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Assessment of Parkinson’s Disease Based on Acoustic Analysis of Hypokinetic Dysarthria
Galáž, Zoltán ; Brezany, Peter (referee) ; Sklenář, Jaroslav (referee) ; Mekyska, Jiří (advisor)
Hypokinetická dysartrie (HD) je častým symptomem vyskytujícím se až u 90% pacientů trpících idiopatickou Parkinsonovou nemocí (PN), která výrazně přispívá k nepřirozenosti a nesrozumitelnosti řeči těchto pacientů. Hlavním cílem této disertační práce je prozkoumat možnosti použití kvantitativní paraklinické analýzy HD, s použitím parametrizace řeči, statistického zpracování a strojového učení, za účelem diagnózy a objektivního hodnocení PN. Tato práce dokazuje, že počítačová akustická analýza je schopná dostatečně popsat HD, speciálně tzv. dysprozodii, která se projevuje nedokonalou intonací a nepřirozeným tempem řeči. Navíc také dokazuje, že použití klinicky interpretovatelných akustických parametrů kvantifikujících různé aspekty HD, jako jsou fonace, artikulace a prozodie, může být použito k objektivnímu posouzení závažnosti motorických a nemotorických symptomů vyskytujících se u pacientů s PN. Dále tato práce prezentuje výzkum společných patofyziologických mechanizmů stojících za HD a zárazy v chůzi při PN. Nakonec tato práce dokazuje, že akustická analýza HD může být použita pro odhad progrese zárazů v chůzi v horizontu dvou let.
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Structural and functional connectivity assessment in patients with Parkinson's disease
Klobušiaková, Patrícia ; Keller, Jiří (referee) ; Mekyska, Jiří (advisor)
Early changes in visuospatial functions predict dementia in Parkinson’s disease (PD). The aim of this work is to assess both structural and functional connectivity of the fasciculus longitudinalis inferior (ILF), which is engaged in visuospatial processing, in PD patients in comparison to healthy controls, and to find associations between connectivity changes and cognitive performance in the patient groups with or without mild cognitive impairment (MCI). To achieve our goal we recruited PD patients with normal cognition (PD-NC, n = 23) and PD with MCI (PD-MCI, n = 21) as well as healthy controls (HC, n = 48). Bidirectional iterative parcellation was used to isolate ILF tracts and their respective endpoints (occipital lobe and anterior temporal lobe) in each subject. The endpoints then served as regions of interest for functional connectivity calculation. We found ILF microstructural connectivity impairment in PD-MCI group, as measured by mean diffusivity, fractional anisotropy and radial diffusivity. In addition, the functional connectivity of ILF tracts was decreased already in the PD-NC. Both structural and functional connectivity deterioration was associated with visuospatial dysfunction in PD-MCI. These changes could serve as potential markers of disease progression or treatment effects monitoring.
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Virtual reality as a tool for diagnosis and therapy in medicine
Kadlec, Jiří ; Mekyska, Jiří (referee) ; Mucha, Ján (advisor)
The use of virtual reality (VR) in the diagnosis and treatment of severe neurodegenerative or neurodevelopmental diseases is a potential alternative to standard methods and is now the subject of many studies and research. One of the objectives of the thesis is a detailed research and analysis of this usage. Another objective is to research and analyze the options of developing VR applications. The main objective of the thesis is the design and implementation of VR application for therapy and diagnosis of patients with Parkinson's disease. The application contain an adaptive environment and three designed exercises based on existing methods for diagnosis and therapy of patients with PD. Among other things, the application also allow you to store exercise data (such as position and rotation data of controls etc.). The implementation was done in the Unity engine with C# as a programming language, with an emphasis on patient adaptation and minimizing the development of VR disease.
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Research of modern articulation features for the analysis of hypokinetic dysarthria
Vrba, Filip ; Zvončák, Vojtěch (referee) ; Galáž, Zoltán (advisor)
This thesis deals with hypokinetic dysarthria, as a disorder of motor speech, which occurs in approximately 70% of patients with Parkinson’s disease (PD). Two newly designed speech parameters for quantification of articulation within HD are analysed in this thesis. This parameters were validated on recording of both healthy and PD speakers. The theoretical part describes conventional and used methods of speech signal processing, parameterization and statistical analysis. In the part of the system implementation is described practical design of new parameters and also methods of their statistical evaluation by correlation analysis and machine learning. The aim of this work is to design new speech parameters for HD diagnostics. The proposed system was implemented in MATLAB software environment.
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Tool for analysis of subject's movements in functional magnetic resonance measurements.
Šejnoha, Radim ; Lamoš, Martin (referee) ; Gajdoš, Martin (advisor)
This diploma thesis deals with an analysis of subject’s movement during measurements with funcional magnetic resonance imaging (fMRI). It focuses on methods of a movement artifacts detection and their removal in fMRI images. Thesis deals with metrics which are used for the movement rate of measured subjects evaluation. Metrics and a correction of movement are implemented into the programme in MATLAB. Comparison of subjects suffering from Parkinson’s disease with a group of healthy control was carried out. Tresholds of individual metrics were suggested and a criterion for the removal of subjects with high movement rate was determined.
