Enhanced classical dysphonia measures and sparse regression for telemonitoring of Parkinson's disease progression

Dysphonia measures are signal processing algorithms that offer an objective method for characterizing voice disorders from recorded speech signals. In this paper, we study disordered voices of people with Parkinson's disease (PD). Here, we demonstrate that a simple logarithmic transformation of these dysphonia measures can significantly enhance their potential for identifying subtle changes in PD symptoms. The superiority of the log-transformed measures is reflected in feature selection results using Bayesian Least Absolute Shrinkage and Selection Operator (LASSO) linear regression. We demonstrate the effectiveness of this enhancement in the emerging application of automated characterization of PD symptom progression from voice signals, rated on the Unified Parkinson's Disease Rating Scale (UPDRS), the gold standard clinical metric for PD. Using least squares regression, we show that UPDRS can be accurately predicted to within six points of the clinicians' observations.