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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.
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Comparison of vowel formants with string and wind instrument formants
Steiner, Dominik ; Musil, Jaroslav (referee) ; Jirásek, Ondřej (advisor)
This thesis deals with the analysis and subsequent comparison of the formant regions of sung vowels of different singing voices with the formants of string and musical instruments based on FFT and LPC analysis. The results of the work are primarily intended for musicians and arrangers for compositional and musical artistic purposes. However, it will also help linguists regarding the analysis of formants and resonant cavities for phonetic and phonological research.
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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.
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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.
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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.
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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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Simulation of Noise Emitted by a Single-stage Gearbox
Motl, Daniel ; Hadaš, Zdeněk (referee) ; Lošák, Petr (advisor)
The level of noise and vibration of gearboxes is one of the most important parameters for today´s customers. This thesis deals with mathematical acoustic behavior modelling and its contribution to the gearbox design using finite element method. The process is presented at three computations models. Two of those were validated with experimental measurement. Acoustic analysis were performed with software FFT ACTRAN.
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Optimisation of electric vehicles gearbox housing with focus on emitted noise
Fürich, Adam ; Prokop, Aleš (referee) ; Řehák, Kamil (advisor)
This thesis deals with the vibration and noise of the gearbox for an electric vehicle. The gearbox itself acts as an emitter of unwanted noise caused by vibration excitation of the internal components of the gearbox. It is therefore necessary to deal with a complex unit such as the gearbox at system level as soon as it is being designed. This can be achieved using analytical tools and software. In this thesis, in order to evaluate the validity of the designed gearbox, a procedure is developed using both technical experiment and numerical simulations. The effect of the bolt preload is not overlooked for the following computational modelling dealing with the radiated noise of the gearbox. The results obtained from the performed technical experiments were then evaluated and compared with the results of the computational models. In this way, it is possible to compare the different approaches, their limitations and weaknesses. Furthermore, the thesis deals with design modifications that are analyzed using computational modeling. The thesis is concluded with a brief description of the application of each approach and an evaluation of the gearbox design modifications.
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Differential analysis of multilingual corpus in patients with neurodegenerative diseases
Kováč, Daniel ; Zvončák, Vojtěch (referee) ; Mekyska, Jiří (advisor)
This diploma thesis focuses on the automated diagnosis of hypokinetic dysarthria in the multilingual speech corpus, which is a motor speech disorder that occurs in patients with neurodegenerative diseases such as Parkinson’s disease. The automatic speech recognition approach to diagnosis is based on the acoustic analysis of speech and subsequent use of mathematical models. The popularity of this method is on the rise due to its objectivity and the possibility of working simultaneously on different languages. The aim of this work is to find out which acoustic parameters have high discriminative power and are universal for multiple languages. To achieve this, a statistical analysis of parameterized speech tasks and subsequent modelling by machine learning methods was used. The analyses were performed for Czech, American English, Hungarian and all languages together. It was found that only some parameters enable the diagnosis of the hypokinetic disorder and are, at the same time, universal for multiple languages. The relF2SD parameter shows the best results, followed by the NST parameter. When classifying speakers of all the languages together, the model achieves accuracy of 59 % and sensitivity of 72 %.
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