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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 improvements of images from 1D gel electrophoresis
Kovář, Martin ; Vítek, Martin (referee) ; Škutková, Helena (advisor)
In this bachelor’s thesis are explained the basic principles of electrophoresis and its modalities with focusing on 1D gel electrophoresis. It describes analysis of an electrophoreogram and causes of its possible distortion, and states specifications of applications of the method in microbiology, genomics and proteomics. The practical part presents development, optimization and outputs of a programme for automatic electrophoreogram analysis, which was created in Matlab environment. The analysis contains lane and band detection and computation of samples’ molecular weight. The ending of the thesis is constituted by evaluating efficiency of detection and accuracy of weight computation.
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Deep Neural Network for Detection of Atrial Fibrillation
Budíková, Barbora ; Ronzhina, Marina (referee) ; Hejč, Jakub (advisor)
Atrial fibrillation is an arrhythmia commonly detected from ECG using its specific characteristics. An early detection of this arrhythmia is a key to prevention of more serious conditions. Nowadays, atrial fibrillation detection is being implemented more often using deep learning. This work presents detection of atrial fibrillation from 12lead ECG using deep convolutional network. In the first section, there is a theoretical context of this work, then there is a description of proposed algorithm. Detection is implemented by a program in Python in two variations and their accuracy is rated by Accuracy and F1 measure. Results of the work are being discussed, mutually compared and compared to other similar publications.
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Analysis of sleep EEG signal
Ježek, Martin ; Kozumplík, Jiří (referee) ; Rozman, Jiří (advisor)
Cílem této práce byl vývoj programu pro automatickou detekci arousalu v signálu spánkového EEG s použitím metod časově-frekvenční analýzy. Předmětem studie bylo 13 celonočních polysomnografických nahrávek (čtyři svody EEG, EMG, EKG a EOG), tj. celkově více než 100 hodin záznamu. Jednalo se o část dat z dřívějších výzkumných prací expertní lékařky v problematice spánku Dr. Emilie Sforzy, Ženeva, Švýcarsko, která rovněž poskytla základní hodnocení těchto dat. V záznamech bylo celkem označeno 1551 arousal událostí. Pro usnadnění výběru konkrétní metody časově-frekvenční analýzy byla následně vytvořena sada nástrojů pro vizualizaci jednotlivých signálů a jejich různých časově-frekvenčních vyjádření. S ohledem na závěry vizuální analýzy, charakter signálu EEG a efektivitu výpočetních metod byla pro analýzu vybrána waveletová transformace s mateřskou vlnkou Daubechies řádu 6. Jednotlivé svody EEG byly dekomponovány do šesti frekvenčních pásem. Z takto odvozených signálů a signálu EMG byly následně stanoveny ukazatele možné přítomnosti události arousalu. Tyto ukazatele byly dále váhovány lineárním klasifikátorem, jehož hodnoty vah byly optimalizovány pomocí genetického algoritmu. Na základě hodnoty lineárního klasifikátoru bylo rozhodnuto o přítomnosti události arousalu v daném svodě EEG – arousal byl detekován, jestliže hodnota klasifikátoru překročila danou mez na dobu více než 3 a méně než 30 vteřin. V celém záznamu pak byl arousal označen, byl-li detekován alespoň v jednom ze svodů EEG. Následně byly odvozeny míry senzitivity a selektivity detekce, jež byly rovněž základem pro stanovení fitness funkce genetického algoritmu. Pro učení genetického algoritmu byly vybrány první čtyři záznamy. Na základě takto optimalizovaných vah vznikl program pro automatickou detekci, který na celém souboru 13 záznamů dosáhl ve srovnání s expertním hodnocením míry senzitivity 76,09%, selektivity 53,26% a specificity 97,66%.
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Methods for infrared thermography with detection of specific facial areas
Kolářová, Dana ; Bernard, Vladan (referee) ; Maryšková, Věra (advisor)
This paper deals with non-contact measurement of temperature in human faces. Principle of measurement of infrared radiation and construction of the thermal imager is described in a literature search. The main part of the paper is design of an algorithm for automatic processing and the detection of regions of interest in thermal images. The theoretical description of used methods is also included in this paper. The aim is to design and implement a program for automatic evaluation of temperature changes in a human face in a sequence of thermal images that were taken with short time delay. As a part of thesis is description of implementation of designed algorithm in programming enviroment MATLAB and the description of the user interface. The program was tested on the experimental data samples. Obtained results and possible limitations are also discused in this paper.
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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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Deep Neural Network for Detection of Atrial Fibrillation
Budíková, Barbora ; Ronzhina, Marina (referee) ; Hejč, Jakub (advisor)
Atrial fibrillation is an arrhythmia commonly detected from ECG using its specific characteristics. An early detection of this arrhythmia is a key to prevention of more serious conditions. Nowadays, atrial fibrillation detection is being implemented more often using deep learning. This work presents detection of atrial fibrillation from 12lead ECG using deep convolutional network. In the first section, there is a theoretical context of this work, then there is a description of proposed algorithm. Detection is implemented by a program in Python in two variations and their accuracy is rated by Accuracy and F1 measure. Results of the work are being discussed, mutually compared and compared to other similar publications.
|
|
|
Automatic improvements of images from 1D gel electrophoresis
Kovář, Martin ; Vítek, Martin (referee) ; Škutková, Helena (advisor)
In this bachelor’s thesis are explained the basic principles of electrophoresis and its modalities with focusing on 1D gel electrophoresis. It describes analysis of an electrophoreogram and causes of its possible distortion, and states specifications of applications of the method in microbiology, genomics and proteomics. The practical part presents development, optimization and outputs of a programme for automatic electrophoreogram analysis, which was created in Matlab environment. The analysis contains lane and band detection and computation of samples’ molecular weight. The ending of the thesis is constituted by evaluating efficiency of detection and accuracy of weight computation.
|
|
|
Methods for infrared thermography with detection of specific facial areas
Kolářová, Dana ; Bernard, Vladan (referee) ; Maryšková, Věra (advisor)
This paper deals with non-contact measurement of temperature in human faces. Principle of measurement of infrared radiation and construction of the thermal imager is described in a literature search. The main part of the paper is design of an algorithm for automatic processing and the detection of regions of interest in thermal images. The theoretical description of used methods is also included in this paper. The aim is to design and implement a program for automatic evaluation of temperature changes in a human face in a sequence of thermal images that were taken with short time delay. As a part of thesis is description of implementation of designed algorithm in programming enviroment MATLAB and the description of the user interface. The program was tested on the experimental data samples. Obtained results and possible limitations are also discused in this paper.
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