National Repository of Grey Literature 8 records found  Search took 0.00 seconds. 
Long-Term Effect of Repetitive Transcranial Magnetic Stimulation on Parkinson’s Disease Patients with Different Severity of Hypokinetic Dysarthria
Novotný, Kryštof
The prevalence of Parkinson’s disease (PD), severe neurodegenerative disorder, has steadily increased. Among the symptoms of PD is hypokinetic dysarthria (HD), a motor speech disorder, characterised by respiratory, articulatory, prosodic and phonatory impairments. It has been demonstrated that both motor and non-motor symptoms of PD can be improved using the repetitive Transcranial Magnetic Stimulation (rTMS). This study analyses acoustic speech characteristics of 19 participants diagnosed with PD before (one pre-stimulus) and after (four poststimulus) evaluation sessions of rTMS treatment. The participants were divided into two groups – receiving either rTMS or sham stimulation (1:1 randomization). Based on the prestimulus subresults of the Test 3F, participants were stratified into two cohorts, according to their possible HD severity level. Speech recordings were also taken during each evaluation session. The outcome of the follow-up acoustic analysis resulted in 16 parameters for each of those sessions. Their evaluation demonstrated the dependence of the effect of rTMS treatment on the severity level. The actively stimulated group of the first cohort showed consistent improvement in articulation and prosody (sham did not) while the actively stimulated group of the second stratified cohort showed consistent improvement in phonation (sham did not). The study provides early preliminary insights into the benefits of rTMS for the alleviation of HD manifestations (symptomatic treatment of PD). In addition, it provides new insights into the possible relationship between the effectiveness of rTMS and the degree of severity in HD.
Analysis of temporal speech impairments in patients with Dementia with Lewy Bodies
Davaajargal, Anar ; Kováč, Daniel (referee) ; Novotný, Kryštof (advisor)
Dementia with Lewy bodies is the second most common neurodegenerative disease of the dementia type. Progressive degradation of motor and cognitive abilities and behavioral changes have a significant impact on the quality of life of the affected person and those around them. Early diagnosis of the disease is therefore crucial for setting up adequate treatment. Due to similarities with other diseases, it may be overlooked or mistaken for another disease in the early stages. There is a lack of extensive research detailing the speech defects specific for this disease, which through acoustic analysis could be used to make a paraclinical diagnosis in a cost-effective, non-invasive, yet efficient manner. The aim of this paper is to survey available knowledge and methods, select and implement appropriate parameters targeting tempo and pauses, and then evaluate the effectiveness of the methods used to discriminate patients from healthy controls. The evaluation was based on Mann-Whitney U test and descriptive statistics. The best results were obtained for parameters MWPM of longer words, median pause length, occurrence of repeated words and NSR, all considering the task with monologue, which generally showed a greater discriminative power than the task that involved reading a text. The analysis suggests temporal parameters suitable for describing speech disorders in the selected disease. At the same time, new custom metrics are introduced and tested in this paper.
The relationship between substantia nigra echogenicity and speech and voice disorders
Adamkovičová, Lenka ; Novotný, Kryštof (referee) ; Mekyska, Jiří (advisor)
Transcranial sonography is a quick, simple and noninvasive examination method that allows to capture the loss of Substantia nigra in the brain. This loss is associated with the development of Lewy body disorders, and a confirmed correlation between Substantia nigra loss and development of dementia with Lewy bodies would allow for more accurate diagnosis of the disease. This thesis aims to investigate the accuracy of automated classification of individuals using a machine learning model, both according to their diagnosis of early DLB and also according to the size of Substantia nigra loss based on TCS examination. Automated acoustic analysis was applied to calculate speech and language parameters, those were statistically processed and then used to train a machine learning model. In a comparison of two binary classification problems it was found, that the model stratified by the size of Substantia nigra loss achieved lower accuracy than the model stratified by a diagnosis to healthy controls and persons with early-stage dementia with Lewy bodies. In addition, no correlation between SN hyperechogenicity and severity of DLB was confirmed.
Monitoring of long-term effects of repetitive transcranial magnetic stimulation on speech and voice in patients with Parkinson's disease
Kaplan, Václav ; Novotný, Kryštof (referee) ; Mekyska, Jiří (advisor)
An individual's response to dopaminergic therapy for Parkinson's Disease (PD), which often presents with hypokinetic dysarthria, varies in its effect on speech disorders. This study examines the long-term effects of alternative treatment using repetitive transcranial magnetic stimulation (rTMS) in PD patients. The aim is to conduct research into acoustic and statistical analyses employed in similar studies in the past, quantify treatment-induced changes using a set of parameters, and statistically evaluate the outcomes. Acoustic parameters describing phonation, articulation, and prosody (areas of speech production) are selected. A database of recordings from patients with mild PD is utilized, from which 18 patients are chosen, participating in one pre-treatment measurement and four post-treatment measurements. Patients are divided into active and sham (placebo) groups. Observable changes in several parameters, particularly in phonation, are noted after rTMS treatment. However, the statistical analysis of acoustic parameters also highlights a significant placebo effect, as good, and often comparable results are observed in the sham group as well.