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Face parameterization using videosequence
Lieskovský, Pavol ; Mekyska, Jiří (referee) ; Rajnoha, Martin (advisor)
This work deals with the problem of face parameterization from the video of a speaking person and estimating Parkinson’s disease and the progress of its symptoms based on face parameters. It describes the syntax and function of the program that was created within this work and solves the problem of face parameterization. The program formats the processed data into a time series of parameters in JSON format. From these data, a dataset was created, based on which artificial intelligence models were trained to predict Parkinson’s disease and the progress of its symptoms. The process of model training and their results are documented within this work.
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Development of modern acoustic features quantifying hypokinetic dysarthria
Kowolowski, Alexander ; Zvončák, Vojtěch (referee) ; Galáž, Zoltán (advisor)
This work deals with designing and testing of new acoustic features for analysis of dysprosodic speech occurring in hypokinetic dysarthria patients. 41 new features for dysprosody quantification (describing melody, loudness, rhythm and pace) are presented and tested in this work. New features can be divided into 7 groups. Inside the groups, features vary by the used statistical values. First four groups are based on absolute differences and cumulative sums of fundamental frequency and short-time energy of the signal. Fifth group contains features based on multiples of this fundamental frequency and short-time energy combined into one global intonation feature. Sixth group contains global time features, which are made of divisions between conventional rhythm and pace features. Last group contains global features for quantification of whole dysprosody, made of divisions between global intonation and global time features. All features were tested on Czech Parkinsonian speech database PARCZ. First, kernel density estimation was made and plotted for all features. Then correlation analysis with medicinal metadata was made, first for all the features, then for global features only. Next classification and regression analysis were made, using classification and regression trees algorithm (CART). This analysis was first made for all the features separately, then for all the data at once and eventually a sequential floating feature selection was made, to find out the best fitting combination of features for the current matter. Even though none of the features emerged as a universal best, there were a few features, that were appearing as one of the best repeatedly and also there was a trend that there was a bigger drop between the best and the second best feature, marking it as a much better feature for the given matter, than the rest of the tested. Results are included in the conclusion together with the discussion.
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Analysis of Speech Signals for the Purpose of Neurological Disorders IT Diagnosis
Mekyska, Jiří ; Dostál, Otto (referee) ; Přibilová, Anna (referee) ; Smékal, Zdeněk (advisor)
This work deals with a design of hypokinetic dysarthria analysis system. Hypokinetic dysarthria is a speech motor dysfunction that is present in approx. 90 % of patients with Parkinson’s disease. The work is mainly focused on parameterization techniques that can be used to diagnose or monitor this disease as well as estimate its progress. Next, features that significantly correlate with subjective tests are found. These features can be used to estimate scores of different scales like Unified Parkinson’s Disease Rating Scale (UPDRS) or Mini–Mental State Examination (MMSE). A protocol of dysarthric speech acquisition is introduced in this work too. In combination with acoustic analysis it can be used to estimate a grade of hypokinetic dysarthria in fields of faciokinesis, phonorespiration and phonetics (correlation with 3F test). Regarding the parameterization, features based on modulation spectrum, inferior colliculus coefficients, bicepstrum, approximate and sample entropy, empirical mode decomposition and singular points are originally introduced in this work. All the designed techniques are integrated into the system concept in way that it can be implemented in a hospital and used for a research on Parkinson’s disease or its evaluation.
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Speech analysis using iOS or Android system
Hejda, Jakub ; Galáž, Zoltán (referee) ; Mekyska, Jiří (advisor)
The telemendicine is rapidly growing industrial branch and gives an opportunity of easier diagnosis needed for much more effective treating methods development. The aim is to develop a smartphone application for the diagnosis of prolonged phonation of pati- ents with Parkinson’s disease. Qt has been chosen s the main framework, allowing the multiplatform development using various combination of languages consisting of C++, QML, Java and JavaScript. Required functionality has been completely implemented. The application guides a user through the process of recording, then executes the analy- sis, saves data into database and displays the history into well-arranged charts, the user can also set a notification to remind him of a recording. The application can be compiled for all widely used mobile and desktop systems.
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Tremometer
Mičánková, Veronika ; Harabiš, Vratislav (referee) ; Chmelař, Milan (advisor)
Bachelor’s thesis analyzes tremor as a movement disorder and shows its relations to diseases such as Parkinson’s disease, which is therefore described more in detail. The other part of the work consists of a brief analysis on the physical sensors of vibrations and other methods that can be used to detect vibrations. Practical part is divided into two. First one describes a design of a tremometer and its constructing and the other one describes realization of a program for evaluation of tremor.
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