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A Thesis submitted to The University of Arizona College of Medicine - Phoenix in partial fulfillment of the requirements for the Degree of Doctor of Medicine. / Obstructive sleep apnea (OSA) is a condition characterized by upper airway obstruction during sleep causing intermittent hypoxia and nighttime awakening. It is a common condition in the United States that is often undiagnosed. It is a significant risk factor for decreased daytime productivity, quality of life, cardiovascular disease, and death. The current gold standard for diagnosis of OSA is laboratory‐based polysomnography (PSG). While PSG is necessary for the diagnosis and monitoring of OSA, many patients have limited access to PSG due to wait times at PSG laboratories or economic or geographic limitations. Portable sleep monitoring has been studied as a possible solution for patients who do not have access to timely PSG. This study aimed to use the Zephyr BioHarness 3, a chest‐worn physical activity monitor that records movement and physiologic data in real‐time, to detect apnea events in patients with suspected OSA undergoing single‐night laboratory PSG. Twenty patients underwent single‐night laboratory‐based PSG while simultaneously wearing the Zephyr BioHarness 3. The Zephyr BioHarness 3 data was analyzed using three methods. First, apnea events were identified in 10‐second windows of Zephyr data via support vector machine, logistic regression, and neural network (sensitivity = 76.0 ± 0.3%, specificity = 62.7 ± 0.2%, accuracy = 63.7 ± 0.1%). Second, apnea events were identified using the mean, median, and variance of the 10‐second windows (sensitivity = 72.3 ± 0.3%, specificity = 69.4 ± 0.1%), accuracy 69.6 ± 0.1%). Third, apnea events were identified using phase‐space transformation of the Zephyr BioHarness 3 data (sensitivity = 76.9 ± 0.3%, specificity = 77.9 ± 0.1 %, accuracy = 77.9 ± 0.1%). The Zephyr BioHarness shows initial promise as a possible OSA screening tool for patients suspected of OSA but who lack access to timely laboratory‐based PSG.

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This project is aimed at sleeping EEG signal, especially at searching of sleeping graphoelements and next at processing signal, witch this segmentation go before. Charakterization of sleeping graphoelements and problems with their classification are outlined here. Principle of two detection methods of k-komplex are explained and processed by Matlab with graphically representation of results. Results of automatic classification are compared with scoring of two experts.

This paper addresses the problem of detecting K-complexes in sleep EEG. The study of sleep has become very essential to diagnose the brain disorders and analysis of brain activities. Since Kcomplex can have a wide variety of shapes it is very difficult to detect the K-complexes manually. In this paper, I present an automatic method for K-complexes detection based wavelet transform,TKEO and method for classification using feedforward multilayer neural network designed in Matlab. Detection performance reached the value approx. from 52,9 to 83,6 %.

The thesis is focused on automatic classification of polysomnographic signals based on various parameters in time and frequency domain. The parameters are acquired from 30 seconds long segments of EEG, EMG and EOG signals recorded during different sleep stages. The parameters used for automatic classification of sleep stages are selected according to statistical analysis. Classification is performed using the SVM method and evaluation of the success of the classification is done using sensitivity, specificity and percentage success. Classification method was implemented using Matlab.

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Title: Marketing research of PSG Zlín fans' satisfaction Objectives: The aim of the diploma thesis is to find PSG Zlín fans' satisfaction and propose actions that would lead to an increase fans' satisfaction in following seasons. Methods: To determine the satisfaction of the fans of the PSG Zlin hockey club it has been used the method of a quantitative research with the help of an electronic questionnaire. This electronic questionnaire based on a large sample of fans enables to uncover sufficient data and analyses fans' satisfaction by various aspects. Results: The analysis and interpretation of data gained from marketing research have found that fans are very satisfied with many aspects. The most important thing for fans is the atmosphere in the stadium and the sport performance. The least interesting aspect was the supporting program, whom the fans are not completely satisfied. Overall, the greatest range for improvement was found in the allocation of a place for families and the improving of service during home matches. Regarding social networks, websites and merchandising exists mostly good satisfaction about the value of 2 (1 - very satisfied, 5 - very dissatisfied). Generally, fans would welcome more information, coverages and interviews behind the scenes of the club. Keywords: sports...

This master thesis deals with classification of sleep stages on the base of polysomnographic signals. On several signals was performed analysis and feature extraxtion in time domain and in frequency domain as well. For feature extraxtion was used EEG, EOG and EMG signals. For classification was selected classification models K-NN, SVM and artifical neural network. Accuracy of classifation is different depending on used method and spleep stages split. The best results achieved classification among stages Wake, REM, and N3, with neural network usage. In this case the succes was 93,1 %.

This thesis deals with wavelet analysis of sleep electroencephalogram to sleep stages scoring. The theoretical part of the thesis deals with the theory of EEG signal creation and analysis. The polysomnography (PSG) is also described. This is the method for simultaneous measuring the different electrical signals; main of them are electroencephalogram (EEG), electromyogram (EMG) and electrooculogram (EOG). This method is used to diagnose sleep failure. Therefore sleep, sleep stages and sleep disorders are also described in the present study. In practical part, some results of application of discrete wavelet transform (DWT) for decomposing the sleep EEGs using mother wavelet Daubechies 2 „db2“ are shown and the level of the seven. The classification of the resulting data was used feedforward neural network with backpropagation errors.