Sleep stage classification based on Hjorth descriptors of EEG signals

Tato bakalářská práce je zaměřena na rozlišování jednotlivých spánkových stádií ze signálů EEG. V její první části je představena klasická vizuální metoda klasifikace spánkových stádií, v části druhé metoda automatizované klasifikace. Jedná se o metodu využívající tří Hjorthových parametrů k vytvoření vektorového prostoru, ve kterém by na základě podobnosti vzniklých útvarů mohla být rozlišována jednotlivá stádia spánku. Hjorthovy parametry jsou počítány jak z celého pásma signálu EEG, tak i v jeho jednotlivých pásmech. V další části práce je provedena analýza hlavních komponent. Hlavní komponenty jsou analogicky s Hjorthovými parametry umístěny do vektorového prostoru a je u nich sledován charakter vzniklých útvarů.This bachelor thesis is focused on the distinction between sleep stages from EEG signals. In its first part the classical method of visual classification of sleep stages is introduced, the second part introduces an automated method for sleep stage scoring. It is a method that uses the three parameters of Hjorth to create a vector space, in which, on the basis of similarity of formed shapes, different stages of sleep could be distinguished. Parameters of Hjorth are calculated from the whole EEG signal, and also from its bands. In the next section of this thesis a principle component analysis is performed. The principle components are placed into a vector space analogously with parameters of Hjorth and the character of formed objects is observed.

Metric Learning for Automatic Sleep Stage Classification

We introduce in this paper a metric learning approach for automatic sleep stage classification based on single-channel EEG data. We show that learning a global metric from training data instead of using the default Euclidean metric, the k-nearest neighbor classification rule outperforms state-of-the-art methods on Sleep-EDF dataset with various classification settings. The overall accuracy for Awake/Sleep and 4-class classification setting are 98.32% and 94.49% respectively. Furthermore, the superior accuracy is achieved by performing classification on a low-dimensional feature space derived from time and frequency domains and without the need for artifact removal as a preprocessing step

Determination of Sleep Stage Separation Ability of Features Extracted from EEG Signals Using Principle Component Analysis

In this study, a method was proposed in order to determine how well features extracted from the EEG signals for the purpose of sleep stage classification separate the sleep stages. The proposed method is based on the principle component analysis known also as the Karhunen-Lo,ve transform. Features frequently used in the sleep stage classification studies were divided into three main groups: (i) time-domain features, (ii) frequency-domain features, and (iii) hybrid features. That how well features in each group separate the sleep stages was determined by performing extensive simulations and it was seen that the results obtained are in agreement with those available in the literature. Considering the fact that sleep stage classification algorithms consist of two steps, namely feature extraction and classification, it will be possible to tell a priori whether the classification step will provide successful results or not without carrying out its realization thanks to the proposed method

Determination of sleep stage separation ability of features extracted from EEG signals using principle component analysis

PMID = 2019205

Polysomnographic data analysis

Tato bakalářská práce se zabývá analýzou polysomnografických signálů, která je založena na analýze parametrů v časové a frekvenční oblasti. Parametry jsou počítány z 30s úseků EEG, EOG a EMG signálů snímaných v průběhu různých spánkových fází. Pomocí vizuální analýzy krabicových grafů, statistické analýzy a následného post-hoc testu jsou vybrány parametry, které jsou vhodné pro následnou detekci spánkových fází. Vybranými parametry v časové oblasti byly pro EOG signály: mobilita, koeficient šikmosti a špičatosti. Pro EEG signály se jednalo o tyto parametry: aktivita, 75. percentil, koeficient špičatosti a mobilita. U EMG signálu to byly 75. percentil a složitost. Z frekvenční oblasti se jednalo o relativní výkonové spektrum frekvenčních pásem alfa, delta a beta.The bachelor´s thesis is focused on analysis of polysomnographic signals based on various parameters in time and frequency domain. The parameters are acquired from 30 seconds long segments of EEG, EOG and EMG signals recorded during different sleep stages. The parameters useful for automatic detection of sleep stages are selected according to both visual analysis of boxplots and statistical analysis via comparison tests. EOG parameters selected in the time domain were mobility, skewness and kurtosis. Among EEG parameters, aktivity, 75. percentile, kurtosis and mobility were selected. Among EMG parameters, 75. percentile and complexity were selected. Finally, the parameters selected in the frequency domain were relative power spectra in alpha, delta and beta bands.

