|
|
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.
|
|
|
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
|
|
|
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.
|
|
|
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.
|
|
|
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.
|
|
|
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
|
guest ::
