Quantitative Evaluation of Cerebellar Ataxia Through Automated Assessment of Upper Limb Movements

et al. 2020

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parametric analysis of cerebellar Ataxia (cA) could be of immense value compared to its subjective clinical assessments. this study focuses on a comprehensive scheme for objective assessment of cA through the instrumented versions of 9 commonly used neurological tests in 5 domains-speech, upper limb, lower limb, gait and balance. Twenty-three individuals diagnosed with CA to varying degrees and eleven age-matched healthy controls were recruited. Wearable inertial sensors and Kinect camera were utilised for data acquisition. Binary and multilabel discrimination power and intra-domain relationships of the features extracted from the sensor measures and the clinical scores were compared using Graph Theory, Centrality Measures, Random Forest binary and multilabel classification approaches. An optimal subset of 13 most important Principal Component (PC) features were selected for CAcontrol classification. This classification model resulted in an impressive performance accuracy of 97% (F1 score = 95.2%) with Holmesian dimensions distributed as 47.7% Stability, 6.3% Timing, 38.75% Accuracy and 7.24% Rhythmicity. Another optimal subset of 11 PC features demonstrated an F1 score of 84.2% in mapping the total 27 PC across 5 domains during CA multilabel discrimination. In both cases, the balance (Romberg) test contributed the most (31.1% and 42% respectively), followed by the peripheral tests whereas gait (Walking) test contributed the least. These findings paved the way for a better understanding of the feasibility of an instrumented system to assist informed clinical decisionmaking.

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confidence: 73%

Objective Assessment of Cerebellar Ataxia: A Comprehensive and Refined Approach

Kashyap

Phan

et al. 2020

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“…The Kinect © captured the marker position at a sampling rate of 30Hz. The maximum frequency of human movement is approximately 20Hz [18], and for ataxic subjects this can be lower [3]. Especially for peripheral limb motion, the requencies are even less.…”

Section: Dysdiadochokinesia (Ddkt) Imumentioning

confidence: 99%

“…Inertial measurement units (IMUs) ohave been used to capture movement kinematics in multiple signal domains [11] to objectively assess the FNT [12,13] and DDKT [12]. The movement of the finger performing the FCT has been tracked using optoelectronic devices ranging from video cameras [14] to VICON [15] and recently Kinect © in our previous study [3] to assess delay in initiating movement and accuracy in reaching the target. While this test identified deficits in accuracy and timing, neither the maintenance of rhythm nor the stability of the execution platform of the moving distal limb were assessed [4].…”

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confidence: 99%

A Comprehensive Scheme for the Objective Upper Body Assessments of Subjects with Cerebellar Ataxia

Tran

Nguyen

et al. 2020

Preprint

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BackgroundCerebellar 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.MethodsInertial 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.ResultsThe selected model accurately (96.4%) predicted the clinical scores for ataxia and correlated well with clinical scores of the severity of ataxia (rho = 0.8, p < 0.001). 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.ConclusionIndividual 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 registrationHuman Research and Ethics Committee, Royal Victorian Eye and Ear Hospital, East Melbourne, Australia (HREC Reference Number: 11/994H/16).

“…IMU based systems were also used eectively in FNT [13,14] and DDKT [13] where the axes of movement characterising ataxia were captured, monitored and evaluated. The movement of the nger performing the FCT has been tracked using optoelectronic devices ranging from video cameras [15] to VICON [16] and recently Kinect © in our previous study [4] to assess delay in initiating movement and accuracy in reaching the target. While this test identies decits in accuracy and timing, neither ability to maintain a rhythm FA/SCAs/Others - (Ax,Ay,Az) with a gyroscope directions(Gx,Gy,Gz), (B)II.…”

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confidence: 99%

A Comprehensive Scheme for the Objective Upper Body Assessments of Subjects with Cerebellar Ataxia

Tran

Nguyen

et al. 2020

Preprint

Self Cite

Background Cerebellar ataxia (CA) is a complex motor disorder that exhibits various symptoms such as lack of movement accuracy, delayed motion and ataxic movements associated with gait, extremity and eye. Accurate assessment of ataxic movements forms an integral part, not only in the process of diagnosis, but also to monitor the severity of the neurodegenerative progression, particularly in a rehabilitation context. However, the current assessment schemes are mostly based on the subjective observation of experienced clinicians. Capturing the movement during standard upper limb tests using readily available motion sensors, this paper is intended to amalgamate the sensory information to obtain a more accurate and objective form of assessment. Methods An assessment scheme involving an inertial measurement system and a Kinect system was considered to quantify the degree of ataxia in four instrumented version of upper extremities tests, i.e. Finger Chase (FCT), Finger Tapping (FTT), Finger to Nose (FNT) and Dysdiadochokinesia (DDKT). Kinematic features from these tests were extracted to quantitatively define ataxic signs such as dysmetria, delay in timing, irregularity and instability. Using Feed backward feature elimination (FBE) and Quadratic discrimination analysis (QDA) and Ridge regression (RR), the features were selectively combined to improve the diagnosis and verify the association with clinical assessments by means of Leave-One-Out cross validation. Clinical ratings of the disease status were recorded using the Scale for the Assessment and Rating of Ataxia (SARA). Results We report statistical significance in identifying ataxia from movement features of the four tests. The combined information from the features provided a high accuracy in diagnosing CA subjects (96.4%) in addition to a promising result in predicting the severity of ataxia due to CA (rho=0.8, p<0.001). 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 achieve the most acceptable outcome among the considered subsets of the 4 tests. Conclusion The analysis of ataxia can be decomposed primarily into four affected dimensions, i.e. stability, timing, accuracy and rhythmicity. In the context of upper limb tests, the results of accurate classification and prediction of severity attributed mostly to the timing. Furthermore, the underlying approach uncovers the appropriate combination with a reduced number of tests for the assessment of CA utilising the clinical resources more effectively. Trial registration Human Research and Ethics Committee, Royal Victorian Eye and Ear Hospital, East Melbourne, Australia (HREC Reference Number: 11/994H/16).