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

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

EEG filtering based on blind source separation (BSS) for early detection of Alzheimer's disease

Objective: Development of an EEG preprocessing technique for improvement of detection of Alzheimer’s disease (AD). The technique is based on filtering of EEG data using blind source separation (BSS) and projection of components which are possibly sensitive to cortical neuronal impairment found in early stages of AD. Method: Artifact-free 20 s intervals of raw resting EEG recordings from 22 patients with Mild Cognitive Impairment (MCI) who later proceeded to AD and 38 age-matched normal controls were decomposed into spatio-temporally decorrelated components using BSS algorithm ‘AMUSE’. Filtered EEG was obtained by back projection of components with the highest linear predictability. Relative power of filtered data in delta, theta, alpha1, alpha2, beta1, and beta 2 bands were processed with Linear Discriminant Analysis (LDA). Results: Preprocessing improved the percentage of correctly classified patients and controls computed with jack-knifing cross-validation from 59 to 73% and from 76 to 84%, correspondingly. Conclusions: The proposed approach can significantly improve the sensitivity and specificity of EEG based diagnosis. Significance: Filtering based on BSS can improve the performance of the existing EEG approaches to early diagnosis of Alzheimer’s disease. It may also have potential for improvement of EEG classification in other clinical areas or fundamental research. The developed method is quite general and flexible, allowing for various extensions and improvements. q 2004 Published by Elsevier Ireland Ltd. on behalf of International Federation of Clinical Neurophysiology

Graph neural networks for electroencephalogram analysis

El objetivo de este trabajo es proporcionar un modelo capaz de identificar la enfermedad de Alzheimer y el Deterioro Cognitivo Leve (DCL) en registros de electroencefalogramas (EEG). A pesar de que los EEGs son una de las pruebas más pruebas utilizadas para los trastornos neurológicos, hoy en día el diagnóstico de estas enfermedades se basa en el comportamiento del paciente. comportamiento del paciente. Esto se debe a que la precisión de los expertos en el reconocimiento visual de los EEGs se estima en torno al 50%. Para resolver las dificultades de la tarea mencionada, esta tesis propone un modelo de Red Neural Gráfica (GNN) para clasificar a los sujetos utilizando únicamente las señales registradas. Para desarrollar el modelo final, primero propusimos varios procedimientos para construir gráficos a partir de las señales de EEGs, explorando diferentes formas de representar la conectividad entre canales, así como métodos para la extracción de de las características relevantes. Por el momento, no hay modelos GNN propuestos para la detección de Alzheimer o DCL. Por lo tanto, utilizamos arquitecturas empleadas en tareas similares y las modificamos para nuestro dominio específico. Por último, se evalúa un conjunto de combinaciones coherentes de grafos y modelos GNN bajo el mismo conjunto de métricas. Además, para las combinaciones con mejor rendimiento, se realiza un estudio del impacto de varios hiperparámetros se lleva a cabo. Con el fin de manejar todos los experimentos posibles, hemos desarrollado un marco de software para construir fácilmente construir los diferentes tipos de gráficos, crear los modelos y evaluar su rendimiento. La mejor combinación de construcción de grafos y diseño de modelos, basada en capas convolucionales de atención a los grafos, conduce a un 92,31% de precisión en la clasificación binaria de sujetos sanos y enfermos de Alzheimer y a un 87,59% de precisión cuando se evalúan también las grabaciones de pacientes con Deterioro Cognitivo Leve, que son comparables a los resultados del estado del arte. resultados del estado del arte. Aunque este trabajo se realiza en un campo novedoso y existen muchas posibilidades aún posibilidades aún por explorar, concluimos que las GNNs muestran capacidades sobrehumanas para la detección de Alzheimer y DCL utilizando EEGs.The aim of this work is to provide a model able to identify Alzheimer's disease and Mild Cognitive Impairment (MCI) in electroencephalogram's (EEGs) recordings. Despite EEGs being one of the most common tests used for neurological disorders, nowadays the diagnose of these diseases is based on the patient's behaviour. This is because expert's accuracy on EEGs visual recognition is estimated to be around 50%. To solve the difficulties of the aforementioned task, this thesis proposes a Graph Neural Network (GNN) model to classify the subjects using only the recorded signals. To develop the final model, first we proposed several procedures to build graphs from the EEGs signals, exploring different ways of representing the inter-channel connectivity as well as methods for relevant features extraction. For the time being, there are not GNN models proposed for Alzheimer or MCI detection. Hence, we used architectures employed by similar tasks and modified them for our specific domain. Finally, a set of coherent combinations of graph and GNN model is evaluated under the same set of metrics. Moreover, for the best performing combinations, a study of the impact of several hyperparameters is carried out. In order to handle all the possible experiments, we developed a software framework to easily build the different types of graphs, create the models and evaluate their performance. The best combination of graph building and model design, based on graph attention convolutional layers, leads to a 92.31% of accuracy in the binary classification of healthy subjects and Alzheimer's patients and to a 87.59% of accuracy when also evaluating MCI patients recordings, these are comparable to state of the art results. Although this work is done within a novel field and there exist many possibilities yet to be explored, we conclude that GNNs show super-human capabilities for Alzheimer and MCI detection using EEGs

Ensemble of classifiers based data fusion of EEG and MRI for diagnosis of neurodegenerative disorders

