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Aperiodic component of EEG power spectrum in Parkinson’s disease patients treated by deep brain stimulation
Chrásková, Sofie Hedvika ; Gajdoš, Martin (referee) ; Lamoš, Martin (advisor)
Parkinson's disease (PD) is one of the most common neurodegenerative diseases. The number of diagnosed patients has doubled in the last 30 years. Symptomatic treatment primarily includes pharmacological therapy, as well as modulation of brain activity using deep brain stimulation (DBS). This work focuses on the electrophysiological changes in patients treated with DBS, which may aid the development of this highly successful therapy. As part of the practical section of the work, the effect of DBS on the so-called aperiodic component of the power spectrum of the EEG signal was examined. The results of the work demonstrate that the long-term effects of DBS have an impact on the aperiodic component. Likewise, the work proves that changes in the aperiodic component can be observed when comparing stimulation on and off. These statements support the conclusions of the latest research, which highlight the potential of the aperiodic component as an input signal for individual therapy of Parkinson's disease using adaptive DBS.
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Case Study of Physiotherapeutic Treatment of a Patient with the Diagnosis M. Parkinson
Stuchlíková, Kateřina ; Neuwirthová, Svatava (advisor) ; Saveljev, Michal (referee)
v Abstract Author: Kateřina Stuchlíková Supervisor: Mgr. Svatava Neuwirthová Title: A case report of physiotherapeutic care for a patient with Parkinson's disease Objectives: The main objective of this bachelor thesis i to process a case study of a ptient diagnosed with Parkinson's disease in the special part and in the theorethical part to provide information about this diagnosis, particularly focusing on etiopathogenesis, diagnosis ant treatment. Methods: The general part includes theoretical information about Parkinson's disease concerning clinical presentation, ethiopathogenesis, pathophysiology, diagnostic options and teratment, including rehabilitation. The special part contains the case report of the patient, whoch was developer during the bachelor's internship at the Nemocnice milosrdných sester svatého Karla Boromejského. Results: The objectives of both the theorethical and special parts were fulfilled. Conclusion: Based on the evaluation of the therapy's efect, it can be stated that physiotherapeutic care was succesful because it had a partially positive impact on the patient's condition. Keywords: Parkinson's disease, physiotherapy, case report, neurodegenerative disease, tremor, rigidity, hypokinesia, bradykinesia, akinesia
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Vliv fyzioterapie na kondici, koordinaci a posturální stabilitu u osob s Parkinsonovou chorobou.
DOMASOVÁ, Martina
Parkinson's disease is a neurodegenerative progressive disease that cannot be cured even today. However, it is possible to influence and slow down its progress to a certain extent. Symptomatic treatment is used to suppress the disease, of which physiotherapy is an integral part. In Parkinson's disease, physiotherapy can suppress the symptoms of this disease, improve the patient's condition and significantly help the patient maintain a higher quality of life for a longer period of time. The first aim of the bachelor thesis was "To draw attention to the importance of physiotherapy in a patient with Parkinson's disease". The second aim was "To map the possibilities of physiotherapy in a patient with Parkinson's disease". Both of these goals were fulfilled and described in the theoretical part in subchapter 1.8.3. In addition, the practical part of this thesis refers to the first one, where it is possible to see the changes in the condition of the patient with Parkinson's that occur when applying group exercise to his regimen. The third aim was "To design an exercise that would lead to the improvement of the condition of the patient with Parkinson's". This goal is dealt with in the practical part of this thesis. Based on the knowledge gained in the theoretical part, an exercise unit was created, which was then applied for two months to a group of seven probands. To evaluate its effect on the condition of patients, input and output kinesiological analyses were compared, including clinical tests to examine fitness, coordination and postural stability (FTSST, TUG, BBS, Pull Test, Push & Release Test).
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Acoustic analysis of emotionally affected sentences in patients with Parkinson's disease
Gavlasová, Radka ; Kováč, Daniel (referee) ; Mekyska, Jiří (advisor)
This thesis focuses on Parkinson's disease and its effect on emotional expression in speech. The aim was to conduct a literature search on acoustic emotional analysis of PD patients and to implement acoustic parameters to distinguish between healthy and diseased individuals. The database used contained recordings of 100 patients with PD and 52 healthy controls for various speech tasks. For this analysis, 7 emotionally coloured sentences and 11 acoustic parameters were selected and implemented in Python. From the statistical analysis, it was found that the most significant parameters include pauses in speech and intensity variability. The XGBoost algorithm with 10-fold stratified cross-validation was used for classification. A total of 10 models were implemented to analyze all tasks together and each task separately. Optimization was performed using randomized search. For the combination of all tasks, the significant parameter was the variability in intensity or speech rate. For the individual speech tasks, variability in intonation and formant areas was highly significant. The best model achieved a 63% success rate (BACC) and 85% sensitivity. The results suggest that emotional prosody affects classification, confirming previous findings and pointing to the need for further investigation in this area.
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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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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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Data Mining with Python
Krestianková, Tamara ; Burgetová, Ivana (referee) ; Zendulka, Jaroslav (advisor)
This thesis deals with principles of data mining process, available Python packages for data mining and a demonstration of Python script capable of data analyisis focused on classification techniques. Created classifiers are able to classify subjects into two groups - healthy people and people suffering from Parkinson's disease - based on their biomedical vocal analysis data.
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State of the art speech features used during the Parkinson disease diagnosis
Bílý, Ondřej ; Smékal, Zdeněk (referee) ; Mekyska, Jiří (advisor)
This work deals with the diagnosis of Parkinson's disease by analyzing the speech signal. At the beginning of this work there is described speech signal production. The following is a description of the speech signal analysis, its preparation and subsequent feature extraction. Next there is described Parkinson's disease and change of the speech signal by this disability. The following describes the symptoms, which are used for the diagnosis of Parkinson's disease (FCR, VSA, VOT, etc.). Another part of the work deals with the selection and reduction symptoms using the learning algorithms (SVM, ANN, k-NN) and their subsequent evaluation. In the last part of the thesis is described a program to count symptoms. Further is described selection and the end evaluated all the result.
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Acoustic analysis of poem recitation in patients with Parkinson's disease
Mucha, Ján ; Smékal, Zdeněk (referee) ; Galáž, Zoltán (advisor)
Diploma thesis is focused on the acoustic analysis of poetry recitation in patients with Parkinson's disease. This disease is associated with speech disorder called hypokinetic dysarthria. One objective of this thesis was familiarization with process, symptoms and treatment of these diseases. In thesis is described preprocessing and parametrization of the speech signal and the binary classification methods. Subsequently, it is the above proposal modular system of auto-diagnosis of Parkinson's disease based on acoustic analysis of the speech. The proposed system is implemented in MATLAB. Classification of calculated parameters is realized using the method of Random forest and Support vector machine. The results of these methods are compared and listed in the thesis. The main objective and the result of this thesis is a system of automatic diagnosis of Parkinson's disease based on acoustic analysis of the poem recitation.
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