A comprehensive scheme for the objective upper body assessments of subjects with cerebellar ataxia

• Published in: Journal of neuroengineering and rehabilitation Vol. 17; no. 1; p. 162
• Main Authors: Tran, Ha, Nguyen, Khoa D, Pathirana, Pubudu N, Horne, Malcolm K, Power, Laura, Szmulewicz, David J
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 04.12.2020; BioMed Central; BMC
• Subjects: Accuracy; Arm; Ataxia; Automation; Cerebellar ataxia; Cerebellum; Diagnosis; Diagnostic systems; Dysdiadochokinesia; Extremities; Extremities, Upper; Feature extraction; Finger; Finger chase; Finger tapping; Finger to nose; Inertial platforms; Kinematics; Medical examination; Motion sensors; Motor task performance; Objective assessment; Physiological aspects; Regression analysis; Regression models; Rhythms; Sensors; Support vector machines; Australia; Feed backward feature elimination; Objective assessment; Finger tapping; Finger chase; Cerebellar ataxia; Finger to nose; Dysdiadochokinesia
• ISSN: 1743-0003; 1743-0003
• DOI: 10.1186/s12984-020-00790-3

Cerebellar ataxia refers to the disturbance in movement resulting from cerebellar dysfunction. It manifests as inaccurate movements with delayed onset and overshoot, especially when movements are repetitive or rhythmic. Identification of ataxia is integral to the diagnosis and assessment of severity, and is important in monitoring progression and improvement. Ataxia is identified and assessed by clinicians observing subjects perform standardised movement tasks that emphasise ataxic movements. Our aim in this paper was to use data recorded from motion sensors worn while subjects performed these tasks, in order to make an objective assessment of ataxia that accurately modelled the clinical assessment. Inertial measurement units and a Kinect© system were used to record motion data while control and ataxic subjects performed four instrumented version of upper extremities tests, i.e. finger chase test (FCT), finger tapping test (FTT), finger to nose test (FNT) and dysdiadochokinesia test (DDKT). Kinematic features were extracted from this data and correlated with clinical ratings of severity of ataxia using the Scale for the Assessment and Rating of Ataxia (SARA). These features were refined using Feed Backward feature Elimination (the best performing method of four). Using several different learning models, including Linear Discrimination, Quadratic Discrimination Analysis, Support Vector Machine and K-Nearest Neighbour these extracted features were used to accurately discriminate between ataxics and control subjects. Leave-One-Out cross validation estimated the generalised performance of the diagnostic model as well as the severity predicting regression model. The selected model accurately ([Formula: see text]) predicted the clinical scores for ataxia and correlated well with clinical scores of the severity of ataxia ([Formula: see text], [Formula: see text]). The severity estimation was also considered in a 4-level scale to provide a rating that is familiar to the current clinically-used rating of upper limb impairments. The combination of FCT and FTT performed as well as all four test combined in predicting the presence and severity of ataxia. Individual bedside tests can be emulated using features derived from sensors worn while bedside tests of cerebellar ataxia were being performed. Each test emphasises different aspects of stability, timing, accuracy and rhythmicity of movements. Using the current models it is possible to model the clinician in identifying ataxia and assessing severity but also to identify those test which provide the optimum set of data. Trial registration Human Research and Ethics Committee, Royal Victorian Eye and Ear Hospital, East Melbourne, Australia (HREC Reference Number: 11/994H/16).