Displaying publications 1 - 20 of 25 in total

Abstract:
Sort:
  1. Characterization of surface electromyography using time domain features for determining hand motion and stages of contraction
    Bin Ahmad Nadzri AA, Ahmad SA, Marhaban MH, Jaafar H
    Australas Phys Eng Sci Med, 2014 Mar;37(1):133-7.
    PMID: 24443218 DOI: 10.1007/s13246-014-0243-3
    Surface electromyography (SEMG) signals can provide important information for prosthetic hand control application. In this study, time domain (TD) features were used in extracting information from the SEMG signal in determining hand motions and stages of contraction (start, middle and end). Data were collected from ten healthy subjects. Two muscles, which are flexor carpi ulnaris (FCU) and extensor carpi radialis (ECR) were assessed during three hand motions of wrist flexion (WF), wrist extension (WE) and co-contraction (CC). The SEMG signals were first segmented into 132.5 ms windows, full wave rectified and filtered with a 6 Hz low pass Butterworth filter. Five TD features of mean absolute value, variance, root mean square, integrated absolute value and waveform length were used for feature extraction and subsequently patterns were determined. It is concluded that the TD features that were used are able to differentiate hand motions. However, for the stages of contraction determination, although there were patterns observed, it is determined that the stages could not be properly be differentiated due to the variability of signal strengths between subjects.
    Matched MeSH terms: Electromyography/methods*
  2. Fulltext A New Approach to Noninvasive-Prolonged Fatigue Identification Based on Surface EMG Time-Frequency and Wavelet Features
    Jamaluddin FN, Ibrahim F, Ahmad SA
    J Healthc Eng, 2023;2023:1951165.
    PMID: 36756137 DOI: 10.1155/2023/1951165
    In sports, fatigue management is vital as adequate rest builds strength and enhances performance, whereas inadequate rest exposes the body to prolonged fatigue (PF) or also known as overtraining. This paper presents PF identification and classification based on surface electromyography (EMG) signals. An experiment was performed on twenty participants to investigate the behaviour of surface EMG during the inception of PF. PF symptoms were induced in accord with a five-day Bruce Protocol treadmill test on four lower extremity muscles: the biceps femoris (BF), rectus femoris (RF), vastus medialis (VM), and vastus lateralis (VL). The results demonstrate that the experiment successfully induces soreness, unexplained lethargy, and performance decrement and also indicate that the progression of PF can be observed based on changes in frequency features (ΔF med and ΔF mean) and time features (ΔRMS and ΔMAV) of surface EMG. This study also demonstrates the ability of wavelet index features in PF identification. Using a naïve Bayes (NB) classifier exhibits the highest accuracy based on time and frequency features with 98% in distinguishing PF on RF, 94% on BF, 9% on VL, and 97% on VM. Thus, this study has positively indicated that surface EMG can be used in identifying the inception of PF. The implication of the findings is significant in sports to prevent a greater risk of PF.
    Matched MeSH terms: Electromyography/methods
  3. Surface electromyographic analysis of the biceps brachii muscle of cricket bowlers during bowling
    Ahamed NU, Sundaraj K, Ahmad B, Rahman M, Ali MA, Islam MA
    Australas Phys Eng Sci Med, 2014 Mar;37(1):83-95.
    PMID: 24477560 DOI: 10.1007/s13246-014-0245-1
    Cricket bowling generates forces with torques on the upper limb muscles and makes the biceps brachii (BB) muscle vulnerable to overuse injury. The aim of this study was to investigate whether there are differences in the amplitude of the EMG signal of the BB muscle during fast and spin delivery, during the seven phases of both types of bowling and the kinesiological interpretation of the bowling arm for muscle contraction mechanisms during bowling. A group of 16 male amateur bowlers participated in this study, among them 8 fast bowlers (FB) and 8 spin bowlers (SB). The root mean square (EMGRMS), the average sEMG (EMGAVG), the maximum peak amplitude (EMGpeak), and the variability of the signal were calculated using the coefficient of variance (EMGCV) from the BB muscle of each bowler (FB and SB) during each bowling phase. The results demonstrate that, (i) the BB muscle is more active during FB than during SB, (ii) the point of ball release and follow-through generated higher signals than the other five movements during both bowling categories, (iii) the BB muscle variability is higher during SB compared with FB, (iv) four statistically significant differences (p<0.05) found between the bowling phases in fast bowling and three in spin bowling, and (v) several arm mechanics occurred for muscle contraction. There are possible clinical significances from the outcomes; like, recurring dynamic contractions on BB muscle can facilitate to clarify the maximum occurrence of shoulder pain as well as biceps tendonitis those are medically observed in professional cricket bowlers, and treatment methods with specific injury prevention programmes should focus on the different bowling phases with the maximum muscle effect. Finally, these considerations will be of particular importance in assessing different physical therapy on bowler's muscle which can improve the ball delivery performance and stability of cricket bowlers.
