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Rules for Determination of Expected P Wave Type in ECG Signals
Maršánová, Lucie
The aim of this work is to improve existing possibilities of automatic detection of pathological P wave in electrocardiogram. For detection of different types of P wave is crucial to choose suitable algorithm. In this paper, establishing of rules for determining which P wave type is in given situation probably present is done. This rules are derived from positions of QRS complexes. Therefore, accurate QRS detector based on a signal filtering by bandpass filter and finding the maximum in a moving window is designed.
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Segmentation of Hidden P Waves Using Deep Learning Methods
Boudová, Markéta ; Ronzhina, Marina (referee) ; Hejč, Jakub (advisor)
The aim of this thesis is segmentation of P waves in ECG signals. The theoretical part of the thesis describes the physiology of the heart and the basics of deep learning methods. Preprocessing of the signals is performed and neural network U-Net is implemented in the Python software environment in the practical part. Afterwards, optimization of network architecture is performed in order to reduce model complexity. Lastly the success rate of the model is evaluated.
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P wave detection in ECG signals
Bajgar, Jiří ; Kozumplík, Jiří (referee) ; Smital, Lukáš (advisor)
The aim of this diploma thesis is to introduce methods of detection of the QRS complex and the subsequent detection of P waves. The intention is to create a program by specified method in the software Matlab which will be able to implement this method. The thesis describes the basic and important methods of detection and subsequent algorithm to detect P waves. Solution of the algorithm is tested on real data. It also describes the automatic signal evaluation and the results of this automatic function.
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A Detections and Measurements in ECG Signals
Toušek, Vojtěch ; Vítek, Martin (referee) ; Kozumplík, Jiří (advisor)
Automatic detection and delineation of ECG characteristic points is a basic procedure of any analyze of ECG using computer. This detection is a necessary step to simplify the work of cardiologists to evaluate long ECG records. In this thesis is proposed and evaluate a method of detection and delineation in a single-lead ECG using dyadic wavelet transform followed by correction in pseudo-orthogonal lead system taken from standard 12-lead system. The method uses information about position of positive maximum – negative minimum pair to detect ECG characteristic waves. At first the QRS complex is detected and than its morphology (waves Q and S) and the onset and end of the complex. After that the T-wave is detected and delineated within a searching window dependent on QRS position. And last the P-wave is detected and delineated. There are used two types of wavelets in developed method, “haar” and “quadratic spline”. The developed method was evaluated on CSE database. When haar wavelet was used the QRS detector sensitivity was 99.14%. In the work is also evaluated the accuracy of delineation characteristic points. As the P-wave and QRS complex delineation produced quite good results the T-wave end delineator produced relatively big deviations. All deviations are presented in histograms.
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Segmentation of Hidden P Waves Using Deep Learning Methods
Boudová, Markéta ; Ronzhina, Marina (referee) ; Hejč, Jakub (advisor)
The aim of this thesis is segmentation of P waves in ECG signals. The theoretical part of the thesis describes the physiology of the heart and the basics of deep learning methods. Preprocessing of the signals is performed and neural network U-Net is implemented in the Python software environment in the practical part. Afterwards, optimization of network architecture is performed in order to reduce model complexity. Lastly the success rate of the model is evaluated.
|
|
|
Rules for Determination of Expected P Wave Type in ECG Signals
Maršánová, Lucie
The aim of this work is to improve existing possibilities of automatic detection of pathological P wave in electrocardiogram. For detection of different types of P wave is crucial to choose suitable algorithm. In this paper, establishing of rules for determining which P wave type is in given situation probably present is done. This rules are derived from positions of QRS complexes. Therefore, accurate QRS detector based on a signal filtering by bandpass filter and finding the maximum in a moving window is designed.
|
|
| |
|
|
P wave detection in ECG signals
Bajgar, Jiří ; Kozumplík, Jiří (referee) ; Smital, Lukáš (advisor)
The aim of this diploma thesis is to introduce methods of detection of the QRS complex and the subsequent detection of P waves. The intention is to create a program by specified method in the software Matlab which will be able to implement this method. The thesis describes the basic and important methods of detection and subsequent algorithm to detect P waves. Solution of the algorithm is tested on real data. It also describes the automatic signal evaluation and the results of this automatic function.
|
|
|
A Detections and Measurements in ECG Signals
Toušek, Vojtěch ; Vítek, Martin (referee) ; Kozumplík, Jiří (advisor)
Automatic detection and delineation of ECG characteristic points is a basic procedure of any analyze of ECG using computer. This detection is a necessary step to simplify the work of cardiologists to evaluate long ECG records. In this thesis is proposed and evaluate a method of detection and delineation in a single-lead ECG using dyadic wavelet transform followed by correction in pseudo-orthogonal lead system taken from standard 12-lead system. The method uses information about position of positive maximum – negative minimum pair to detect ECG characteristic waves. At first the QRS complex is detected and than its morphology (waves Q and S) and the onset and end of the complex. After that the T-wave is detected and delineated within a searching window dependent on QRS position. And last the P-wave is detected and delineated. There are used two types of wavelets in developed method, “haar” and “quadratic spline”. The developed method was evaluated on CSE database. When haar wavelet was used the QRS detector sensitivity was 99.14%. In the work is also evaluated the accuracy of delineation characteristic points. As the P-wave and QRS complex delineation produced quite good results the T-wave end delineator produced relatively big deviations. All deviations are presented in histograms.
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