Sleep EEG analysis

Tato bakalářská práce se zabývá analýzou spánkových EEG, která je provedena pomocí výpočtu vybraných parametrů z časové a frekvenční oblasti. Parametry se počítají z jednotlivých úseků EEG signálů, které odpovídají jednotlivým spánkovým fázím. Na základě analýzy se rozhodne, které parametry EEG jsou vhodné pro automatickou detekci fází a která metoda je vhodnější pro hodnocení dat v hypnogramu. K analýze byl použit program MATLAB, ve kterém byla daná data porovnána.This thesis deals with the analysis of EEG during various sleep stages, which is done by calculating the selected parameters from the time and frequency domain. These parameters are calculated from individual segments of EEG signals that correspond with various sleep stages. Based on the analysis it decides which EEG parameters are appropriate for the automatic detection of the phases and which method is more suitable for evaluation of data in hypnogram. The programme MATLAB was used for the analysis and also for the comparison of chosen data.

Sağlık sektöründe apriori algoritması ile bir veri madenciliği uygulaması

06.03.2018 tarihli ve 30352 sayılı Resmi Gazetede yayımlanan “Yükseköğretim Kanunu İle Bazı Kanun Ve Kanun Hükmünde Kararnamelerde Değişiklik Yapılması Hakkında Kanun” ile 18.06.2018 tarihli “Lisansüstü Tezlerin Elektronik Ortamda Toplanması, Düzenlenmesi ve Erişime Açılmasına İlişkin Yönerge” gereğince tam metin erişime açılmıştır.Son yıllarda tıp alanındaki teknolojik gelişmeler ile birlikte artan veri hacmi insan algısı ile verileri analiz etmeyi imkânsız kılmıştır. Tıbbi verilerin hayati önem taşıması nedeniyle hata oranının minimum olduğu bilişim teknolojilerinden destek alma yoluna gidilmeye başlanmıştır. Özellikle tıbbi veri tabanlarında veri analizi, karar destek sistemlerinin oluşturulması, yönetim birimimde bilgilere etkili ve hızlı bir şekilde ulaşılabilmesi bakımından bilgisayarlar uzmanlara büyük kolaylıklar sağlamaktadır. Bu hedef doğrultusunda önceden bilinmeyen, ilk bakışta fark edilemeyen, veri içinde gizli kalmış anlamlı ve değerli bilgiler elde edilebilmesinden dolayı veri madenciliği optimum çözüm olmuştur.Bu tez kapsamında, veri madenciliğinin tıpta kullanıldığı alanlar, veri tabanlarında bilgi keşfi süreçleri, veri madenciliği, veri madenciliğinde kullanılan birliktelik analizi ve Apriori algoritması hakkında bilgiler verilmiştir.Bu tez çalışmasında Sakarya Üniversitesi personeline uygulanan, olası migren teşhisine yönelik anket sonuçlarında, sık geçen öğelerin keşfedilmesinde en yaygın olarak bilinen Apriori algoritması yardımıyla, birliktelik kuralları aranmıştır. Apriori algoritmasını uygulayabilmek için .net platformunda web tabanlı bir yazılım geliştirilmiştir. Bu yazılım sayesinde Apriori algoritmasının işleyişi adım adım takip edilebilmektedir. Çalışmanın sonunda elde edilmesi hedeflenen birliktelik kurallarına ulaşılmıştır.Anahtar kelimeler: Veri Madenciliği, Medikal Veri Madenciliği, Klinik Veri Madenciliği, Apriori Algoritması, Birliktelik KurallarıRecently biomedical sciences, biology and medicine have undergone tremendous advances in their technologies and therefore have generated huge amounts of biomedical information and data sets. It seems impossible to analyze this amount of data obtained. Since medical and biological data are vital for patients minimum error rates in diagnosis, therapy and prognosis levels are required. Therefore it shall be easy and extremely fast to reach previous and recent data analysis in medical databases and construction of decision support systems is crucial. Computers are appropriate solutions. Nevertheless a method is required to turn all these information and data to expressive knowledge and to expose the secret meanings of the collected data mass. Data mining is the optimum solution method to reach these goals.In this thesis study, application fields of data mining in medicine, knowledge discovery processes in databases, data mining, association rules in data mining and Apriori algorithm is discussed.A survey study was held to obtain data about migraine disease in Sakarya University. Random surveyors of academic and administrative staff of Sakarya University participated in the study. Association rules were sought by the help of Apriori algorithm, one of the most common algorithms used in related applications. A web based software was developed in ?.net? platform to apply Apriori algorithm. This software enables monitoring the processing levels of the algorithm step by step. At the end of study projected association rules are acquired.Key Words: Data Mining, Medical Data Mining, Clinical Data Mining, Apriori Algorithm, Association Rule

