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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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Health assessment using smart devices
Vargová, Enikö ; Filipenská, Marina (referee) ; Němcová, Andrea (advisor)
This thesis deals with the possibilities of non-invasive determination of blood glucose from photoplethysmographic signals. Elevated blood sugar is often associated with disease called diabetes mellitus. Diabetes is one of the world’s major chronic diseases. Untreated diabetes is often a cause of death. The aim of the work is to propose methods for glycemic classification and prediction. Two datasets have been created by recording the PPG signals using two smart devices (a smart wristband and a smartphone), along with their blood glucose levels measured in an invasive way. The PPG signals were preprocessed, and suitable features were extracted from them. Various machine-learning models for glycemic classification and prediction were created.
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Machine Learning from Intrusion Detection Systems
Dostál, Michal ; Očenášek, Pavel (referee) ; Hranický, Radek (advisor)
The current state of intrusion detection tools is insufficient because they often operate based on static rules and fail to leverage the potential of artificial intelligence. The aim of this work is to enhance the open-source tool Snort with the capability to detect malicious network traffic using machine learning. To achieve a robust classifier, useful features of network traffic were choosed, extracted from the output data of the Snort application. Subsequently, these traffic features were enriched and labeled with corresponding events. Experiments demonstrate excellent results not only in classification accuracy on test data but also in processing speed. The proposed approach and the conducted experiments indicate that this new method could exhibit promising performance even when dealing with real-world data.
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Metasearch for Reviews on the Czech Web
Šmahel, Michal ; Doležal, Jan (referee) ; Smrž, Pavel (advisor)
The main purpose of this work is to create a metasearch engine for review articles with built-in sentiment analysis. In addition, a complex survey of main text extraction tools and web browser automation tools for web crawling has been carried out to achieve of the best possible results. The resulting metasearch engine provides a web interface for searching relevant review articles, thus saving time spent on manual searching. Thanks to multi-level transformer-based filtering, it can return 10—15 relevant review articles on frequently reviewed topics in about 4 minutes with no effort, just by clicking on a button.
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Classification of global environmental systems according to the level of anthropogenic transformation
Hrdina, Aleš
The topic of the doctoral thesis is the development of a comprehensive classification of global environmental systems based on a geographical synthesis of abiotic, biotic and anthropogenic factors. The dramatic changes in the Earth's natural environment, the noticeable loss of biodiversity and the increasing impact of human activity in many different aspects raise the need for a comprehensive classification that provides an appropriate spatial framework for assessing the impacts of these changes. Several global classifications have been developed in the past, but most of them only work with various natural environmental gradients (especially climate or relief). However, most regions of the world have been so fundamentally affected or even completely transformed by human activity that the omission of anthropogenic factors in comprehensive environmental classifications may lead to erroneous conclusions. For this reason, new global environmental classifications have recently begun to emerge abroad that attempt to deal with anthropogenic changes to the natural environment and include them in a comprehensive assessment. The proposal of a methodology and the actual creation of the classification of global environmental systems based on abiotic gradients, biodiversity distribution and spatial...
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Classification of global environmental systems according to the level of anthropogenic transformation
Hrdina, Aleš ; Romportl, Dušan (advisor) ; Boltižiar, Martin (referee) ; Václavík, Tomáš (referee)
The topic of the doctoral thesis is the development of a comprehensive classification of global environmental systems based on a geographical synthesis of abiotic, biotic and anthropogenic factors. The dramatic changes in the Earth's natural environment, the noticeable loss of biodiversity and the increasing impact of human activity in many different aspects raise the need for a comprehensive classification that provides an appropriate spatial framework for assessing the impacts of these changes. Several global classifications have been developed in the past, but most of them only work with various natural environmental gradients (especially climate or relief). However, most regions of the world have been so fundamentally affected or even completely transformed by human activity that the omission of anthropogenic factors in comprehensive environmental classifications may lead to erroneous conclusions. For this reason, new global environmental classifications have recently begun to emerge abroad that attempt to deal with anthropogenic changes to the natural environment and include them in a comprehensive assessment. The proposal of a methodology and the actual creation of the classification of global environmental systems based on abiotic gradients, biodiversity distribution and spatial...
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Emotion Recognition from Acted and Spontaneous Speech
Atassi, Hicham ; Přibil, Jiří (referee) ; Zahradník, Pavel (referee) ; Smékal, Zdeněk (advisor)
Dizertační práce se zabývá rozpoznáním emočního stavu mluvčích z řečového signálu. Práce je rozdělena do dvou hlavních častí, první část popisuju navržené metody pro rozpoznání emočního stavu z hraných databází. V rámci této části jsou představeny výsledky rozpoznání použitím dvou různých databází s různými jazyky. Hlavními přínosy této části je detailní analýza rozsáhlé škály různých příznaků získaných z řečového signálu, návrh nových klasifikačních architektur jako je například „emoční párování“ a návrh nové metody pro mapování diskrétních emočních stavů do dvou dimenzionálního prostoru. Druhá část se zabývá rozpoznáním emočních stavů z databáze spontánní řeči, která byla získána ze záznamů hovorů z reálných call center. Poznatky z analýzy a návrhu metod rozpoznání z hrané řeči byly využity pro návrh nového systému pro rozpoznání sedmi spontánních emočních stavů. Jádrem navrženého přístupu je komplexní klasifikační architektura založena na fúzi různých systémů. Práce se dále zabývá vlivem emočního stavu mluvčího na úspěšnosti rozpoznání pohlaví a návrhem systému pro automatickou detekci úspěšných hovorů v call centrech na základě analýzy parametrů dialogu mezi účastníky telefonních hovorů.
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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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A convolutional neural network for image segmentation
Mitrenga, Michal ; Petyovský, Petr (referee) ; Jirsík, Václav (advisor)
The aim of the bachelor thesis is to learn more about the problem of convolutional neural networks and to realize image segmentation. This theme includes the field of computer vision, which is used in systems of artificial intelligence. Special Attention is paid to the image segmentation process. Furthermore, the thesis deals with the basic principles of artificial neural networks, the structure of convolutional neural networks and especially with the description of individual semantic segmentation architectures. The chosen SegNet architecture is used in a practical application along with a pre-learned network. Part of the work is a database of CamVid images, which is used for training. For testing, a custom image database is created. Practical part is focused on CNN training and searching for unsuitable parameters for network learning using SW Matlab.
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Classification of the vascular tree in fundus images
Tebenkova, Iuliia ; Kolář, Radim (referee) ; Odstrčilík, Jan (advisor)
Retinal image analysis plays a very important role, as human gets around 90% of environment information with the help of eyes. Automation of process of retinal image analysis promotes to improve the efficiency of retinal medical examinations. The following thesis is dedicated to automatic classification methods of retinal vascular system images obtained from a digital fundus camera. Vessel classification method using classifier on the base of neural networks, which is trained and then tested on the retinal vessel segments, is investigated and implemented. In this thesis anatomical retinal survey, properties of image data from digital fundus camera and retinal image classification methods are briefly described. The last chapter is devoted to the evaluation of efficiency of retinal vessel classification with automatic methods.
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