Sequence matching and deep learning techniques for early stage detection of parkinsons disease through multi modalities

Abstract

Parkinson s Disease (PD) is the second most frequently occurring newlineneurodegenerative disease. It causes motor and non-motor symptoms. newlineIn general, PD is detected by using gait, handwriting, voice, genes, and neuroimages. newlineTraditional diagnostic approaches such as Relief feature selection, newlinestatistical Chi square model-based feature selection etc. are suffering from newlinesubjectivity i.e. subtle to medical practitioner eyes. Moreover, non-motor PD newlineexhibit only mild symptoms and hence its detection using the traditional newlinemethods is very challenging and hence it needs new and automated methods. newlineMostly PD is detected on later stages due to the usage of incorrect atypical newlinefeatures and identification methods. Therefore, it is difficult to classify using newlinesuch methods and it may lead to possible misclassification. In addition to newlinethis, the already existing fully automated early-stage identification techniques newlineare lacking in accuracy, more sensitive to smaller input data points, does not newlineperfectly handle feature redundancy. So, there is a need to fill the research newlinegap present in the early stage detection methods. This research work attempts newlineto bridge this gap by proposing new techniques for detection of PD by newlineextending the conventional machine learning and deep learning-based newlineapproaches on different modalities. In this thesis, we propose a new multi-modality based hybrid model for the early stage detection of PD. This proposed multi-modality based work newlineconsists of 3 main methods such as voice, gene, and neuro-image-based newlineapproaches. In the First approach, the most discriminant features of newlinebiomedical voice data that are occurring due to the vocal impairment in PD newlinepatients are analysed. This approach is mainly used for identifying the newlineLate-onset Parkinson s Disease (LOPD) detection with a higher recognition newlinerate. In the Second approach, the gene sequences responsible for causing PD newlineand their variants are identified. In the third approach, MRI neuro-images are newlineused to identify the structural variations that occur in the mid-brain in order to newlinerecognize the PD more accurately. The correlation-based feature set selection, newlinePrincipal Component Analysis, and Genetic Algorithm (GA) are applied in newlinethis work for identifying the discriminant biomedical voice features. Here, the newlineGA has been employed with Deep Neuro-Genetic Algorithm (DN-GA) based newlineclassifier to select the features and to perform the classification. newline newline