    Matched MeSH terms: Electromyography/methods*
  4. Investigation of the EMG activity of erector spinae and trapezius muscles during Islamic prayer (Salat)
    Rabbi MF, Ghazali KH, Mohd II, Alqahtani M, Altwijri O, Ahamed NU
    J Back Musculoskelet Rehabil, 2018;31(6):1097-1104.
    PMID: 29945343 DOI: 10.3233/BMR-170988
    This study aimed to investigate the electrical activity of two muscles located at the dorsal surface during Islamic prayer (Salat). Specifically, the electromyography (EMG) activity of the erector spinae and trapezius muscles during four positions observed while performing Salat, namely standing, bowing, sitting and prostration, were investigated. Seven adult subjects with an average age of 28.1 (± 3.8) years were included in the study. EMG data were obtained from their trapezius and erector spinae muscles while the subjects maintained the specific positions of Salat. The EMG signal was analysed using time and frequency domain features. The results indicate that the trapezius muscle remains relaxed during the standing and sitting positions while the erector spinae muscle remains contracted during these two positions. Additionally, during the bowing and prostration positions of Salat, these two muscles exhibit the opposite activities: the trapezius muscle remains contracted while the erector spinae muscle remains relaxed. Overall, both muscles maintain a balance in terms of contraction and relaxation during bowing and prostration position. The irregularity of the neuro-muscular signal might cause pain and prevent Muslims from performing their obligatory prayer. This study will aid the accurate understanding of how the back muscles respond in specific postures during Salat.
    Matched MeSH terms: Electromyography/methods*
  5. A systematic review of muscle activity assessment of the biceps brachii muscle using mechanomyography
    Talib I, Sundaraj K, Lam CK, Sundaraj S
    J Musculoskelet Neuronal Interact, 2018 12 01;18(4):446-462.
    PMID: 30511949
    This systematic review aims to categorically analyses the literature on the assessment of biceps brachii (BB) muscle activity through mechanomyography (MMG). The application of our search criteria to five different databases identified 319 studies. A critical review of the 48 finally selected records, revealed the diversity of protocols and parameters that are employed in MMG-based assessments of BB muscle activity. The observations were categorized into the following: muscle torque, fatigue, strength and physiology. The available information on the muscle contraction protocol, sensor(s), MMG signal parameters and obtained results were then tabulated based on these categories for further analysis. The review affirms that - 1) MMG is suitable for skeletal muscle activity assessment and can be employed potentially for further investigation of the BB muscle activity and condition (e.g., force, torque, fatigue, and contractile properties), 2) a majority of the records focused on static contractions of the BB, and the analysis of dynamic muscle contractions using MMG is thus a research gap, and 3) very few studies have focused on the analysis of BB muscle activity under externally stimulated contractions. Taken together, the findings of this review on BB activity assessment using MMG affirm the potential of MMG as an alternative tool.
    Matched MeSH terms: Electromyography/methods*
  6. Fulltext Machine Learning-Based Diabetic Neuropathy and Previous Foot Ulceration Patients Detection Using Electromyography and Ground Reaction Forces during Gait
    Haque F, Reaz MBI, Chowdhury MEH, Ezeddin M, Kiranyaz S, Alhatou M, et al.
    Sensors (Basel), 2022 May 05;22(9).
