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Identification of sleep disorders based on actigraphy data and sleep diaries
Molík, Miroslav ; Mekyska, Jiří (referee) ; Mikulec, Marek (advisor)
This master’s thesis deals with prediction of Parkinson's disease using sleep parameters from actigraphy and sleep diaries. The goal is to design a machine learning approach, which will be able to recognize pacients suffering from Parkinson's disease. For training dataset supplied by St. Anne's University Hospital Brno was used, which was variously modified for achieving best possible results. These adjustments were evaluated according to the results of the trained models and based on these results, two models (achieving test accuracies of 85 and 82%) were selected.
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Speech Enhancement using Cancelling of Dissonant Components
Studený, Radim ; Mekyska, Jiří (referee) ; Smékal, Zdeněk (advisor)
This work is supposed to remove interferences from speech signal and so increase its clarity, quality of degraded signal and signal-to-noise ratio. The most common sources of interference could be street noise, wind coming on a microfon, speech on the background or music.The metod described in this work remove frequence bands of a signal, which in relation to the fundamental frequency of a speech are disonant. Be specific, to reference tone C there are F#, B a C# tones.
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Functional connectivity and brain structure assessment in patients at risk of synucleinopathies
Klobušiaková, Patrícia ; Gajdoš, Martin (referee) ; Mekyska, Jiří (advisor)
Synucleinopathy is a neurodegenerative disorder characterized by the presence of pathological protein -synuclein in neurons. So far, treatment that could heal or permanently stop this disease is not known. The aim of this work is to identify prodromal stages of synucleinopathies using functional connectivity processed applying graph metrics and assessing cortical thickness and subcortical structures volumes from magnetic resonance imaging data, and to verify specificity and sensitivity of combinations of parameters that sufficiently differentiate patients in risk of synucleinopathies. To accomplish this goal, we collected data from patients in the risk of synucleinopathy (preDLB, n = 27) and healthy controls (HC, n = 28). We found reduced volume of right pallidum and increased hippocampal volume to cortical volume ratio, increased normalised clustering coefficient and higher modularity in the preDLB group in comparison to HC. These four parameters were modeled using machine learning. The resulting model differentiated preDLB and HC with balanced accuracy of 88 %, specificity of 89 % and sensitivity of 86 %. The findings of this thesis can serve as the basis for further studies searching for specific MRI markers of prodromal stage of synucleinopathy that could be targeted with therapy in the future.
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Automated diagnosis of sleep disorders using wearable devices
Sigmund, Jan ; Mekyska, Jiří (referee) ; Mikulec, Marek (advisor)
Sleep disorders induce many negative repercussions. Furthermore, research about their connection to cognitive health is increasing in numbers. This thesis concerns detection of poor sleep quality via raw actigraphy data. Existing method for assessing sleep was selected, it’s performance was validated against polysomnography on 27 patients. Used algorithm defines sleep as the absence of change in arm angle. Resulting 81 % sensitivity, 62 % specificity and 78 % accuracy is different from the outcome in the pilot study. Two approaches, to determine sleep quality were used. Both are based on comparing sleep features – first, with National Sleep Foundation recommendations and second, with control group without sleep disorders (7 persons). The goal was to pinpoint the remaining 19 patients with diagnosis. The recommendation for SOL, WASO, NA>5 and SE had higher sensitivity (75 %), lower specificity (71 %) and identical accuracy (74 %). These approaches were then also tested on 7-day actigraphy, consisting of 27 subjects, that are presumed to have prodromal dementia with Lewy bodies. Same principle was applied to try to predict LBD and thereby address the link between sleep quality and neurodegeneration. This resulted in 86 % sensitivity, 38 % specificity and 63 % accuracy. With regard to achieving solid sensitivity in all cases and good accuracy this could be used to indicate sleep quality.
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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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Speech signal processing via web interface
Markovič, Michal ; Smékal, Zdeněk (referee) ; Mekyska, Jiří (advisor)
This work is resolving the possibility of processing a speech signal by using the web interface. A record of the speech signal is made and then saved and stored in servers. Then it’s possible to analyse and process the record with the help of a web application written in the C++ programming language. The results are shown in website.
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Acoustic call analysis application for Android system
Hejda, Jakub ; Mucha, Ján (referee) ; Mekyska, Jiří (advisor)
The telemedicine’s capabilities are rapidly expanding due to technological advances in a smarphone development. The goal of this thesis was to suggest the architecture and prepare the design providing acquisition, processing and synchronization of voice para- meters recorded by patients with Parkinson’s disease and to implement such system. The architecture was completed successfully, it consists of the mobile application able to record patient’s calls, the server application introducing an interface to store and synchronize the data and to provide them to the web application, where doctors can see the data and analyze it. Implementation of the server application was finished according to the design and to the requirements for robustness and security as well as the web application. By an extension of the existing mobile application for recording voice calls there was developed a huge system for the analysis of this disease.
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Analysis and Modelling of Medical Images
Vrba, Jan ; Mekyska, Jiří (referee) ; Smékal, Zdeněk (advisor)
The main objective of this thesis is an analysis of assessment techniques face, and cephalometric evaluation methods that are suitable for treatment of jaw position and design methods for treatment of jaw. At the same time the emphasis is on studying the methods,java advance imaging, which are used for the curvature of the image and should be able to meet the objectives of the assignment. These adjustments can be made using the Warp. Result of this work is an application developed in JAVA programming language, which demonstrates the best method for modifying the image. This method is WarpGrid. The application was made in the development environment eclipse. With this application, depending on the mouse action is possible to modify the image.
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Acoustic analysis of sentences complicated for articulation in patients with Parkinson's disease
Kiska, Tomáš ; Smékal, Zdeněk (referee) ; Mekyska, Jiří (advisor)
This work deals with a design of hypokinetic dysarthria analysis system. Hypokinetic dysarthria is a speech motor dysfunction that is present in approx. 90 % of patients with Parkinson’s disease. 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, VAI, etc.). The work is mainly focused on parameterization techniques that can be used to diagnose or monitor this disease as well as estimate its progress. A protocol of dysarthric speech acquisition is described in this work too. In combination with acoustic analysis it can be used to estimate a grade of hypokinetic dysarthria in fields of faciokinesis, phonorespiration and phonetics (correlation with 3F test). Regarding the parameterization, new features based on method RASTA. The analysis is based on parametrization sentences complicated for articulation. Experimental dataset consists of 101 PD patients with different disease progress and 53 healthy controls. For classification with feature selection have selected method mRMR.
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Application for the calculation of speech features describing hypokinetic dysarthria
Hynšt, Miroslav ; Mekyska, Jiří (referee) ; Kiska, Tomáš (advisor)
This thesis is about design and implementation of application for computing speech parameters on people with Parkinson disease. At the beginning is generaly described Parkinson disease and Hypokinetic dysarthria and how it affects the speech and speech parameters when it occurs. Mainly there are described areas of speech like phonation, prosody, articulation and fluent speech. As a part of next topic this thesis describes specific speech parameters with bigger meaning during diagnosis Parkinson disease and it's progress over the time. There are also mentioned few significant studies dealing with examination of speech of the subjects with diagnoses of Parkinson disease and computing some speech parameters in order to analyze their speech impairments. Part of the thesis is description of implemented standalone application for calculating, exporting and visualizing of speech parameters from selected sound records.
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