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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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Game Web Portal - Object-oriented Programming
Hůla, Vladimír ; Galáž, Zoltán (referee) ; Povoda, Lukáš (advisor)
This thesis deals with design and implementation of programming structure of game web portal. The web portal will serve as a communication and information center to simplify coordination among players and allow them to gain new experience. In the thesis are analyzed the needs of players, existing solutions and their drawbacks. The results of this analysis are used to design individual functions of the portal. Implementation of the most important parts of the website has been described, the implementation of these parts were evaluated and some enhancemets were eventually suggested. In the end of this thesis several functionalities were suggested, which could extend the portal in the future.
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Enhancement of image quality for security forces
Varga, Adam ; Galáž, Zoltán (referee) ; Burget, Radim (advisor)
This bachelor thesis deals with image quality enhancement for security forces. Image quality enhancement in this case means increasing the resolution of image data by using super-resolution techniques using models of deep convolutional neural networks. The thesis in its theoretical part describes the principles of the operation of this technique and in its practical part is presented the work with selected state-of-the-art models in the area of super-resolution.
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Diagnosis and progress monitoring of Parkinson’s disease using dysgraphia analysis methods
Markovič, Michal ; Galáž, Zoltán (referee) ; Mekyska, Jiří (advisor)
Parkinson’s disease causes among other symptoms also writing disorder. Parkinson's dysgrafia is disease the writing of parkinsonics. The aim of the work is to show the importance of examinig the parametres of Parkinson's dysgrafia and to find writing parametres, which could distinguish healthy subjects from the pacient and also it could monitoring progress of pakinson's disease. Some of the parametrs showed marked differences and therefore could distinguish healthy people from those with Parkinson’s disease.
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Degree of Parkinson's disease estimation based on acoustic analysis of speech
Ustohalová, Iveta ; Kiska, Tomáš (referee) ; Galáž, Zoltán (advisor)
The diploma thesis deals with the non-invasive analysis of progression of Parkinson´s disease using the acoustic analysis of speach. Hypokinetic dysarthria in connection with Parkinson´s disease as well as speech parameters are described in this work. Speech parameters are sorted according to the speech component they affect. The work uses the phonation of vowels "a" speech task as the most commonly used speech task in the field of pathological speech processing, because of its resistance to demographic and linguistic characteristics of the speakers. Based on obtained knowledge, in MATLAB development enviroment were created systém for UPDRS III scale estimation. The UPDRS III scale is based on subjective diagnosis given by the doctor. At first, one individual parameter is used for the UPDRS III scale value estimation. Then the feature selection using SFFS algorithm is applied to gain feature combination with minimal estimation errror. Attention i salso paid to correlation between individual symptoms and UPDSR III scale.
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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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Objectification of the Test 3F - dysarthric profile based on acoustic analysis
Bezůšek, Marek ; Galáž, Zoltán (referee) ; Mekyska, Jiří (advisor)
Test 3F is used to diagnose the extent of motor speech disorder – dysarthria for czech speakers. The evaluation of dysarthric speech is distorted by subjective assessment. The motivation behind this thesis is that there are not many automatic and objective analysis tools that can be used to evaluate phonation, articulation, prosody and respiration of speech disorder. The aim of this diploma thesis is to identify, implement and test acoustic features of speech that could be used to objectify and automate the evaluation. These features should be easily interpretable by the clinician. It is assumed that the evaluation could be more precise because of the detailed analysis that acoustic features provide. The performance of these features was tested on database of 151 czech speakers that consists of 51 healthy speakers and 100 patients. Statistical analysis and methods of machine learning were used to identify the correlation between features and subjective assesment. 27 of total 30 speech tasks of Test 3F were identified as suitable for automatic evaluation. Within the scope of this thesis only 10 tasks of Test 3F were tested because only a limited part of the database could be preprocessed. The result of statistical analysis is 14 features that were most useful for the test evaluation. The most significant features are: MET (respiration), relF0SD (intonation), relSEOVR (voice intensity – prosody). The lowest prediction error of the machine learning regression models was 7.14 %. The conclusion is that the evaluation of most of the tasks of Test 3F can be automated. The results of analysis of 10 tasks shows that the most significant factor in dysarthria evaluation is limited expiration, monotone voice and low variabilty of speech intensity.
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OpenCPU system usage in data processing
Yerpeissov, Serik ; Galáž, Zoltán (referee) ; Mžourek, Zdeněk (advisor)
Bachelor thesis deals with the OpenCPU system usage in data processing. The work is divided into theoretical introduction which contains a description of data processing in information and communication technologies, task and purpose of data processing, possibilities and practical usage of OpenCPU, practical usage and models of R, then the HTTP protocol and virtualization systems. Furthermore, the work deals with the realization of a sensor in OpenCPU environment through the PHP, HTML
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Analysis of impact of noise in recordings on the automated detection of hypokinetic dysarthria
Havelková, Nikola ; Galáž, Zoltán (referee) ; Kováč, Daniel (advisor)
This thesis deals with the automated detection of hypokinetic dysarthria by analysing the influence of noise present in recordings. Appropriate single-channel methods, specifically the spectral subtraction and Kalman filter, are selected and implemented in the MATLAB R2022a to enhance speech. These methods are also used for noise-free recordings, to which additive white noise was added. Afterwards, the effectiveness of these methods is objectively evaluated by using signal-to-noise ratio values. After enhancing of speech, interferences are extracted from the recordings. The effect of the presence of noise, as well as its subsequent suppression by individual methods, is then evaluated by statistical analysis, specifically using the Kruskal-Wallis test and the post hoc Dunn’s test. The probability of distributing parameters of clean, noisy and enhanced recordings, for which the effect of noise is significant, according to statistical tests, are plotted using violin and box graphs. Finally, the classification was done by logistic regression with the help of machine learning, where the effect of the presence of noise and subsequent speech enhancement on automated detection of hypokinetic dysarthria was described according to the area values under the ROC curve.
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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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