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Automatic detection of ischemia in ECG
Noremberczyk, Adam ; Potočňák, Tomáš (referee) ; Ronzhina, Marina (advisor)
This thesis discusses the utilization of the artificial neural networks (ANN) for detection of coronary artery disease (CAD) in frequency area. The first part of this thesis is orientated towards the theoretical knowledge. Describes the issue of ECG pathological changes. ECQ are converted to frequency area. Described statistical methods and methods for automatic detection of CAD and MI. Explained the issue of the perceptron and ANN. The second deals with use of Neural Network Toolbox MATLAB®. This part focuses on counting and finding suitable parameters and making connection of band. At the end of the thesis UNS is used to detect ischemic parameters and the results are discussed. Average values for the best settings are 100% accuracy.
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Detection of poorly differentiated cardiac arrhythmias
Kantor, Marek ; Ronzhina, Marina (referee) ; Novotná, Petra (advisor)
This thesis focusses on the detection methods of atrial fibrilation, atrial flutter and sinus rhythm from ECG. Thesis also concentrate on the description of this arrhythmias and the learning algorithms used. In this thesis are implemented several classification approaches. For extraction of features is used convolution neural network and classification artifitial neural network. Selected 1D CNN method achived classification accuracy global F1 - score is 91 %. Moreover, the proposed CNN optimized with GA appears to be fast shallow network with better accuracy than the deep network. Created model are used for classification other type of arrhythmias too.
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QRS detection based on Stockwell transform
Kašík, Ondřej ; Kozumplík, Jiří (referee) ; Smital, Lukáš (advisor)
This bachelor´s thesis deals with the detection of QRS complexes in ECG record. The thesis provides a brief information related to the heart anatomy, generation of electrical signals in the heart, recording and description of the ECG record. In more detail, there is a description of the detection of QRS complexes by various methods and realization of a detector based on Stockwell transform, Shannon energy and adaptive thresholding. The evaluation process of the detection efficiency is also included. Sensitivity and positive prediction of the proposed detector on the complete MIT-BIH Arrhythmia database reached 99.80 % and 99.88 % respectively.
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Compression and Quality Assessment of ECG Signals
Němcová, Andrea ; Tkacz,, Professor Ewaryst (referee) ; Kudrna,, Petr (referee) ; Vítek, Martin (advisor)
Ztrátová komprese signálů EKG je užitečná a v současnosti stále se rozvíjející oblast. Stále se vyvíjí nové a nové kompresní algoritmy. V této oblasti ale chybí standardy pro hodnocení kvality signálu po kompresi. Existuje tedy sice mnoho různých kompresních algoritmů, které ale buď nelze objektivně porovnat vůbec, nebo jen zhruba. V oblasti komprese navíc nikde není popsáno, zda mají na výkon kompresních algoritmů vliv patologie, popřípadě jaký. Tato dizertační práce poskytuje přehled všech nalezených metod pro hodnocení kvality signálů EKG po kompresi. Navíc bylo vytvořeno 10 nových metod. V rámci práce byla provedena analýza všech těchto metod a na základě jejích výsledků bylo doporučeno 12 metod vhodných pro hodnocení kvality signálu EKG po kompresi. Také je zde představen nový kompresní algoritmus „Single-Cycle Fractal-Based (SCyF)“. Algoritmus SCyF je inspirován metodou založenou na fraktálech a využívá jednoho cyklu signálu EKG jako domény. Algoritmus SCyF byl testován na čtyřech různých databázích, přičemž kvalita signálů po kompresi byla vyhodnocena 12 doporučenými metodami. Výsledky byly porovnány s velmi populárním kompresním algoritmem založeným na vlnkové transformaci, který využívá metodu „Set Partitioning in Hierarchical Trees (SPIHT)“. Postup testování zároveň slouží jako příklad, jak by měl vypadat standard hodnocení výkonu kompresních algoritmů. Dále bylo statisticky prokázáno, že existuje rozdíl mezi kompresí fyziologických a patologických signálů. Patologické signály byly komprimovány s nižší efektivitou a kvalitou než signály fyziologické.
