TY - JOUR
T1 - Assessment and Classification of Early-Stage Multiple Sclerosis with Inertial Sensors
T2 - Comparison Against Clinical Measures of Disease State
AU - Greene, Barry R.
AU - Rutledge, Stephanie
AU - McGurgan, Iain
AU - McGuigan, Christopher
AU - O'Connell, Karen
AU - Caulfield, Brian
AU - Tubridy, Niall
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/7/1
Y1 - 2015/7/1
N2 - A cross-sectional study on patients with early-stage multiple sclerosis (MS) was conducted to examine the reliability of manual and automatic mobility measures derived from shank-mounted inertial sensors during the Timed Up and Go (TUG) test, compared to control subjects. Furthermore, we aimed to determine if disease status [as measured by the Multiple Sclerosis Impact Scale (MSIS-20) and the Expanded Disability Status Score (EDSS)] can be explained by measurements obtained using inertial sensors. We also aimed to determine if patients with early-stage MS could be automatically distinguished from healthy controls subjects, using inertial parameters recorded during the TUG test. The mobility of 38 patients (aged 25-65 years, 14 M, 24 F), diagnosed with relapsing-remitting MS and 33 healthy controls (14 M, 19 F, age 50-65), was assessed using the TUG test, while patients wore inertial sensors on each shank. Reliability analysis showed that 36 of 53 mobility parameters obtained during the TUG showed excellent intrasession reliability, while nine of 53 showed moderate reliability. This compared favorably with the reliability of the mobility parameters in healthy controls. Exploratory regression models of the EDSS and MSIS-20 scales were derived, using mobility parameters and an elastic net procedure in order to determine which mobility parameters influence disease state. A cross-validated elastic net regularized regression model for MSIS-20 yielded a mean square error (MSE) of 1.1 with 10 degrees of freedom (DoF). Similarly, an elastic net regularized regression model for EDSS yielded a cross-validated MSE of 1.3 with 10 DoF. Classification results show that the mobility parameters of participants with early-stage MS could be distinguished from controls with 96.90% accuracy. Results suggest that mobility parameters derived from MS patients while completing the TUG test are reliable, are associated with disease state in MS, and may have utility in screening for early-stage MS.
AB - A cross-sectional study on patients with early-stage multiple sclerosis (MS) was conducted to examine the reliability of manual and automatic mobility measures derived from shank-mounted inertial sensors during the Timed Up and Go (TUG) test, compared to control subjects. Furthermore, we aimed to determine if disease status [as measured by the Multiple Sclerosis Impact Scale (MSIS-20) and the Expanded Disability Status Score (EDSS)] can be explained by measurements obtained using inertial sensors. We also aimed to determine if patients with early-stage MS could be automatically distinguished from healthy controls subjects, using inertial parameters recorded during the TUG test. The mobility of 38 patients (aged 25-65 years, 14 M, 24 F), diagnosed with relapsing-remitting MS and 33 healthy controls (14 M, 19 F, age 50-65), was assessed using the TUG test, while patients wore inertial sensors on each shank. Reliability analysis showed that 36 of 53 mobility parameters obtained during the TUG showed excellent intrasession reliability, while nine of 53 showed moderate reliability. This compared favorably with the reliability of the mobility parameters in healthy controls. Exploratory regression models of the EDSS and MSIS-20 scales were derived, using mobility parameters and an elastic net procedure in order to determine which mobility parameters influence disease state. A cross-validated elastic net regularized regression model for MSIS-20 yielded a mean square error (MSE) of 1.1 with 10 degrees of freedom (DoF). Similarly, an elastic net regularized regression model for EDSS yielded a cross-validated MSE of 1.3 with 10 DoF. Classification results show that the mobility parameters of participants with early-stage MS could be distinguished from controls with 96.90% accuracy. Results suggest that mobility parameters derived from MS patients while completing the TUG test are reliable, are associated with disease state in MS, and may have utility in screening for early-stage MS.
KW - Classification
KW - TUG test
KW - inertial sensors
KW - mobility
KW - multiple sclerosis
KW - regularization
KW - reliability
UR - http://www.scopus.com/inward/record.url?scp=84938354528&partnerID=8YFLogxK
U2 - 10.1109/JBHI.2015.2435057
DO - 10.1109/JBHI.2015.2435057
M3 - Article
C2 - 26087505
AN - SCOPUS:84938354528
SN - 2168-2194
VL - 19
SP - 1356
EP - 1361
JO - IEEE Journal of Biomedical and Health Informatics
JF - IEEE Journal of Biomedical and Health Informatics
IS - 4
M1 - 7126914
ER -