    PMID: 35591196 DOI: 10.3390/s22093507
    Diabetic neuropathy (DN) is one of the prevalent forms of neuropathy that involves alterations in biomechanical changes in the human gait. Diabetic foot ulceration (DFU) is one of the pervasive types of complications that arise due to DN. In the literature, for the last 50 years, researchers have been trying to observe the biomechanical changes due to DN and DFU by studying muscle electromyography (EMG) and ground reaction forces (GRF). However, the literature is contradictory. In such a scenario, we propose using Machine learning techniques to identify DN and DFU patients by using EMG and GRF data. We collected a dataset from the literature which involves three patient groups: Control (n = 6), DN (n = 6), and previous history of DFU (n = 9) and collected three lower limb muscles EMG (tibialis anterior (TA), vastus lateralis (VL), gastrocnemius lateralis (GL)), and three GRF components (GRFx, GRFy, and GRFz). Raw EMG and GRF signals were preprocessed, and different feature extraction techniques were applied to extract the best features from the signals. The extracted feature list was ranked using four different feature ranking techniques, and highly correlated features were removed. In this study, we considered different combinations of muscles and GRF components to find the best performing feature list for the identification of DN and DFU. We trained eight different conventional ML models: Discriminant analysis classifier (DAC), Ensemble classification model (ECM), Kernel classification model (KCM), k-nearest neighbor model (KNN), Linear classification model (LCM), Naive Bayes classifier (NBC), Support vector machine classifier (SVM), and Binary decision classification tree (BDC), to find the best-performing algorithm and optimized that model. We trained the optimized the ML algorithm for different combinations of muscles and GRF component features, and the performance matrix was evaluated. Our study found the KNN algorithm performed well in identifying DN and DFU, and we optimized it before training. We found the best accuracy of 96.18% for EMG analysis using the top 22 features from the chi-square feature ranking technique for features from GL and VL muscles combined. In the GRF analysis, the model showed 98.68% accuracy using the top 7 features from the Feature selection using neighborhood component analysis for the feature combinations from the GRFx-GRFz signal. In conclusion, our study has shown a potential solution for ML application in DN and DFU patient identification using EMG and GRF parameters. With careful signal preprocessing with strategic feature extraction from the biomechanical parameters, optimization of the ML model can provide a potential solution in the diagnosis and stratification of DN and DFU patients from the EMG and GRF signals.
    Matched MeSH terms: Electromyography/methods
  7. Fulltext Effect of Forearm Postures and Elbow Joint Angles on Elbow Flexion Torque and Mechanomyography in Neuromuscular Electrical Stimulation of the Biceps Brachii
    Uwamahoro R, Sundaraj K, Feroz FS
    Sensors (Basel), 2023 Sep 29;23(19).
    PMID: 37836995 DOI: 10.3390/s23198165
    Neuromuscular electrical stimulation plays a pivotal role in rehabilitating muscle function among individuals with neurological impairment. However, there remains uncertainty regarding whether the muscle's response to electrical excitation is affected by forearm posture, joint angle, or a combination of both factors. This study aimed to investigate the effects of forearm postures and elbow joint angles on the muscle torque and MMG signals. Measurements of the torque around the elbow and MMG of the biceps brachii (BB) muscle were conducted in 36 healthy subjects (age, 22.24 ± 2.94 years; height, 172 ± 0.5 cm; and weight, 67.01 ± 7.22 kg) using an in-house elbow flexion testbed and neuromuscular electrical stimulation (NMES) of the BB muscle. The BB muscle was stimulated while the forearm was positioned in the neutral, pronation, or supination positions. The elbow was flexed at angles of 10°, 30°, 60°, and 90°. The study analyzed the impact of the forearm posture(s) and elbow joint angle(s) on the root-mean-square value of the torque (TQRMS). Subsequently, various MMG parameters, such as the root-mean-square value (MMGRMS), the mean power frequency (MMGMPF), and the median frequency (MMGMDF), were analyzed along the longitudinal, lateral, and transverse axes of the BB muscle fibers. The test-retest interclass correlation coefficient (ICC21) for the torque and MMG ranged from 0.522 to 0.828. Repeated-measure ANOVAs showed that the forearm posture and elbow flexion angle significantly influenced the TQRMS (p < 0.05). Similarly, the MMGRMS, MMGMPF, and MMGMDF showed significant differences among all the postures and angles (p < 0.05). However, the combined main effect of the forearm posture and elbow joint angle was insignificant along the longitudinal axis (p > 0.05). The study also found that the MMGRMS and TQRMS increased with increases in the joint angle from 10° to 60° and decreased at greater angles. However, during this investigation, the MMGMPF and MMGMDF exhibited a consistent decrease in response to increases in the joint angle for the lateral and transverse axes of the BB muscle. These findings suggest that the muscle contraction evoked by NMES may be influenced by the interplay between actin and myosin filaments, which are responsible for muscle contraction and are, in turn, influenced by the muscle length. Because restoring the function of limbs is a common goal in rehabilitation services, the use of MMG in the development of methods that may enable the real-time tracking of exact muscle dimensional changes and activation levels is imperative.
