Journal article
Balance Deficits due to Cerebellar Ataxia: A Machine Learning and Cloud-Based Approach
T Ngo, PN Pathirana, MK Horne, L Power, DJ Szmulewicz, SC Milne, LA Corben, M Roberts, MB Delatycki
IEEE Transactions on Biomedical Engineering | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | Published : 2021
Abstract
Cerebellar ataxia (CA) refers to the disordered movement that occurs when the cerebellum is injured or affected by disease. It manifests as uncoordinated movement of the limbs, speech, and balance. This study is aimed at the formation of a simple, objective framework for the quantitative assessment of CA based on motion data. We adopted the Recurrence Quantification Analysis concept in identifying features of significance for the diagnosis. Eighty-six subjects were observed undertaking three standard neurological tests (Romberg's, Heel-shin and Truncal ataxia) to capture 213 time series inertial measurements each. The feature selection was based on engaging six different common techniques to..
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Awarded by Florey Institute of Neuroscience
Awarded by National Health and Medical Research Council (NHMRC) of Australia
Funding Acknowledgements
Manuscript received April 23, 2020; revised August 17, 2020; accepted September 29, 2020. Date of publication October 12, 2020; date of current version April 21, 2021. This work was supported by the Florey Institute of Neuroscience under Grant GNT1101304 and in part by the National Health and Medical Research Council (NHMRC) of Australia under Grant APP1129595. (Corresponding author: Thang Ngo.)