The prevalence of Alzheimer\u27s disease (AD), Parkinson\u27s disease (PD), and mild cognitive impairment (MCI) are rising at an alarming rate as the average age of the population increases, especially in developing nations. The efficacy of the new medical treatments critically depends on the ability to diagnose these diseases at the earliest stages. To facilitate the availability of early diagnosis in community hospitals, an accurate, inexpensive, and noninvasive diagnostic tool must be made available. As biomarkers, the event related potentials (ERP) of the electroencephalogram (EEG) - which has previously shown promise in automated diagnosis - in addition to volumetric magnetic resonance imaging (MRI), are relatively low cost and readily available tools that can be used as an automated diagnosis tool. 16-electrode EEG data were collected from 175 subjects afflicted with Alzheimer\u27s disease, Parkinson\u27s disease, mild cognitive impairment, as well as non-disease (normal control) subjects. T2 weighted MRI volumetric data were also collected from 161 of these subjects. Feature extraction methods were used to separate diagnostic information from the raw data. The EEG signals were decomposed using the discrete wavelet transform in order to isolate informative frequency bands. The MR images were processed through segmentation software to provide volumetric data of various brain regions in order to quantize potential brain tissue atrophy. Both of these data sources were utilized in a pattern recognition based classification algorithm to serve as a diagnostic tool for Alzheimer\u27s and Parkinson\u27s disease. Support vector machine and multilayer perceptron classifiers were used to create a classification algorithm trained with the EEG and MRI data. Extracted features were used to train individual classifiers, each learning a particular subset of the training data, whose decisions were combined using decision level fusion. Additionally, a severity analysis was performed to diagnose between various stages of AD as well as a cognitively normal state. The study found that EEG and MRI data hold complimentary information for the diagnosis of AD as well as PD. The use of both data types with a decision level fusion improves diagnostic accuracy over the diagnostic accuracy of each individual data source. In the case of AD only diagnosis, ERP data only provided a 78% diagnostic performance, MRI alone was 89% and ERP and MRI combined was 94%. For PD only diagnosis, ERP only performance was 67%, MRI only was 70%, and combined performance was 78%. MCI only diagnosis exhibited a similar effect with a 71% ERP performance, 82% MRI performance, and 85% combined performance. Diagnosis among three subject groups showed the same trend. For PD, AD, and normal diagnosis ERP only performance was 43%, MRI only was 66%, and combined performance was 71%. The severity analysis for mild AD, severe AD, and normal subjects showed the same combined effect

Assessing the Potential of Data Augmentation in EEG Functional Connectivity for Early Detection of Alzheimer’s Disease

Electroencephalographic (EEG) signals are acquired non-invasively from electrodes placed on the scalp. Experts in the field can use EEG signals to distinguish between patients with Alzheimer’s disease (AD) and normal control (NC) subjects using classification models. However, the training of deep learning or machine learning models requires a large number of trials. Datasets related to Alzheimer’s disease are typically small in size due to the lack of AD patient samples. The lack of data samples required for the training process limits the use of deep learning techniques for further development in clinical settings. We propose to increase the number of trials in the training set by means of a decomposition–recombination system consisting of three steps. Firstly, the original signals from the training set are decomposed into multiple intrinsic mode functions via multivariate empirical mode decomposition. Next, these intrinsic mode functions are randomly recombined across trials. Finally, the recombined intrinsic mode functions are added together as artificial trials, which are used for training the models. We evaluated the decomposition–recombination system on a small dataset using each subject’s functional connectivity matrices as inputs. Three different neural networks, including ResNet, BrainNet CNN, and EEGNet, were used. Overall, the system helped improve ResNet training in both the mild AD dataset, with an increase of 5.24%, and in the mild cognitive impairment dataset, with an increase of 4.50%. The evaluation of the proposed data augmentation system shows that the performance of neural networks can be improved by enhancing the training set with data augmentation. This work shows the need for data augmentation on the training of neural networks in the case of small-size AD datasets.Fil: Jia, Hao. Universitat de Vic; España. Nankai University; ChinaFil: Huang, Zihao. Nankai University; ChinaFil: Caiafa, César Federico. Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Instituto Argentino de Radioastronomía. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto Argentino de Radioastronomía; ArgentinaFil: Duan, Feng. Nankai University; ChinaFil: Zhang, Yu. Lehigh University; Estados UnidosFil: Sun, Zhe. Juntendo University; ChinaFil: Solé Casals, Jordi. Universitat de Vic; Españ

Characterization of the spontaneous EEG activity in the Alzheimer's disease continuum: from local activation to network organization

La presente Tesis Doctoral se presenta como un compendio de cuatro publicaciones indexadas en el Journal Citation Reports. El objetivo de estas publicaciones es la caracterización de los cambios neuronales subyacentes en las diferentes etapas de la enfermedad de Alzheimer (EA) y su etapa prodrómica, el deterioro cognitivo leve (DCL), siguiendo tres niveles de análisis: activación local, interacción entre pares de sensores, y organización de red. Los principales cambios encontrados a medida que progresa la enfermedad son: (i) una lentificación, y una pérdida de complejidad e irregularidad de la actividad EEG espontánea; (ii) una disminución significativa de la conectividad en bandas altas de frecuencia y un aumento en las bandas bajas; y (iii) una pérdida en la integración y la segregación de las redes neuronales. Estos hallazgos han proporcionado información adicional sobre las alteraciones cerebrales de la EA en sus diferentes etapas, útiles para comprender mejor sus mecanismos fisiopatológicos.Departamento de Teoría de la Señal y Comunicaciones e Ingeniería TelemáticaDoctorado en Tecnologías de la Información y las Telecomunicacione