Severity estimation of finger-tapping caused by Parkinson's disease by using linear discriminant regression analysis

We propose a linear discriminant regression analysis (LDRA) that provides an estimated severity marker for discriminating between healthy and patient groups and estimating severities of the patient group simultaneously. This method combines an evaluation function for discriminating between two groups and one for estimating severities of one group. The combined function is optimized to obtain an equation for calculating estimated severities. The method was evaluated with finger-tapping data of healthy and Parkinson's disease (PD) groups and PD severities assessed by a doctor. As a result, the discrimination ability of LDRA (AUC: 0.8835) was higher than that of discriminant analysis (DA. AUC: 0.8442), which is a conventional method for classification, and the regression ability of LDRA (mean square error (MSE): 1.27) was superior to that of multiple regression analysis (MRA. MSE: 1.68), which is a conventional method for regression. The results show that LDRA is an effective method for estimating the presence and severity of Parkinson's disease.