Analysis of encephalic lesions of different natures in magnetic resonance and computed tomography images from self-organizing maps

Orientadores: Fabiano Reis, Li Li MinTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Ciências MédicasResumo: Com o advento de conjunto de dados cada vez maiores (Big Data), dentre os quais se incluem imagens médicas com crescente qualidade de resoluções espacial, espectral e radiométrica e, portanto, com maior número de pixels, espectros de varredura e níveis de cinza, faz-se útil o uso de técnicas matemáticas avançadas, especialmente as não-supervisionadas, para aprimorar a segmentação do tecido cerebral em agrupamentos (clusters) distintos, possibilitando uma melhor visualização da área acometida por patologia. Este trabalho teve por objetivo segmentar imagens de sistema nervoso central (SNC) em patologias de três naturezas distintas - neoplásica (tumores), desmielinizante (esclerose múltipla) e vascular (acidente vascular cerebral isquêmico -AVCi). Foram realizados três estudos, descritos nos Artigos I, II e III, nos quais foram analisadas imagens de SNC de pacientes (ressonância magnética - RM, nos Artigos I e II, e tomografia computadorizada - TC, no artigo III) com diagnóstico de, respectivamente, neoplasia, esclerose múltipla tipo remitente-recorrente e AVCi. As imagens foram transformadas em matrizes com valores da escala de cinza para cada pixel, em cada aquisição, e processadas por ferramentas com capacidade de execução de Mapas Auto-Organizáveis (SOM), SiroSOM e Weka, que permitiram a construção de mapa neural com treinamento de neurônios e, posteriormente, particionamento dos mesmos em agrupamentos por K-Médias. As novas matrizes, com assimilação de clusters para cada pixel, foram novamente reconstruídas em imagens, que foram submetidas à avaliação de médicos com formação consolidada prévia em neurorradiologia. Os trabalhos I e II confirmam a capacidade geral de segmentação de imagens médicas por meio de SOM com razoável precisão de delimitação de bordas em RM. O artigo III revelou um grau insatisfatório de exatidão de delineação de bordas de lesões à segmentação de TC, porém com potencial identificável de melhora da acurácia se novos e mais amplos estudos forem realizados, com base no material publicadoAbstract: With the upcoming of ever bigger datasets (Big Data), among those medical images, ever-growing on spatial, spectral and radiometric resolutions and, hence, in the number of pixels, spectrums and digital numbers (DNs), the use of advanced mathematical algorithms, especially non-supervised neural networks, plays a role on improving automated segmentation of the human brain into smaller, distinct clusters, providing a better visualization of the comprised, pathological regions. This work aimed to segment central nervous system (CNS) images from pathologies of three different natures - neoplasms, demyelinating and vascular (ischemic stroke). We performed three studies, each one described in distinct articles, I, II and III, on which medical CNS images (MRI for Articles I and II and CT for article III) from patients with confirmed diagnosis of, respectively, neoplasms, relapsing-remitting multiple sclerosis and ischemic stroke were analyzed. The images were transformed in matrices of gray values for each pixel, in each acquisition, and then processed by Kohonen's Self Organizing Maps (SOM) via capable software - SiroSOM or Weka, through training of neurons belonging to the built neural map, followed by clustering by K Means. The newly created matrices with cluster values for each pixel were then rebuilt back to new images that were appreciated by physicians skilled in neuroradiology. The research confirms the general ability of medical image segmentation by SOM, with reasonable border delimitation in MRI, in articles I and II. Article III revealed a non-satisfactory precision of lesion border delineation on CT, but there is a likely chance of improvement of accuracy, if further, deeper studies based on the publication data could be performedDoutoradoNeurologiaDoutor em Ciências Médicas88881.132052/2016-01CAPE

Estagiamento automático do sono utilizando um canal de EEG e uma rede neural artificial com alta representação cortical

Tese (Doutorado) - Universidade Federal de Santa Catarina, Centro Tecnológico

Uyku evrelerinin EEG işaretleri kullanılarak sınıflandırılmasında yeni bir yaklaşım

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.Bu çalışmada uyku evrelerini sınıflandırma amacıyla EEG işaretlerinden çıkartılan özelliklerin uyku evrelerini ne ölçüde ayrıştırdığını belirlemek amacıyla yöntemler sunulmuş ve olasılıksal sinir ağları ile uygulaması yapılmıştır. Sunulan yöntemler, Karhunen-Loeve dönüşümü olarak da bilinen temel bileşen analizine dayalıdır. Uyku evrelerini sınıflandırmada sunulan özellikler ana üç grupta toplanmıştır: (i) zaman-uzayı özellikleri, (ii) frekans-uzayı özellikleri, (iii) hibrit özellikler. Yapılan detaylı simülasyonlarla her bir gruptaki özelliklerin uyku evrelerini ne oranda ayrıştırdığı belirlenmiş ve elde edilen önbilginin literatürdeki sonuçlarla uyumlu olduğu görülmüştür. Sunulan yöntemler sayesinde sınıflandırıcın başarılı sonuç verip vermeyeceğini ve sınıflandırma başarısının analiz kısmında elde edilen sonuçlarla uyumlu olup olmadığını sınıflandırıcıyı gerçekleştirmeden söylemek mümkündür. Temel bileşen analizi ile hangi özelliklerin sınıflandırma için daha iyi sonuç vereceği tespit edilmeye çalışılmıştır. Simülasyonlar hibrit özelliklerinin en iyi ayrıştırmayı verdiğini göstermiştir. Daha sonra, sadece hibrit özellikler kullanılarak temel bileşen analizi ve kernel tabanlı temel bileşen analizi ile uyku evrelerinin ne oranda ayrıştığı incelenmiştir. Yapılan bu ayrıştırma sonuçlarının geçerliliği olasılıksal sinir ağları kullanılarak gösterilmiştir. Çalışma sonunda temel bileşen analizi ve kernel tabanlı temel bileşen analizinin ayrıştırma yetenekleri olasılıksal sinir ağı aracılığıyla karşılaştırılmıştır.In this study, methods were presented in order to determine how well features extracted from the EEG signals separate the sleep stages for the purpose of sleep stage classification and they were implemented using probabilistic neural networks. The proposed methods are based on the principle component analysis also known as the Karhunen-Loéve transform. The 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 methods. Principles component analysis was used in order to determine the features that give the best results. Simulations showed that hybrid features give the best separation. Then, how well sleep stages separate was investigated with principle component analysis and kernel based principle component analysis by using only hybrid features. The validity of these separation results was shown by using probabilistic neural networks. Finally, separation capabilities of principle component analysis and kernel based principle component analysis was compared by means of probabilistic neural network

