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Maximization of ECG signals diagnostic yield
Beháňová, Andrea ; Smital, Lukáš (referee) ; Vítek, Martin (advisor)
This bachelor thesis deals with the maximization of ECG signals diagnostic yield. In the theoretical section we deal with the physiology of the heart, electrocardiography, types of ECG noises. It describes some known methods for the estimation of quality of the ECG signal. The practical section contains two parts. The first one contains a continuous Signal-to-Noise Ratio (SNR). It includes generating artificial ECG signal, artificial myopotentials and implementation of Adaptive Wiener Wiener Filtrate (AWWF). After verification of the correctness of the filter on the artificial data, we started to use real data from MIT-BIH database. The second part involves a segmentation process that divides the ECG signal into three categories: a signal suitable for full analysis, suitable for detection of QRS complexes and a signal unsuitable for further analysis.
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Real-Time Estimation Of ECG Signal Quality
Beháňová, Andrea
In this study, we focus on the estimation of ECG signal quality. It consists of two parts, first includes generating artificial ECG, artificial myopotentials, implementation of Adaptive Wavelet Wiener Filter and continuous calculation of the Signal-to-Noise Ratio (SNR). The second part includes segmentation process, which sorts parts of ECG signal into three categories: suitable for full wave analysis, good for QRS detection and unsuitable for further processing.
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Maximization of ECG signals diagnostic yield
Beháňová, Andrea ; Smital, Lukáš (referee) ; Vítek, Martin (advisor)
This bachelor thesis deals with the maximization of ECG signals diagnostic yield. In the theoretical section we deal with the physiology of the heart, electrocardiography, types of ECG noises. It describes some known methods for the estimation of quality of the ECG signal. The practical section contains two parts. The first one contains a continuous Signal-to-Noise Ratio (SNR). It includes generating artificial ECG signal, artificial myopotentials and implementation of Adaptive Wiener Wiener Filtrate (AWWF). After verification of the correctness of the filter on the artificial data, we started to use real data from MIT-BIH database. The second part involves a segmentation process that divides the ECG signal into three categories: a signal suitable for full analysis, suitable for detection of QRS complexes and a signal unsuitable for further analysis.
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