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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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Automated segmentation of the diadochokinetic task for remote monitoring of hypokinetic dysarthria
Svojanovský, Jan ; Mekyska, Jiří (referee) ; Kováč, Daniel (advisor)
The study describes health problems associated with Parkinson’s disease, especially hypokinetic dysarthria. It also points out the subjective and objective methods used to determine the severity of the disease. One of these methods is a diadochokinetic (DDK) task based on rapid syllable repetition to test the functionality of the articulatory apparatus (e.g., tongue, lips, or vocal cords). Correct speech production can also be examined by a speech therapist in the 3F test, which scores the severity of disorders in different areas of speech production. Next, the approaches of other authors, also dealing with the automated search of syllables in the speech signal, are described. The thesis also discusses some features of human speech that are needed for training a machine learning model. These features were computed for each of the 30 ms segments of a DDK task. The main goal is the automated detection and classification of [Pa]-[Ta]-[Ka] syllables in the recordings. For this purpose, an algorithm using a logistic regression was applied. The resulting average accuracy of syllable detection in the recordings was 89.4 %, average sensitivity 59.0 % and average specificity 93.79 %. The identification of individual syllable types was successful with an average accuracy of 90.78 %, an average sensitivity of 59.0 % and an average specificity of 95.39 %. Considering that the predicted onset was not located directly on the manually annotated onset, but in its close vicinity (up to ±3 segments), the average detection sensitivity and average syllable type classification sensitivity were 96.9 % and 85.1 % respectively, with an average difference between manually annotated and automatically segmented syllable onsets of 10.35 ms. The average accuracy of classification of speakers into healthy and PN patients using logistic regression (with speech parameters obtained after automated segmentation) was only 43.92 %, sensitivity 70.0 % and specificity 30.61 % (threshold 70 %). Using linear regression, the clinical scores of the 3F test were predicted. For faciokinesis, the root mean square error (RMSE) was 2.764 after manual syllable annotation and 3.271 after automated segmentation. The RMSE values for phonetics were 3.657 (manual) and 0.753 (automated). The developed algorithm can detect syllables in DDK tasks with relative success, and thus it is possible to determine parameters quantifying speech disorders with low differences with manual segmentation. If the recordings of DDK tasks meet the conditions for computing all these parameters, the algorithm could be used to classify speakers into healthy subjects and PN patients, for whom it could additionally assess the severity of dysarthria.
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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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Neuropsychological differential diagnosis of selected movement disorders
Tegelová, Vendula
Title: Neuropsychological Differential Diagnosis of selected Movement Disorders Author: Bc. et Bc. Vendula Tegelová Supervisor: Mgr. et Mgr. Tomáš Nikolai, Ph.D. Number of pages and characters: 106; 198 530 Number of appendices: 1 Number of references: 206 Abstract: The diploma thesis deals with the insight into the problems of the extrapyramidal system and its diseases and the key structures of the extrapyramidal system - basal ganglia. Furthermore, a basic description of Parkinson's disease (PN), progressive supranular palsy (PSP) and multisystem atrophy (MSA) with emphasis on the cognitive aspects of these diseases. Thesis also deals with neuropsychological examination in clinical practice, in more detail screening methods of cognitive functions, which are a necessary part of the diagnosis of cognitive deficits. We briefly describe selected screening methods, including the Mattis dementia scale (DRS2), which is used in the empirical part of the work. The theoretical part concludes with a brief search of studies that examine using DRS2, but also other screening methods, cognitive deficit and cognitive profile, especially in PN, PSP and MSA, but also in other neurodegenerative diseases. The empirical part of the work focuses on the analysis of DRS2 results in patients with PN, PSP and MSA, taking...
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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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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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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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