    Matched MeSH terms: Electromyography/methods
  8. Fulltext The effectiveness of FES-evoked EMG potentials to assess muscle force and fatigue in individuals with spinal cord injury
    Ibitoye MO, Estigoni EH, Hamzaid NA, Wahab AK, Davis GM
    Sensors (Basel), 2014;14(7):12598-622.
    PMID: 25025551 DOI: 10.3390/s140712598
    The evoked electromyographic signal (eEMG) potential is the standard index used to monitor both electrical changes within the motor unit during muscular activity and the electrical patterns during evoked contraction. However, technical and physiological limitations often preclude the acquisition and analysis of the signal especially during functional electrical stimulation (FES)-evoked contractions. Hence, an accurate quantification of the relationship between the eEMG potential and FES-evoked muscle response remains elusive and continues to attract the attention of researchers due to its potential application in the fields of biomechanics, muscle physiology, and rehabilitation science. We conducted a systematic review to examine the effectiveness of eEMG potentials to assess muscle force and fatigue, particularly as a biofeedback descriptor of FES-evoked contractions in individuals with spinal cord injury. At the outset, 2867 citations were identified and, finally, fifty-nine trials met the inclusion criteria. Four hypotheses were proposed and evaluated to inform this review. The results showed that eEMG is effective at quantifying muscle force and fatigue during isometric contraction, but may not be effective during dynamic contractions including cycling and stepping. Positive correlation of up to r = 0.90 (p < 0.05) between the decline in the peak-to-peak amplitude of the eEMG and the decline in the force output during fatiguing isometric contractions has been reported. In the available prediction models, the performance index of the eEMG signal to estimate the generated muscle force ranged from 3.8% to 34% for 18 s to 70 s ahead of the actual muscle force generation. The strength and inherent limitations of the eEMG signal to assess muscle force and fatigue were evident from our findings with implications in clinical management of spinal cord injury (SCI) population.
    Matched MeSH terms: Electromyography/methods*
  9. Evaluation of repetitive isometric contractions on the heads of triceps brachii muscle during grip force exercise
    Ali MA, Sundaraj K, Ahmad RB, Ahamed NU, Islam MA, Sundaraj S
    Technol Health Care, 2014;22(4):617-25.
    PMID: 24990168 DOI: 10.3233/THC-140833
    Normally, surface electromyography electrodes are used to evaluate the activity of superficial muscles during various kinds of voluntary contractions of muscle fiber. The objective of the present study was to investigate the effect of repetitive isometric contractions on the three heads of the triceps brachii muscle during handgrip force exercise.
    Matched MeSH terms: Electromyography/methods*
  10. Mechanomyography and muscle function assessment: a review of current state and prospects
    Ibitoye MO, Hamzaid NA, Zuniga JM, Abdul Wahab AK
    Clin Biomech (Bristol, Avon), 2014 Jun;29(6):691-704.
    PMID: 24856875 DOI: 10.1016/j.clinbiomech.2014.04.003
    Previous studies have explored to saturation the efficacy of the conventional signal (such as electromyogram) for muscle function assessment and found its clinical impact limited. Increasing demand for reliable muscle function assessment modalities continues to prompt further investigation into other complementary alternatives. Application of mechanomyographic signal to quantify muscle performance has been proposed due to its inherent mechanical nature and ability to assess muscle function non-invasively while preserving muscular neurophysiologic information. Mechanomyogram is gaining accelerated applications in evaluating the properties of muscle under voluntary and evoked muscle contraction with prospects in clinical practices. As a complementary modality and the mechanical counterpart to electromyogram; mechanomyogram has gained significant acceptance in analysis of isometric and dynamic muscle actions. Substantial studies have also documented the effectiveness of mechanomyographic signal to assess muscle performance but none involved comprehensive appraisal of the state of the art applications with highlights on the future prospect and potential integration into the clinical practices. Motivated by the dearth of such critical review, we assessed the literature to investigate its principle of acquisition, current applications, challenges and future directions. Based on our findings, the importance of rigorous scientific and clinical validation of the signal is highlighted. It is also evident that as a robust complement to electromyogram, mechanomyographic signal may possess unprecedented potentials and further investigation will be enlightening.
