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Darbe pirmiausia aprašoma EKG prasmė ir kompiuterinės analizės raida. Apžvelgiami pagrindiniai EKG kompiuterinės analizės uždaviniai – EKG įvedimas, saugojimas, parametrų nustatymas, klasifikacija ir interpretacija. Ištirta prieinama EKG analizės programinė įranga, kuri yra arba atviro kodo, arba komercinė (Demo versijos). Nustatyta, kad EKG analizės programinė įranga pagal paskirtį yra trijų tipų: EKG modeliavimo, mokymo ir analizės. Literatūros analizė parodė, kad atviros EKG kompiuterinės analizės programos yra daugiausia skirtos EKG mokymui. Išnagrinėtos EKG modeliavimo, mokymo ir analizės programinės įrangos aprašai pateikti lentelėse. Atlikti eksperimentai su realiais iš duomenų bazės atsisiųstais EKG įrašais. Eksperimentuose naudotos laisvai prieinamos Octave sistema sukurtos programos. Darbe sukurta vaizdinė vartotojo sąsaja, apjungianti šias programas, kuria demonstruojamas šių programų veikimas: EKG nuskaitymas, filtravimas ir parametrų nustatymas. / In this master work firstly is described the aim and development of ECG computer’s analysis. Also the main tasks of ECG computer’s analysis are analyzed including ECG input, saving, parameters’ detection, classification and interpretation. Besides available ECG analysis program is investigated which is an open source code or commercial (Demo version). ECG analysis software can be divided to the three types: ECG modeling, ECG learning and ECG analysis. The analysis given in the references showed that an open ECG software is mainly intended for ECG learning. All these investigated programs are described in the provided tables. The real ECG records from the data bases were taken and the experiments have been performed using the programs which were created by the Octave system. In this work the graphic interface was created which combined all these above mentioned programs. This graphic interface demonstrated the whole process of the program comprising getting ECG data, filtering and parameters detection.

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This thesis focuses on the development of a GNU Health Module for electrocardiogram monitoring and the development of an application providing a fundamental electrocardiogram analysis. The theoretical part contains a brief introduction to hospital information systems including electronic patient record and healthcare data standards information, followed by a description of the GNU Health application and the implementation of the electrocardiogram analysis, written in the Python programming language. The practical part deals with the development of the GNU Health Monitoring module and the external application for signal analysis. The results, disscussion and the conclusion follow.

Atrial fibrillation (AF) is our society's most common cardiac arrhythmic disease, leading to increased morbidity and mortality. Predicting AF episodes during sinus rhythm based on electrocardiograms (ECGs) allows timely interventions. It is known, that changes in selected ECG morphology features are a predictor for the onset of AF, but no systematic investigation of different ECG features' temporal changes has been performed so far. We split sinus rhythm episodes of 60 minutes preceding AF from the MIT-BIH AF database into segments of 5 minutes with 50% overlap (n=644) and calculated 155 features of different domains per segment. Logistic regression analyses between the segments preceding AF and others revealed the most significant effects for segments ending 5 minutes before AF onset, with PQ interval slope (p < 0.01), PQ interval correlation (p < 0.05), and median RR time (p < 0.05) being the most relevant features. A decision tree ensemble, trained with all features, achieved an accuracy of 0.87 when distinguishing 8 segment clusters. Our results confirm expected changes in ECG features (e.g., PQ interval) before AF episodes, indicating impaired atrial excitation, and show that the combination of interpretable features is sufficient to discriminate at different points in time before AF onset. For advanced analyses, more extensive databases should be included.

This master´s thesis deals with the analysis of experimental telemetrical ECG records with intention to determine the long-term influence of anticancer drug sunitinib on the electrical activity of heart. A laboratory rat was chosen as a model organism for the experimental study carried out at the Department of Physiology at Faculty of Medicine, Masaryk university. The sunitinib was applied to the rats at an early age and the ECG was measured with a 20-week delay using the Stellar telemetry system. To measure the effect of sunitinib on the electrical activity of the heart chambers, an analysis of the duration of the RR and QT interval and the width of the QRS complex was chosen. These parameters were detected by the wavelet transform method. Statistical analysis was performed using nonparametric tests - the Wilcoxon signed rank test, the MannWhitney test and the Friedman Test. The obtained results suggest that the use of sunitinib has no long-term effect on the observed parameters for the chosen animal model. After extension of the study, the results obtained could contribute to assess the effect of drugs on electrical activity of the human heart several decades after sunitinib treatment termination.

Sympathovagal imbalance is known to precede the on-set of atrial fibrillation (AF) and has been analyzed extensively based on heart rate variability (HRV). However, the relationship between sympathetic and vagal effects before AF onset and their influence on various HRV features have not been fully elucidated. QT interval variability (QTV) reflects sympathetic activity and may therefore provide further insights into this relationship. Using the time delay stability (TDS) method, we investigated temporal changes in coupling behavior before AF onset between 20 vagal or sympathovagal-associated HRV and QTV features. We applied the TDS method to 26 electrocardiograms from the MIT-BIH AF database with at least one hour of sinus rhythm preceding AF onset. Sinus rhythm segments were split into 5-minute windows with 50 % overlap. Logistic regression analysis revealed significantly (p<0.01) increased coupling between QTV and vagal HRV features from 20 to 15 minutes before AF onset. We found similar behavior between QTV and sympathovagal HRV features. This indicates sympathetic predominance increasing until 15 minutes before the onset of AF and decreasing towards vagal predominance right before AF onset. Our results provide new insights into temporal changes of sympathovagal imbalance preceding AF onset and may improve the prediction of AF in clinical applications.

The electrocardiograph (ECG) is a common clinical and biomedical research tool used for both diagnostic and prognostic purposes. In recent years computer aided analysis of the ECG has enabled cardiographic patterns to be found which were hitherto not apparent. Many of these analyses rely upon the segmentation of the ECG into separate time delimited waveforms. The instants delimiting these segments are called the

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%.

This thesis deals with evaluation EKG in terms of classification rhythm and analysis HRV. In theoretic part of work are described basics of heart physiology and its usual pathology, basics of electrocardiography, evaluation EKG and standard methods of HRV evaluation. In practical part are described algorithms used in created application. Mainly describes technique of rhythm evaluation, ectopic rhythms and delineation error elimination, data preparing for HRV evaluation, drift removal from DES and HRV evaluation methods. Created program was tested on CSE and MIT- BIH database records. For lack of suitable data and absence of tested data, it wasn’t possible to test all the classification rules of used algorithms. Tested part of program appears reliable and functional.