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Automatic detection of graphoelements in sleep EEG
Balcarová, Anežka ; Ronzhina, Marina (referee) ; Kozumplík, Jiří (advisor)
This project is aimed at sleeping EEG signal, especially at searching of sleeping graphoelements and next at processing signal, witch this segmentation go before. Charakterization of sleeping graphoelements and problems with their classification are outlined here. Principle of two detection methods of k-komplex are explained and processed by Matlab with graphically representation of results. Results of automatic classification are compared with scoring of two experts.
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Automatic delineation of ECG signals
Vítek, Martin ; Tyšler, Milan (referee) ; Halámek,, Josef (referee) ; Kozumplík, Jiří (advisor)
This dissertation deals with QRS complex detection and ECG delineation. The theoretical part of the work describes basics of electrocardiography, QRS detection approaches, ECG delineation approaches, the standard CSE database and the wavelet transform theory. The practical part of the work describes designed methods of QRS complex detection and ECG delineation. The designed methods are based on a continuous wavelet transform, appropriate scales, appropriate mother wavelet, cluster analysis and leads transformation. The introduced algorithms were evaluated on the standard CSE database. The obtained results are better, than directly comparable results of other methods and accomplished given database criteria. The robustness of designed algorithms was successfully tested on CSE database signals modified by compression and filtering. The proposed ECG delineation algorithm was successfully used as a tool for evaluation of diagnostic distortion of ECG signals modified by compression.
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Image Compression Using the Wavelet Transform
Kontra, Matúš ; Drábek, Vladimír (referee) ; Bařina, David (advisor)
Wavelet transform belongs to modern methods used to compress data. It's application modifies data in such way, that we can use and store them in much more efficient way. Focus of this thesis lies in theoretical basis required to understand this method and its implementation. Next section shifts focus to quantization and coding - operations used to further reduce size of our data, which are provided by the SPIHT algorithm.
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Optimal detection of QRS boundaries in ECG signals
Spáčil, Jakub ; Hrubeš, Jan (referee) ; Vítek, Martin (advisor)
This diploma thesis deals with location optimal wavelet for detecton charakterics points of QRS complex in ECG signals. The first part of this thesis deals with description of heart, genesis of electric signals on heart and problem of noise. The second part describes the wavelet transform and the designed program and the third part evaluate detection results. The created program is working with 10 ECG signals from the CSE database and is testing 12 different mother wavelets. The program was developed in Matlab environment and is based on the finding zero-points in the transformed signal.
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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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Time-frequency analysis
Tráge, David ; Hadinec, Michal (referee) ; Kubásek, Radek (advisor)
The aim of this bachelor`s thesis is to explore possibilities of solving time-, frequency analysis a their combination time-frequency analysis by different methods for example Fourier transform a Wavelet transform. Going through this project we will get know each transformation and we will make clear procedure of their solving and first of all their advantages and disadvantages in view of accuracy of frequention`s mark in time.
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Filtering methods for NMR measurements
Zvěřina, Lukáš ; Rajmic, Pavel (referee) ; Gescheidtová, Eva (advisor)
The subject of this thesis is the principle of modern filtering techniques of signals acquired by nuclear magnetic resonance technique. In the NMR images a disturbing element is almost always present, especially noise, which causes useful signal and image degradation. The noise is a random signal, related to the errors of measurement and evaluation. The noise brings no information about the behaviour of the signal and it is unwanted signal component. The bachelor thesis is therefore focused mainly on the removal of the interfering parts of the signals. It exploits the fact that the nowadays widely expanding wavelet transform is closely connected with the banks of the digital filters. The subsequent section deals with experimental filtering of signals by implementation the wavelet transform in Matlab.
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Simple wavelet filter of ECG signals
Doležel, Jiří ; Ronzhina, Marina (referee) ; Smital, Lukáš (advisor)
The work deals with wavelet transfom and its possibilities of using it for elimination muscle noise from ECG signals. The first part of this thesis describes basic properties of ECG signal, the most common types of noise and describes basic types of wavelet transform, which are used for filtering the signals. Others parts describe a process of ECG signals wavelet filter design and afterwards the most appropriate setting are described. Finally results of filtration are evaluated, based on improved SNR, and compared with other author’s results.
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Lossless Image Compression Using Wavelet Transform
Tumpach, Jiří ; Polok, Lukáš (referee) ; Bařina, David (advisor)
This work focuses on wavelet transform and its use in image compression particularly on a comparation between classical tensor product wavelets and new kind of second generation wavelet also known as red-black wavelet transform. Although brief comparison of EBCOT modifications, color transforms, wavelets and predictors are discussed too. A framework for an evaluation of some current methods is constructed and results across different image groups are presented. In addition, C++ library was created. Proposed lossless compression methods are better then JPEG 2000 and PNG.
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