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Analysis of Speech Signals for the Purpose of Neurological Disorders IT Diagnosis
Mekyska, Jiří ; Dostál, Otto (referee) ; Přibilová, Anna (referee) ; Smékal, Zdeněk (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. The work is mainly focused on parameterization techniques that can be used to diagnose or monitor this disease as well as estimate its progress. Next, features that significantly correlate with subjective tests are found. These features can be used to estimate scores of different scales like Unified Parkinson’s Disease Rating Scale (UPDRS) or Mini–Mental State Examination (MMSE). A protocol of dysarthric speech acquisition is introduced 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, features based on modulation spectrum, inferior colliculus coefficients, bicepstrum, approximate and sample entropy, empirical mode decomposition and singular points are originally introduced in this work. All the designed techniques are integrated into the system concept in way that it can be implemented in a hospital and used for a research on Parkinson’s disease or its evaluation.
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Speech analysis using iOS or Android system
Hejda, Jakub ; Galáž, Zoltán (referee) ; Mekyska, Jiří (advisor)
The telemendicine is rapidly growing industrial branch and gives an opportunity of easier diagnosis needed for much more effective treating methods development. The aim is to develop a smartphone application for the diagnosis of prolonged phonation of pati- ents with Parkinson’s disease. Qt has been chosen s the main framework, allowing the multiplatform development using various combination of languages consisting of C++, QML, Java and JavaScript. Required functionality has been completely implemented. The application guides a user through the process of recording, then executes the analy- sis, saves data into database and displays the history into well-arranged charts, the user can also set a notification to remind him of a recording. The application can be compiled for all widely used mobile and desktop systems.
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Tremometer
Mičánková, Veronika ; Harabiš, Vratislav (referee) ; Chmelař, Milan (advisor)
Bachelor’s thesis analyzes tremor as a movement disorder and shows its relations to diseases such as Parkinson’s disease, which is therefore described more in detail. The other part of the work consists of a brief analysis on the physical sensors of vibrations and other methods that can be used to detect vibrations. Practical part is divided into two. First one describes a design of a tremometer and its constructing and the other one describes realization of a program for evaluation of tremor.
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Use of Statistical Methods for Progression Evaluation of Parkinson’s Disease
Pecha, Jiří ; Mekyska, Jiří (referee) ; Smékal, Zdeněk (advisor)
This master’s thesis takes aim with the use of statistical methods for progression evaluation of Parkinson’s disease. There is a brief description of Parkinson’s disease. It is further stated processing and evaluation of values of speech parameters which are affected by Parkinson’s disease. The thesis describes the process using the values of classification and regression trees and evaluate results using the mean absolute error and estimated error. Processing and evaluation of values was done in MATLAB software.
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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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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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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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Movement Abnormalities Classification using Genetic Programming
Chudárek, Aleš ; Mrázek, Vojtěch (referee) ; Drahošová, Michaela (advisor)
When suppressing the symptoms of Parkinson's disease, the correct dosage of drugs is critical for the patient. Improper dosing can either cause insufficient suppression of symptoms or, conversely, side effects, such as dyskinesia, occur with high doses. Dyskinesia is manifested by involuntary muscle movement. This work deals with the automated classification of dyskinesia from motion data recorded using a triaxial accelerometer located on the patient's body. In this work, the classifier of dyskinesia is automatically designed using Cartesian genetic programming. The designed classifier achieves very good quality of classification of severe dyskinesia (AUC = 0,94), which is a comparable result to the techniques presented in scientific literature.
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Application of statistical analysis of speech in patients with Parkinson's disease
Bijota, Jan ; Mžourek, Zdeněk (referee) ; Galáž, Zoltán (advisor)
This thesis deals with speech analysis of people who suffer from Parkinson’s disease. Purpose of this thesis is to obtain statistical sample of speech parameters which helps to determine if examined person is suffering from Parkinson’s disease. Statistical sample is based on hypokinetic dysarthria detection. For speech signal pre-processing DC-offset removal and pre-emphasis are used. The next step is to divide signal into frames. Phonation parameters, MFCC and PLP coefficients are used for characterization of framed speech signal. After parametrization the speech signal can be analyzed by statistical methods. For statistical analysis in this thesis Spearman’s and Pearson’s correlation coefficients, mutual information, Mann-Whitney U test and Student’s t-test are used. The thesis results are the groups of speech parameters for individual long czech vowels which are the best indicator of the difference between healthy person and patient suffering from Parkinson’s disease. These result can be helpful in medical diagnosis of a patient.
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