A multiple motion sensors index for motor state quantification in Parkinson's disease. (June 2020) - Aghanavesi, Somayeh; Westin, Jerker; Bergquist, Filip; Nyholm, Dag; Askmark, Håkan; Aquilonius, Sten Magnus; Constantinescu, Radu; Medvedev, Alexander; Spira, Jack; Ohlsson, Fredrik; Thomas, Ilias; Ericsson, Anders; et al.
Highlights: A methodology to quantify motor states of Parkinson's disease patients is presented. A machine learning model that fuses information from multiple motion sensors is proposed. Comparison of algorithms using data from multiple and single sensors. Using stepwise regression and support vector machines provided the best results. Upper limb sensor data during walking was most related to clinical rating scores. Abstract: Aim: To construct a Treatment Response Index from Multiple Sensors (TRIMS) for quantification of motor state in patients with Parkinson's disease (PD) during a single levodopa dose. Another aim was to compare TRIMS to sensor indexes derived from individual motor tasks. Method: Nineteen PD patients performed three motor tests including leg agility, pronation-supination movement of hands, and walking in a clinic while wearing inertial measurement unit sensors on their wrists and ankles. They performed the tests repeatedly before and after taking 150% of their individual oral levodopa-carbidopa equivalent morning dose.Three neurologists blinded to treatment status, viewed patients' videos and rated their motor symptoms, dyskinesia, overall motor state based on selected items of Unified PD Rating Scale (UPDRS) part III, Dyskinesia scale, and Treatment Response Scale (TRS). To build TRIMS, out of initially 178 extracted features from upper- and lower-limbs data, 39 features were selected by stepwise regression method and were used as input to support vector machines to be mapped to mean reference TRS scores using 10-fold cross-validation method. Test-retest reliability, responsiveness to medication, and correlation to TRS as well as other UPDRS items were evaluated for TRIMS. Results: The correlation of TRIMS with TRS was 0.93. TRIMS had good test-retest reliability (ICC = 0.83). Responsiveness of the TRIMS to medication was good compared to TRS indicating its power in capturing the treatment effects. TRIMS was highly correlated to dyskinesia ( R  = 0.85), bradykinesia ( R  = 0.84) and gait ( R  = 0.79) UPDRS items. Correlation of sensor index from the upper-limb to TRS was 0.89. Conclusion: Using the fusion of upper- and lower-limbs sensor data to construct TRIMS provided accurate PD motor states estimation and responsive to treatment. In addition, quantification of upper-limb sensor data during walking test provided strong results.