    Matched MeSH terms: Electromyography/methods
  11. Fulltext Cross-talk in mechanomyographic signals from the forearm muscles during sub-maximal to maximal isometric grip force
    Islam MA, Sundaraj K, Ahmad RB, Sundaraj S, Ahamed NU, Ali MA
    PLoS One, 2014;9(5):e96628.
    PMID: 24802858 DOI: 10.1371/journal.pone.0096628
    This study aimed: i) to examine the relationship between the magnitude of cross-talk in mechanomyographic (MMG) signals generated by the extensor digitorum (ED), extensor carpi ulnaris (ECU), and flexor carpi ulnaris (FCU) muscles with the sub-maximal to maximal isometric grip force, and with the anthropometric parameters of the forearm, and ii) to quantify the distribution of the cross-talk in the MMG signal to determine if it appears due to the signal component of intramuscular pressure waves produced by the muscle fibers geometrical changes or due to the limb tremor.
    Matched MeSH terms: Electromyography/methods
  12. Fulltext Surface electromyography signal processing and classification techniques
    Chowdhury RH, Reaz MB, Ali MA, Bakar AA, Chellappan K, Chang TG
    Sensors (Basel), 2013;13(9):12431-66.
    PMID: 24048337 DOI: 10.3390/s130912431
    Electromyography (EMG) signals are becoming increasingly important in many applications, including clinical/biomedical, prosthesis or rehabilitation devices, human machine interactions, and more. However, noisy EMG signals are the major hurdles to be overcome in order to achieve improved performance in the above applications. Detection, processing and classification analysis in electromyography (EMG) is very desirable because it allows a more standardized and precise evaluation of the neurophysiological, rehabitational and assistive technological findings. This paper reviews two prominent areas; first: the pre-processing method for eliminating possible artifacts via appropriate preparation at the time of recording EMG signals, and second: a brief explanation of the different methods for processing and classifying EMG signals. This study then compares the numerous methods of analyzing EMG signals, in terms of their performance. The crux of this paper is to review the most recent developments and research studies related to the issues mentioned above.
    Matched MeSH terms: Electromyography/methods*
  13. Fulltext EMG-based facial gesture recognition through versatile elliptic basis function neural network
    Hamedi M, Salleh ShH, Astaraki M, Noor AM
    Biomed Eng Online, 2013;12:73.
    PMID: 23866903 DOI: 10.1186/1475-925X-12-73
    Recently, the recognition of different facial gestures using facial neuromuscular activities has been proposed for human machine interfacing applications. Facial electromyograms (EMGs) analysis is a complicated field in biomedical signal processing where accuracy and low computational cost are significant concerns. In this paper, a very fast versatile elliptic basis function neural network (VEBFNN) was proposed to classify different facial gestures. The effectiveness of different facial EMG time-domain features was also explored to introduce the most discriminating.
    Matched MeSH terms: Electromyography/methods*
  14. Design and development of an automated, portable and handheld tablet personal computer-based data acquisition system for monitoring electromyography signals during rehabilitation
    Ahamed NU, Sundaraj K, Poo TS
    Proc Inst Mech Eng H, 2013 Mar;227(3):262-74.
    PMID: 23662342
    This article describes the design of a robust, inexpensive, easy-to-use, small, and portable online electromyography acquisition system for monitoring electromyography signals during rehabilitation. This single-channel (one-muscle) system was connected via the universal serial bus port to a programmable Windows operating system handheld tablet personal computer for storage and analysis of the data by the end user. The raw electromyography signals were amplified in order to convert them to an observable scale. The inherent noise of 50 Hz (Malaysia) from power lines electromagnetic interference was then eliminated using a single-hybrid IC notch filter. These signals were sampled by a signal processing module and converted into 24-bit digital data. An algorithm was developed and programmed to transmit the digital data to the computer, where it was reassembled and displayed in the computer using software. Finally, the following device was furnished with the graphical user interface to display the online muscle strength streaming signal in a handheld tablet personal computer. This battery-operated system was tested on the biceps brachii muscles of 20 healthy subjects, and the results were compared to those obtained with a commercial single-channel (one-muscle) electromyography acquisition system. The results obtained using the developed device when compared to those obtained from a commercially available physiological signal monitoring system for activities involving muscle contractions were found to be comparable (the comparison of various statistical parameters) between male and female subjects. In addition, the key advantage of this developed system over the conventional desktop personal computer-based acquisition systems is its portability due to the use of a tablet personal computer in which the results are accessible graphically as well as stored in text (comma-separated value) form.
