wavelet transforms (DWT). The statistical features of the signals (sEMG, envelope of the squared sEMG and wavelet processed sEMG) were determined and analyzed. The analysis of the features suggested that the features of the envelope of the squared sEMG signals were sufficient to be used for high-efficiency classification and control signal generation. A hall-effect sensor based switching mechanism was introduced for controlling the duration of the activation of the device. The control signals were wirelessly transmitted to the assistive device (robotic vehicle). The training and the subsequent use of the developed control system were easy.

scholarly journals Motor unit recruitment strategies investigated by surface EMG variables

2002 ◽  
Vol 92 (1) ◽  
pp. 235-247 ◽  
Author(s):  
Dario Farina ◽  
Mauro Fosci ◽  
Roberto Merletti
Keyword(s):  
Biceps Brachii ◽  
Motor Units ◽  
Surface Emg ◽  
Recruitment Strategy ◽  
Isometric Contractions ◽  
Size Principle ◽  
Constant Torque ◽  
Emg Signal ◽  
Power Spectral ◽  
The Central Nervous System

During isometric contractions of increasing strength, motor units (MUs) are recruited by the central nervous system in an orderly manner starting with the smallest, with muscle fibers that usually show the lowest conduction velocity (CV). Theory predicts that the higher the velocity of propagation of the action potential, the higher the power at high frequencies of the detected surface signal. These considerations suggest that the power spectral density of the surface detected electromyogram (EMG) signal may give indications about the MU recruitment process. The purpose of this paper is to investigate the potential and limitations of spectral analysis of the surface EMG signal as a technique for the investigation of muscle force control. The study is based on a simulation approach and on an experimental investigation of the properties of surface EMG signals detected from the biceps brachii during isometric linearly increasing torque contractions. Both simulation and experimental data indicate that volume conductor properties play an important role as confounding factors that may mask any relation between EMG spectral variables and estimated CV as a size principle parameter during ramp contractions. The correlation between spectral variables and CV is thus significantly lower when the MU pool is not stable than during constant-torque isometric contractions. Our results do not support the establishment of a general relationship between spectral EMG variables and torque or recruitment strategy.

scholarly journals Smart System for Prediction of Accurate Surface Electromyography Signals Using an Artificial Neural Network

2019 ◽  
Vol 11 (1) ◽  
pp. 25 ◽  
Author(s):  
Osama Dorgham ◽  
Ibrahim Al-Mherat ◽  
Jawdat Al-Shaer ◽  
Sulieman Bani-Ahmad ◽  
Stephen Laycock
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Electrical Potential ◽  
Eccentric Contraction ◽  
Surface Emg ◽  
Mean Square ◽  
Ann Model ◽  
Semg Signal ◽  
Artificial Neural ◽  
Semg Signals

Bioelectric signals are used to measure electrical potential, but there are different types of signals. The electromyography (EMG) is a type of bioelectric signal used to monitor and recode the electrical activity of the muscles. The current work aims to model and reproduce surface EMG (SEMG) signals using an artificial neural network. Such research can aid studies into life enhancement for those suffering from damage or disease affecting their nervous system. The SEMG signal is collected from the surface above the bicep muscle through dynamic (concentric and eccentric) contraction with various loads. In this paper, we use time domain features to analyze the relationship between the amplitude of SEMG signals and the load. We extract some features (e.g., mean absolute value, root mean square, variance and standard deviation) from the collected SEMG signals to estimate the bicep’ muscle force for the various loads. Further, we use the R-squared value to depict the correlation between the SEMG amplitude and the muscle loads by linear fitting. The best performance the ANN model with 60 hidden neurons for three loads used (3 kg, 5 kg and 7 kg) has given a mean square error of 1.145, 1.3659 and 1.4238, respectively. The R-squared observed are 0.9993, 0.99999 and 0.99999 for predicting (reproduction step) of smooth SEMG signals.

scholarly journals Error reduction in EMG signal decomposition

2014 ◽  
Vol 112 (11) ◽  
pp. 2718-2728 ◽  
Author(s):  
Joshua C. Kline ◽  
Carlo J. De Luca
Keyword(s):  
Motor Unit ◽  
Decomposition Methods ◽  
Surface Emg ◽  
Error Reduction ◽  
Motor Unit Action Potential ◽  
Reduction Algorithm ◽  
Emg Signal ◽  
Motor Unit Firing ◽  
Time Required ◽  
Semg Signals

Decomposition of the electromyographic (EMG) signal into constituent action potentials and the identification of individual firing instances of each motor unit in the presence of ambient noise are inherently probabilistic processes, whether performed manually or with automated algorithms. Consequently, they are subject to errors. We set out to classify and reduce these errors by analyzing 1,061 motor-unit action-potential trains (MUAPTs), obtained by decomposing surface EMG (sEMG) signals recorded during human voluntary contractions. Decomposition errors were classified into two general categories: location errors representing variability in the temporal localization of each motor-unit firing instance and identification errors consisting of falsely detected or missed firing instances. To mitigate these errors, we developed an error-reduction algorithm that combines multiple decomposition estimates to determine a more probable estimate of motor-unit firing instances with fewer errors. The performance of the algorithm is governed by a trade-off between the yield of MUAPTs obtained above a given accuracy level and the time required to perform the decomposition. When applied to a set of sEMG signals synthesized from real MUAPTs, the identification error was reduced by an average of 1.78%, improving the accuracy to 97.0%, and the location error was reduced by an average of 1.66 ms. The error-reduction algorithm in this study is not limited to any specific decomposition strategy. Rather, we propose it be used for other decomposition methods, especially when analyzing precise motor-unit firing instances, as occurs when measuring synchronization.