睡眠ステージの知識データベースに基づく条件付確立を用いた自動判定法

Sleep consists of non-rapid eye movement (NREM) and rapid eye movement (REM) states. NREM is further subdivided into Stage I, II, III and IV. The most well-known criteria for sleep stage scoring were published by Rechtschaffen and Kales in 1968. Each state is characterized by a different type of brain wave activity. Currently, sleep stage scoring has been widely used for evaluating the condition of sleep and diagnosing the sleep related disorders in hospitals and institutions. Automatic sleep stage determination can free the clinicians from the heavy task of visual inspection on sleep stages. Rule-based waveform detection methods, according to Rechtschaffen and Kales criteria, have been developed in many studies. However, Rechtschaffen and Kales criteria including typical waveforms of healthy persons under ideal condition for sleep stage scoring are insufficient to cover the variable sleep data of patients under usual condition in hospitals. The conventional rule-base methods have the similar limitations for clinical practice. An expert knowledge-based probabilistic method is developed in order to overcome the limitation of conventional rule-based methods. The visual inspection of sleep stage scoring by a qualified clinician is adopted as the expert knowledge. According to the visual inspection on a set of training data, an expert knowledge database is established in terms of probability density functions of parameters for various sleep stages. A set of characteristic parameters are defined as candidates. The probability density functions for various sleep stages are developed by using Cauchy distribution to approximately estimate the parameter distribution on histogram. The parameter which is effective for sleep stage discrimination is selected automatically. Sleep stages is determined automatically by the maximum value of conditional probabilities. An amendment function is developed to modify the decision making of sleep stage by the expert knowledge-based method, which is designed according to the additional rules by clinician for the continuity of stage II and onset/offset of stage REM. The developed expert knowledge-based automatic sleep stage determination system has flexible performance for clinical practice

Mapa fan no estagiamento automático do sono

Tese (doutorado) - Universidade Federal de Santa Catarina, Centro Tecnológico.Este trabalho apresenta um novo método para a classificação automática dos estágios do sono utilizando um canal do eletroencefalograma (C3). O sistema de classificação consiste de três módulos: o pré-processamento com extração de características, o classificador Mapa FAN, e um pós-processamento. A extração de característica consiste na ordenação e na redução de dimensão do sinal digital em cada época. O Mapa FAN é um sistema de classificação baseado na tecnologia de redes neurais artificiais, onde cada classe de padrões é representada por um mapa (matriz bidimensional). Para todo o padrão de entrada, cada mapa gera o grau de ativação daquele padrão em relação à classe que o mapa representa. Os Mapas foram treinados com os dados de um paciente e testados em outros sete pacientes, usando épocas de 30 segundos. Após a classificação feita pelo Mapa FAN, um sistema de regras de inferência, baseadas em estudos sobre a evolução do sono durante a noite, é aplicado para corrigir algumas falhas da classificação. Os resultados da classificação comparados com os de um especialista humano alcançaram uma concordância de 60 a 80%. Esses resultados estão dentro dos limites encontrados por outros pesquisadores, o que comprova a eficácia do modelo. Portando, o sistema Mapa FAN pode ser utilizado neste e em outros problemas de reconhecimento de padrões que apresentam multidimensionalidade

Automation of Sleep Staging

This thesis primarily covers the automation problem for sleep versus awake detection, which is sometimes accomplished by differentiating the various sleep stages prior to clustering. This thesis documents various experimentation into areas where the performance can be improved, including classifer design and feature selection from EEG, EOG and Context. In terms of classifers, it was found that the neural network MLP outperforms the continuous Hidden Markov Model with an accuracy of 91.91%, and additional performance requires better feature sets and more training data. Improved EEG features based on time frequency representation were optimized to differentiate Awake with 93.52% sensitivity and 94.60% specificity, differentiate REM with 96.12% sensitivity and 93.63% specificity, differentiate Stages II and III with 96.81% sensitivity and 89.28% specificity, and differentiate Stages III and IV with 93.60% sensitivity and 90.43% specificity. Due to the limited data set, an example of applying contextual information using a One-Cycle-Duo-Direction model was built and shown to improve EEG features by up to 10%. This level of performance is comparable if not superior to the human scorer accuracy of 88% to 94%. This thesis improved some aspects of sleep staging automation, but due to the limitations on resources, the full potential of these improvements could not be demonstrated. To further develop these improvements, additional data sets customized by sleep staging experts is crucial

Sistema para detecção e classificação automática de apnéias do sono a partir de registros polissonográficos

Tese (doutorado) - Universidade Federal de Santa Catarina, Centro Tecnológico. Programa de Pós-Graduação em Engenharia Elétrica