Assessing Laryngeal Neuromotor Activity from Phonation

Neurodegenerative motor disorders affect the neuromuscular system challenging daily life and normal activity. Parkinson's Disease (PD) is among the most prevalent ones, with a large impact and rising prevalence rates. Speech is most affected by PD as far as phonatory and articulatory performance is concerned. Neuromotor activity (NMA) alterations have an impact on larynx muscles responsible for vocal fold adduction and abduction, hampering phonation stability and regularity. The main muscular articulators involved in phonation control are the cricothyroid (tensor) and thyroarytenoid (relaxer) systems, regulated by two distinct direct neuromotor pathways, activated by the precentral gyrus laryngeal control areas. These articulations control the musculus vocalis, directly responsible for regular vocal fold vibration. An indirect estimation of the muscular tension produced by inverse filtering may split into two independent channels, assumed to be the tensor and relaxer neuromotor pathways such as the differential neuromotor activity (DNMA). The amplitude distributions of both DNMA channels allow comparing phonations from PD-affected persons (PDPs) and age-matched healthy control participants (HCPs) with respect to a set of reference mid-age normative participants (RSPs). The comparisons are carried out by Jensen-Shannon distributions of PDP and HCP phonations with respect to those of RSPs. A dataset of 96 phonation samples from participants balanced by gender is used to train a set of decision tree classifiers (DTCs) to distinguish PDP from HCP phonation. The best results from 10-fold cross-validation offered accumulated mismatches of 0.09 and 0.1292 for male and female subsets. The sensitivity, specificity, and accuracy of the classification results when separating PDP from HCP phonatios were 93.33%, 88.23%, and 90.63% (male PDP versus HCP) and 92.86%, 83.33%, and 87.50% (female PDP versus HCP), providing a stratification of PDPs and HCPs by objective disease grading from explainable AI (XAI) methods.