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.

This semester thesis is focused on automated measurement of ECG signal. The theoretical part describes the rise and options ECG signal. Furthermore, the issue is staged principal components analysis, whose output is used as input signal for seasons. They describe the basic methods used in measurement to ECG signal. The practical part is designed in measurement algorithm for ECG signal that has been tested on basic CSE database. The results are discussed in the conclusion.

EKG är en metod som registrerar hjärtats elektriska aktivitet, för att upptäcka eventuella sjukdomar eller störningar i hjärtats rytm. Tidigare studier har visat att det sker förändringar i EKG:t om bröstelektroderna inte placeras korrekt. Syftet med studien är att undersöka hur QRS-komplexen förändras vid felaktig placering av bröstelektroderna. Vi registrerade tre EKG från 30 frivilliga patienter: -Ett vilo-EKG där elektroderna placeras rätt. Vid det andra EKG flyttades bröstelektroderna upp ett intercostalrum och vid det tredje EKG flyttades elektroderna istället ner ett intercostalrum. Därefter undersöktes amplitudförändringar i QRS-komplex och T-våg och skillnader i datortolkning. Studien utfördes på Avdelningen för klinisk fysiologi på Skånes universitetssjukhus i Malmö.Vi fann att R-vågsamplituden i V1 blev lägre då elektroderna placerades ett intercostalrum upp (p=0,014) och istället blev högre då elektroderna placerades ett intercostalrum ner (inte statistiskt signifikant). R-vågsamplituderna i V5 och V6 ökade då elektroderna placerades ett intercostalrum upp, medan de minskade då elektroderna placerades ett intercostalrum ner. Det skedde skillnader i datortolkningen hos 37 % av patienterna.Slutsatsen är att det sker förändringar vid felaktig placering av bröstelektroderna både ett intercostalrum upp respektive ett intercostalrum ner. Man riskerar att överdiagnostisera septal infarkt om V1-V2 placeras för högt och underdiagnostisera vänsterkammarhypertrofi om V4-V6 placeras för långt ner. / Resting-ECG is a method of recording the heart's electrical activity, to detect cardiac diseases or disorders of heart rhythm. Previous studies have shown that changes occur in the ECG when the chest electrodes are not placed correctly. The aim of the study was to examine how the ECG changes upon improper placement of chest electrodes. We included 30 patients with three different ECGs: A resting ECG in which electrodes are placed in the right place. The second ECG was recorded with chest electrodes moved up an intercostal room. At the third ECG the electrodes were instead moved an intercostal room down. Both amplitude and differences in computer interpretation were examined from all three recordings.The study was performed at the Department of Clinical Physiology at Skåne University Hospital in Malmö.The R wave amplitude in V1 increased when the chest electrodes were placed too high (p=0.014), and decreased when the electrodes were placed too low (not statistically significant). The R wave amplitudes in V5 and V6 increased when the electrodes were placed too high and decreased when they were placed too low. There were differences in the computerized interpretations of the ECGs in 37% of the patients.We conclude that there are changes in the ECG when the chest electrodes are not correctly placed. There is a risk of over diagnosing septal myocardial if V1-V2 is placed too high and diagnosing left ventricular hypertrophy if V4-V6 are placed too far down.

In many developing countries health care conditions are poor and there is a lack of healthcare professionals and diagnostics tools. Cheap and easy-to-use diagnostics tools have been developed to make practicing medicine easier under these conditions. However, signal monitors can be many and spread out, making it hard for the limited number of medical workers to handle. The monitors are also stationary, making mobile supervision impossible. In this thesis a solution is suggested, made of a hardware setup consisting of an Arduino UNO and Bluetooth module paired with an application, capable of analog to digital conversion, wireless transfer and display of medical signals. Furthermore, two different QRS detection algorithms are tested, a larger and accurate model called Pan-Tompkins and a smaller and faster, moving average based filtering system. The transmission circuit as well as the signal displayed showed promise. However, the analog to digital conversion was noisy due to the power source. The tested algorithms showed that speed and low computational requirements are traded for precision.

