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Premature atrial contraction detection in ECG
Mistrová, Jana ; Ronzhina, Marina (referee) ; Novotná, Petra (advisor)
This thesis discuses detection of premature atrial contraction from ECG. In the first part, thesis describes electrocardiogram, cardiac conduction system and extrasystoles. Extrasystoles are premature contraction caused by ectopic heartbeats. Next part is devoted to signal preprocessing, the feature description methods, reduction of feature vector and methods of classification. Realized method and results of classifier are discused in the last part.
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Classification of heart beats using artificial neuronal networks
Doležalová, Radka ; Vítek, Martin (referee) ; Ronzhina, Marina (advisor)
This work deals with using of artificial neural networks (ANN) for ECG classification. The issue of ECG and ANN technique are described theoretically at first, the next section describes use of Matlab to design ANN and graphical user interface. ECG data (namely QRST segments from the orthogonal X- lead from seven phases of the experiment) obtained from experiments in isolated hearts of rabbits are used for learning and testing of the classifier. The result of this work is the software with GUI that allows user to set various parameters and structure of ANN. After learning phase, ANN realized in this work able to classify cardiac cycles according to their morphology into seven groups.
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Heart rate variability in decreasing of alertness level
Strublová, Tereza ; Provazník, Ivo (referee) ; Janoušek, Oto (advisor)
Objective of this study is to familiarize with problems of measuring heart rate variability. At first there is described physiology of vascular system, then practical measuring of heart rate variability during falling asleep. At the end of this project is the statistical processing of results and comparison of the differences between heart rate variability during sleep and wakefulness.
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Detection of premature ventricular contractions in ECG
Kantor, Marek ; Ronzhina, Marina (referee) ; Novotná, Petra (advisor)
This thesis focusses on the detection methods of extrasystoles from ECG and description of electrocardiogram, cardiac conduction system, extrasystoles and ventricular tachycardia. Extrasystoles are premature ventricular contraction caused by ectopic heartbeats. Classification is based on signal preprocessing, detection of R peak, the heartbeat segmentation, the feature description methods, normalization of features and the learning algorithms used. Selected and realized methods achieved classification accuracy ACC = 98 %, sensitivity SE = 100 % and specificity SP = 96,1 %. Gained features are also used for detection bundle branch block.
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Analysis of Atrial Fibrillation Heart Rate Dynamics
Tesařová, Tereza ; Ronzhina, Marina (referee) ; Hejč, Jakub (advisor)
Bachalor thesis focuses on clinical tracking of heart rate abnormalities to specify the level of variability in diverse-lengthy ECG signals, with a direct way to analyze atrial fibrillation and accuracy increasing its detection of actual available methods. The principle of heart electrophysiology and also the impulse genesis is mentioned in a introductory part of work, including elementary classification of cardiac arrhytmias and their deflection from normal sinus rhythm. The physiological analysis of atrial fibrilation dynamic structure helps with the application of detection algorithms in a MATLAB program and sets an underlying basis for an appropriate discussion in a final statistical ROC analysis.
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Analyzer of cardiac waveform
Zmeškal, Ladislav ; Říha, Kamil (referee) ; Číž, Radim (advisor)
The thesis describes design, algorithmization and realization of graphical application for recording EKG and PPG signal using LabJack UE9 tool in Matlab program, it also describes subsequent deposition of recorded signals and their processing, such as optional selection, cropping and filtering. Furthermore there are described types of filters, methods for detecting basic parameters of EKG and PPG signals and methods for detecting R waves and Systolic peaks. Based on detection of those parameters, algorithms for computing average heart rate and finding arrhythmias were designed and tested. Last part of the thesis includes an evaulation which compares values detected by designed algorithms with values from public database which includes reference annotation.
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Delineation of ECG signals using methods combining
Zahradník, Radek ; Smital, Lukáš (referee) ; Vítek, Martin (advisor)
The aim of this work is to study and describe the principles and method of delineation of ECG signals. Learn and describe about method of cluster analysis. In this work was created and described three different methods of delineations of ECG signals. Created algorithms were tested on complete CSE database. With cluster analysis were combine created methods. The obtained results from realized methods and combined method were compared with others known methods. At the end of this work is evaluate efficiency of detection of combined method.
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Adaptive Filtering of Biological Signals
Šmíd, Karel ; Kozumplík, Jiří (referee) ; Provazník, Ivo (advisor)
Objective of this diploma work was to study methods of adaptive filtering and their use in suppression of noise in biological signals. Adaptive filtering represents effective means of suppression of parasitic nonstationary disturbances in a useful signal. The task was to design various types of adaptive filters and implement an adaptation algorithm in Matlab programming environment. It namely included suppression of powerline noise at 50 Hz and 100 Hz in ECG signals with minimization useful components disturbing. The realized filters were verified on real ECH signals and their efficiency was evaluated.
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Filtering of the ECG Signals
Slezák, Roman ; Kolář, Radim (referee) ; Kozumplík, Jiří (advisor)
Main objective of this thesis was to learn about possibility of suppression of narrow band disturbances. We focused on use of Lynn’s filters. The objective was to realize these filters with respect to a fast algorithm of filtering. Concretely, for suppression of drift we realized the fast high pass filter with flexible cut off frequency. Then we realized filter for suppression of electrical network disturbance. We have realized these filters for sampling frequencies 250 and 500 Hz and we tested them with real ECG signals. Then we have evaluated their efficiency.
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ECG classification using methods of HRV analysis
Caha, Martin ; Vítek, Martin (referee) ; Ronzhina, Marina (advisor)
This paper deals with the classification of ECG measured from isolated rabbit hearts during the experiment with repeated ischemia. Classification features were calculated using the methods of heart rate variability analysis. The results were statistically evaluated. Heart rate variability parameters were calculated using Kubios HRV, other calculations were performed in MATLAB. Artificial neural network was created to classify the analyzed parameters to specific groups.
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