Study on The Likelihood Ratio Used to Evaluate the Muscle Tension of Upper Limb Spasm in Hemiplegia Patients
PDF

Keywords

Mechanomyography
Muscle spasm
Modify Ashworth scale (MAS)
Passive movement
Spasticity assessment

DOI

10.26689/jcnr.v5i4.2302

Abstract

Aim: To evaluate the clinical usability of mechanomyography (MMG) in the evaluation of upper-limb spasticity states of hemiplegia patients with likelihood ratio analysis. Methods: The MMG signals from the 30 hemiplegia patients’ biceps and triceps were recorded with three-dimensional wireless accelerometer (Trigno Wireless System, Delsys Inc), when they extended or bent their elbow passively. At the same time, the physiotherapist assessed the MAS (Modified Ashworth Scale) of all the patients who participated in the study and would be divided them into four groups based on the MAS values (MAS0, MAS1, MAS1+ and MAS2). The MMG sensors were built with triaxial accelerometers named as X, Y and Z that represent the muscle fibers lengthwise movement, the cross movement and the vertical the muscle moving direction, respectively. The root mean square (RMS) value of the MMG signal was calculated for analysis. Likelihood ratio analysis were used in the study. Results: All of the variables of the X, Y, Z axis of signals of MMG of BB and TB have related with muscle spasticity grading during passive elbow flexion in multinomial logistic regression (P=0.000, P<0.001). The regression coefficient of Y axis signal of MMG of BB is the largest. The 5 variables of the X, Y, Z axis of signals of MMG of BB and X, Y axis of TB have related with Muscle spasticity grading during passive elbow extension in multinomial logistic regression (P=0.000, P<0.001). The regression coefficient of Y axis signal of MMG of BB is the largest in equation and the Y axis signal of MMG of TB is second large. Conclusions: The effect of agonist is more than the antagonist during the MAS assessment, especially the muscle fibers cross movement and the vertical movement by the MMG assessment.

References

Kawczy?ski A, Nie H, Jaskolska A, et al., 2007, Mechanomyography and Electromyography During and After Fatiguing Shoulder Eccentric Contractions in Males and Females. Scandinavian Journal of Medicine & Science in Sports, 17(2): 172-179.

Tarata MT, 2003, Mechanomyography Versus Electromyography, in Monitoring the Muscular Fatigue. Biomed Eng Online, 2(3).

You M. 2011, Research on Prosthetic Control System Based on Myograph (MMG) and Electromyography (EMG) Signals. Central South University, 2-5.

Alves-Kotzev N, 2010, Mechanomyography as an Access Pathway for Binary and Multifunction Control. University of Toronto.

Wu Z, Wang H, Wang J, et al., 2014, Myograph Assessment of Elbow Joint Spasticity in Patients with Hemiplegia After Stroke. Journal of Integrated Traditional Chinese and Western Medicine Cardio-Cerebrovascular Disease, (12):1456-1458.

Veltink P, Slycke H, et al., Three-dimensional inertial sensing of foot movements for automatic tuning of a two-channel implantable drop-foot stimulator. Medical Engineering & Physics .2003:25,21–28.

Antonelli MG, Zobel PB, Giacomin J, 2009, Use of MMG Signals for the Control of Powered Orthotic Devices: Development of a Rectus Femoris Measurement Protocol. Assist Technology 2009 Spring, 21(1):1-12.

Shinohara M, Sogaard K, 2006, Mechanomyography for Studying Force Fluctuations and Muscle Fatigue. Exercise and Sport Sciences Reviews, 34(2):59-64.