This paper presents a novel methodology in which the Unified Parkinson's Disease Rating Scale (UPDRS) data processed with a rule-based decision algorithm is used to predict the state of the Parkinson's Disease patients. The research was carried out to investigate whether the advancement of the Parkinson's Disease can be automatically assessed. For this purpose, past and current UPDRS data from 47 subjects were examined. The results show that, among other classifiers, the rough set-based decision algorithm turned out to be most suitable for such automatic assessment.Virtual slidesThe virtual slide(s) for this article can be found here:http://www.diagnosticpathology.diagnomx.eu/vs/1563339375633634.
Abstract.In this paper an approach to build a brain computer-based hemispheric synchronization system is presented. The concept utilizes the wireless EEG signal registration and acquisition as well as advanced pre-processing methods. The influence of various filtration techniques of EOG artifacts on brain state recognition is examined. The emphasis is put on brain state recognition using band pass filtration for separation of individual brain rhythms. In particular, the recognition of alpha and beta states is examined to assess whether synchronization occurred. Two independent methods of hemispheric synchronization analysis are given, i.e. the first consisted in calculating statistical parameters for the entire signal registered and the second one in using wavelet-based feature statistics for different lengths of time windows, and then discussed. Perspectives of the system development are shown in the conclusions.
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