Tensor Analysis and Fusion of Multimodal Brain Images

Current high-throughput data acquisition technologies probe dynamical systems with different imaging modalities, generating massive data sets at different spatial and temporal resolutions posing challenging problems in multimodal data fusion. A case in point is the attempt to parse out the brain structures and networks that underpin human cognitive processes by analysis of different neuroimaging modalities (functional MRI, EEG, NIRS etc.). We emphasize that the multimodal, multi-scale nature of neuroimaging data is well reflected by a multi-way (tensor) structure where the underlying processes can be summarized by a relatively small number of components or "atoms". We introduce Markov-Penrose diagrams - an integration of Bayesian DAG and tensor network notation in order to analyze these models. These diagrams not only clarify matrix and tensor EEG and fMRI time/frequency analysis and inverse problems, but also help understand multimodal fusion via Multiway Partial Least Squares and Coupled Matrix-Tensor Factorization. We show here, for the first time, that Granger causal analysis of brain networks is a tensor regression problem, thus allowing the atomic decomposition of brain networks. Analysis of EEG and fMRI recordings shows the potential of the methods and suggests their use in other scientific domains.Comment: 23 pages, 15 figures, submitted to Proceedings of the IEE

Damage identification in structural health monitoring: a brief review from its implementation to the Use of data-driven applications

The damage identification process provides relevant information about the current state of a structure under inspection, and it can be approached from two different points of view. The first approach uses data-driven algorithms, which are usually associated with the collection of data using sensors. Data are subsequently processed and analyzed. The second approach uses models to analyze information about the structure. In the latter case, the overall performance of the approach is associated with the accuracy of the model and the information that is used to define it. Although both approaches are widely used, data-driven algorithms are preferred in most cases because they afford the ability to analyze data acquired from sensors and to provide a real-time solution for decision making; however, these approaches involve high-performance processors due to the high computational cost. As a contribution to the researchers working with data-driven algorithms and applications, this work presents a brief review of data-driven algorithms for damage identification in structural health-monitoring applications. This review covers damage detection, localization, classification, extension, and prognosis, as well as the development of smart structures. The literature is systematically reviewed according to the natural steps of a structural health-monitoring system. This review also includes information on the types of sensors used as well as on the development of data-driven algorithms for damage identification.Peer ReviewedPostprint (published version

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

Nonconvulsive Epileptic Seizure Detection in Scalp EEG Using Multiway Data Analysis

Nonconvulsive status epilepticus is a condition where the patient is exposed to abnormally prolonged epileptic seizures without evident physical symptoms. Since these continuous seizures may cause permanent brain damage, it constitutes a medical emergency. This paper proposes a method to detect nonconvulsive seizures for a further nonconvulsive status epilepticus diagnosis. To differentiate between the normal and seizure electroencephalogram (EEG), a K-Nearest Neighbor, a Radial Basis Support Vector Machine, and a Linear Discriminant Analysis classifier are used. The classifier features are obtained from the Canonical Polyadic Decomposition (CPD) and Block Term Decomposition (BTD) of the EEG data represented as third order tensor. To expand the EEG into a tensor, Wavelet or Hilbert-Huang transform are used. The algorithm is tested on a scalp EEG database of 139 seizures of different duration. The experimental results suggest that a Hilbert-Huang tensor representation and the CPD analysis provide the most suitable framework for nonconvulsive seizure detection. The Radial Basis Support Vector Machine classifier shows the best performance with sensitivity, specificity, and accuracy values over 98%. A rough comparison with other methods proposed in the literature shows the superior performance of the proposed method for nonconvulsive epileptic seizure detection

Design and validation of a structural health monitoring system for aeronautical structures.

