Wenbo Ge1, Christian J Lueck2,3, Deborah Apthorp1,4, Hanna Suominen1,5,6. 1. Research School of Computer Science, Australian National University, Canberra, ACT, Australia. 2. Department of Neurology, Canberra Hospital, Canberra, ACT, Australia. 3. Australian National University Medical School, Canberra, ACT, Australia. 4. School of Psychology, University of New England, Armidale, NSW, Australia. 5. Machine Learning Research Group, Data61/CSIRO, Canberra, ACT, Australia. 6. Department of Future Technologies, University of Turku, Turku, Finland.
Abstract
BACKGROUND: Postural sway may be useful as an objective measure of Parkinson's disease (PD). Existing studies have analyzed many different features of sway using different experimental paradigms. We aimed to determine what features have been used to measure sway and then to assess which feature(s) best differentiate PD patients from controls. We also aimed to determine whether any refinements might improve discriminative power and so assist in standardizing experimental conditions and analysis of data. METHODS: In this systematic review of the literature, effect size (ES) was calculated for every feature reported by each article and then collapsed across articles where appropriate. The influence of clinical medication status, visual state, and sampling rate on ES was also assessed. RESULTS: Four hundred and forty-three papers were retrieved. 25 contained enough information for further analysis. The most commonly used features were not the most effective (e.g., PathLength, used 14 times, had ES of 0.47, while TotalEnergy, used only once, had ES of 1.78). Increased sampling rate was associated with increased ES (PathLength ES increased to 1.12 at 100 Hz from 0.40 at 10 Hz). Measurement during "OFF" clinical status was associated with increased ES (PathLength ES was 0.83 OFF compared to 0.21 ON). CONCLUSIONS: This review identified promising features for analysis of postural sway in PD, recommending a sampling rate of 100 Hz and studying patients when OFF to maximize ES. ES complements statistical significance as it is clinically relevant and is easily compared across experiments. We suggest that machine learning is a promising tool for the future analysis of postural sway in PD.
BACKGROUND: Postural sway may be useful as an objective measure of Parkinson's disease (PD). Existing studies have analyzed many different features of sway using different experimental paradigms. We aimed to determine what features have been used to measure sway and then to assess which feature(s) best differentiate PDpatients from controls. We also aimed to determine whether any refinements might improve discriminative power and so assist in standardizing experimental conditions and analysis of data. METHODS: In this systematic review of the literature, effect size (ES) was calculated for every feature reported by each article and then collapsed across articles where appropriate. The influence of clinical medication status, visual state, and sampling rate on ES was also assessed. RESULTS: Four hundred and forty-three papers were retrieved. 25 contained enough information for further analysis. The most commonly used features were not the most effective (e.g., PathLength, used 14 times, had ES of 0.47, while TotalEnergy, used only once, had ES of 1.78). Increased sampling rate was associated with increased ES (PathLength ES increased to 1.12 at 100 Hz from 0.40 at 10 Hz). Measurement during "OFF" clinical status was associated with increased ES (PathLength ES was 0.83 OFF compared to 0.21 ON). CONCLUSIONS: This review identified promising features for analysis of postural sway in PD, recommending a sampling rate of 100 Hz and studying patients when OFF to maximize ES. ES complements statistical significance as it is clinically relevant and is easily compared across experiments. We suggest that machine learning is a promising tool for the future analysis of postural sway in PD.
Authors: Ana Claudia de Souza Fortaleza; Martina Mancini; Patty Carlson-Kuhta; Laurie A King; John G Nutt; Eliane Ferrari Chagas; Ismael Forte Freitas; Fay B Horak Journal: Gait Posture Date: 2017-05-10 Impact factor: 2.840
Authors: Alan L Whone; Ray L Watts; A Jon Stoessl; Margaret Davis; Sven Reske; Claude Nahmias; Anthony E Lang; Olivier Rascol; Maria J Ribeiro; Philippe Remy; Werner H Poewe; Robert A Hauser; David J Brooks Journal: Ann Neurol Date: 2003-07 Impact factor: 10.422