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.

This diploma thesis deals with cluster analysis for long-term electrocardiograms (ECG) clustering. The linear filtration is used for ECG preprocessing. The ECG sign segmenting in single heart cycles is based on the detection QRS complex and consequently to an application of dynamic time warping algorithms. To an application of all these mentioned processes and to results interpretation, a program called Cluster analysis has been created in the Matlab background. The results of this diploma thesis confirm that cluster analysis is able to distinguish cardiac arrhythmias which are typical with their shape distinctness of normal heart cycles.

The thesis deals with possibilities of using wavelet transform in the field of surface electrocardiogram (ECG) signals denoising and ECG signals measuring. Several algorithms have been used to detect and estimate T-wave alternans (TWA), such as spectral method (SM), Poincaré Mapping (PM) or correlation method (CM). T-wave alternans, also called repolarization alternans, is a phenomenon appearing in the electrocardiogram as a consistent fluctuation in the repolarization morphology on every-other-beat basis. Electrical TWA has been recognized as a marker of electrical instability, and has been shown to be related with patients at increased risk for ventricular arrhytmias. Presence of TWA has been reported in a wide range of clinical and experimental situations including long QT syndrome, myocardial infarction, angina pectoris, acute ischemia, etc. Projected methods of detection TWA are realized in Matlab software, and they are experimentally verified on real ECG signals from the European ST-T Database.

This project investigates methods of construction the wavelet-based QRS-complex detector. QRS-complex detection is very important, because it helps automatically calculate heart rate and in some cases it is used for compression ECG signal. The design of QRS detector can be made with many methods, in this project were mentioned and consequently tested only a few variants. The principle of designed detector used a wavelet-based decomposition of the original ECG signal to several frequency-coded bands. These bands are consequently transformed to absolute values and with the help of the threshold value are marked positions of assumed QRS complexes. Then are these assumed positions from all bands compared between themselves. If the position is confirmed at least at one nearby band, then is this position marked as true QRS complex. To increase efficiency of designed detector, two modifications were additionally mentioned. The first one, using the envelope of the signal, had rather negative effect on detectors efficiency. The second modification, using combined signal from three pseudoorthogonal leads, had reversely very good effect on detectors efficiency. In the end, the designed detector and all its modifications were tested on signals from CSE library (exactly on leads II, V2 and V6).

This master’s thesis is engaged in usage of cluster analysis for ECG signal to separate normal QRS complexes from abnormal ones. For this, it is used two algorithms created in professional computing interface MATLAB. The outputs from this master’s thesis are dendrograms, which divide QRS complexes into abnormal and normal clusters, and Pearson correlation coefficients.

The master’s thesis is focused on the possibilities of measuring heart rate of ECG signal and its use in therapeutic game of ECG biofeedback. This thesis describes the way of measuring ECG with acquisition unit Biopac and analyzes signal processing for measurement of heart rate – algorithms of QRS detection, HRV analysis. Realisation of therapeutic is designed for applications in Matlab.

This master's thesis deals with an analysis of principles of ECG signals detection and delineation. The theoretical part describes heart electrophysiology and electrocardiography basics. Next, the most important QRS detection and ECG delineation algorithms are introduced. Especially the wavelet transform methods are described. In the practical part proper delineation algoriythm was realized. It was tested on the standard CSE database, then it was modified on data of isolated rabbit heartsand the results are published in the conclusion.

A T wave peak and offset detector based on an unpublished lead transformation that can be briefly described as multilead linear regression was proposed and implemented afterwards. Potential of the transformation as a useful QRS detection tool was revealed later on. Proposed QRS detector was put to the test of CSE database. Results were compared with work of other authors. Results of T wave peak and offset detector were introduced in visual way.

This master's thesis describes basics principles of QRS complex detection. It is focused on QRS detection using zero crossing counts method. There are described princips and program realization of this method. The other part is focused on genetic optimalization algorithm. There are presented obtained optimalization results on standard CSE and MIT-BIH database. The quality of the detector is compared with other authors. The optimalized QRS detector achieves comparable results with other authors. The part of the thesis is graphical user interface which supply view on modified ECG signal and detection results.

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.