ANALYSIS OF SURFACE EMG SIGNALS UNDER FATIGUE AND NON-FATIGUE CONDITIONS USING B-DISTRIBUTION BASED QUADRATIC TIME FREQUENCY DISTRIBUTION

2015 ◽  
Vol 15 (02) ◽  
pp. 1540028 ◽  
Author(s):  
P. A. KARTHICK ◽  
G. VENUGOPAL ◽  
S. RAMAKRISHNAN
Keyword(s):  
Frequency Distribution ◽  
Biceps Brachii ◽  
Surface Emg ◽  
Median Frequency ◽  
Biceps Brachii Muscle ◽  
Spectral Entropy ◽  
Time Frequency ◽  
Neuromuscular Conditions ◽  
Time Frequency Distribution ◽  
Semg Signals

In this paper, an attempt has been made to analyze surface electromyography (sEMG) signals under non-fatigue and fatigue conditions using time-frequency based features. The sEMG signals are recorded from biceps brachii muscle of 50 healthy volunteers under well-defined protocol. The pre-processed signals are divided into six equal epochs. The first and last segments are considered as non-fatigue and fatigue zones respectively. Further, these signals are subjected to B-distribution based quadratic time-frequency distribution (TFD). Time frequency based features such as instantaneous median frequency (IMDF) and instantaneous mean frequency (IMNF) are extracted. The expression of spectral entropy is modified to obtain instantaneous spectral entropy (ISPEn) from the time-frequency spectrum. The results show that all the extracted features are distinct in both conditions. It is also observed that the values of all features are higher in non-fatigue zone compared to fatigue condition. It appears that this method is useful in analysing various neuromuscular conditions using sEMG signals.

scholarly journals Methodology Proposal of EMG Hand Movement Classification Based on Cross Recurrence Plots

2019 ◽  
Vol 2019 ◽  
pp. 1-15
Author(s):  
M. A. Aceves-Fernandez ◽  
J. M. Ramos-Arreguin ◽  
E. Gorrostieta-Hurtado ◽  
J. C. Pedraza-Ortega
Keyword(s):  
Hand Movement ◽  
Surface Emg ◽  
Noisy Environment ◽  
Recurrence Plots ◽  
Machine Learning Methods ◽  
Nonlinear Features ◽  
Quantification Analysis ◽  
Cross Recurrence Quantification ◽  
Cross Recurrence Quantification Analysis ◽  
Semg Signals

Dealing with electromyography (EMG) signals is often not simple. The nature of these signals is nonstationary, noisy, and high dimensional. These EMG characteristics make their predictability even more challenging. Cross recurrence plots (CRPs) have demonstrated in many works their capability of detecting very subtle patterns in signals often buried in a noisy environment. In this contribution, fifty subjects performed ten different hand movements with each hand with the aid of electrodes placed in each arm. Furthermore, the nonlinear features of each subject’s signals using cross recurrence quantification analysis (CRQA) have been performed. Also, a novel methodology is proposed using CRQA as the mainstream technique to detect and classify each of the movements presented in this study. Additional tools were presented to determine to which extent this proposed methodology is able to avoid false classifications, thus demonstrating that this methodology is feasible to classify surface EMG (SEMG) signals with good accuracy, sensitivity, and specificity. Lastly, the results were compared with traditional machine learning methods, and the advantages of using the proposed methodology above such methods are highlighted.

Conduction velocity of low-threshold motor units during ischemic contractions performed with surface EMG feedback

2005 ◽  
Vol 98 (4) ◽  
pp. 1487-1494 ◽  
Author(s):  
Dario Farina ◽  
Marco Gazzoni ◽  
Federico Camelia
Keyword(s):  
Motor Unit ◽  
Conduction Velocity ◽  
Action Potential Duration ◽  
Surface Emg ◽  
Mean Power ◽  
Single Motor Unit ◽  
Single Motor ◽  
Power Spectral ◽  
Low Threshold ◽  
Spectral Frequency

The aim of this study was to analyze the effect of ischemia on low-threshold motor unit conduction velocity. Nine subjects were trained to isolate the activity of a single motor unit (target motor unit) in the abductor pollicis brevis muscle with feedback on surface EMG signals recorded with a 16-electrode linear array. After training, the subjects activated the target motor unit at ∼8 pulses per second (pps) for five 3-min-long contractions. During the third and fourth contractions, a cuff inflated at 180 mmHg around the forearm induced ischemia of the hand. The exerted force (mean ± SE, 4.6 ± 2.1% of the maximal voluntary contraction force), discharge rate (8.6 ± 0.4 pps), interpulse interval variability (34.8 ± 2.5%), and peak-to-peak amplitude of the target motor unit action potentials (176.6 ± 18.2 μV) were not different among the five contractions. Conduction velocity, mean power spectral frequency, and action potential duration were the same in the beginning of the five contractions (2.8 ± 0.2 m/s, 195.2 ± 10.5 Hz, and 5.4 ± 0.3 ms, respectively) and changed over the 3 min of sustained activation only during the fourth contraction. Conduction velocity and mean power spectral frequency decreased (10.05 ± 1.8% and 8.50 ± 2.18% during the 3 min, respectively) and action potential duration increased (8.2 ± 4.6% in the 3 min) during the fourth contraction. In conclusion, 1) subjects were able to isolate the activity of a single motor unit with surface EMG visual feedback in ischemic conditions maintained for 16 min, and 2) the activation-induced decrease in single motor unit conduction velocity was significantly larger with ischemia than with normal circulation, probably due to the alteration of mechanisms of ion exchange across the fiber membrane.

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