Electrocardiogram is a pictorial representation of the electrical activity of heart beats. Because of the direct relationship between the ECG waveform and interval of the heart beats, it is possible that doctor can diagnose cardiac disease and monitor patient conditions from the unusual ECG waveforms. Based on the wavelet transform, this work introduces an algorithm to detect QRS complex. In particular, the quadratic spline wavelet has been adopted. The thesis first reviews wavelet transform briefly, then develops a QRS detention algorithm, which is then tested by using the MIT-BIH arrhythmia database. It is hoped that the proposed QRS detection algorithm can be a useful tool for medical personnel who are interested in using QRS information to explore their research work.

This thesis deals with implementation of detector QRS complex with use of wavelet transform. The first part is focused on formation and possibility to measure cardiac activity. The other part of thesis we will familiarise with the different possibilities of detection QRS complex and we intimately focus on wavelet transform that will be used for a project of detection QRS complex. The practical part of thesis focuses on the project of detector in programming language Matlab and his different setting. This projected detector has been tested with CSE database. Achieved results of projected detector are evaluated with the results of others authors.

This diploma thesis deals with location optimal wavelet for detecton charakterics points of QRS complex in ECG signals. The first part of this thesis deals with description of heart, genesis of electric signals on heart and problem of noise. The second part describes the wavelet transform and the designed program and the third part evaluate detection results. The created program is working with 10 ECG signals from the CSE database and is testing 12 different mother wavelets. The program was developed in Matlab environment and is based on the finding zero-points in the transformed signal.

This diploma thesis focuses on determination of the QT interval in ECG. The thoretical part desribes physiology of the heart, electronic activity of the heart and possible methods of ECG measurement. The theoretical part also describes methods of signal processing, the processed signal is then used for determination of the QT interval. The practical part focuses on two chosen methods of QT interval determination and on implementation of the methods in a computer program. The results part is evaluation of measured QT intervals and a comparision of the results with reference values.

In this thesis, many machine learning algorithms were applied to electrocardiogram (ECG), spectral analysis, and Field Programmable Gate Arrays (FPGAs). In ECG, QRS complexes are useful for measuring the heart rate and for the segmentation of ECG signals. QRS complexes were detected using WaveletCNN Autoencoder filters and ConvLSTM detectors. The WaveletCNN Autoencoders filters the ECG signals using the wavelet filters, while the ConvLSTM detects the spatial temporal patterns of the QRS complexes. For the spectral analysis topic, the detection of chemical compounds using spectral analysis is useful for identifying unknown substances. However, spectral analysis algorithms require vast amounts of data. To solve this problem, B-spline neural networks were developed for the generation of infrared and ultraviolet/visible spectras. This allowed for the generation of large training datasets from a few experimental measurements. Graphical Processing Units (GPUs) are good for training and testing neural networks. However, using multiple GPUs together is hard because PCIe bus is not suited for scattering operations and reduce operations. FPGAs are more flexible as they can be arranged in a mesh or toroid or hypercube configuration on the PCB. These configurations provide higher data throughput and results in faster computations. A general neural network framework was written in VHDL for Xilinx FPGAs. It allows for any neural network to be trained or tested on FPGAs. / Graduate

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The Hilbert transform is a widely used transform in signal processing. In this thesis we explore its use for three different applications: electrocardiography, the Hilbert-Huang transform and modulation. For electrocardiography, we examine how and why the Hilbert transform can be used for QRS complex detection. Also, what are the advantages and limitations of this method? The Hilbert-Huang transform is a very popular method for spectral analysis for nonlinear and/or nonstationary processes. We examine its connection with the Hilbert transform and show limitations of the method. Lastly, the connection between the Hilbert transform and single-sideband modulation is investigated.

The topic of this master's thesis is the analysis of ECG signals using wavelet transform. In the introductory chapters there is a brief description of heart anatomy, the emergence and spread of potentials, which evocating activities of myocardium. There is an overview of techniques used for ECG signals analysis and explanation of ECG curve diagnostic importance. Work also containts an ECG signal analysis common procedure explanation and different approaches brief overview. The main part of this work is an application detecting significant intervals in the ECG signal, developed in Matlab. In several chapters the detection procedure is described in more details and gave reasons for chosen methods. In the last chapter there is a preview of several signals as a result of developed application, together with evaluation of the tests carried out at the CSE database. Detector sensitivity was quantified over 99,10%.

The aim of diploma thesis named “QRS detection using wavelet transform” is to simplify and accelerate the work of doctors. This can be achieved by using application for QRS detection, which can use one of four proposed algorithms. Application shows basic informations about inserted electrocardiogram. Part of this program is a graphical window with displayed record and with coloured marks on detected QRS complexes. By another algorythm are marks color-coded due to accurancy percentil of every detected complex. This program is designed for a several hours record from Holter ECG monitoring.

Cardiovascular diseases are the leading cause of death worldwide, and are estimated to kill over 17 million people each year, about 31% of all deaths. In the clinic, the first diagnostic procedure for a suspected cardiac abnormality is often acquisition of an electrocardiogram (ECG), which measures the electrical potential of the heart at the body surface. Understanding the mechanisms underlying generation of the ECG waveforms is crucial for optimal clinical benefit. Computer simulations possess several strengths as a tool to gain this understanding, particularly in terms of human-specificity, flexibility, repeatability, and ethics. The ventricles make up the majority of the cardiac volume and are therefore responsible for the majority of ECG waveforms. Ventricular disorders are the most life-threatening, because the ventricles are responsible for pumping blood to the body. Due to their size it has only recently become possible to perform biophysically detailed simulations of the ventricles and torso using supercomputers. In this thesis, multiscale, mathematical models of the ventricles and torso using the Chaste software library are simulated on high performance computing systems. A description is included of the performance enhancements made in Chaste to improve resource efficiency and accelerate job turnaround, particularly in data storage and the auxiliary tasks of post-processing and data conversion. A novel model of ventricular activation is presented and parametrized using multi-modal human data, and successfully used to simulate normal and pathological QRS complexes. Similarly, repolarization gradients are imposed based on the literature and result in a variety of T waves. Finally, the developed human whole-ventricular and torso models are utilized to gain new insights into possible ionic mechanisms underlying the clinical manifestations of the early repolarization syndrome. Overall, this thesis presents a novel framework for simulation of the human ECG using high performance computers, with possible applications in basic science and computational medicine.