A Review of Electromyography Signal Analysis Techniques for Musculoskeletal Disorders

Zawawi, Tengku Nor Shuhada Tengku; Abdullah, Abdul Rahim; Jopri, Mohd Hatta; Sutikno, Tole; Saad, Norhashimah Mohd; Sudirman, Rubita

doi:10.11591/ijeecs.v11.i3.pp1136-1146

Cited by 12 publications

(3 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Variations in the value of MDF reflect modifications in the distribution of the signal power spectrum throughout its bandwidth, which can be associated with changes in the the conduction velocity of the action potential through muscle fibers and in the tissue filter effects, among other factors [ 37 , 38 ]. For this reason, MDF has been widely used to characterize changes in the spectral content of sEMG signals in the context of muscle force production and fatigue [ 37 , 39 ].…”

Section: Methodsmentioning

confidence: 99%

Characterization of Pelvic Floor Activity in Healthy Subjects and with Chronic Pelvic Pain: Diagnostic Potential of Surface Electromyography

Albaladejo-Belmonte

Tarazona-Motes

Nohales-Alfonso

et al. 2021

Sensors

View full text Add to dashboard Cite

Chronic pelvic pain (CPP) is a highly disabling disorder in women usually associated with hypertonic dysfunction of the pelvic floor musculature (PFM). The literature on the subject is not conclusive about the diagnostic potential of surface electromyography (sEMG), which could be due to poor signal characterization. In this study, we characterized the PFM activity of three groups of 24 subjects each: CPP patients with deep dyspareunia associated with a myofascial syndrome (CPP group), healthy women over 35 and/or parous (>35/P group, i.e., CPP counterparts) and under 35 and nulliparous (<35&NP). sEMG signals of the right and left PFM were recorded during contractions and relaxations. The signals were characterized by their root mean square (RMS), median frequency (MDF), Dimitrov index (DI), sample entropy (SampEn), and cross-correlation (CC). The PFM activity showed a higher power (>RMS), a predominance of low-frequency components (<MDF, >DI), greater complexity (>SampEn) and lower synchronization on the same side (<CC) in CPP patients, with more significant differences in the >35/P group. The same trend in differences was found between healthy women (<35&NP vs. >35/P) associated with aging and parity. These results show that sEMG can reveal alterations in PFM electrophysiology and provide clinicians with objective information for CPP diagnosis.

show abstract

Section: Methodsmentioning

confidence: 99%

Characterization of Pelvic Floor Activity in Healthy Subjects and with Chronic Pelvic Pain: Diagnostic Potential of Surface Electromyography

Albaladejo-Belmonte

Tarazona-Motes

Nohales-Alfonso

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…These techniques relied heavily on manual functions developed by doctors and neuroradiologists in the field [76]. Every patient might have different observed data, and the interpretation of the data depends on the experience of those skilled in the art, this can lead to errors within and between observers [77]. Segmentation ensues by dividing digital images into multiple segments into nonoverlapped areas that share characteristics such as shape, intensity, or texture to locate and identify objects and boundaries in an image [18], [78]- [89].…”

Section: Segmentation Techniquesmentioning

confidence: 99%

Brain stroke computed tomography images analysis using image processing: A Review

Ali

Abdullah

Saad

et al. 2021

IJ-AI

Self Cite

View full text Add to dashboard Cite

<span lang="EN-US">Stroke is the second-leading cause of death globally; therefore, it needs immediate treatment to prevent the brain from damage. Neuroimaging technique for stroke detection such as computed tomography (CT) has been widely used for emergency setting that can provide precise information on an obvious difference between white and gray matter. CT is the comprehensively utilized medical imaging technology for bone, soft tissue, and blood vessels imaging. A fully automatic segmentation became a significant contribution to help neuroradiologists achieve fast and accurate interpretation based on the region of interest (ROI). This review paper aims to identify, critically appraise, and summarize the evidence of the relevant studies needed by researchers. Systematic literature review (SLR) is the most efficient way to obtain reliable and valid conclusions as well as to reduce mistakes. Throughout the entire review process, it has been observed that the segmentation techniques such as fuzzy C-mean, thresholding, region growing, k-means, and watershed segmentation techniques were regularly used by researchers to segment CT scan images. This review is also impactful in identifying the best automated segmentation technique to evaluate brain stroke and is expected to contribute new information in the area of stroke research.</span>

show abstract

“…For the hand gestures, four gestures are chosen. The researchers used a Feedforward Artificial Neural Network is used as classifier that trained to recognize the gestures which later used for human-robot interaction [21][22][23][24][25]. This paper present the classification of EMG signal for multiple hand gestures based on neural network using by using Arduino IDE, CoolTerm software and Microsoft Excel for data collection and finally, MATLAB is used for classification part using artificial neural network.…”

mentioning

confidence: 99%

Classification of EMG signal for multiple hand gestures based on neural network

Abu

Rosleesham

Suboh

et al. 2020

IJEECS

View full text Add to dashboard Cite

<span>This paper presents the classification of EMG signal for multiple hand gestures based on neural network. In this study, the Electromyography is used to measure the muscle cell’s electrical activities which is commonly represented in a function time. Every muscle has their own signals, which was produced in every movement. Surface electromyography (sEMG) is used as a non-invasive technique for acquiring the EMG signal. The development of sensors’ detection and measuring the EMG have been improved and have become more precise while maintaining a small size. In this paper, the main objective is to identify the hand gestures based on: (1) Cylindrical Grasp, (2) Supination (Twist Left), (3) Pronation (Twist Right), (4) Resting Hand and (5) Open Hand that are predefined by using Arduino IDE, CoolTerm software and Microsoft Excel before using artificial neural network for classifying purposes in MATLAB. Finally, the extraction of the EMG patterns for each movement went through features extraction of the signals which is used to train the classifier in MATLAB to classify signals in the neural network. The features extracted are using mean absolute value (MAV), median, waveform length (WL) and root mean square (RMS). The Artificial Neural Network (ANN) produced accuracy of 80% for training and testing for 10 hidden neurons layer.</span>

show abstract

A Review of Electromyography Signal Analysis Techniques for Musculoskeletal Disorders

Cited by 12 publications

References 45 publications

Characterization of Pelvic Floor Activity in Healthy Subjects and with Chronic Pelvic Pain: Diagnostic Potential of Surface Electromyography

Characterization of Pelvic Floor Activity in Healthy Subjects and with Chronic Pelvic Pain: Diagnostic Potential of Surface Electromyography

Brain stroke computed tomography images analysis using image processing: A Review

Classification of EMG signal for multiple hand gestures based on neural network

Contact Info

Product

Resources

About