NDLI: Assessing electrical impedance alterations in spinal muscular atrophy via the finite element method

Content Provider	IEEE Xplore Digital Library
Author	Jafarpoor, M. Spieker, A.J. Jia Li Minhee Sung Darras, B.T. Rutkove, S.B.
Copyright Year	2011
Description	Author affiliation: Children's Hospital Boston, Department of Neurology, Boston, MA 02215 USA (Darras, B.T.) \|\| Beth Israel Deaconess Medical Center, Department of Neurology, Boston, MA 02215 USA (Jafarpoor, M.; Spieker, A.J.; Jia Li; Minhee Sung; Rutkove, S.B.)
Abstract	Electrical impedance myography (EIM) is a surface-based, non-invasive technique of evaluation of muscle health, involving the application of high frequency, low-amplitude current to the skin over a muscle of interest. Results from a previous animal study suggest that the finite element method can relate disease-induced changes in electrical properties of the muscle to alterations in surface impedance measurements; however, whether such an approach will prove useful in human models is uncertain. Therefore, to further investigate this question, we have created a single finite element model of the human biceps muscle using data from one healthy subject and one with spinal muscular atrophy (SMA), each of whom had comparable age, limb girth, muscle size, and subcutaneous fat thickness. Since healthy human tissue was unavailable, permittivity and conductivity measurements were obtained from five healthy and five advanced amyotrophic lateral sclerosis rat gastrocnemius muscles immediately after sacrifice; their data were input into the human biceps model and the expected surface voltages calculated. We then compared the results of this model to the actual surface EIM data for both individuals. Although the actual resistance and reactance values varied and the peak values were displaced, the resulting maximum phase predicted by the model approximated that obtained with surface recordings. These results support that alterations in the primary characteristics of muscle impact the surface impedance measurements in meaningful and likely predictable ways.
Starting Page	1871
Ending Page	1874
File Size	592070
Page Count	4
File Format	PDF
ISBN	9781424441211
ISSN	1557170X
e-ISBN	9781457715891
e-ISBN	9781424441228
DOI	10.1109/IEMBS.2011.6090531
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-08-30
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Muscles Humans Finite element methods Data models Permittivity Animals Conductivity
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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