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Cluster analysis in biosignal processing
Kalous, Stanislav ; Archalous, Tomáš (referee) ; Kolářová, Jana (advisor)
This diploma thesis deals with cluster analysis for long-term electrocardiograms (ECG) clustering. The linear filtration is used for ECG preprocessing. The ECG sign segmenting in single heart cycles is based on the detection QRS complex and consequently to an application of dynamic time warping algorithms. To an application of all these mentioned processes and to results interpretation, a program called Cluster analysis has been created in the Matlab background. The results of this diploma thesis confirm that cluster analysis is able to distinguish cardiac arrhythmias which are typical with their shape distinctness of normal heart cycles.
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Automatic delineation of ECG signals
Tiurina, Mariia ; Kozumplík, Jiří (referee) ; Vítek, Martin (advisor)
This semester’s thesis deals about delineation ECG signals. Semester work consists of two parts. The theoretical part provides a brief introduction to the principles of electrocardiography, sensing methods electrocardiography recording and description of some well-known methods for seasons auto electrocardiographic signals. The practical part is a program that is implemented in programming environment Matlab and the results of its testing twelve lead signals from the CSE database. Using statistical methods, the results are compared with the results of the listed authors methods
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Detection of the repolarization parameters from ECG
Brandejs, Jakub ; Halámek, Josef (referee) ; Veselý, Petr (advisor)
A T wave peak and offset detector based on an unpublished lead transformation that can be briefly described as multilead linear regression was proposed and implemented afterwards. Potential of the transformation as a useful QRS detection tool was revealed later on. Proposed QRS detector was put to the test of CSE database. Results were compared with work of other authors. Results of T wave peak and offset detector were introduced in visual way.
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Deep learning based QRS delineator
Malina, Ondřej ; Ronzhina, Marina (referee) ; Smíšek, Radovan (advisor)
This thesis deals with the issue of automatic measurement of the duration of QRS complexes in ECG signals. Special emphasis is then placed on the possibility of automatic detection of QRS complexes while exciting cardiac tissue with a pacemaker. The content of this work is divided into four logical units, where the first part deals with the heart as an organ. It describes the origin and spread of excitement in the heart, its possible pathologies and their manifestations in ECG recording, it also deals with pacing and measuring ECG recording during simultaneous pacing. The second part of the thesis contains a brief introduction to the topic of machine and deep learning. The third part of the thesis contains a search of current approaches using methods based on deep learning to solve the detection of QRSd. The fourth part deals with the design and implementation of its own model of deep learning, able to detect the beginnings and ends of QRS complexes from ECG recordings. It describes the data preprocessing implemented in the MATLAB programming environment. The actual implementation of the model was performed in the Python using the PyTorch and NumPy moduls.
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Deep learning based QRS delineator
Malina, Ondřej ; Ronzhina, Marina (referee) ; Smíšek, Radovan (advisor)
This thesis deals with the issue of automatic measurement of the duration of QRS complexes in ECG signals. Special emphasis is then placed on the possibility of automatic detection of QRS complexes while exciting cardiac tissue with a pacemaker. The content of this work is divided into four logical units, where the first part deals with the heart as an organ. It describes the origin and spread of excitement in the heart, its possible pathologies and their manifestations in ECG recording, it also deals with pacing and measuring ECG recording during simultaneous pacing. The second part of the thesis contains a brief introduction to the topic of machine and deep learning. The third part of the thesis contains a search of current approaches using methods based on deep learning to solve the detection of QRSd. The fourth part deals with the design and implementation of its own model of deep learning, able to detect the beginnings and ends of QRS complexes from ECG recordings. It describes the data preprocessing implemented in the MATLAB programming environment. The actual implementation of the model was performed in the Python using the PyTorch and NumPy moduls.
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Breathing Rate Estimation from the ECG and PPG Signals
Blaude, Ondřej ; Němcová, Andrea (referee) ; Kozumplík, Jiří (advisor)
This bachelor’s thesis is focused on breath rate estimation. In this thesis, methods of breath rate estimation from the ECG and PPG signal are described and implemented. In the first two chapters the basics of ECG measurements are described, as well as the measurements of the respiratory system activity and the PPG signal. The third chapter then describes the ECG/PPG-derived respiratory signal estimation methods, later on the breath rate determination. In the fourth chapter, chosen methods are implemented and the created algorithms are applied on real data from the BIDMC database.
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Deep learning based QRS delineator
Malina, Ondřej ; Hejč, Jakub (referee) ; Smíšek, Radovan (advisor)
This thesis deals with the issue of automatic measurement of the duration of QRS complexes in ECG signals. Special emphasis is then placed on the possibility of automatic detection of QRS complexes while exciting cardiac tissue with a pacemaker. The content of this work is divided into four logical units, where the first part deals with the heart as an organ. It describes the origin and spread of excitement in the heart, its possible pathologies and their manifestations in ECG recording, it also deals with pacing and measuring ECG recording during simultaneous pacing. The second part of the thesis contains a brief introduction to the topic of machine and deep learning. The third part of the thesis contains a search of current approaches using methods based on deep learning to solve the detection of QRSd. The fourth part deals with the design and implementation of its own model of deep learning, able to detect the beginnings and ends of QRS complexes from ECG recordings. It describes the data preprocessing implemented in the MATLAB programming environment. The actual implementation of the model was performed in the Python using the PyTorch and NumPy moduls.
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Detection of QRS complex in resting ECG and exercise ECG
Ondráček, Pavel ; Odstrčilík, Jan (referee) ; Kašpar, Jakub (advisor)
This thesis deals with methods for detection of QRS complex in ECG signal. There is described electrical activity of cardiac muscle and the ECG signal itself. There are also described some of the methods for detection of QRS complex and detailed procedure of realization of the two methods in matlab. Also, the efficiency of each method is discussed and compared at the end of the thesis.
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Delineation of ECG signals using phasor transform
Koupil, Michal ; Vítek, Martin (referee) ; Maršánová, Lucie (advisor)
TThis project deals with delineation of ECG signals using phasor transform. This method is rather new in the field of automatic delineation of ECG and it is not widely used in practice yet. In the theoretical part, the relation between heart activity and ECG is described. A phasor transform is also described. In the practical part, a program was implemented in the computing environment of MATLAB. Its purpose is to delineate the signals. At first, it detects waves’ peaks, then its beginning and starting points. In the next part, analysis of the results is done, as well as comparison with other authors. The testing was performed on the QT signals from the Physionet database.
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Automatic delineation of ECG signals
Tiurina, Mariia ; Kozumplík, Jiří (referee) ; Vítek, Martin (advisor)
This semester’s thesis deals about delineation ECG signals. Semester work consists of two parts. The theoretical part provides a brief introduction to the principles of electrocardiography, sensing methods electrocardiography recording and description of some well-known methods for seasons auto electrocardiographic signals. The practical part is a program that is implemented in programming environment Matlab and the results of its testing twelve lead signals from the CSE database. Using statistical methods, the results are compared with the results of the listed authors methods
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