Paper Info
Title
Towards a Simplified Estimation of Muscle Activation Pattern from MRI and EMG Using Electrical Network and Graph Theory.
Abstract
Muscle functional MRI (mfMRI) is an imaging technique that assess muscles' activity, exploiting a shift in the T2-relaxation time between resting and active state on muscles. It is accompanied by the use of electromyography (EMG) to have a better understanding of the muscle electrophysiology; however, a technique merging MRI and EMG information has not been defined yet. In this paper, we present an anatomical and quantitative evaluation of a method our group recently introduced to quantify its validity in terms of muscle pattern estimation for four subjects during four isometric tasks. Muscle activation pattern are estimated using a resistive network to model the morphology in the MRI. An inverse problem is solved from sEMG data to assess muscle activation. The results have been validated with a comparison with physiological information and with the fitting on the electrodes space. On average, over 90% of the input sEMG information was able to be explained with the estimated muscle patterns. There is a match with anatomical information, even if a strong subjectivity is observed among subjects. With this paper we want to proof the method's validity showing its potential in diagnostic and rehabilitation fields.
Year
DOI
Venue
2020
10.3390/s20030724
SENSORS
Keywords
Field
DocType
MRI,EMG,graph theory,electrical network,muscle activity,forearm
Graph theory,Electrical network,Pattern recognition,Electromyography,Muscle activation,Electronic engineering,Active state,Inverse problem,Artificial intelligence,Engineering,Isometric exercise,Electrophysiology
Journal
Volume
Issue
ISSN
20
3
1424-8220
Citations 
PageRank 
References 
0
0.34
0
Authors
7
Name
Order
Citations
PageRank
Enrico Piovanelli100.34
Davide Piovesan200.34
Shouhei Shirafuji32010.19
Becky Su400.34
Natsue Yoshimura500.34
Yousuke Ogata600.34
Jun Ota7527109.77