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Diagnosing Parkinson's disease from analysis of speech recording
Vymlátil, Petr ; Trzos, Michal (referee) ; Lněnička, Jakub (advisor)
This thesis is focused on diagnosing Parkinson’s disease from analysis of speech recording. Introduction of this work deals with description of voice production mechanism, it’s basic qualities and influence of hypokinetic dysarthria on speech. In next chapter, there is described voice signal and some methods of it’s preprocessing. Next part continues dealing with description of chosen individual symptoms, which are needed for PD diagnosing, followed by definition of chosen reduction methods and classifiers. There is a comparison of classify succes of naive bayes classifier, depending on chosen reduction method in last chapter of this work.
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Degree of Parkinson's disease estimation based on acoustic analysis of speech
Ustohalová, Iveta ; Kiska, Tomáš (referee) ; Galáž, Zoltán (advisor)
The diploma thesis deals with the non-invasive analysis of progression of Parkinson´s disease using the acoustic analysis of speach. Hypokinetic dysarthria in connection with Parkinson´s disease as well as speech parameters are described in this work. Speech parameters are sorted according to the speech component they affect. The work uses the phonation of vowels "a" speech task as the most commonly used speech task in the field of pathological speech processing, because of its resistance to demographic and linguistic characteristics of the speakers. Based on obtained knowledge, in MATLAB development enviroment were created systém for UPDRS III scale estimation. The UPDRS III scale is based on subjective diagnosis given by the doctor. At first, one individual parameter is used for the UPDRS III scale value estimation. Then the feature selection using SFFS algorithm is applied to gain feature combination with minimal estimation errror. Attention i salso paid to correlation between individual symptoms and UPDSR III scale.
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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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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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Analysis of Parkinson's disease using segmental speech parameters
Mračko, Peter ; Mekyska, Jiří (referee) ; Smékal, Zdeněk (advisor)
This project describes design of the system for diagnosis Parkinson’s disease based on speech. Parkinson’s disease is a neurodegenerative disorder of the central nervous system. One of the symptoms of this disease is disability of motor aspects of speech, called hypokinetic dysarthria. Design of the system in this work is based on the best known segmental features such as coefficients LPC, PLP, MFCC, LPCC but also less known such as CMS, ACW and MSC. From speech records of patients affected by Parkinson’s disease and also healthy controls are calculated these coefficients, further is performed a selection process and subsequent classification. The best result, which was obtained in this project reached classification accuracy 77,19%, sensitivity 74,69% and specificity 78,95%.
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Research of speech features quantifying diadochokinetic (DDK) tasks
Kukučka, Peter ; Smékal, Zdeněk (referee) ; Mekyska, Jiří (advisor)
Speech processing methods were studied to calculate parameters of pacient with Parkinon's disease. Main focus of this work is to examine diadochokinetic (DDK) tests. Algorithm for parameters extraction was proposed. It works in more parts. DC is removed from speech signal, preemphasis aplicated. Envelope of input signal is calculated, peaks of syllables are detected. Parameters and statistical results of Mann-Whitney U~test are calculated from detected peaks. Proposed algorithm is implemented in Matlab.
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Preliminary Acoustic Analysis of Noise Components in Patients with Parkinson's Disease
Galáž, Z.
This paper deals with acoustic analysis of noise components extracted from speech signals of patients with Parkinson’s disease (PD) who recited a poem. Experimental dataset consisted of 97 PD patients with different disease progress and 55 healthy controls (HC). The analysis is based on parametrization of 2 rhymes recitation using dysphonia features. We obtained classification accuracy 76.66% for female speakers, 69.65% for male speakers and 69.24% for the mixture of both genders.
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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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Analysis of phonation in patients with Parkinson's disease
Kopřiva, Tomáš ; Smékal, Zdeněk (referee) ; Mekyska, Jiří (advisor)
This work deals with analysis of phonation in patients with Parkinson’s disease (PD). Approximately 90% of patients with Parkinson’s disease suffer from speech motor dysfunction called hypokinetic dysarthria. System for Parkinson’s disease analysis from speech signals is proposed and several types of features are examined. Czech Parkinson’s speech database called PARCZ is used for classification. This dataset consists of 84 PD patients and 49 healthy controls. Results are evaluated in two ways. Firstly, features are individually analysed by Spearman correlation, mutual information and Mann-Whitney U test. Classification is based on random forests along with leave-one-out validation. Secondly, SFFS algorithm is employed for feature selection in order to get the best classification result. Proposed system is tested for each gender individually and both genders together as well. Best result for both genders together is expressed by accuracy 89,47 %, sensitivity 91,67% and specificity 85,71 %. Results of this work showed that the most important vowel realizations for phonation analysis are sustained vowels pronounced with maximum or minimum intensity (not whispering).
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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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