25 research outputs found
New signal processing and machine learning methods for EEG data analysis of patients with Alzheimer's disease
Les malalties neurodegeneratives sĂłn un conjunt de malalties que afecten al cervell. Aquestes malalties estan relacionades amb la pèrdua progressiva de l'estructura o la funciĂł de les neurones, incloent-hi la mort d'aquestes. La malaltia de l'Alzheimer Ă©s una de les malalties neurodegeneratives mĂ©s comunes. Actualment, no es coneix cap cura per a l'Alzheimer, però es creu que hi ha un grup de medicaments que el que fan Ă©s retardar-ne els principals sĂmptomes. Aquests s'han de prendre en les primeres fases de la malaltia ja que sinĂł no tenen efecte. Per tant, el diagnòstic precoç de la malaltia de l'Alzheimer Ă©s un factor clau.
En aquesta tesis doctoral s'han estudiat diferents aspectes relacionats amb la neurociència per investigar diferents eines que permetin realitzar un diagnòstic precoç de la malaltia en qĂĽestiĂł. Per fer-ho, s'han treballat diferents aspectes com el preprocessament de dades, l'extracciĂł de caracterĂstiques, la selecciĂł de caracterĂstiques i la seva posterior classificaciĂł.Neurodegenerative diseases are a group of disorders that affect the brain. These diseases are related with changes in the brain that lead to loss of brain structure or loss of neurons, including the dead of some neurons. Alzheimer's disease (AD) is one of the most well-known neurodegenerative diseases. Nowadays there is no cure for this disease. However, there are some medicaments that may delay the symptoms if they are used during the first stages of the disease, otherwise they have no effect. Therefore early diagnose is presented as a key factor.
This PhD thesis works different aspects related with neuroscience, in order to develop new methods for the early diagnose of AD. Different aspects have been investigated, such as signal preprocessing, feature extraction, feature selection and its classification
Eliminació d'artefactes en EGG mitjançant l'ús de la Multivariate Empirical Mode Decomposition
Curs 2010-2011La tècnica de l’electroencefalograma (EEG) és una de les tècniques més
utilitzades per estudiar el cervell. En aquesta tècnica s’enregistren els
senyals elèctrics que es produeixen en el còrtex humà a través d’elèctrodes
col•locats al cap. Aquesta tècnica, però, presenta algunes limitacions a l’hora
de realitzar els enregistraments, la principal limitaciĂł es coneix com a
artefactes, que sĂłn senyals indesitjats que es mesclen amb els senyals EEG.
L’objectiu d’aquest treball de final de mà ster és presentar tres nous mètodes
de neteja d’artefactes que poden ser aplicats en EEG. Aquests estan basats
en l’aplicació de la Multivariate Empirical Mode Decomposition, que és una
nova tècnica utilitzada per al processament de senyal.
Els mètodes de neteja proposats s’apliquen a dades EEG simulades que
contenen artefactes (pestanyeigs), i un cop s’han aplicat els procediments de
neteja es comparen amb dades EEG que no tenen pestanyeigs, per
comprovar quina millora presenten. Posteriorment, dos dels tres mètodes de
neteja proposats s’apliquen sobre dades EEG reals.
Les conclusions que s’han extret del treball són que dos dels nous
procediments de neteja proposats es poden utilitzar per realitzar el
preprocessament de dades reals per eliminar pestanyeigs.Abstract
The electroencephalogram (EEG) is one of the most used techniques to study
the brain. This technique records the electric potentials generated in the
human cortex with electrodes attached to the scalp. However, this technique
presents several shortcomings. The more important shortcoming is the
presence of artifacts, which are undesired signals that disturb the EEG time
series. These artifacts are due to muscle action.
The aim of this Master Final Project is to present three new procedures to
clean artifacts of EEG data. The new procedures are based on the
application of the Multivariate Empirical Mode Decomposition, which is a
new technique used in data processing.
The proposed methods are applied to simulated EEG data with artifacts
(eye blinks). Once the cleaning methods are applied, clean data is compared
with EEG data without eye blinks to quantify the improvement of the data.
Subsequently, two of the presented methods are applied to real data to show
that the procedures can be applied to actual recordings.