Structural Health Monitoring (SHM) is an area where the main objective is the verification of the state or the health of the structures in order to ensure proper performance and maintenance cost savings using a sensor network attached to the structure, continuous monitoring and algorithms. Different benefits are derived from the implementation of SHM, some of them are: knowledge about the behavior of the structure under different loads and different environmental changes, knowledge of the current state in order to verify the integrity of the structure and determine whether a structure can work properly or whether it needs to be maintained or replaced and, therefore, to reduce maintenance costs. The paradigm of damage identification (comparison between the data collected from the structure without damages and the current structure in orderto determine if there are any changes) can be tackled as a pattern recognition problem. Some statistical techniques as Principal Component Analysis (PCA) or Independent Component Analysis (ICA) are very useful for this purpose because they allow obtaining the most relevant information from a large amount of variables. This thesis uses an active piezoelectric system to develop statistical data driven approaches for the detection, localization and classification of damages in structures. This active piezoelectric system is permanently attached to the surface of the structure under test in order to apply vibrational excitations and sensing the dynamical responses propagated through the structure at different points. As pattern recognition technique, PCA is used to perform the main task of the proposed methodology: to build a base-line model of the structure without damage and subsequentlyto compare the data from the current structure (under test) with this model. Moreover, different damage indices are calculated to detect abnormalities in the structure under test. Besides, the localization of the damage can be determined by means of the contribution of each sensor to each index. This contribution is calculated by several different methods and their comparison is performed. To classify different damages, the damage detection methodology is extended using a Self-Organizing Map (SOM), which is properly trained and validated to build a pattern baseline model using projections of the data onto the PCAmodel and damage detection indices. This baseline is further used as a reference for blind diagnosis tests of structures. Additionally, PCA is replaced by ICAas pattern recognition technique. A comparison between the two methodologies is performed highlighting advantages and disadvantages. In order to study the performance of the damage classification methodology under different scenarios, the methodology is tested using data from a structure under several different temperatures. The methodologies developed in this work are tested and validated using different structures, in particular an aircraft turbine blade, an aircraft wing skeleton, an aircraft fuselage,some aluminium plates and some composite matarials plates.La monitorización de daños en estructuras (SHM por sus siglas en inglés) es un área que tiene como principal objetivo la verificación del estado o la salud de la estructura con el fin de asegurar el correcto funcionamiento de esta y ahorrar costos de mantenimiento. Para esto se hace uso de sensores que son adheridos a la estructura, monitorización continua y algoritmos. Diferentes beneficios se obtienen de la aplicación de SHM, algunos de ellos son: el conocimiento sobre el desempeño de la estructura cuando esta es sometida a diversas cargas y cambios ambientales, el conocimiento del estado actual de la estructura con el fin de determinar la integridad de la estructura y definir si esta puede trabajar adecuadamente o si por el contrario debe ser reparada o reemplazada con el correspondiente beneficio del ahorro de gastos de mantenimiento. El paradigma de la identificación de daños (comparación entre los datos obtenidos de la estructura sin daños y la estructura en un estado posterior para determinar cambios) puede ser abordado como un problema de reconocimiento de patrones. Algunas técnicas estadísticas tales como Análisis de Componentes Principales (PCA por sus siglas en inglés) o Análisis de Componentes Independientes (ICA por sus siglas en ingles) son muy útiles para este propósito puesto que permiten obtener la información más relevante de una gran cantidad de variables. Esta tesis hace uso de un sistema piezoeléctrico activo para el desarrollo de algoritmos estadísticos de manejo de datos para la detección, localización y clasificación de daños en estructuras. Este sistema piezoeléctrico activo está permanentemente adherido a la superficie de la estructura bajo prueba con el objeto de aplicar señales vibracionales de excitación y recoger las respuestas dinámicas propagadas a través de la estructura en diferentes puntos. Como técnica de reconocimiento de patrones se usa Análisis de Componentes Principales para realizar la tarea principal de la metodología propuesta: construir un modelo PCA base de la estructura sin daño y posteriormente compararlo con los datos de la estructura bajo prueba. Adicionalmente, algunos índices de daños son calculados para detectar anormalidades en la estructura bajo prueba. Para la localización de daños se usan las contribuciones de cada sensor a cada índice, las cuales son calculadas mediante varios métodos de contribución y comparadas para mostrar sus ventajas y desventajas. Para la clasificación de daños, se amplia la metodología de detección añadiendo el uso de Mapas auto-organizados, los cuales son adecuadamente entrenados y validados para construir un modelo patrón base usando proyecciones de los datos sobre el modelo PCA base e índices de detección de daños. Este patrón es usado como referencia para realizar un diagnóstico ciego de la estructura. Adicionalmente, dentro de la metodología propuesta, se utiliza ICA en lugar de PCA como técnica de reconocimiento de patrones. Se incluye también una comparación entre la aplicación de las dos técnicas para mostrar las ventajas y desventajas. Para estudiar el desempeño de la metodología de clasificación de daños bajo diferentes escenarios, esta se prueba usando datos obtenidos de una estructura sometida a diferentes temperaturas. Las metodologías desarrolladas en este trabajo fueron probadas y validadas usando diferentes estructuras, en particular un álabe de turbina, un esqueleto de ala y un fuselaje de avión, así como algunas placas de aluminio y de material compuest

A multi-way data analysis approach for structural health monitoring of a cable-stayed bridge

© The Author(s) 2018. A large-scale cable-stayed bridge in the state of New South Wales, Australia, has been extensively instrumented with an array of accelerometer, strain gauge, and environmental sensors. The real-time continuous response of the bridge has been collected since July 2016. This study aims at condition assessment of this bridge by investigating three aspects of structural health monitoring including damage detection, damage localization, and damage severity assessment. A novel data analysis algorithm based on incremental multi-way data analysis is proposed to analyze the dynamic response of the bridge. This method applies incremental tensor analysis for data fusion and feature extraction, and further uses one-class support vector machine on this feature to detect anomalies. A total of 15 different damage scenarios were investigated; damage was physically simulated by locating stationary vehicles with different masses at various locations along the span of the bridge to change the condition of the bridge. The effect of damage on the fundamental frequency of the bridge was investigated and a maximum change of 4.4% between the intact and damage states was observed which corresponds to a small severity damage. Our extensive investigations illustrate that the proposed technique can provide reliable characterization of damage in this cable-stayed bridge in terms of detection, localization and assessment. The contribution of the work is threefold; first, an extensive structural health monitoring system was deployed on a cable-stayed bridge in operation; second, an incremental tensor analysis was proposed to analyze time series responses from multiple sensors for online damage identification; and finally, the robustness of the proposed method was validated using extensive field test data by considering various damage scenarios in the presence of environmental variabilities