Multiway Array Decomposition Analysis of EEGs in Alzheimer’s Disease

Methods for the extraction of features from physiological datasets are growing needs as clinical investigations of Alzheimer’s disease (AD) in large and heterogeneous population increase. General tools allowing diagnostic regardless of recording sites, such as different hospitals, are essential and if combined to inexpensive non-invasive methods could critically improve mass screening of subjects with AD. In this study, we applied three state of the art multiway array decomposition (MAD) methods to extract features from electroencephalograms (EEGs) of AD patients obtained from multiple sites. In comparison to MAD, spectral-spatial average filter (SSFs) of control and AD subjects were used as well as a common blind source separation method, algorithm for multiple unknown signal extraction (AMUSE). We trained a feed-forward multilayer perceptron (MLP) to validate and optimize AD classification from two independent databases. Using a third EEG dataset, we demonstrated that features extracted from MAD outperformed features obtained from SSFs AMUSE in terms of root mean squared error (RMSE) and reaching up to 100% of accuracy in test condition. We propose that MAD maybe a useful tool to extract features for AD diagnosis offering great generalization across multi-site databases and opening doors to the discovery of new characterization of the disease

Data fusion of complementary information from parietal and occipital event related potentials for early diagnosis of Alzheimer\u27s disease

The number of the elderly population affected by Alzheimer\u27s disease is rapidly rising. The need to find an accurate, inexpensive, and non-intrusive procedure that can be made available to community healthcare providers for the early diagnosis of Alzheimer\u27s disease is becoming an increasingly urgent public health concern. Several recent studies have looked at analyzing electroencephalogram signals through the use of many signal processing techniques. While their methods show great promise, the final outcome of these studies has been largely inconclusive. The inherent difficulty of the problem may be the cause of this outcome, but most likely it is due to the inefficient use of the available information, as many of these studies have used only a single EEG source for the analysis. In this contribution, data from the event related potentials of 19 available electrodes of the EEG are analyzed. These signals are decomposed into different frequency bands using multiresolution wavelet analysis. Two data fusion approaches are then investigated: i.) concatenating features before presenting them to a classification algorithm with the expectation of creating a more informative feature space, and ii.) generating multiple classifiers each trained with a different combination of features obtained from various stimuli, electrode, and frequency bands. The classifiers are then combined through the weighted majority vote, product and sum rule combination schemes. The results indicate that a correct diagnosis performance of over 80% can be obtained by combining data primarily from parietal and occipital lobe electrodes. The performance significantly exceeds that reported from community clinic physicians, despite their access to the outcomes of longitudinal monitoring of the patients

Diagnóstico da doença de Alzheimer em intervalos de curta duração utilizando o EEG