    Matched MeSH terms: Electromyography/methods*
  15. Fulltext Human facial neural activities and gesture recognition for machine-interfacing applications
    Hamedi M, Salleh ShH, Tan TS, Ismail K, Ali J, Dee-Uam C, et al.
    Int J Nanomedicine, 2011;6:3461-72.
    PMID: 22267930 DOI: 10.2147/IJN.S26619
    The authors present a new method of recognizing different human facial gestures through their neural activities and muscle movements, which can be used in machine-interfacing applications. Human-machine interface (HMI) technology utilizes human neural activities as input controllers for the machine. Recently, much work has been done on the specific application of facial electromyography (EMG)-based HMI, which have used limited and fixed numbers of facial gestures. In this work, a multipurpose interface is suggested that can support 2-11 control commands that can be applied to various HMI systems. The significance of this work is finding the most accurate facial gestures for any application with a maximum of eleven control commands. Eleven facial gesture EMGs are recorded from ten volunteers. Detected EMGs are passed through a band-pass filter and root mean square features are extracted. Various combinations of gestures with a different number of gestures in each group are made from the existing facial gestures. Finally, all combinations are trained and classified by a Fuzzy c-means classifier. In conclusion, combinations with the highest recognition accuracy in each group are chosen. An average accuracy >90% of chosen combinations proved their ability to be used as command controllers.
    Matched MeSH terms: Electromyography/methods*
  16. Strength and fatigue of lumbar extensor muscles in older adults
    Ajit Singh DK, Bailey M, Lee R
    Muscle Nerve, 2011 Jul;44(1):74-9.
    PMID: 21488056 DOI: 10.1002/mus.21998
    Loss of lumbar extensor muscle strength and fatigue resistance may contribute to functional disability.
    Matched MeSH terms: Electromyography/methods
  17. Reliability of measurement of muscle fiber conduction velocity using surface EMG
    Harba MI, Teng LY
    Front Med Biol Eng, 1999;9(1):31-47.
    PMID: 10354908
    Cross-correlating two surface EMG signals detected at two different locations along the path of flow of action potential enables the measurement of the muscle fiber average conduction velocity in those active motor units monitored by the electrodes. The position of the peak of the cross-correlation function is the time delay between the two signals and hence the velocity may be deduced. The estimated velocity using this technique has been observed previously to depend on the location of the electrodes on the muscle surface. Different locations produced different estimates. In this paper we present a measurement system, analyze its inherent inaccuracies and use it for the purpose of investigating the reliability of measurement of conduction velocity from surface EMG. This system utilizes EMG signals detected at a number of locations on the biceps brachii, when under light tension, to look for any pattern of variations of velocity as a function of location and time. It consists of a multi-electrode unit and a set of eight parallel on-line correlators. The electrode unit and the parallel correlators ensure that these measurements are carried out under the same physical and physiological conditions of the muscle. Further, the same detected signals are used in different measurement configurations to try to understand the reasons behind the observed variations in the estimated velocity. The results obtained seem to suggest that there will always be an unpredictable random component superimposed on the estimated velocity, giving rise to differences between estimates at different locations and differences in estimates with time at the same location. Many factors contribute to this random component, such as the non-homogeneous medium between the muscle fibers and the electrodes, the non-parallel geometry and non-uniform conduction velocity of the fibers, and the physical and physiological conditions of the muscle. While it is not possible to remove this random component completely from the measurement, the user must be aware of its presence and how to reduce its effects.
    Matched MeSH terms: Electromyography/methods*
  18. AANEM - IFCN glossary of terms in neuromuscular electrodiagnostic medicine and ultrasound
    Dengler R, de Carvalho M, Shahrizaila N, Nodera H, Vucic S, Grimm A, et al.
    Clin Neurophysiol, 2020 07;131(7):1662-1663.
