Quantitative assessment of cerebellar ataxia, through automated limb functional tests.

BACKGROUND: Cerebellar damage can often result in disabilities affecting the peripheral regions of the body. These include poor and inaccurate coordination, tremors and irregular movements that often manifest as disorders associated with balance, gait and speech. The severity assessment of Cerebellar ataxia (CA) is determined by expert opinion and is likely to be subjective in nature. This paper investigates automated versions of three commonly used tests: Finger to Nose test (FNT), test for upper limb Dysdiadochokinesia Test (DDK) and Heel to Shin Test (HST), in evaluating disability due to CA. METHODS: Limb movements associated with these tests are measured using Inertial Measurement Units (IMU) to capture the disability. Kinematic parameters such as acceleration, velocity and angle are considered in both time and frequency domain in three orthogonal axes to obtain relevant disability related information. The collective dominance in the data distributions of the underlying features were observed though the Principal Component Analysis (PCA). The dominant features were combined to substantiate the correlation with the expert clinical assessments through Linear Discriminant Analysis. Here, the Pearson correlation is used to examine the relationship between the objective assessments and the expert clinical scores while the performance was also verified by means of cross validation. RESULTS: The experimental results show that acceleration is a major feature in DDK and HST, whereas rotation is the main feature responsible for classification in FNT. Combining the features enhanced the correlations in each domain. The subject data was classified based on the severity information based on expert clinical scores. CONCLUSION: For the predominantly translational movement in the upper limb FNT, the rotation captures disability and for the DDK test with predominantly rotational movements, the linear acceleration captures the disability but cannot be extended to the lower limb HST. The orthogonal direction manifestation of ataxia attributed to sensory measurements was determined for each test. TRIAL REGISTRATION: Human Research and Ethics Committee, Royal Victorian Eye and Ear Hospital, East Melbourne, Australia (HREC Reference Number: 11/994H/16).

A Bayesian Network Approach for Friedreich Ataxia Severity Classification using Probability Modelling.

Friedreich ataxia (FRDA) requires an objective measure of severity to overcome the shortcoming of clinical scales when applied to trials for treatments. This is hindered due to the rarity of the disease resulting in small datasets. Further, the published quantitative measures for ataxia do not incorporate or underutilise expert knowledge. Bayesian Networks (BNs) provide a structure to adopt both subjective and objective measures to give a severity value while addressing these issues. The BN presented in this paper uses a hybrid learning approach, which utilises both subjective clinical assessments as well as instrumented measurements of disordered upper body movement of individuals with FRDA. The final model's estimates gave a 0.93 Pearson correlation with low error, 9.42 root mean square error and 7.17 mean absolute error. Predicting the clinical scales gave 94% accuracy for Upright Stability and Lower Limb Coordination and 67% accuracy for Functional Staging, Upper Limb Coordination and Activities of Daily Living.Clinical relevance- Due to the nature of rare diseases conventional machine learning is difficult. Most clinical trials only generate small datasets. This approach allows the combination of expert knowledge with instrumented measures to develop a clinical decision support system for the prediction of severity.

Objective Assessment of Progression and Disease Characterization of Friedreich Ataxia via an Instrumented Drinking Cup: Preliminary Results.

The monitoring of disease progression in certain neurodegenerative conditions can significantly be quantified with the help of objective assessments. The severity assessment of diseases like Friedreich ataxia (FRDA) are usually based on different subjective measures. The ability of a participant with FRDA to perform standard neurological tests is the most common way of assessing disease progression. In this feasibility study, an Ataxia Instrumented Measurement-Cup (AIM-C) is proposed to quantify the disease progression of 10 participants (mean age 39 years, onset of disease 16.3 years) in longitudinal timepoints. The device consists of a sensing system with the provision of extracting both kinetic and kinematic information while engaging in an activity closely associated with activities of daily living (ADL). A common functional task of simulated drinking was used to capture features that possesses disease progression information as well as certain other features which intrinsically correlate with commonly used clinical scales such as the modified Friedreich Ataxia Rating Scale (mFARS), the Functional Staging of Ataxia score and the ADL scale. Frequency and time-frequency domain features allowed the longitudinal assessment of participants with FRDA. Furthermore, both kinetic and kinematic measures captured clinically relevant features and correlated 85% with clinical assessments.

Quantitative Assessment of Friedreich Ataxia via Self-Drinking Activity.

Effective monitoring of the progression of neurodegenerative conditions can be significantly improved by objective assessments. Clinical assessments of conditions such as Friedreich's Ataxia (FA), currently rely on subjective measures commonly practiced in clinics as well as the ability of the affected individual to perform conventional tests of the neurological examination. In this study, we propose an ataxia measuring device, in the form of a pressure canister capable of sensing certain kinetic and kinematic parameters of interest to quantify the impairment levels of participants particularly when engaged in an activity that is closely associated with daily living. In particular, the functional task of simulated drinking was utilised to capture characteristic features of disability manifestation in terms of diagnosis (separation of individuals with FA and controls) and severity assessment of individuals diagnosed with the debilitating condition of FA. Time and frequency domain analysis of these biomarkers enabled the classification of individuals with FA and control subjects to reach an accuracy of 98% and a correlation level reaching 96% with the clinical scores.

Quantitative Assessment of Friedreich Ataxia through the self-drinking activity.

The progression of neurodegenerative conditions can be effectively monitored and improved by using objective assessments. The conditions such as Friedreich Ataxia (FA) are clinically assessed by means of subjective measures commonly practised in clinics. Here, we propose a device capable of measuring ataxia, in the form of a `cup' capable of sensing certain kinematic parameters of interest while engaging in an activity that is closely related to daily living. In this study, the functional task of 'drinking' was utilised to diagnose participants with FA and capture features in terms of diagnosis (separation) and correlation with the clinical scales. Frequency domain analysis was incorporated enabling the classification of control subjects and FA patients to an accuracy of 88% with a correlation of 90% with the clinical scores.

Quantitative Assessment of Cerebella Ataxia, through Automated Limb-Coordination tests.

The disabilities affecting the peripheral regions of the body can be often as a result of cerebellar damage. Tremor, poor and inaccurate coordination, and irregular movements associated with gait, balance and speech are some of the manifestations. Conventionally expert opinion determines severity of Cerebellar Ataxia (CA) and the assessment is likely to be inherently subjective. The automated versions of two commonly used tests: Finger to Nose test (FNT) and Heel to Shin Test (HST), are investigated in this paper for evaluating disability and movement deficits due to CA. Limb movements are measured using Inertial Measurement Units (IMU) that captures the disability related information, using kinematic parameters such as acceleration and angular velocity considered in both time and frequency domain. Using the Singular Spectrum Analysis (SSA), the collective dominance in the data distributions of the underlying features were observed. The dominant features were combined to substantiate the correlation with the expert clinical assessments through Linear Discriminant and Regression analysis where the classifier performance was also verified by means of cross-validation. This study found that IMU features captured characteristic movements as intention tremor in FNT and not in HST. In FNT predominantly consisting of translational movements, the rotation was a dominant feature whereas acceleration were observed to be more dominant for the case of HST.