As condições de vida nos países desenvolvidos têm melhorado significativamente nas últimas décadas. Consequentemente aumentou a esperança média de vida e o número de doenças neurodegenerativas associadas ao envelhecimento como a Doença de Alzheimer (DA). Esta doença provoca demência, afeta o córtex cerebral e causa determinadas alterações na atividade elétrica do cérebro. Como tal, a análise dos sinais do eletroencefalograma (EEG) pode revelar carências estruturais e funcionais associadas à DA. Muito embora, diversos estudos sobre a atividade do EEG tenham revelado uma desaceleração do ritmo cerebral, juntamente com uma redução na complexidade dos sinais e uma perda de conectividade funcional do cérebro, o valor de diagnóstico ainda é muito limitado e importa por isso continuar a pesquisa para ajudar a reduzir o impacto da DA. Por conseguinte, neste estudo analisou-se a atividade espontânea do EEG de pacientes com diagnóstico de DA e um grupo de controlo composto por sujeitos cognitivamente normais, através de diferentes parâmetros espectrais. Utilizou-se a Power Spectral Density (PSD) baseada na Transformada de Fourier de Curta Duração (STFT) e a PSD baseada na Transformada de Wavelet (WT). Os melhores resultados foram obtidos com a PSD determinada pela STFT e deram uma correlação de coeficiente 0.963 no conjunto de teste com uma Rede Neuronal Artificial (RNA) desenvolvida com o algoritmo de treino Levenberg-Marquardt (trainlm), a função Logística Sigmoidal (logsig) e com 16 nós de entrada na camada escondida.As condições de vida nos países desenvolvidos têm melhorado significativamente nas últimas décadas. Consequentemente aumentou a esperança média de vida e o número de doenças neurodegenerativas associadas ao envelhecimento como a Doença de Alzheimer (DA). Esta doença provoca demência, afeta o córtex cerebral e causa determinadas alterações na atividade elétrica do cérebro. Como tal, a análise dos sinais do eletroencefalograma (EEG) pode revelar carências estruturais e funcionais associadas à DA. Muito embora, diversos estudos sobre a atividade do EEG tenham revelado uma desaceleração do ritmo cerebral, juntamente com uma redução na complexidade dos sinais e uma perda de conectividade funcional do cérebro, o valor de diagnóstico ainda é muito limitado e importa por isso continuar a pesquisa para ajudar a reduzir o impacto da DA. Por conseguinte, neste estudo analisou-se a atividade espontânea do EEG de pacientes com diagnóstico de DA e um grupo de controlo composto por sujeitos cognitivamente normais, através de diferentes parâmetros espectrais. Utilizou-se a Power Spectral Density (PSD) baseada na Transformada de Fourier de Curta Duração (STFT) e a PSD baseada na Transformada de Wavelet (WT). Os melhores resultados foram obtidos com a PSD determinada pela STFT e deram uma correlação de coeficiente 0.963 no conjunto de teste com uma Rede Neuronal Artificial (RNA) desenvolvida com o algoritmo de treino Levenberg-Marquardt (trainlm), a função Logística Sigmoidal (logsig) e com 16 nós de entrada na camada escondida. Living conditions in developed countries have improved significantly in recent decades. Consequently has increased the life expectancy and the number of neurodegenerative diseases associated with aging as Alzheimer's Disease (AD). This disease leads to dementia, affects the cerebral cortex and causes certain changes in the electrical activity of brain. Therefore, the analysis of electroencephalogram signals (EEG) may reveal structural and functional deficiencies associated with AD. Although several studies about the EEG activity revealed a slowdown of brain rhythms along with a reduction in the complexity of signals and a loss of functional connectivity of brain, the diagnosis value is still very limited and we must continue the search trying to help to reduce AD impact. Therefore, in this study was analyzed the spontaneous EEG activity of patients with AD and a control group consisting of cognitively normal subjects through different spectral parameters. We used the Power Spectral Density (PSD) based on Short Time Fourier Transform (STFT) and the PSD based on Wavelet Transform (WT). The best results were performed using the PSD determined by STFT and they gave a correlation coefficient of 0.963 in the test set with an Artificial Neural Network (ANN) developed with the training algorithm Levenberg-Marquardt (trainlm), a Logistic sigmoid function (logsig) and with 16 nodes in the hidden layer

Independent component analysis for naive bayes classification

Ph.DDOCTOR OF PHILOSOPH

A method for detection of Alzheimer's disease using ICA-enhanced EEG measurements

OBJECTIVE: Many researchers have studied automatic EEG classification and recently a lot of work has been done on artefact-removal from EEG data using independent component analyses (ICA). However, demonstrating that a ICA-processed multichannel EEG measurement becomes more interpretable compared to the raw data (as is usually done in work on ICA-processing of EEG data) does not yet prove that detection of (incipient) anomalies is also better possible after ICA-processing. The objective of this study is to show that ICA-preprocessing is useful when constructing a detection system for Alzheimer's disease. METHODS AND MATERIAL: The paper describes a method for detection of EEG patterns indicative of Alzheimer's disease using automatic pattern recognition techniques. Our method incorporates an artefact removal stage based on ICA prior to automatic classification. The method is evaluated on measurements of a length of 8s from two groups of patients, where one group is in an initial stage of the disease (28 patients), whereas the other group is in a more progressed stage (15 patients). Both setups include a control group that should be classified as normal (10 and 21, respectively). RESULTS: Our final classification results for the group with severe Alzheimer's disease are comparable to the best results from literature. We show that ICA-based reduction of artefacts improves classification results for patients in an initial stage. CONCLUSION: We conclude that a more robust detection of Alzheimer's disease related EEG patterns may be obtained by employing ICA as ICA based pre-processing of EEG data can improve classification results for patients in an initial stage of Alzheimer's disease