    PMID: 32354605 DOI: 10.1016/j.clinph.2020.03.014
    Modern neuromuscular electrodiagnosis (EDX) and neuromuscular ultrasound (NMUS) require a universal language for effective communication in clinical practice and research and, in particular, for teaching young colleagues. Therefore, the AANEM and the IFCN have decided to publish a joint glossary as they feel the need for an updated terminology to support educational activities in neuromuscular EDX and NMUS in all parts of the world. In addition NMUS has been rapidly progressing over the last years and is now widely used in the diagnosis of disorders of nerve and muscle in conjunction with EDX. This glossary has been developed by experts in the field of neuromuscular EDX and NMUS on behalf of the AANEM and the IFCN and has been agreed upon by electronic communication between January and November 2019. It is based on the glossaries of the AANEM from 2015 and of the IFCN from 1999. The EDX and NMUS terms and the explanatory illustrations have been updated and supplemented where necessary. The result is a comprehensive glossary of terms covering all fields of neuromuscular EDX and NMUS. It serves as a standard reference for clinical practice, education and research worldwide.
    Matched MeSH terms: Electromyography/methods
  19. Analysis of the crosstalk in mechanomyographic signals along the longitudinal, lateral and transverse axes of elbow flexor muscles during sustained isometric forearm flexion, supination and pronation exercises
    Talib I, Sundaraj K, Lam CK
    J Musculoskelet Neuronal Interact, 2020 06 01;20(2):194-205.
    PMID: 32481235
    OBJECTIVE: To analyse the influence of muscle fibre axis on the degree of crosstalk in mechanomyographic (MMG) signals during sustained isometric forearm flexion, pronation and supination exercises performed at 80% maximum voluntary contraction (MVC) at an elbow joint angle of 90°.

    METHODS: MMG signals in longitudinal, lateral and transverse directions of muscle fibres were recorded from the elbow flexors of twenty-five male subjects using triaxial accelerometers. Cross-correlation coefficients were used to quantify the degree of crosstalk in all nine possible pairs of fibre axes, all muscle pairs and all exercises.

    RESULTS: MMG root mean square (RMS) was statistically significant among the fibre axes (p<0.05, η2=0.17- 0.34) except for biceps brachii and brachioradialis in supination and brachialis in flexion. Overall mean crosstalk values in the three muscle pairs (biceps brachii & brachialis, brachialis & brachioradialis and brachioradialis & biceps brachii) were found to be 6.09-52.17%, 4.01-61.42% and 2.16-51.85%, respectively. Crosstalk values showed statistical significance among all nine axes pairs (p<0.05, η2=0.16-0.51) except for biceps brachii & brachialis during pronation. The transverse axes pair generated the lowest mean crosstalk values (2.16-9.14%).

    CONCLUSION: MMG signals recorded using accelerometers from the transverse axes of muscle fibres in the elbow flexors are unique and yield the least amount of crosstalk.

    Matched MeSH terms: Electromyography/methods
  20. Classification of ankle joint movements based on surface electromyography signals for rehabilitation robot applications
    Al-Quraishi MS, Ishak AJ, Ahmad SA, Hasan MK, Al-Qurishi M, Ghapanchizadeh H, et al.
    Med Biol Eng Comput, 2017 May;55(5):747-758.
    PMID: 27484411 DOI: 10.1007/s11517-016-1551-4
    Electromyography (EMG)-based control is the core of prostheses, orthoses, and other rehabilitation devices in recent research. Nonetheless, EMG is difficult to use as a control signal given the complex nature of the signal. To overcome this problem, the researchers employed a pattern recognition technique. EMG pattern recognition mainly involves four stages: signal detection, preprocessing feature extraction, dimensionality reduction, and classification. In particular, the success of any pattern recognition technique depends on the feature extraction stage. In this study, a modified time-domain features set and logarithmic transferred time-domain features (LTD) were evaluated and compared with other traditional time-domain features set (TTD). Three classifiers were employed to assess the two feature sets, namely linear discriminant analysis (LDA), k nearest neighborhood, and Naïve Bayes. Results indicated the superiority of the new time-domain feature set LTD, on conventional time-domain features TTD with the average classification accuracy of 97.23 %. In addition, the LDA classifier outperformed the other two classifiers considered in this study.
    Matched MeSH terms: Electromyography/methods
Related Terms
Filters
Contact Us

Please provide feedback to Administrator ([email protected])

External Links