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. 2015 Jul;19(4):1356-61.
doi: 10.1109/JBHI.2015.2435057. Epub 2015 Jun 17.

Assessment and Classification of Early-Stage Multiple Sclerosis With Inertial Sensors: Comparison Against Clinical Measures of Disease State

Assessment and Classification of Early-Stage Multiple Sclerosis With Inertial Sensors: Comparison Against Clinical Measures of Disease State

Barry R Greene et al. IEEE J Biomed Health Inform. .

Abstract

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.

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