Assessing movement of articulatory organs based on acoustic analysis of speech
Novotný, Kryštof ; Galáž, Zoltán (referee) ; Mekyska, Jiří (advisor)
Hypokinetic dysarthria is a motor speech disorder often present during Parkinson’s disease. It affects the speech system, including articulatory abilities. There are several speech parameters describing this domain, so it is suggested to deal with their mutual comparison. This work aims to design and describe an algorithm for calculating the parameters of articulation, adapted for the Czech language, and then compare their discriminative power. The acoustic analysis of speech included in it is done via the Praat program and basic machine learning algorithms such as Expectation-Maximization, Kmeans and linear regression are used for the subsequent data processing. The Mann-Whitney U test and representatives of linear, nonlinear and ensemble machine learning models using cross-validation and balanced accuracy are used for evaluation. The results are scripts for automatic assessment of vowel space area, for calculating articulation parameters and for their evaluation. The outputs of the analysis of two different databases (PARCZ and CoBeN) prove that differences in articulation can indeed be observed between normal and dysarthric speech. Based on the mutual comparison of results, it is therefore proposed in the work which parameters and models of machine learning are being appropriate for further dealing with this issue.
Analysis of speech disorders in patients with a high risk of developing Lewy body diseases
Novotný, Kryštof ; Kováč, Daniel (referee) ; Mekyska, Jiří (advisor)
Lewy bodies diseases (one of the most common neurodegenerative disorders) have the same pathological basis, but the individual representatives differ in their clinical manifestations. Different diseases affect the mental or physical side of the patient to a greater or lesser extent. This work assumes that thanks to the acoustic analysis of speech, it is possible to distinguish individual diseases from one another, because the disorders of the cognitive and motor aspects of a patient reflect in speech in specific ways. The thesis aims to describe the clinical features of the main representatives of the Lewy bodies diseases, to investigate their impact on speech, to propose characterizing acoustic parameters and then to compare their discriminative power. Speech recordings from the CoBeN and preLBD databases are used as input data for the proposed algorithm. Descriptive statistics, Mann-Whitney U test, FDR correction and XGBoost machine learning model using stratified cross-validation and balanced accuracy are used for subsequent evaluation. The result are scripts for the automated calculation of speech parameters from the database and their evaluation. The results of the analysis prove that the selected diseases can really be distinguished from each other and from a healthy control based on the manifestations in speech, already in the prodromal stages.
Using games and contests in English language teaching at elementary school level.
NOVOTNÝ, Kryštof
Abstract This Diploma Thesis deals with using games and competitions in teaching of English for the first level of primary schools. The aim of the thesis is to characterize the role of the game in English language teaching and its possible use in order to develop key language skills. The theoretical part deals with the psychological characteristics of the younger school age, then the theory of play and the developmental stages of children's play. In the last part of the thesis, there are described the different types of didactic games. The practical part consists of two parts. The first part presents didactic games, that are properly commented in terms of general educational and language objectives. Each of them was taught and reflected in terms of its effectiveness. The second chapter of the practical part presents the results of the research, which was conducted in the form of a guided interview with English language teachers at the elementary school, dealing with their experience in using English language lessons at primary school.
Assessing movement of articulatory organs based on acoustic analysis of speech
Novotný, Kryštof ; Galáž, Zoltán (referee) ; Mekyska, Jiří (advisor)
Hypokinetic dysarthria is a motor speech disorder often present during Parkinson’s disease. It affects the speech system, including articulatory abilities. There are several speech parameters describing this domain, so it is suggested to deal with their mutual comparison. This work aims to design and describe an algorithm for calculating the parameters of articulation, adapted for the Czech language, and then compare their discriminative power. The acoustic analysis of speech included in it is done via the Praat program and basic machine learning algorithms such as Expectation-Maximization, Kmeans and linear regression are used for the subsequent data processing. The Mann-Whitney U test and representatives of linear, nonlinear and ensemble machine learning models using cross-validation and balanced accuracy are used for evaluation. The results are scripts for automatic assessment of vowel space area, for calculating articulation parameters and for their evaluation. The outputs of the analysis of two different databases (PARCZ and CoBeN) prove that differences in articulation can indeed be observed between normal and dysarthric speech. Based on the mutual comparison of results, it is therefore proposed in the work which parameters and models of machine learning are being appropriate for further dealing with this issue.

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1 Novotný, K.
5 Novotný, Kamil
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