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Analysis of heart rate variability in animal models
Řehořková, Iveta ; Ronzhina, Marina (referee) ; Kolářová, Jana (advisor)
This paper deals with the analysis of heart rate variability (HRV) in animal models. The first part discusses, the basic information concerning the ECG, both in humans and in individual laboratory animals. This is followed by an introduction to the topic of heart rate variability, a description of methods for its determination and the effects of pathologies on HRV values. Prior to the practical section, the methods of ECG acquisition in animal models are discussed, and the function of the perfusion model used in capturing data for this work is also described in detail. The last part deals with the analysis of HRV provided data, performed in Matlab environment and an evaluation of the chosen methods.
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Application of neural networks for classification of T-wave alternations
Procházka, Tomáš ; Harabiš, Vratislav (referee) ; Hrubeš, Jan (advisor)
This thesis deals with analysis of T-wave Alternans (TWA), periodical changes of T wave in ECG signal. Presence of these alternans may predict higher risk of sudden cardiac death. From the several possible ways of TWA classification, the training algorithms of self organizing maps are used in this thesis. Result of the thesis is a program, which in the first step detects QRS complexes in the signal. Then, in the next step, gained reference points are used for T-waves detection. Detected waves are represented by a vector of significant points, which is used as an input for artificial neural network. Final output of the program is a decision about presence of TWA in the signal and its rate.
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Detection of atrial fibrillation in short-term ECG
Ambrožová, Monika ; Janoušek, Oto (referee) ; Ronzhina, Marina (advisor)
Atrial fibrillation is diagnosed in 1-2% of the population, in next decades, it expects a significant increase in the number of patients with this arrhythmia in connection with the aging of the population and the higher incidence of some diseases that are considered as risk factors of atrial fibrillation. The aim of this work is to describe the problem of atrial fibrillation and the methods that allow its detection in the ECG record. In the first part of work there is a theory dealing with cardiac physiology and atrial fibrillation. There is also basic descreption of the detection of atrial fibrillation. In the practical part of work, there is described software for detection of atrial fibrillation, which is provided by BTL company. Furthermore, an atrial fibrillation detector is designed. Several parameters were selected to detect the variation of RR intervals. These are the parameters of the standard deviation, coefficient of skewness and kurtosis, coefficient of variation, root mean square of the successive differences, normalized absolute deviation, normalized absolute difference, median absolute deviation and entropy. Three different classification models were used: support vector machine (SVM), k-nearest neighbor (KNN) and discriminant analysis classification. The SVM classification model achieves the best results. Results of success indicators (sensitivity: 67.1%; specificity: 97.0%; F-measure: 66.8%; accuracy: 92.9%).
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Automatic delineation of ECG signals
Jurek, Jakub ; Janoušek, Oto (referee) ; Vítek, Martin (advisor)
This project deals with basic description of ECG signal and some of known methods of delineation ECG´s individual parts. Next this work deals with detailed description of the method of authors Laguna, Jané, Caminal and realization of the complete delineation algorithm based on this method. Next this work deals with testing this algorithm on the CSE database, evaluation results and comparing results with results of authors of the method.
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ECG signal classification
Smělý, Tomáš ; Harabiš, Vratislav (referee) ; Hrubeš, Jan (advisor)
This thesis deals with classification of different types of time courses of ECG signals. Main objective was to recognize the normal cycles and several forms of arrhythmia and to classify the exact types of them. Classification has been done with usage of algorithms of Neural Networks in Matlab program, with its add-on (Neural Network Toolbox). The result of this thesis is application, which makes possible to load an ECG signal, pre-process it and classify its each cycle into five classes. Percentage results of this classification are in the conclusion of this thesis.
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Measurement of ECG signal for TWA analysis
Řezáč, Petr ; Vítek, Martin (referee) ; Kozumplík, Jiří (advisor)
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.
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