Processing of EEG signals in frequency domain

Práce se zabývá spánkovým signálem EEG. Je zaměřena na zpracování signálu, které předchází testům stacionarity. Zároveň se věnuje problematice stacionarity spánkového EEG signálu s uvedením tří typů segmentace. Jsou vysvětleny principy tří typů segmentace a zpracovány jejich realizace v prostředí MatLab způsobem, jenž umožňuje volbu různých vstupních parametrů. S pomocí výsledků takto vytvořených funkcí je zkoumána změna stacionarity v různých fázích spánku. Dalším aspektem této práce je zkoumání změn ve vývoji absolutních a relativních výkonů v čase v závislosti na přechodu ze stádia bdělosti do stádia spánku. Výskyt změn je pro tento přechod vyhledáván i v časovém vývoji koherenčních spekter.Sleep EEG is occupied by this project. It is aimed at processing signal which precede test of stationarity. It also deals with the issue of stationarity of sleep EEG with indicating of three types of segmentations. Principles of three types of segmentation are explained and their realization is compiled in the environment of Matlab by the way which enables to choose different input parameters. The change of stationarity is examined in various stages of sleep with the help of the results thus created functions. Another aspect of this study is the investigation of changes in the development of absolute and relative power over time according to transition between stage of wakefulness and stage of sleep. It is searched for the occurrence of these changes also in the development of the coherence spectra.

Sleep EEG analysis

Tato bakalářská práce se zabývá analýzou spánkových elektroencefalogramů, která je založená na výpočtu vybraných parametrů z časové a frekvenční oblasti. Tyto parametry jsou počítány z jednotlivých úseků EEG signálů odpovídajícím různým spánkovým fázím. Na základě statistické analýzy se dá rozhodnout, které parametry EEG jsou vhodné pro následnou automatickou detekci jednotlivých spánkových fází. Pro výběr, zobrazení a analýzu EEG byl vytvořen program s grafickým uživatelským rozhraním v prostředí MATLAB.The bachelor´s thesis is focused on analysis of sleep electroencephalograms based on extraction of chosen parameters in time and frequency domain. The parameters are acquired from segments of EEG signals coincident with sleep stages. The parameters used for automatic detection of sleep stages are selected according to statistical analysis. The program with a graphical user interface for selection, display and analysis EEG was created using Matlab.

Automatic sleep scoring using polysomnographic data

Tato diplomová práce se zabývá automatickou klasifikací polysomnografických záznamů, která je založená na analýze parametrů v časové a frekvenční oblasti. Parametry jsou počítány z 30s úseků EEG, EMG a EOG signálů snímaných v průběhu různých spánkových fází. Pomocí statistické analýzy jsou vybrány parametry, které jsou vhodné pro následnou automatickou klasifikaci spánkových fází. Klasifikace je poté provedena pomocí metody SVM a zhodnocení úspěšnosti klasifikace je provedeno pomocí senzitivity, specificity a procentuální úspěšnosti. Práce byla provedena v programovém prostředí MATLAB.The thesis is focused on automatic classification of polysomnographic signals based on various parameters in time and frequency domain. The parameters are acquired from 30 seconds long segments of EEG, EMG and EOG signals recorded during different sleep stages. The parameters used for automatic classification of sleep stages are selected according to statistical analysis. Classification is performed using the SVM method and evaluation of the success of the classification is done using sensitivity, specificity and percentage success. Classification method was implemented using Matlab.