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Changes in pulmonary function and cardiac activity during reflex rolling stimulation according to Vojta
Charvátová, Zuzana ; Nováková, Tereza (advisor) ; Martínek, Milan (referee)
Author: Zuzana Charvátová Title: Changes in pulmonary function and cardiac activity during reflex rolling stimulation according to Vojta Objectives: The aim of this study is to evaluate accompanying non-locomotive manifestations within the comprehensive response to stimulation of trigger zones from the concept of reflex locomotion according to Professor Vojta, which are a reflection of the function of the autonomic nervous system. Changes in respiratory parameters and changes in cardiac activity have been selected from these manifestations. Methods: The research was conducted on 26 healthy adult women aged 19-25 years. Reflex rolling according to Vojta from the supine position (RO - 1st phase) was chosen. Pressure stimulation was applied from the left chest trigger zone between the 6th and 7th rib for 10 minutes. Each subject also underwent apparent (sham) stimulation in the same position (RO - 1st phase), lasting 10 minutes, but on the right side of the chest between the 2nd and 3rd rib. Due to the possible lingering effect of pressure stimulation during VRL, sham stimulation was always preceded by experimental stimulation. A 10-minute break was taken between the two cycles. Data for the analysis of changes in lung function were obtained by spirometric measurements of FVC, FEV1, and PEF before...
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Laboratory exercise using Vernier systems
Kubová, Dominika ; Mézl, Martin (referee) ; Králík, Martin (advisor)
The aim of this thesis is to design a measurement procedure in the LabVIEW development environment for three laboratory exercises using an ECG sensor, a blood pressure sensor and a spirometer from Vernier. In the theoretical part, the physiological introduction, the problems of the given devices and measurement methods are described. The practical part focuses on the development and verification of the proposed measurement protocols, which are attached as appendices to the thesis. It also includes support files for the exercises, including scripts for basic data acquisition and calibration. These materials are intended to serve as teaching tools enabling effective training and understanding of measurement methods in medical diagnostics.
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Atrial fibrillation localization for burden assessment
Martinásková, Klára ; Ředina, Richard (referee) ; Filipenská, Marina (advisor)
The diploma thesis deals with the problem of detection of atrial fibrillation from ECG recordings and localization of given fibrillation segments in signals with paroxysmal fibrillation. A research is done on atrial fibrillation, the origin of this pathology and methods of fibrillation detection from ECG recordings using deep learning. Subsequently, a convolutional neural network model with residual blocks is implemented in Python to classify short (3 s) segments of the ECG signal. Subsequently, the classification results are processed and the segments with paroxysmal fibrillation are localized in the signals with fibrillation. With the classification and localization, the burden assessment of fibrillation is further evaluated. The implemented classifier on the test set achieves an F1 score of 96,15 %. When the sections with fibrillation are localized by the algorithm, MAE of 0,95 s for detecting the beginnings and 1,29 s for detecting the ends with respect to the reference positions is achieved. The estimated patient's burden assessment is compared with the actual values and achieves MAE of 3 %
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ECG arrhythmia detection
Pchálková, Aneta ; Filipenská, Marina (referee) ; Novotná, Petra (advisor)
This thesis describes the principles of ECG, the physiology of arrhythmias, their origin, and manifestations in the ECG, focusing on ventricular extrasystoles and bundle branch blocks. It examines contemporary methods for detecting these arrhythmias and acquiring the necessary features for their implementation. The work also covers data handling, including data preprocessing. Classification of ventricular extrasystoles and bundle branch blocks is implemented using k-nearest neighbors models.
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ECG signal quality annotation
Hluší, Veronika ; Smital, Lukáš (referee) ; Vítek, Martin (advisor)
This thesis deals with the topic of annotation of the quality of ECG recordings. Theo- retical part of the thesis contains a description of methods dealing with the ECG signal quality annotation. The practical part deals with designing and implementation of pur- posed method, which enables continuous estimation of the quality of ECG recordings.The implemented method is tested on publicly available records and the results are evaluated.
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ECG biometrics using deep learning
Repčík, Tomáš ; Mézl, Martin (referee) ; Vičar, Tomáš (advisor)
This diploma thesis makes a comprehensive overview of various approaches to the usage of ECG as biometry. ECG of people was measured with ECG capable wristband for training and testing purposes. The measurements were gathered during the four-month period. The neural networks of various types were trained on these data, and the feedforward convolutional neural networks have the best performance. These models reached a true acceptance rate 98,9% and a true rejection rate 99,5% on average. After training, the models have been visualised with a variety of techniques and essentials parts of ECG for verification have been described. The thesis also describes the first implementation of the Android application.
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Automatic diagnosis of the 12-lead ECG using deep learning
Blaude, Ondřej ; Smital, Lukáš (referee) ; Provazník, Valentine (advisor)
The aim of this diploma thesis is to investigate the problematics of automatic ECG diagnostics, namely on twelve-lead recordings. In the first chapter the heart and its electrical activity measurement is described shortly. In addition to that, the abnormalities which are going to be classified in this thesis are also briefly described. In the second chapter, it is described how the ECG was diagnosed earlier, by classical methods that preceded deep learning. Some of the shortcomings that the classical methods have compared to deep learning are also described here. The third part already pays attention to deep learning itself, and its contribution and advantages compared to classical methods. Convolutional neural networks and their individual blocks are also described here, later attention is paid to selected architectures that were used in some studies. The fourth chapter already focuses on the practical part, in which the data used from the PhysioNet database, the proposed algorithm and its implementation are described in more detail. In the fifth chapter the results are discussed and compared to the corresponding publications.
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Interpreting the learning process of an atrial fibrillation classifier
Lichtblauová, Anna ; Ředina, Richard (referee) ; Novotná, Petra (advisor)
In the theoretical part of the bachelor thesis the problems of atrial fibrillation (AF) detection and principles of convolutional neural networks (CNN) are discussed. Next, two classifiers were created in the practical part. The first was designed to classify sinus rhythm, atrial fibrillation and other pathologies, while the second further distinguished the category "atrial fibrillation" according to whether it was present in the whole recording or only in a part of it. The resulting accuracies are 82.12 \% and 85.14 \% for the first and second classifiers, respectively.
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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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