The results point out that the use of two of the cleaning procedures proposed
to correct eye blinks may be a good procedure for EEG signal preprocessing.Director/a: Jordi Solé Casal
EEG signal analysis via a cleaning procedure based on multivariate empirical mode decomposition
IJCCI 2012Artifacts are present in most of the electroencephalography (EEG) recordings, making it difficult to interpret
or analyze the data. In this paper a cleaning procedure based on a multivariate extension of empirical mode
decomposition is used to improve the quality of the data. This is achieved by applying the cleaning method
to raw EEG data. Then, a synchrony measure is applied on the raw and the clean data in order to compare
the improvement of the classification rate. Two classifiers are used, linear discriminant analysis and neural
networks. For both cases, the classification rate is improved about 20%
Empirical mode decomposition-based face recognition system
In this work we explore the multivariate empirical mode decomposition combined with a Neural Network
classifier as technique for face recognition tasks. Images are simultaneously decomposed by means of EMD
and then the distance between the modes of the image and the modes of the representative image of each
class is calculated using three different distance measures. Then, a neural network is trained using 10- fold
cross validation in order to derive a classifier. Preliminary results (over 98 % of classification rate) are
satisfactory and will justify a deep investigation on how to apply mEMD for face recognition
A Theta-Band EEG Based Index for Early Diagnosis of Alzheimer’s Disease
Despite recent advances, early diagnosis of Alzheimer’s disease (AD) from electroencephalography (EEG) remains
a difficult task. In this paper, we offer an added measure through which such early diagnoses can potentially be improved. One
feature that has been used for discriminative classification is changes in EEG synchrony. So far, only the decrease of synchrony
in the higher frequencies has been deeply analyzed. In this paper, we investigate the increase of synchrony found in narrow
frequency ranges within the θ band. This particular increase of synchrony is used with the well-known decrease of synchrony
in the band to enhance detectable differences between AD patients and healthy subjects. We propose a new synchrony ratio
that maximizes the differences between two populations. The ratio is tested using two different data sets, one of them containing
mild cognitive impairment patients and healthy subjects, and another one, containing mild AD patients and healthy subjects.
The results presented in this paper show that classification rate is improved, and the statistical difference between AD patients
and healthy subjects is increased using the proposed ratio
A hybrid feature selection approach for the early diagnosis of Alzheimer's disease
Objective. Recently, significant advances have been made in the early diagnosis of Alzheimer’s disease from EEG. However, choosing suitable measures is a challenging task. Among other measures, frequency Relative Power and loss of complexity have been used with promising results. In the present study we investigate the early diagnosis of AD using synchrony measures and frequency Relative Power on EEG signals, examining the changes found in different frequency ranges. Approach. We first explore the use of a single feature for computing the classification rate, looking for the best frequency range. Then, we present a multiple feature classification system that outperforms all previous results using a feature selection strategy. These two approaches are tested in two different databases, one containing MCI and healthy subjects (patients age: 71.9 ± 10.2, healthy subjects age: 71.7 ± 8.3), and the other containing Mild AD and healthy subjects (patients age: 77.6 ± 10.0; healthy subjects age: 69.4± 11.5). Main Results. Using a single feature to compute classification rates we achieve a performance of 78.33% for the MCI data set and of 97.56 % for Mild AD. Results are clearly improved using the multiple feature classification, where a classification rate of 95% is found for the MCI data set using 11 features, and 100% for the Mild AD data set using 4 features. Significance. The new features selection method described in this work may be a reliable tool that could help to design a realistic system that does not require prior knowledge of a patient's status. With that aim, we explore the standardization of features for MCI and Mild AD data sets with promising results
Improving Early Diagnosis of Alzheimer's Disease Using Synchrony Measures
It is well-known that Alzheimer's disease causes changes on the
electroencephalography of the patients. However those changes are difficult to
parameterize. In this paper a new ratio between synchrony in 0 and a band is
investigated in arder to get an early diagnosis of Mild Alzheimer's patients. The
presented ratio is compared using two types of classifiers, Linear Discriminan!
Analysis and Artificial Neural Networks, with values of synchrony in the standard
frequency bands. Presented results improve using the ratio in the linear classifier.
Using the non-linear classifĂer, best results are obtained using synchrony measures
in 0 and a band simultaneously
Towards a low-complex breathing monitoring system based on acoustic signals
Monitoring the breathing is required in many applications of medical and health fields, but it can be used also in new game applications, for example. In this work, an automatic system for monitoring the breathing is presented. The system uses the acoustic signal recorded by a standard microphone placed in the area of the nostrils. The system is based on a low-complex signal parameterization performed on non-overlapped frames. From this parameterization, a reduced set of real parameters is obtained frame-to-frame. These parameters feed a classifier that performs a classification in three stages: inspiration, transition or retention and expiration providing a fine monitoring of the respiration process. As all of those algorithms are of low complexity and the auxiliary equipment required could only be a standard microphone from a conventional Bluetooth Headset, the system could be able to run in a smartphone device. © 2013 Springer-Verlag Berlin Heidelberg