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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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Detection of atrial fibrillation in ECG
Húsková, Michaela ; Vítek, Martin (referee) ; Maršánová, Lucie (advisor)
Aim of this thesis is description of problems of atrial fibrillation and methods that could be used for detection in the electrocardiogram. The introductory part of the theoretical analysis deals with the principle of electrophysiology of the heart and mainly the pathophysiology of atrial fibrillation. Additionally the work is focused on describing methods on automatic atrial fibrillation detection and capabilities of PhysioNet database. In the practical part methods are implemented in the MATLAB environment. After using the statistics to evaluate the quality of the parameters, the automatic classification of the data was performed by the method of The Nearest Neighbour. Finally, the accuracy of testing is presented.
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Detection of atrial fibrillation in long-term ECG records
Imramovská, Klára ; Kozumplík, Jiří (referee) ; Maršánová, Lucie (advisor)
The thesis deals with problems of automatic detection of atrial fibrillation in long-term ECG records. The preface of the theoretical part describes the electrophysiology of the heart and the principle of atrial fibrillation. Furthermore, it introduces methods of automatic detection of atrial fibrillation. In the practical part a method which uses the symbolic dynamics and a calculation of Shannon entropy is implemented in the MATLAB software environment. The method is tested on signals from the MIT-BIH Atrial Fibrillation Database and the Long-Term AF Database. Lastly, the accuracy of the classification is compared with methods described in different papers.
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Detection of paroxysmal atrial fibrillation and atrial flutter
Krmela, Jan ; Němcová, Andrea (referee) ; Smíšek, Radovan (advisor)
This bachelor thesis deals with the problem of atrial fibrillation and flutter, the pathophysiology of these arrhythmias and their automatic detection. It includes a theoretical introduction necessary to understand the basal anatomy of the heart, its function, the origin and description of the electrocardiogram and a chapter on cardiac arrhythmias. It also includes a review of automatic detection of atrial fibrillation. The databases used in the practical part of the thesis are also described. The implementation of heart rhythm classification and automatic detection of the beginning and end of paroxysmal episodes is performed in MATLAB environment, the proposed algorithm is tested on the described databases and the results are evaluated.
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Advanced classification of cardiac arrhythmias in ECG
Sláma, Štěpán ; Hejč, Jakub (referee) ; Ronzhina, Marina (advisor)
This work focuses on a theoretical explanation of heart rhythm disorders and the possibility of their automatic detection using deep learning networks. For the purposes of this work, a total of 6884 10-second ECG recordings with measured eight leads were used. Those recordings were divided into 5 groups according to heart rhythm into a group of records with atrial fibrillation, sinus rhythms, supraventricular rhythms, ventricular rhythms, and the last group consisted of the others records. Individual groups were unbalanced represented and more than 85 % of the total number of data are sinus rhythm group records. The used classification methods served effectively as a record detector of the largest group and the most effective of all was a procedure consisting of a 2D convolutional neural network into which data entered in the form of scalalograms (classification procedure number 3). It achieved results of precision of 91%, recall of 96% and F1-score values of 0.93. On the contrary, when classifying all groups at the same time, there were no such quality results for all groups. The most efficient procedure seems to be a variant composed of PCA on eight input signals with the gain of one output signal, which becomes the input of a 1D convolutional neural network (classification procedure number 5). This procedure achieved the following F1-score values: 1) group of records with atrial fibrillation 0.54, 2) group of sinus rhythms 0.91, 3) group of supraventricular rhythms 0.65, 4) group of ventricular rhythms 0.68, 5) others records 0.65.
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Dynamic time warping in biosignal processing
Novobilský, Petr ; Provazník, Ivo (referee) ; Kolářová, Jana (advisor)
The thesis deals with one of the non-linear methods for signal processing - dynamic time warping (DTW). The method observes shape changes, which should be used in biomedical signals processing. The thesis involves the method description and consecution for finding DTW optimal way. The method is applied on the number series in the edutainment program, on the group of simulated signals and real electrocardiograms (ECG). ECG recordings were gained by performing experiments on the Masaryk University and their aim was clarifying the influence of voltage-sensitive dye on the heart tissue. One-lead ECG was described in time domain, frequency domain, time-frequency domain and subsequently remitted to DTW algorithm. The method outcomes evaluates the diversity rate of ECG signals obtained in each experiment stages. During the data evaluation were followed up the changes in process of the tension-sensible paint application and the stage of scouring toward control. The difference of elaborating signals group was verified in the time domain (37,5 %), in the frequency domain (75 %) and in the time-frequency domain (25 %). However, due to the small data group was not possible to explicitly approve the activity of voltage-sensitive dye on the heart tissue and to determinate limiting value of minimum algorithm way DTW for each heart round electrocardiogram classification. In the more data group analysis it is supposed to manifest the trend of growth heart round ECG differences in the stage of staining toward the stage of scouring.
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Heart beat representation for classification
Smíšek, Radovan ; Janoušek, Oto (referee) ; Ronzhina, Marina (advisor)
Selection of ECG segment plays a significant role in design of a heart beat classifier. The type of selected segments influences the classification not only in regard to the type and maximum number of recognized pathological groups but also in regard to the complexity of classification model, which consequently creates indirect demands on the memory of the computer technology used as well as on the time needed for the classification. The thesis is focused on the comparison of success rates of the ECG heart beat classifications in different input segments. The input segments used were QRST, RST, ST-T, QRS, and T. The ECG signal was obtained from isolated rabbit hearts and divided into individual types according to the T-wave amplitude and changes in the ST segment. The signal subsequently enters the artificial neural network where it is classified into predefined types. The network used had twenty-four neurons in the first layer and one neuron in the second layer. Efficiency of the classification is in the conclusion of this thesis.
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Detection of atrial fibrillation using ECG Signals
Běhunčíková, Vendula ; Ronzhina, Marina (referee) ; Kozumplík, Jiří (advisor)
Atrial fibrillation is one of the most common cardiac rhythm disorders. The prevalence of atrial fibrillation is reported at 1-6 % of the adult population. The chances of developing atrial fibrillation increase with age. An early detection of this arrhythmia is a key to prevent more serious conditions. Many ways have been found to detect atrial fibrillation episodes in ECG including deep learning methods. The aim of this bachelor’s thesis is to describe the problem of atrial fibrillation and the methods used for detection in the ECG record, design an atrial fibrillation detector and test its results. Detector is implemented using a Matlab R2020b software.
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Delineation of ECG signals using leads transformation
Ondroušek, Lukáš ; Ronzhina, Marina (referee) ; Vítek, Martin (advisor)
The goal of this work is to study the principles of delineation of ECG signals, wavelet transformation and transformation approaches to increase the number of available leads. Consequently, the knowledge was used to create delineation algorithm in Matlab. The algorithm was tested on complete CSE database. The obtained results were compared with the criteria which are set for the CSE database. In this work were realized six transformation approaches to increase the number of available leads. All of them were analyzed by delineation algorithm. In the work was examined, whether the transformation increase the efficiency of detection.
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