The primary goal of this study was to investigate computerized assessment methods to classify motor dysfunctioning of patients with Parkinsonâs disease on the clinical scale. In this proposed system, machine learningâbased computerized assessment methods were introduced to assess the motor performance of patients with Parkinsonâs disease. Biomechanical parameters were acquired from six exercises through wearable inertial sensors: SensFoot V2 and SensHand V1. All patients were evaluated via neurologist by means of the clinical scale. The average rating was calculated from all exercise ratings given by clinicians to estimate overall rating for each patient. Patients were divided in two groups: slightâmild patients with Parkinsonâs disease and moderateâsevere patients with Parkinsonâs disease according to average rating (â0: slight and mildâ and â1: moderate and severeâ). Feature selection methods were used for the selection of significant features. Selected features were trained in support vector machine, logistic regression, and neural network to classify the two groups of patients. The highest classification accuracy obtained by support vector machine classifier was 79.66%, with 0.8790 area under the curve. A 76.2% classification accuracy was obtained with 0.7832 area under the curve through logistic regression. A 83.10% classification accuracy was obtained by neural network classifier, with 0.889 area under the curve. Strong distinguishability of the models between the two groups directs the high possibility of motor impairment classification through biomechanical parameters in patients with Parkinsonâs disease based on the clinical scale.
|Titolo:||Biomechanical parameter assessment for classification of Parkinson's disease on clinical scale|
|Data di pubblicazione:||2017|
|Appare nelle tipologie:||1.1 Articolo su Rivista/Article|