Made available in DSpace on 2016-03-15T19:37:40Z (GMT). No. of bitstreams: 1 Luiz Carlos Ferreira Rodrigues.pdf: 3281430 bytes, checksum: ce62f748aa1e8330c7d6402e06d3d41f (MD5) Previous issue date: 2012-03-02 / The cardiopathies are currently, according the Ministério da Saúde, the second biggest cause of mortality among the Brazilians, behind only the brain vascular diseases. The motivation for the work here presented is the identification and classification of cardiopathies registered in Electrocardiogram exams, ECG, such as premature contractions, branches blocks, tachycardia and other rhythms disturbance. Due its easy application and low cost, the ECG is one of the resources more commonly used by researchers and health professionals in the assessment of cardiac conditions. The computational application developed in this study relies in the application of Wavelets Transforms for the digital signal processing of ECG, in extracting the morphologic characteristics, dynamics and spectral of the cycles of the signal and in the submission of these characteristics to two Support Vector Machines (SVM). The output of these two SVM's are combined as input to a Bayesian Network for the identification and classification of the cardiopathies. The characteristic of each cycle, morphologic and spectral, has it dimensionality reduced by Principal Component Analysis (PCA). The spectral characteristics are extracted by the extractions of the Wavelets Transforms coefficients of the signal, whilst the dynamics characteristics are defined by the interval between the global maxima of each cycle. For development, testings and validations of the application we utilize the MIT-BIH Arrhythmia database, made available by Massachusetts Institute of Technology (MIT). At the end of this work we demonstrate that the application is able to recognize and classify 8 types of heart beats in ECG records, with an medium accuracy above 95,0%. / As cardiopatias são atualmente, segundo o Ministério da Saúde, a segunda maior causa de mortalidade entre brasileiros, ficando atrás apenas das doenças cerebrovasculares. A motivação do trabalho aqui apresentado é a identificação e classificação de cardiopatias registradas em exames de Eletrocardiograma, o ECG, tais como contrações prematuras, bloqueio de ramos, taquicardias e outros distúrbios de ritmo. Devido a sua fácil aplicação e baixo custo, o ECG é um dos recursos mais largamente utilizados por pesquisadores e profissionais da saúde na avaliação da saúde do coração. A aplicação computacional desenvolvida neste estudo concentra-se no uso de Transformadas Wavelets para o processamento digital dos sinais de ECG, na extração das características morfológicas, dinâmicas e espectrais de ciclos do sinal e na submissão dessas características a duas Máquinas de Vetores de Suporte (SVM). Os resultados das SVM's são combinadas em uma Rede Bayesiana para a identificação e classificação das cardiopatias. As características morfológicas de cada ciclo do sinal são extraídas através de Análise de Componentes Principais (PCA), as características espectrais são extraídas através da decomposição do sinal em coeficientes de Transformadas Wavelets enquanto as características dinâmicas são definidas pelos intervalos entre o máximo global de cada ciclo. Para desenvolvimento, testes e validação da aplicação foi utilizado o Banco de Arritmias MIT-BIH, disponibilizado pelo Massachusetts Institute of Technology (MIT). Neste trabalho demonstramos que a aplicação desenvolvida é capaz de reconhecer e classificar 8 tipos de batimentos cardíacos em registros de ECG, com uma acurácia média total de classificação superior a 95,0%.

This thesis deals with the issue of automatic measurement of the duration of QRS complexes in ECG signals. Special emphasis is then placed on the possibility of automatic detection of QRS complexes while exciting cardiac tissue with a pacemaker. The content of this work is divided into four logical units, where the first part deals with the heart as an organ. It describes the origin and spread of excitement in the heart, its possible pathologies and their manifestations in ECG recording, it also deals with pacing and measuring ECG recording during simultaneous pacing. The second part of the thesis contains a brief introduction to the topic of machine and deep learning. The third part of the thesis contains a search of current approaches using methods based on deep learning to solve the detection of QRSd. The fourth part deals with the design and implementation of its own model of deep learning, able to detect the beginnings and ends of QRS complexes from ECG recordings. It describes the data preprocessing implemented in the MATLAB programming environment. The actual implementation of the model was performed in the Python using the PyTorch and NumPy moduls.

This thesis deals with the detection of QRS complexes from electrocardiograms using time-frequency analysis. Detection procedures are based on wavelet and Stockwell transform. The theoretical part describes the basics of electrocardiography, then introduces common approaches to time-frequency analysis, such as short-time Fourier transform (STFT), wavelet transform and Stockwell transform. These algorithms were tested on a set of electrograms from the MIT-BIH and CSE-MO1 arrhythmia database. For the CSE database worked best the method based on the wavelet transform with the filter bank Symlet4, with the resulting value of sensitivity 100 % and positive predictivity 99.86%. For the MIT database had the best performance the detector using the Stockwell transform with values of sensitivity 99.54% and positive predictivity 99.68%. The results were compared with the values of other authors mentioned in the text.

This thesis deals with the issue of automatic measurement of the duration of QRS complexes in ECG signals. Special emphasis is then placed on the possibility of automatic detection of QRS complexes while exciting cardiac tissue with a pacemaker. The content of this work is divided into four logical units, where the first part deals with the heart as an organ. It describes the origin and spread of excitement in the heart, its possible pathologies and their manifestations in ECG recording, it also deals with pacing and measuring ECG recording during simultaneous pacing. The second part of the thesis contains a brief introduction to the topic of machine and deep learning. The third part of the thesis contains a search of current approaches using methods based on deep learning to solve the detection of QRSd. The fourth part deals with the design and implementation of its own model of deep learning, able to detect the beginnings and ends of QRS complexes from ECG recordings. It describes the data preprocessing implemented in the MATLAB programming environment. The actual implementation of the model was performed in the Python using the PyTorch and NumPy moduls.

This thesis discusses the design and the utilization of the artificial neural networks as ECG classifiers and the detectors of heart diseases in ECG signal especially myocardial ischaemia. The changes of ST-T complexes are the important indicator of ischaemia in ECG signal. Different types of ischaemia are expressed particularly by depression or elevation of ST segments and changes of T wave. The first part of this thesis is orientated towards the theoretical knowledges and describes changes in the ECG signal rising close to different types of ischaemia. The second part deals with to the ECG signal pre-processing for the classification by neural network, filtration, QRS detection, ST-T detection, principal component analysis. In the last part there is described design of detector of myocardial ischaemia based on artificial neural networks with utilisation of two types of neural networks back – propagation and self-organizing map and the results of used algorithms. The appendix contains detailed description of each neural networks, description of the programme for classification of ECG signals by ANN and description of functions of programme. The programme was developed in Matlab R2007b.

This work deals with ECG signal measuring and methods of its processing. It compares some of the QRS detection methods and describes some of the testing databases. In this work a detector of QRS complex is realized, it is based on the approach of zero crossings. Next section makes combination of results from separate leads to one, which improves efficiency of detection. One section of this work deals with design and realization delination of ECG signal. In the last part outputs of this delineation are compared with the results of the other authors.