An electroglottographical analysis-based discriminant function model differentiating multiple sclerosis patients from healthy controls

In this study, we produced a discriminant function equation that can differentiate MS patients from healthy controls, using electroglottographic variables not analyzed in a previous study. We applied stepwise linear discriminant function analysis in order to produce a function and score derived from electroglottographic variables extracted from a previous study. The derived discriminant function ’s statistical significance was determined via Wilk’sλ test (and the associatedp value). Finally, a 2  × 2 confusion matrix was used to determine the function’s predictive accuracy, whereas the cross-validated predictive accuracy is estimated via the “leave-one-out” classification process. Discriminant function analysis (DFA) was used to create a linear function of continuous predictors. DFA produced the following model (Wilk’sλ = 0.043,χ2  = 388.588,p <  0.0001, Tables 3 and 4): D (MS vs controls) = 0.728*DQx1 mean monologue + 0.325*CQx monologue + 0.298*DFx1 90% range monologue + 0.443*DQx1 90% range reading − 1.490*DQx1 90% range monologue. The derived discriminant score (S1) was used subsequently in order to form the coordinates of a ROC cu rve. Thus, a cutoff score of − 0.788 for S1 corresponded to a perfect classification (100% sensitivity and 100% specificity,p = 1.67e−22). Consistent with previous findings, electroglottographic evaluation represents an easy to implement and potentially important assessment in MS patients, achieving ...
Source: Neurological Sciences - Category: Neurology Source Type: research