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	Electroencephalogram (EEG) and machine learning based classification of depression: unveiling hidden patterns for early detection Jurkechová, Adriana ; Malik, Aamir Saeed (referee) ; Zaheer, Muhammad Asad (advisor) Táto práca sa zaoberá predspracovaním EEG signálov, extrakciou vlastností a klasifikáciou pacientov s depresiou a zdravou kontrolnou skupinou. Na klasifikáciu bolo zväžených a ohodnotených 5 modelov strojového učenia. Získané poznatky potvrdzujú výsledky z predchádzajúcich výskumov a poukazujú na dôležitosť veľkého a diverzného datasetu. Táto práca pracuje s verejne dostupným datasetom. Detailed record
	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. Detailed record
	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. Detailed record

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