Clasificación de las fases del sueño utilizando señales EEG

La identificación eficaz de las fases del sueño es de gran ayuda para el tratamiento de problemas del sueño como la apnea obstructiva (OSA), insomnio o narcolepsia. De esta manera, se puede mejorar la calidad de vida de los pacientes. La clasificación de estas fases pueden realizarla expertos del sueño de manera manual, basándose en señales PSG (Polisomnograma).No obstante, esto requiere mucho tiempo y para realizar una polisomnografía se necesitan muchas señales. Con un clasificador automáticobasado en señales EEGla detección sería más rápida y efectiva. En este trabajo se ha realizado una investigación de estudios ya realizados de la detección automática de las fases del sueño y se ha experimentadocon las señales EEG de 4sujetos sanos: se hanextraído un conjuntode características y se ha evaluadodel rendimiento de diferentes clasificadores. Con el clasificador KNN,7 características y 8 canales EEG, se han clasificado las fases del sueño con un F1 scoredel 51,41%. Como líneas de investigación futuras para mejorar los resultados se ha propuesto añadir característicasespectrales y reducir el número de canales, entre otras

Detection of breast cancer microcalcifications in digitized mammograms. Developing segmentation and classification techniques for the processing of MIAS database mammograms based on the Wavelet Decomposition Transform and Support Vector Machines.

Mammography is used to aid early detection and diagnosis systems. It takes an x-ray image of the breast and can provide a second opinion for radiologists. The earlier detection is made, the better treatment works. Digital mammograms are dealt with by Computer Aided Diagnosis (CAD) systems that can detect and analyze abnormalities in a mammogram. The purpose of this study is to investigate how to categories cropped regions of interest (ROI) from digital mammogram images into two classes; normal and abnormal regions (which contain microcalcifications). The work proposed in this thesis is divided into three stages to provide a concept system for classification between normal and abnormal cases. The first stage is the Segmentation Process, which applies thresholding filters to separate the abnormal objects (foreground) from the breast tissue (background). Moreover, this study has been carried out on mammogram images and mainly on cropped ROI images from different sizes that represent individual microcalcification and ROI that represent a cluster of microcalcifications. The second stage in this thesis is feature extraction. This stage makes use of the segmented ROI images to extract characteristic features that would help in identifying regions of interest. The wavelet transform has been utilized for this process as it provides a variety of features that could be examined in future studies. The third and final stage is classification, where machine learning is applied to be able to distinguish between normal ROI images and ROI images that may contain microcalcifications. The result indicated was that by combining wavelet transform and SVM we can distinguish between regions with normal breast tissue and regions that include microcalcifications

Detección automática de spindles en niños con apnea obstructiva del sueño mediante técnicas de deep learning

La Apnea Obstructiva del Sueño (AOS) infantil es un trastorno muy prevalente a nivel mundial (1-5% de la población), que tiene graves consecuencias en la salud y afecta el desarrollo de los niños afectados. Gracias a los nuevos avances tecnológicos, se ha observado que pequeñas oscilaciones presentes fundamentalmente durante las fases N2 y N3 del sueño entre 11-16 Hz, conocidas como spindles del sueño. Estos spindles están íntimamente relacionadas con el proceso cognitivo de las personas en general y de los niños en particular. Este descubrimiento abre una nueva línea de investigación orientada a desarrollar algoritmos que detecten automáticamente spindles empleando para ello la señal de electroencelafograma (EEG), y que sirvan para estudiar posibles trastornos en función de su número, densidad y características concretas. Además, estas nuevas técnicas permiten disminuir la carga de trabajo y la variabilidad inter-observador en la labor de detección de dichas oscilaciones en las señales EEG. El objetivo de este Trabajo Fin de Máster ha sido evaluar la utilidad de algoritmos basados en técnicas de deep learning para detectar spindles del sueño en señales EEG de niños de entre 6 y 9 años con sospecha de AOS. La mayoría de los estudios científicos publicados hasta la fecha actual sobre la detección de spindles se ha centrado principalmente en pacientes adultos, lo que, junto con las diferencias de la AOS en sujetos adultos provoca que los modelos de detección de spindles no sean fácilmente generalizables a la población infantil.Pediatric Obstructive Sleep Apnea Syndrome (OSA) in children is a high prevalent global disorder (affecting 1-5% of the population), adversely impacting the health and development of affected children. Recent technological advances have revealed that small oscillations, primarily occurring during the N2 and N3 sleep stages within the 11-16 Hz frequency range, known as sleep spindles. Sleep spindles are closely related to the cognitive process of both the general population and, particularly, children. This discovery has prompted research focused on developing automated algorithms for sleep spindle detection using electroencephalogram (EEG) signals, enabling the examination of potential disorders based on spindle number, density, and specific characteristics. Furthermore, these new techniques hold the potential to alleviate the workload and inter-observer variability associated within the task of detecting these oscillations with EEG recordings. The aim of this Master’s Thesis was to evaluate the usefulness of deep-learning algorithms for detecting sleep spindles in EEG signals of children between 6 and 9 years old with suspected OSA as a continuation of the previous Bachelor’s degree thesis. Prior research spindle detection has mainly focused on adult patients, which, together with the differences in OSA characteristics between adults and children, leads to spindle detection models that are not easily generalizable to the pediatric population.Departamento de Teoría de la Señal y Comunicaciones e Ingeniería TelemáticaMáster en Ingeniería de Telecomunicació

