Visual Diagnostic Method For Parkinson's Disease Bsed On Speech Features

Parkinson's disease (PD) is one of highest incident neurodegenerative diseases with along time course and a significant prevalence. As the etiology is currently unkown, thetreatement for this disease is to alleviate but can not to be cured. If the patients could betreated appropriately, most of them could control the disease's development and preservethe ability of basic self care. As a result of that, early diagnosis of Parkinson's disease is ofgreat significance both for family and for society. The dysphonia is a typical symptom forearly Parkinson's disease patient, so the Parkinson's disease diagnosis in dysphoniameasurement is one of active fields.In this paper, we propose the visual diagnostic method for Parkinson's disease basedon speech features by visual combined classifer named multi-dimensional filter. Thepurpose of that is not only to explore the way about PD's diagnose, but also to improve thetheory and method in visual pattern recongnition about multiple graph representation forhigh-dimensional data. It is of great significance both for information fusion, patternrecognition research and Parkinson's disease diagnosis based on speech features.At first, we proposed the multi-dimensional filter classifier framework based on thetheory of visual classification, and improve it from the data representation, domainconstruction and weight calculation. Stage of data representation, a new type ofrepresentation named chromatic graphical representation is proposed to bridge the gapbetween graphical representation and category distribution. It inherits the merits oftraditional and represent the category information by chromatic of the current sample,which makes it more suitable than the traditional in visual pattern recognition applications;One of the basic issues in pattern recognition is to calculate the boundary betweendifferent categories. Stage of domain construction, a novel method for that based oncomputational geometry named active expansion is proposed. The whole space couldexpress the category information and the boundary is obtained by active expanding forbase points to non-base points; Stage of weight calculation, fuzzy and regularity areproposed for weight based on category spatial distribution characteristics. Two methods analyze the space visual information from the statistical and structure respectively.Comprehensive data experiments show that the multi-dimensional filter classifier not onlyinherits the characteristics about visualization features, but also gets equivalentperformance to the popular classifiers, and outweighs in some dataset.In subsequential, the relationship between speech features and PD symptom isresearched. The physical explainations of speech features and application characteristicsare clarified by experiments, which provide a reliable basis for early diagnosis ofParkinson's disease base on dysphonia. At the same time, we analyze the differentcharacteristics of different vowel separation between classes, which is the foundation forautomatic diagnosis of Parkinson's disease.At last, in the experiments about diagnosis the PD based on dysphonia, theParkinson dataset and Parkinson telemonitering dataset are employeed for visual diagnosisand determine the course of the disease. Experiments show that the application of visualpattern recognition, not only completed the entire diagnostic process of visualization toassist the discovery of new diagnostic indicators, also to obtain a higher classificationaccuracy than the classical machine diagnoses.