National Repository of Grey Literature 120 records found  1 - 10nextend  jump to record: Search took 0.01 seconds. 
Automatic detection of stress using biological signals
Votýpka, Tomáš ; Kozumplík, Jiří (referee) ; Smíšek, Radovan (advisor)
Bachelor's thesis is focused on stress detection. This thesis defines the concept of stress, analyzes the appropriate biological signals for stress detection, presents databases of biological signals, that were used for stress detection and mentions methods of automatic stress detection. Then, a stress detection program was implemented in the MATLAB software environment. A freely available database of non-EEG signals was used to implement the program. Models classifying stress were created using 4 machine learning methods for binary classification and 3 machine learning methods for classifying 4 psychical states. Efficiency of the classification was summarized in the conclusion of this thesis.
Clustering of ECG cycles
Němečková, Karolína ; Kozumplík, Jiří (referee) ; Ronzhina, Marina (advisor)
This bachelor thesis deals with application of cluster analysis to different ECG records in order to identify particular cardiac pathologies. The work is mainly focused on the detection of premature atrial and premature ventricular beats. Presented approach is based on the signal correlation and further beat type identification and beats clustering via specific ECG features. By evaluation the method on test data, we obtained TPR 73.40 %, FPR 91.00 %, PPV 29.00 %, ACC 90.00 %, F1 41.40 % for PAC detection and TPR 76.50 %, FPR 94.20 %, PPV 45.90 %, ACC 93.10 %, F1 57.40 % for PVC detection. Pure F1 and PPV is due to high number of false positive detections mainly in noisy ECG or ECG with manifested atrial fibrillation.
Detection of QRS complexes in multilead ECG signals
Dufková, Barbora ; Němcová, Andrea (referee) ; Kozumplík, Jiří (advisor)
The aim of this thesis is to acquaint the reader with the basic methods of QRS detection in a multilead ECG signals and with the possibilities of implemetation of these methods in Matlab. Firstly, the methods of signal preprocessing, which are based on orthogonal and pseudoorthogonal leads, are described. Then there are described some implemented and also some more advanced unrealized methods.The implemented methods are tested on the CSE database. The last part of the work is a comparison with the results of other authors who also tested their algorithms on the CSE database.
Detection of QRS complexes in ECG signals
Zhorný, Lukáš ; Ronzhina, Marina (referee) ; Kozumplík, Jiří (advisor)
This thesis deals with the detection of QRS complexes from electrocardiograms using time-frequency analysis. Detection procedures are based on wavelet and Stockwell transform. The theoretical part describes the basics of electrocardiography, then introduces common approaches to time-frequency analysis, such as short-time Fourier transform (STFT), wavelet transform and Stockwell transform. These algorithms were tested on a set of electrograms from the MIT-BIH and CSE-MO1 arrhythmia database. For the CSE database worked best the method based on the wavelet transform with the filter bank Symlet4, with the resulting value of sensitivity 100 % and positive predictivity 99.86%. For the MIT database had the best performance the detector using the Stockwell transform with values of sensitivity 99.54% and positive predictivity 99.68%. The results were compared with the values of other authors mentioned in the text.
Automatic ECG signal quality assesment
Malý, Tomáš ; Smital, Lukáš (referee) ; Kozumplík, Jiří (advisor)
This thesis deals with issues of automatic quality estimation of ECG signals. The main aim of this thesis is to implement own algorithm for classifying ECG signals into three classes of quality. Theoretical part of the thesis contains mostly description of recording electrical activity of the heart, anatomy and physiology of the heart, electrocardiography, different types of ECG signals interference and two of the chosen methods for quality estimation. Implementation of the chosen methods is presented in the practical part. Result of this thesis are two implemented algorithms, which are based on methods described in the theoretical part. The first of two is based on detection of R-waves, validation of physiological assumptions and the subsequent calculation of the correlation coefficient between adaptive template and interfered signal. Second is based on calculation of a continuous SNR value over time, which is then thresholded. The robustness of the methods was verified on the three specified real ECG signals, which are all available on UBMI including annotation of specific signal parts. Those 24-hour long signals were recorded by Holter monitor, which is described in the theoretical part of the thesis. Achieved results of individual methods, including their comparison with annotation and statistical evaluation are presented in the conclusion of this thesis.
Surrogate data analysis for assessing the significance of interaction between cardiovascular signals
Javorčeková, Lenka ; Kozumplík, Jiří (referee) ; Svačinová, Jana (advisor)
The aim of this diploma thesis was to get familiar with methods to generate surrogates and how to apply them on cardiovascular signals. The first part of this diploma thesis describes the basic theory of baroreflex function and methods to generate surrogate data. Surrogate data were generated from data, acquired from the database, by using three different methods. In the next part of this diploma thesis, coherence significance between blood pressure and heart intervals was calculated by using surrogates. In the end two hypotheses were defined and tested by which it was detected whether the orthostatic change of the measurement position has effect on the causal coherence change and baroreflex function.
Analysis of pulse wave velocity variability
Benešová, Lenka ; Kozumplík, Jiří (referee) ; Svačinová, Jana (advisor)
This diploma thesis deals with the variability of pulse wave velocity. It studies the variability of cardiovascular signals. It presents the research of measurement of pulse wave velocity and its analysis in physiology and pathological physiology. Applies spectral analysis in Matlab to a data set. It evaluates and reviews the results of this analysis
Detection of K-complexes in sleep EEG signals
Hlaváčová, Kristýna ; Ronzhina, Marina (referee) ; Kozumplík, Jiří (advisor)
This master’s thesis deals with issues of the detection of K-complexes in EEG sleep signals. Record from an electroencephalograph is important for non-invasive diagnosis and research of brain activity. The scanned signal is used to examine sleep phases, disturbances, states of consciousness and the effects of various substances. This work follows the automatic detection of K-complexes, because the manual labeling of graphoelements is complicated. Two approaches were used –Stockwell transform and bandpass filtration followed by TKEO operator application. All algorithms were created in the MATLAB R2014a.
Automatic sleep scoring
Schwanzer, Miroslav ; Kozumplík, Jiří (referee) ; Ronzhina, Marina (advisor)
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 %.
Detection of atrial fibrillation in long-term ECG records
Imramovská, Klára ; Kozumplík, Jiří (referee) ; Maršánová, Lucie (advisor)
The thesis deals with problems of automatic detection of atrial fibrillation in long-term ECG records. The preface of the theoretical part describes the electrophysiology of the heart and the principle of atrial fibrillation. Furthermore, it introduces methods of automatic detection of atrial fibrillation. In the practical part a method which uses the symbolic dynamics and a calculation of Shannon entropy is implemented in the MATLAB software environment. The method is tested on signals from the MIT-BIH Atrial Fibrillation Database and the Long-Term AF Database. Lastly, the accuracy of the classification is compared with methods described in different papers.

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