Assessment scales in stroke: clinimetric and clinical considerations

As stroke care has developed, there has been a need to robustly assess the efficacy of interventions both at the level of the individual stroke survivor and in the context of clinical trials. To describe stroke-survivor recovery meaningfully, more sophisticated measures are required than simple dichotomous end points, such as mortality or stroke recurrence. As stroke is an exemplar disabling long-term condition, measures of function are well suited as outcome assessment. In this review, we will describe functional assessment scales in stroke, concentrating on three of the more commonly used tools: the National Institutes of Health Stroke Scale, the modified Rankin Scale, and the Barthel Index. We will discuss the strengths, limitations, and application of these scales and use the scales to highlight important properties that are relevant to all assessment tools. We will frame much of this discussion in the context of "clinimetric" analysis. As they are increasingly used to inform stroke-survivor assessments, we will also discuss some of the commonly used quality-of-life measures. A recurring theme when considering functional assessment is that no tool suits all situations. Clinicians and researchers should chose their assessment tool based on the question of interest and the evidence base around clinimetric properties

A kernel method for non-linear systems identification – infinite degree volterra series estimation

Volterra series expansions are widely used in analyzing and solving the problems of non-linear dynamical systems. However, the problem that the number of terms to be determined increases exponentially with the order of the expansion restricts its practical application. In practice, Volterra series expansions are truncated severely so that they may not give accurate representations of the original system. To address this problem, kernel methods are shown to be deserving of exploration. In this report, we make use of an existing result from the theory of approximation in reproducing kernel Hilbert space (RKHS) that has not yet been exploited in the systems identification field. An exponential kernel method, based on an RKHS called a generalized Fock space, is introduced, to model non-linear dynamical systems and to specify the corresponding Volterra series expansion. In this way a non-linear dynamical system can be modelled using a finite memory length, infinite degree Volterra series expansion, thus reducing the source of approximation error solely to truncation in time. We can also, in principle, recover any coefficient in the Volterra series

Multiple-model approach to non-linear kernel-based adaptive filtering

Kernel methods now provide standard tools for the solution of function approximation and pattern classification problems. However, it is typically assumed that all data are available for training. More recently, various approaches have been proposed for extending kernel methods to sequential problems whereby the model is updated as each new data point arrives. Whilst these approaches have proven successful in estimating the basic parameters, the problem of estimating the hyperparameters which determine the overall model behaviour, remains essentially unsolved. In this paper a novel approach to the hyperparameters is presented based on a multiple model framework. An ensemble of models with different hyperparameters is trained in parallel, the outputs of which are subsequently combined based on a predictive performance measure. This new approach is sucessfully demonstrated in a standard benchmark time series problem