The work deals with the generation of ECG signals using generative adversarial networks (GAN). It examines in detail the basics of artificial neural networks and the principles of their operation. It theoretically describes the use and operation and the most common types of failures of generative adversarial networks. In this work, a general procedure of signal preprocessing suitable for GAN training was derived, which was used to compile a database. In this work, a total of 3 different GAN models were designed and implemented. The results of the models were visually displayed and analyzed in detail. Finally, the work comments on the achieved results and suggests further research direction of methods dealing with the generation of ECG signals.

The aim of this thesis is to analyse high-frequency ECG to detect some heart diseases. This is performed with averaging of selected QRS complexes for each lead of the signal; these are thenfilteredin range 500-1 000 Hz. After that the envelope of the signal is done and here the peaks are detected. Based on mutual positions of this peaks, it is possible to detectwhat kind od signal we treat.

The aim of this thesis is to analyse high-frequency ECG to detect some heart diseases. This is performed with averaging of selected QRS complexes for each lead of the signal; these are then filtered in range 500-1 000 Hz. After that the envelope of the signal is done and here the peaks are detected. Based on mutual positions of this peaks, it is possible to detect what kind od signal we treat.

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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This diploma thesis is focused on testing ECG monitors and ECG instruments. There are discussed origin and charakteristics of ECG signal. The ECG signal is presented by standard leads. This is the reason, why the way of making leads is mentioned. One chapter is aimed at generating ECG signal. There are set requierements for the ECG generator. These requierements results from ECG instrument possibilities and from ECG signal character. Next part of this diploma thesis includes hardware design of each part of the system. The system was designed with respect to mobility and 12-leads systems compatibility. Finaly the thesis includes design of the controll software.

Main objective of this thesis was to learn about possibility of suppression of narrow band disturbances. We focused on use of Lynn’s filters. The objective was to realize these filters with respect to a fast algorithm of filtering. Concretely, for suppression of drift we realized the fast high pass filter with flexible cut off frequency. Then we realized filter for suppression of electrical network disturbance. We have realized these filters for sampling frequencies 250 and 500 Hz and we tested them with real ECG signals. Then we have evaluated their efficiency.

Objective of this diploma work was to study methods of adaptive filtering and their use in suppression of noise in biological signals. Adaptive filtering represents effective means of suppression of parasitic nonstationary disturbances in a useful signal. The task was to design various types of adaptive filters and implement an adaptation algorithm in Matlab programming environment. It namely included suppression of powerline noise at 50 Hz and 100 Hz in ECG signals with minimization useful components disturbing. The realized filters were verified on real ECH signals and their efficiency was evaluated.

Master’s thesis deals with the measurement of biological signals for the effect of psychological burden. To monitor this effect was elected Stroop test, which is in the psychology used to detect disorders of attention and concentration. EEG and ECG signals during Stroop test were obtained using the EEG recording systém, in the context of research evoked potentials. As a part of the work it has been designed custom application for analyzing and interpreting data and statistical analysis by t-test.

This work concerns with problems of the QT sequences detection in ECG signal. The first part introduces electrophysiology of heart and his electrical manifestation. For main detection were used two algorithms, which were afterwards modified and programmed in Matlab. The work includes developed custom applications for measurement and evaluation of the length of QT interval. The final lengths of the QT interval, which we got from this two methods, were compared with reference length.