PET image classification using HHT-based features through fractal sampling

Medical image classification is currently a challenging task that can be used to aid the diagnosis of different brain diseases. Thus, exploratory and discriminative analysis techniques aiming to obtain rep- resentative features from the images, play a decisive role in the design of effective Computer Aided Diagnosis (CAD) systems, which is spe- cially important in the early diagnosis of dementias. In this work we present a technique that allows extracting discriminative features from Positron Emission Tomography (PET) by means of an Empirical Mode Decomposition-based (EEMD) method. This requires to transform the 3D PET image into a time series which is addressed by sampling the image using a fractal-based method which allows to preserve the spa- tial relationship among voxels. The devised technique has been used to classify images from the Alzheimer's Disease Neuroimaging Initiat- ive (ADNI) achieving up to a 90.5% accuracy in a differential diagnosis task (AD vs. controls), which proves that the information retrieved by our methodology is significantly linked to the disease.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Empirical Functional PCA for 3D Image Feature Extraction Through Fractal Sampling.

Medical image classification is currently a challenging task that can be used to aid the diagnosis of different brain diseases. Thus, exploratory and discriminative analysis techniques aiming to obtain representative features from the images play a decisive role in the design of effective Computer Aided Diagnosis (CAD) systems, which is especially important in the early diagnosis of dementia. In this work, we present a technique that allows using specific time series analysis techniques with 3D images. This is achieved by sampling the image using a fractal-based method which preserves the spatial relationship among voxels. In addition, a method called Empirical functional PCA (EfPCA) is presented, which combines Empirical Mode Decomposition (EMD) with functional PCA to express an image in the space spanned by a basis of empirical functions, instead of using components computed by a predefined basis as in Fourier or Wavelet analysis. The devised technique has been used to classify images from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and the Parkinson Progression Markers Initiative (PPMI), achieving accuracies up to 93% and 92% differential diagnosis tasks (AD versus controls and PD versus Controls, respectively). The results obtained validate the method, proving that the information retrieved by our methodology is significantly linked to the diseases.This work was partly supported by the MINECO/ FEDER under TEC2015-64718-R and PSI2015- 65848-R projects and the Consejer´ıa de Innovaci´on, Ciencia y Empresa (Junta de Andaluc´ıa, Spain) under the Excellence Project P11-TIC-7103 as well as the Salvador deMadariaga Mobility Grants 2017. Data collection and sharing for this project was funded by the ADNI (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Depart ment of Defense award number W81XWH-12-2- 0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contribu tions from the following: AbbVie, Alzheimer’s Asso ciation; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol Myer Squibb Company; CereSpir, Inc.; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Ho mann-La Roche Ltd and its ali ated company Genentech, Inc.; Fujirebio; GE Health care; IXICO Ltd.; Janssen Alzheimer Immunother apy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; P zer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clin ical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coor dinated by the Alzheimer’s Disease Cooperative Study at the University of California, San Diego. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern Cali fornia. PPMI a public-private partnership is funded by the Michael J. Fox Foundation for Parkinson’s Research and funding partners, including [list the full names of all of the PPMI funding partners found at www.ppmi-info.org/fundingpartners]

Hilbert-Huang Transform: biosignal analysis and practical implementation

Any system, however trivial, is subjected to data analysis on the signals it produces. Over the last 50 years the influx of new techniques and expansions of older ones have allowed a number of new applications, in a variety of fields, to be analysed and to some degree understood. One of the industries that is benefiting from this growth is the medical field and has been further progressed with the growth of interdisciplinary collaboration. From a signal processing perspective, the challenge comes from the complex and sometimes chaotic nature of the signals that we measure from the body, such as those from the brain and to some degree the heart. In this work we will make a contribution to dealing with such systems, in the form of a recent time-frequency data analysis method, the Hilbert-Huang Transform (HHT), and extensions to it. This thesis presents an analysis of the state of the art in seizure and heart arrhythmia detection and prediction methods. We then present a novel real-time implementation of the algorithm both in software and hardware and the motivations for doing so. First, we present our software implementation, encompassing realtime capabilities and identifying elements that need to be considered for practical use. We then translated this software into hardware to aid real-time implementation and integration. With these implementations in place we apply the HHT method to the topic of epilepsy (seizures) and additionally make contributions to heart arrhythmias and neonate brain dynamics. We use the HHT and some additional algorithms to quantify features associated with each application for detection and prediction. We also quantify significance of activity in such a way as to merge prediction and detection into one framework. Finally, we assess the real-time capabilities of our methods for practical use as a biosignal analysis tool

Fusion of musical contents, brain activity and short term physiological signals for music-emotion recognition

In this study we propose a multi-modal machine learning approach, combining EEG and Audio features for music emotion recognition using a categorical model of emotions. The dataset used consists of film music that was carefully created to induce strong emotions. Five emotion categories were adopted: Fear, Anger, Happy, Tender and Sad. EEG data was obtained from three male participants listening to the labeled music excerpts. Feature level fusion was adopted to combine EEG and Audio features. The results show that the multimodal system outperformed the EEG mono modal system. Additionally, we evaluated the contribution of each audio feature in the classification performance of the multimodal system. Preliminary results indicate a significant contribution of individual audio features in the classification accuracy, we also found that various audio features that noticeably contributed in the classification accuracy were also reported in previous research studying the correlation between audio features and emotion ratings using the same dataset.

