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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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Automatic diagnosis of the 12-lead ECG using deep learning
Blaude, Ondřej ; Chmelík, Jiří (referee) ; Provazník, Valentine (advisor)
The aim of this diploma thesis is to investigate the problematics of automatic ECG diagnostics, namely on twelve-lead recordings. This problem is solved by standard methods such as random forest, artificial neural networks or K-nearest neighbors. However, thanks to its ability to independently extract symptoms, deep learning methods are also popular. All these methods are described in the theoretical part. In the practical part, deep learning models were designed, functionality support was verified using data from the PhysioNet database. Two pilot models were created and subsequently optimized. From the entire parameter optimization procedure, three models are available, of which the best accuracy achieves an F1 score of 87.35% and 83.7%, and the second best achieves an F1 score of 77.74% and an accuracy of 84.53%. The results achieved are discussed and compared with those of similar publications.
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Automatic diagnosis of the 12-lead ECG using deep learning
Blaude, Ondřej ; Smital, Lukáš (referee) ; Provazník, Valentine (advisor)
The aim of this diploma thesis is to investigate the problematics of automatic ECG diagnostics, namely on twelve-lead recordings. In the first chapter the heart and its electrical activity measurement is described shortly. In addition to that, the abnormalities which are going to be classified in this thesis are also briefly described. In the second chapter, it is described how the ECG was diagnosed earlier, by classical methods that preceded deep learning. Some of the shortcomings that the classical methods have compared to deep learning are also described here. The third part already pays attention to deep learning itself, and its contribution and advantages compared to classical methods. Convolutional neural networks and their individual blocks are also described here, later attention is paid to selected architectures that were used in some studies. The fourth chapter already focuses on the practical part, in which the data used from the PhysioNet database, the proposed algorithm and its implementation are described in more detail. In the fifth chapter the results are discussed and compared to the corresponding publications.
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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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