Étude des substrats cérébraux associés au traitement sémantique dans le vieillissement pathologique et normal

Introduction : Les patients atteints de la variante sémantique de l'aphasie primaire progressive (vsAPP) et de trouble neurocognitif majeur dû à la maladie d'Alzheimer (MA) ont des déficits langagiers qui nuisent à leur qualité de vie. Ces déficits sont notamment liés à une atteinte du traitement de la signification des mots ou sémantique. Les thérapies actuelles, telles que les thérapies de rééducation du langage et la pharmacothérapie, ont une efficacité limitée pour améliorer les habiletés langagières chez les personnes atteintes de vsAPP et de MA. Une des raisons pouvant contribuer aux limites de l'efficacité de ces thérapies est qu'ils ne ciblent pas de façon spécifique les substrats cérébraux liés à l'atteinte du traitement sémantique. Or, ces substrats cérébraux ont besoin d'être mieux définis. Plusieurs mesures liées à l'anatomie, à l'activité électrophysiologique et à la modulation de l'activité cérébrale permettent d'étudier les substrats cérébraux associés au traitement sémantique. En ce qui a trait à l'anatomie, il est possible de mesurer le volume de matière grise du cerveau. Comme mesures de l'activité électrophysiologique, on note le potentiel évoqué N400 et la puissance des oscillations au repos. La modulation de l'activité cérébrale permet, pour sa part, d'explorer des liens de causalité entre des processus cognitifs et des réseaux cérébraux. Différentes techniques permettent d'obtenir ces mesures, telles la morphométrie cérébrale, l'électroencéphalographie (EEG) et la stimulation transcrânienne à courant continu (STCC). Objectif : L'objectif général de cette thèse est d'étudier les substrats cérébraux associés au traitement sémantique, au cours du vieillissement, afin de contribuer au développement d'approches thérapeutiques pour améliorer le langage dans le vieillissement pathologique. Hypothèse : L'anatomie et l'activité électrophysiologique cérébrale sont associées au traitement sémantique dans le vieillissement pathologique et normal, et la modulation de l'activité cérébrale permet d'explorer des liens de causalité entre le traitement sémantique et des régions cérébrales. Méthode : Pour atteindre notre objectif, nous avons effectué 4 études. L'étude 1 a pour but d'identifier les régions du cerveau dont le volume de matière grise est lié aux habiletés en lecture de mots, laquelle implique un traitement sémantique, chez des patients atteints de vsAPP et de MA et des aînés sains à l'aide de la morphométrie cérébrale. L'étude 2 a pour but d'étudier le traitement sémantique via le comportement et le potentiel évoqué N400 dans la MA et le vieillissement normal, par le biais d'une revue systématique. L'étude 3 a pour but d'étudier l'activité cérébrale associée au traitement sémantique chez des aînés sains en comparaison à celle de jeunes adultes avec l'EEG. L'étude 4 a pour but d'identifier les régions du cerveau auxquelles la STCC peut être appliquée pour moduler le traitement sémantique par le biais d'une revue de littérature. Résultats : L'étude 1 a révélé que le volume de matière grise du lobe temporal antérieur gauche est associé au nombre d'erreurs commises lors de la lecture de mots, qui implique un traitement sémantique. L'étude 2 a révélé des différences dans le potentiel évoqué N400 entre les personnes atteintes de MA et les aînés sains, ainsi qu'entre ces derniers et les adultes plus jeunes. L'étude 3 a révélé que, malgré un comportement similaire entre les aînés et les jeunes, l'activité électrophysiologique cérébrale associée au traitement sémantique diffère entre les groupes d'âge. L'étude 4 a révélé que la STCC appliquée à des régions des cortex frontal, temporal et pariétal peut moduler le traitement sémantique chez des adultes en santé. Conclusion : Des mesures de l'anatomie et de l'activité électrophysiologique, dont le volume de matière grise du lobe temporal du cerveau et le potentiel évoqué N400, sont associés au traitement sémantique dans le vieillissement pathologique et normal. Les mesures de la modulation de l'activité cérébrale renforcent