Nondestructive Testing (NDT)

The aim of this book is to collect the newest contributions by eminent authors in the field of NDT-SHM, both at the material and structure scale. It therefore provides novel insight at experimental and numerical levels on the application of NDT to a wide variety of materials (concrete, steel, masonry, composites, etc.) in the field of Civil Engineering and Architecture

Information Theory and Its Application in Machine Condition Monitoring

Condition monitoring of machinery is one of the most important aspects of many modern industries. With the rapid advancement of science and technology, machines are becoming increasingly complex. Moreover, an exponential increase of demand is leading an increasing requirement of machine output. As a result, in most modern industries, machines have to work for 24 hours a day. All these factors are leading to the deterioration of machine health in a higher rate than before. Breakdown of the key components of a machine such as bearing, gearbox or rollers can cause a catastrophic effect both in terms of financial and human costs. In this perspective, it is important not only to detect the fault at its earliest point of inception but necessary to design the overall monitoring process, such as fault classification, fault severity assessment and remaining useful life (RUL) prediction for better planning of the maintenance schedule. Information theory is one of the pioneer contributions of modern science that has evolved into various forms and algorithms over time. Due to its ability to address the non-linearity and non-stationarity of machine health deterioration, it has become a popular choice among researchers. Information theory is an effective technique for extracting features of machines under different health conditions. In this context, this book discusses the potential applications, research results and latest developments of information theory-based condition monitoring of machineries

Brainwave-Based Human Emotion Estimation using Deep Neural Network Models for Biofeedback

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonEmotion is a state that comprehensively represents human feeling, thought and behavior, thus takes an important role in interpersonal human communication. Emotion estimation aims to automatically discriminate different emotional states by using physiological and nonphysiological signals acquired from human to achieve effective communication and interaction between human and machines. Brainwaves-Based Emotion Estimation is one of the most common used and efficient methods for emotion estimation research. The technology reveals a great role for human emotional disorder treatment, brain computer interface for disabilities, entertainment and many other research areas. In this thesis, various methods, schemes and frameworks are presented for Electroencephalogram (EEG) based human emotion estimation. Firstly, a hybrid dimension featurere duction scheme is presented using a total of 14 different features extracted from EEG recordings. The scheme combines these distinct features in the feature space using both supervised and unsupervised feature selection processes. Maximum Relevance Minimum Redundancy (mRMR) is applied to re-order the combined features into max-relevance with the emotion labels and min-redundancy of each feature. The generated features are further reduced with Principal Component Analysis (PCA) for extracting the principal components. Experimental results show that the proposed work outperforms the state-of-art methods using the same settings at the publicly available Database for Emotional Analysis using Physiological Signals (DEAP) data set. Secondly, a disentangled adaptive noise learning β-Variational autoencoder (VAE) combinewithlongshorttermmemory(LSTM)modelwasproposedfortheemotionrecognition based on EEG recordings. The experiment is also based on the EEG emotion public DEAPdataset. At first, the EEG time-series data are transformed into the Video-like EEG image data through the Azimuthal Equidistant Projection (AEP) to original EEG-sensor 3-D coordinates to perform 2-D projected locations of electrodes. Then Clough-Tocher scheme is applied for interpolating the scattered power measurements over the scalp and for estimating the values in-between the electrodes over a 32x32 mesh. After that, the βVAE LSTM algorithm is used to estimate the accuracy of the quadratic (arousal-valence) classification. The comparison between the β VAE-LSTM model and other classic methods is conducted at the same experimental setting that shows that the proposed model is effective. Finally, a novel real-time emotion detection system based on the EEG signals from a portable headband was presented, integrated into the interactive film ‘RIOT’. At first, the requirement of the interactive film was collected and the protocol for data collection using a portable EEG sensor (Emotiv Epoc) was designed. Then, a portable EEG emotion database (PEED) is built from 10 participants with the emotion labels using both self-reporting and video annotation tools. After that, various feature extraction, feature selection, validation scheme and classification methods are explored to build a practical system for the real-time detection. In the end, the emotion detection system is trained and integrated into the interactive film for real-time implementation and fully evaluated. The experimental results demonstrate the system with satisfied emotion detection accuracy and real-time performance

Sensor-based Nonlinear and Nonstationary Dynaimc Analysis of Online Structural Health Monitoring

This dissertation focuses on robust online Structural Health Monitoring (SHM) framework for civil engineering structures. The proposed framework improves the diagnostic and prognostic schemes for damage-state awareness and structural life prediction in civil engineering structures. The underlying research achieves three main objectives, namely, (1) sensor placement optimization using partial differential equation modeling and Fisher information matrix, (2) structural damage detection using quasi-recursive correlation dimension (QRCD), and (3) structural damage prediction using online empirical mode decomposition (EMD).The research methodology includes three research tasks: Firstly, to formulate the optimal criteria for the sensor placement optimization damage detection problem based upon a partial differential equation (PDE) analytical model. The PDE model is derived and then validated through experimental results using correlation analysis. Secondly, to develop a novel quasi-recursive correlation dimension method for structural damage detection. The QRCD algorithm is integrated with an attractor analysis and overlapping windowing technique. Thirdly, to design an online structural damage prediction method based on empirical mode decomposition. The proposed SHM prediction scheme consists of two steps: prediction based change point detection using Hilbert instantaneous phase, and damage severity prediction using the energy index of the most representative intrinsic mode function (IMF).Study results show that; (1) the proposed optimal sensor placement method leads to an optimal spatial location for a collection of sensors, which are sensitive to structural damage, (2) the proposed damage detection algorithm can significantly alleviate the complexity of computation for correlation dimension to approximate O(N), making the online monitoring of nonlinear/nonstationary processes more applicable and efficient; and (3) the proposed empirical mode decomposition method for online damage prediction overcomes the boundary effects of the sifting process, and it has significant prediction accuracy improvement (greater than 30%) over other commonly used prediction techniques.Industrial Engineering & Managemen