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Quantitative assessment of cerebellar ataxia with kinematic sensing during rhythmic tapping
conference contribution
posted on 2018-01-01, 00:00 authored by Khoa Dinh Nguyen, Pubudu PathiranaPubudu Pathirana, Malcolm Horne, Laura Power, David SzmulewiczThe aim of this study is to investigate the validity of an entropy-based objective assessment of cerebellar ataxia patients performing rhythmic tapping. Previous research conducted, particularly in time and frequency domains, tested the adherence of patients to more stringent experimental requirements. These requirements may inadvertently cause higher
level brain functions to influence the performance and possibly
obscure the cerebella related disabilities in the data stream. In this study, a multiscale entropy-based learning process that overcomes this practical limitation was considered. In
particular, assessment techniques with less restrictions on the
tapping duration were considered. Thirty-three patients were
engaged in the test, with three levels of severity 0 (normal),
1 (moderate) and 2 (severe) ranked by specialist clinicians.
The performance of each model was evaluated using leave-oneout
cross validation. Results from both time-frequency features
and entropy features extracted and characterized the cerebellar
condition captured during the finger and foot tapping tests
(with over 80% accuracy). Strong correlations with clinical
assessment-based scoring were observed with the entropy based
approach for both tests, although the correlation with timefrequency
features were less convincing.
level brain functions to influence the performance and possibly
obscure the cerebella related disabilities in the data stream. In this study, a multiscale entropy-based learning process that overcomes this practical limitation was considered. In
particular, assessment techniques with less restrictions on the
tapping duration were considered. Thirty-three patients were
engaged in the test, with three levels of severity 0 (normal),
1 (moderate) and 2 (severe) ranked by specialist clinicians.
The performance of each model was evaluated using leave-oneout
cross validation. Results from both time-frequency features
and entropy features extracted and characterized the cerebellar
condition captured during the finger and foot tapping tests
(with over 80% accuracy). Strong correlations with clinical
assessment-based scoring were observed with the entropy based
approach for both tests, although the correlation with timefrequency
features were less convincing.