le rôle de régions cérébrales temporales, frontales et pariétales dans le traitement sémantique. Ces études fournissent des pistes quant aux régions cérébrales qui pourraient être ciblées pour améliorer le traitement sémantique dans le vieillissement pathologique, tel que par l'utilisation de techniques de neuromodulation non-invasive.Introduction: Patients with the semantic variant of primary progressive aphasia (svPPA) and with Alzheimer's disease (AD) show language impairments that affect their quality of life. One of the main sources of language impairment in these populations is that they present with abnormal processing of the meaning of words (semantic processing deficits). Treatment options currently available, namely pharmacotherapy and language therapy, have limited effectiveness to improve language abilities in these patients. It is possible that a therapy that would directly target the neural substrates involved in semantic processing, unlike currently available therapies, could have a positive impact on language abilities. However, these neural substrates are not that well characterized. Neural substrates of semantic processing can be studied via different anatomical and electrophysiological brain measures, or through neuromodulation of brain activity. To study the structural brain anatomy and electrophysiological brain activity, one can measure respectively gray matter volume and the N400 event-related potential. The measurement of the effects of neuromodulation further allows to explore causal links between cognitive processes and targeted brain regions. Different techniques enable to collect these measures, such as voxel-based morphometry (VBM), electroencephalography (EEG) and transcranial direct current stimulation (tDCS). Objective: The main objective of this thesis is to study the brain substrates related to semantic processing in aging in order to contribute to the development of treatments aiming at improving language abilities. Hypothesis: Anatomical and electrophysiological brain measures are associated with semantic processing in pathological and healthy aging, and neuromodulation allows to explore causal links between semantic processing and brain regions. Methods: To achieve our objective, we conducted 4 studies. Study 1 aims at identifying the brain regions in which gray matter volume is associated with whole-word reading (which implies semantic processing) in patients with svPPA or AD and healthy elderly adults using VBM. Study 2 aims at investigating the N400 event-related potential, associated with semantic processing, in AD and healthy aging through a systematic review. Study 3 aims at investigating the electrophysiological brain activity associated with semantic processing in healthy elderly adults in comparison to young adults using EEG. Study 4 aims at identifying the brain regions that could be targeted with tDCS to modulate semantic processing through a literature review. Results: Study 1 revealed that gray matter volume of the left anterior temporal lobe is associated with the number of errors in whole-word reading, which implies semantic processing. Study 2 revealed differences in the N400 event-related potential between patients with AD and healthy elderly adults, as well as between healthy elderly and younger adults. Study 3 revealed that, despite a similar behavioral performance between elderly and younger adults, some of the electrophysiological activity patterns associated with semantic processing differed between the two age groups. Study 4 revealed that tDCS delivered over the frontal, temporal and parietal cortices can modulate semantic processing in healthy adults. Conclusion: Anatomical and electrophysiological brain measures, including gray matter volume and the N400 event-related potential, are associated with semantic processing in pathological and healthy aging. Neuromodulation measures strengthen the role of temporal, frontal and parietal brain regions in semantic processing. These studies outline brain regions that could be targeted with non-invasive neuromodulation techniques to improve semantic processing in pathological aging