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

TL;DR

This study uses a novel machine learning-based multivariate test, ts-AUC, to identify posturographic features associated with fall risk in Parkinsonian syndromes, revealing significant differences that traditional methods miss.

Contribution

The paper introduces the ts-AUC, a non-parametric multivariate test, demonstrating its effectiveness over standard methods in analyzing complex posturographic data for fall risk assessment.

Findings

ts-AUC detected significant differences between fallers and non-fallers

Open-eyes protocol revealed notable posturographic differences

Fallers showed increased antero-posterior movements and posturographic area

Abstract

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