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QRS complex detection in multilead ECG signals
Šlancar, Matěj ; Smital, Lukáš (referee) ; Kozumplík, Jiří (advisor)
The aim of this thesis is to introduce the principles of software QRS detection, which is based on different combinations of orthogonal (pseudoorthogonal) leads. The thesis describes the main components of the ECG signal, a selection of methods that can be used for QRS detection in orthogonal leads and finally the evaluation of the effectiveness of the chosen methods and a comparing the approaches with the results of other authors. Functionality of detection algorithm has been tested on signals of CSE standard library.
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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.
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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.
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K-complex detection in sleep EEG
Bjelová, Martina ; Mézl, Martin (referee) ; Králík, Martin (advisor)
This paper addresses detecting of K-complexes in sleeping EEG records. Polysomnography is the method, which is used for diagnostic and following therapy of many sleep disorders. For identifnging of sleep stages it is fundamental to know graphoelements, in which they are situate. K-complex is important indicator of second sleep stange and hence is essencial to know to detect this pattern. In this paper we focus on design and implementation of more algorithms for detection of these patterns with various characteristics. Among the proposed methods, the wavelet transform method was best evaluated. Performance of this detection reached values the average senzitivity 63,83 % and average positive predictive value 44,07 %.
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Automatic detection of K-complexes in sleep EEG signals
Pecníková, Michaela ; Ronzhina, Marina (referee) ; Kozumplík, Jiří (advisor)
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 %.
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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 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.
|
|
|
K-complex detection in sleep EEG
Bjelová, Martina ; Mézl, Martin (referee) ; Králík, Martin (advisor)
This paper addresses detecting of K-complexes in sleeping EEG records. Polysomnography is the method, which is used for diagnostic and following therapy of many sleep disorders. For identifnging of sleep stages it is fundamental to know graphoelements, in which they are situate. K-complex is important indicator of second sleep stange and hence is essencial to know to detect this pattern. In this paper we focus on design and implementation of more algorithms for detection of these patterns with various characteristics. Among the proposed methods, the wavelet transform method was best evaluated. Performance of this detection reached values the average senzitivity 63,83 % and average positive predictive value 44,07 %.
|
|
|
QRS complex detection in multilead ECG signals
Šlancar, Matěj ; Smital, Lukáš (referee) ; Kozumplík, Jiří (advisor)
The aim of this thesis is to introduce the principles of software QRS detection, which is based on different combinations of orthogonal (pseudoorthogonal) leads. The thesis describes the main components of the ECG signal, a selection of methods that can be used for QRS detection in orthogonal leads and finally the evaluation of the effectiveness of the chosen methods and a comparing the approaches with the results of other authors. Functionality of detection algorithm has been tested on signals of CSE standard library.
|
|
|
Automatic detection of K-complexes in sleep EEG signals
Pecníková, Michaela ; Ronzhina, Marina (referee) ; Kozumplík, Jiří (advisor)
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 %.
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