Abstract Personalized health monitoring and prediction are indispensable in advancing healthcare delivery, particularly amidst the escalating prevalence of chronic illnesses and the aging population. Deep learning (DL) stands out as a promising avenue for crafting personalized health monitoring systems adept at forecasting health outcomes with precision and efficiency. As personal health data becomes increasingly accessible, DL‐based methodologies offer a compelling strategy for enhancing healthcare provision through accurate and timely prognostications of health conditions. This article offers a comprehensive examination of recent advancements in employing DL for personalized health monitoring and prediction. It summarizes a diverse range of DL architectures and their practical implementations across various realms, such as wearable technologies, electronic health records (EHRs), and data accumulated from social media platforms. Moreover, it elucidates the obstacles encountered and outlines future directions in leveraging DL for personalized health monitoring, thereby furnishing invaluable insights into the immense potential of DL in this domain.

Neurodegenerative diseases pose a formidable challenge to medical research, demanding a nuanced understanding of their progressive nature. In this regard, latent generative models can effectively be used in a data-driven modeling of different dimensions of neurodegeneration, framed within the context of the manifold hypothesis. This paper proposes a joint framework for a multi-modal, common latent generative model to address the need for a more comprehensive understanding of the neurodegenerative landscape in the context of Parkinson’s disease (PD). The proposed architecture uses coupled variational autoencoders (VAEs) to joint model a common latent space to both neuroimaging and clinical data from the Parkinson’s Progression Markers Initiative (PPMI). Alternative loss functions, different normalization procedures, and the interpretability and explainability of latent generative models are addressed, leading to a model that was able to predict clinical symptomatology in the test set, as measured by the unified Parkinson’s disease rating scale (UPDRS), with R2 up to 0.86 for same-modality and 0.441 cross-modality (using solely neuroimaging). The findings provide a foundation for further advancements in the field of clinical research and practice, with potential applications in decision-making processes for PD. The study also highlights the limitations and capabilities of the proposed model, emphasizing its direct interpretability and potential impact on understanding and interpreting neuroimaging patterns associated with PD symptomatology.

A growing body of literature has proved that general type-2 fuzzy control systems (GT2 FCSs) are reliable, robust, and safe control systems against severe external disturbances, high levels of noise, and uncertainty that are inevitable in real-world applications. Further studies on the GT2 FCSs are therefore needed to provide new insights into these control systems, in particular over their stability and computational complexity problems. Unlike the existing works on stability analysis of GT2 FCSs in the time domain, the aim of this article is to assess the stability properties of these systems in the frequency domain through a novel intuitive method. Before delving into stability analysis, an initial step involves reducing computational complexity. This is achieved by introducing a streamlined version of the GT2 fuzzy controller (GT2 FC). This simplified architecture is constructed using a series of zSlices-based interval type-2 fuzzy-logic controls (zIT2 FLCs) situated at specific zLevels. Each zIT2 FLC is composed of two embedded type-1 fuzzy FLCs (T1 FLCs). The subsequent phase entails a methodical procedure for assessing stability based on the existence of limit cycles. The initial stage involves the linearization of the simplified GT2 FC through the derivation of its describing function (DF). Next, a combination of the parameter plane approach and the particle swarm optimization (PSO) technique is leveraged. These techniques serve the purpose of pinpointing the regions corresponding to limit cycles and asymptotic stability with a focus on improving the stability boundary. Following this, the analysis shifts toward quantifying the system’s resilience in the face of uncertainty. Stability margins required to generate a limit cycle are calculated using stability equations. This step provides a measure of how robust the closed-loop system remains under varying degrees of uncertainty. Finally, three simulation examples are presented to justify the advantages of the proposed approach.

The combination of different sources of information is currently one of the most relevant aspects in the diagnostic process of several diseases. In the field of neurological disorders, different imaging modalities providing structural and functional information are frequently available. Those modalities are usually analyzed separately, although a joint of the features extracted from both sources can improve the classification performance of Computer-Aided Diagnosis (CAD) tools. Previous studies have computed independent models from each individual modality and combined them in a subsequent stage, which is not an optimum solution. In this work, we propose a method based on the principles of siamese neural networks to fuse information from Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET). This framework quantifies the similarities between both modalities and relates them with the diagnostic label during the training process. The resulting latent space at the output of this network is then entered into an attention module in order to evaluate the relevance of each brain region at different stages of the development of Alzheimer's disease. The excellent results obtained and the high flexibility of the method proposed allow fusing more than two modalities, leading to a scalable methodology that can be used in a wide range of contexts.

Parkinson's Disease (PD) is the second most prevalent neurodegenerative disorder among adults. Although its triggers are still not clear, they may be due to a combination of different types of biomarkers measured through medical imaging, metabolomics, proteomics or genetics, among others. In this context, we have proposed a Computer-Aided Diagnosis (CAD) system that combines structural and functional imaging data from subjects in Parkinson's Progression Markers Initiative dataset by means of an Ensemble Learning methodology trained to identify and penalize input sources with low classification rates and/ or high-variability. This proposal improves results published in recent years and provides an accurate solution not only from the point of view of image preprocessing (including a comparison between different intensity preservation techniques), but also in terms of dimensionality reduction methods (Isomap). In addition, we have also introduced a bagging classification schema for scenarios with unbalanced data. As shown by our results, the CAD proposal is able to detect PD with [Formula: see text] of balanced accuracy, and opens up the possibility of combining any number of input data sources relevant for PD.