Automatic recognition of sleep spindles in EEG by using artificial neural networks

In this paper, we introduce a two-stage procedure based on artificial neural networks for the automatic recognition of sleep spindles (SSs) in a multi-channel electroencephalographic signal. In the first stage, a discrete perceptron is used to eliminate definite non-SSs. The pre-classification done in the first stage not only reduces the computation time but also increases the overall detection performance of the procedure. In the second stage, the remaining SS candidates after pre-classification procedure are aimed to be separated from each other by an artificial neural network that would function as a post-classifier. Two different networks, i.e. a backpropagation multilayer perceptron and radial basis support vector machine (SVM), are proposed as the post-classifier and compared in terms of their classification performances. Visual evaluation, by two electroencephalographers (EEGers), of 19 channel EEG records of 6 subjects showed that the best performance is obtained with a radial basis SVM providing an average sensitivity of 94.6% and an average false detection rate of 4.0%. (C) 2004 Elsevier Ltd. All rights reserved

Critères spatial et spectral pour la détection des fuseaux du sommeil en EEG

Les fuseaux du sommeil sont des oscillations relativement rapides d’environ une seconde qui caractérisent le stade 2 du sommeil observé en EEG. Cette activité sporadique aurait un rôle dans la protection du sommeil et les processus de mémoire et de plasticité cérébrale. Plusieurs détecteurs automatiques ont été proposés pour assister ou remplacer l’expert dans l’identification des fuseaux. La problématique persistante est que ces algorithmes détectent en général trop d’évènements et que le compromis entre sensibilité (Se) et spécificité (Sp) est délicat à atteindre. Dans le présent travail, on propose un détecteur semi-automatique et supervisé qui ajoute une phase de spécificité spatiale et fréquentielle à une phase sensible basée sur des critères validés dans la littérature. Dans la phase sensible, les évènements candidats sélectionnés (bande 10Hz-16Hz) sont ceux dont les caractéristiques d’amplitude et de rapport spectral rejettent une hypothèse nulle (p < 0.1), soit l’évènement considéré n’est pas un fuseau. Cette hypothèse nulle est construite à partir des évènements se manifestant durant les stades REM identifiés par un expert. Dans la phase spécifique, une classification hiérarchique des candidats est faite sur les caractéristiques de fréquence et de position spatiale (axe antéro-postérieur). La classe sélectionnée est celle regroupant la majorité d’un ensemble de fuseaux marqués par l’expert. À la première phase, on obtient Se = 93.2% et Sp = 89.0%. À la deuxième phase, on obtient Se = 85.4% et Sp = 95.5%. Les résultats suggèrent que l’aspect spatio-fréquentiel est caractéristique des fuseaux et peut contribuer à perfectionner les méthodes de détection automatique

3D Facial Feature Extraction and Recognition. An investigation of 3D face recognition: correction and normalisation of the facial data, extraction of facial features and classification using machine learning techniques.

Face recognition research using automatic or semi-automatic techniques has emerged over the last two decades. One reason for growing interest in this topic is the wide range of possible applications for face recognition systems. Another reason is the emergence of affordable hardware, supporting digital photography and video, which have made the acquisition of high-quality and high resolution 2D images much more ubiquitous. However, 2D recognition systems are sensitive to subject pose and illumination variations and 3D face recognition which is not directly affected by such environmental changes, could be used alone, or in combination with 2D recognition. Recently with the development of more affordable 3D acquisition systems and the availability of 3D face databases, 3D face recognition has been attracting interest to tackle the limitations in performance of most existing 2D systems. In this research, we introduce a robust automated 3D Face recognition system that implements 3D data of faces with different facial expressions, hair, shoulders, clothing, etc., extracts features for discrimination and uses machine learning techniques to make the final decision. A novel system for automatic processing for 3D facial data has been implemented using multi stage architecture; in a pre-processing and registration stage the data was standardized, spikes were removed, holes were filled and the face area was extracted. Then the nose region, which is relatively more rigid than other facial regions in an anatomical sense, was automatically located and analysed by computing the precise location of the symmetry plane. Then useful facial features and a set of effective 3D curves were extracted. Finally, the recognition and matching stage was implemented by using cascade correlation neural networks and support vector machine for classification, and the nearest neighbour algorithms for matching. It is worth noting that the FRGC data set is the most challenging data set available supporting research on 3D face recognition and machine learning techniques are widely recognised as appropriate and efficient classification methods