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Falling in Parkinsonian syndromes (PS) is associated with posturalinstability and consists a common cause of disability among PS patients.Current posturographic practices record the body's center-of-pressuredisplacement (statokinesigram) while the patient stands on a force platform.Statokinesigrams, after appropriate signal processing, can offer numerousposturographic features, which however challenges the efforts for validstatistics via standard univariate approaches. In this work, we present thets-AUC, a non-parametric multivariate two-sample test, which we employ toanalyze statokinesigram differences among PS patients that are fallers (PSf)and non-fallers (PSNF). We included 123 PS patients who were classified intoPSF or PSNF based on clinical assessment and underwent simple Romberg Test(eyes open/eyes closed). We analyzed posturographic features using bothmultiple testing with p-value adjustment and the ts-AUC. While the ts-AUCshowed significant difference between groups (p-value = 0.01), multiple testingdid not show any such difference. Interestingly, significant difference betweenthe two groups was found only using the open-eyes protocol. PSF showedsignificantly increased antero-posterior movements as well as increasedposturographic area, compared to PSNF. Our study demonstrates the superiorityof the ts-AUC test compared to standard statistical tools in distinguishing PSFand PSNF in the multidimensional feature space. This result highlights moregenerally the fact that machine learning-based statistical tests can be seen asa natural extension of classical statistical approaches and should beconsidered, especially when dealing with multifactorial assessments.

Revealing posturographic features associated with the risk of falling in patients with Parkinsonian syndromes via machine learning