The prevalence of dementia is currently increasing worldwide. This syndrome produces a deterioration in cognitive function that cannot be reverted. However, an early diagnosis can be crucial for slowing its progress. The Clock Drawing Test (CDT) is a widely used paper-and-pencil test for cognitive assessment in which an individual has to manually draw a clock on a paper. There are a lot of scoring systems for this test and most of them depend on the subjective assessment of the expert. This study proposes a computer-aided diagnosis (CAD) system based on artificial intelligence (AI) methods to analyze the CDT and obtain an automatic diagnosis of cognitive impairment (CI). This system employs a preprocessing pipeline in which the clock is detected, centered and binarized to decrease the computational burden. Then, the resulting image is fed into a Convolutional Neural Network (CNN) to identify the informative patterns within the CDT drawings that are relevant for the assessment of the patient's cognitive status. Performance is evaluated in a real context where patients with CI and controls have been classified by clinical experts in a balanced sample size of [Formula: see text] drawings. The proposed method provides an accuracy of [Formula: see text] in the binary case-control classification task, with an AUC of [Formula: see text]. These results are indeed relevant considering the use of the classic version of the CDT. The large size of the sample suggests that the method proposed has a high reliability to be used in clinical contexts and demonstrates the suitability of CAD systems in the CDT assessment process. Explainable artificial intelligence (XAI) methods are applied to identify the most relevant regions during classification. Finding these patterns is extremely helpful to understand the brain damage caused by CI. A validation method using resubstitution with upper bound correction in a machine learning approach is also discussed.

Deep learning has become a primary choice in medical image analysis due to its powerful representation capability. However, most existing deep learning models designed for medical image classification can only perform well on a specific disease. The performance drops dramatically when it comes to other diseases. Generalizability remains a challenging problem. In this paper, we propose an evolutionary attention-based network (EDCA-Net), which is an effective and robust network for medical image classification tasks. To extract task-related features from a given medical dataset, we first propose the densely connected attentional network (DCA-Net) where feature maps are automatically channel-wise weighted, and the dense connectivity pattern is introduced to improve the efficiency of information flow. To improve the model capability and generalizability, we introduce two types of evolution: intra- and inter-evolution. The intra-evolution optimizes the weights of DCA-Net, while the inter-evolution allows two instances of DCA-Net to exchange training experience during training. The evolutionary DCA-Net is referred to as EDCA-Net. The EDCA-Net is evaluated on four publicly accessible medical datasets of different diseases. Experiments showed that the EDCA-Net outperforms the state-of-the-art methods on three datasets and achieves comparable performance on the last dataset, demonstrating good generalizability for medical image classification.

COVID-19 pneumonia started in December 2019 and caused large casualties and huge economic losses. In this study, we intended to develop a computer-aided diagnosis system based on artificial intelligence to automatically identify the COVID-19 in chest computed tomography images. We utilized transfer learning to obtain the image-level representation (ILR) based on the backbone deep convolutional neural network. Then, a novel neighboring aware representation (NAR) was proposed to exploit the neighboring relationships between the ILR vectors. To obtain the neighboring information in the feature space of the ILRs, an ILR graph was generated based on the k-nearest neighbors algorithm, in which the ILRs were linked with their k-nearest neighboring ILRs. Afterward, the NARs were computed by the fusion of the ILRs and the graph. On the basis of this representation, a novel end-to-end COVID-19 classification architecture called neighboring aware graph neural network (NAGNN) was proposed. The private and public data sets were used for evaluation in the experiments. Results revealed that our NAGNN outperformed all the 10 state-of-the-art methods in terms of generalization ability. Therefore, the proposed NAGNN is effective in detecting COVID-19, which can be used in clinical diagnosis.

Implantable high-density multichannel neural recording microsystems provide simultaneous recording of brain activities. Wireless transmission of the entire recorded data causes high bandwidth usage, which is not tolerable for implantable applications. As a result, a hardware-friendly compression module is required to reduce the amount of data before it is transmitted. This paper presents a novel compression approach that utilizes a spike extractor and a vector quantization (VQ)-based spike compressor. In this approach, extracted spikes are vector quantized using an unsupervised learning process providing a high spike compression ratio (CR) of 10–80. A combination of extracting and compressing neural spikes results in a significant data reduction as well as preserving the spike waveshapes. The compression performance of the proposed approach was evaluated under variant conditions. We also developed new architectures such that the hardware blocks of our approach can be implemented more efficiently. The compression module was implemented in a 180-nm standard CMOS process achieving a SNDR of 14.49[Formula: see text]dB and a classification accuracy (CA) of 99.62% at a CR of 20, while consuming 4[Formula: see text][Formula: see text]W power and 0.16[Formula: see text]mm2 chip area per channel.

The automation in the diagnosis of medical images is currently a challenging task. The use of Computer Aided Diagnosis (CAD) systems can be a powerful tool for clinicians, especially in situations when hospitals are overflowed. These tools are usually based on artificial intelligence (AI), a field that has been recently revolutionized by deep learning approaches. These alternatives usually obtain a large performance based on complex solutions, leading to a high computational cost and the need of having large databases. In this work, we propose a classification framework based on sparse coding. Images are first partitioned into different tiles, and a dictionary is built after applying PCA to these tiles. The original signals are then transformed as a linear combination of the elements of the dictionary. Then, they are reconstructed by iteratively deactivating the elements associated with each component. Classification is finally performed employing as features the subsequent reconstruction errors. Performance is evaluated in a real context where distinguishing between four different pathologies: control versus bacterial pneumonia versus viral pneumonia versus COVID-19. Our system differentiates between pneumonia patients and controls with an accuracy of 97.74%, whereas in the 4-class context the accuracy is 86.73%. The excellent results and the pioneering use of sparse coding in this scenario evidence that our proposal can assist clinicians when their workload is high.

Spatial normalization helps us to compare quantitatively two or more input brain scans. Although using an affine normalization approach preserves the anatomical structures, the neuroimaging field is more common to find works that make use of nonlinear transformations. The main reason is that they facilitate a voxel-wise comparison, not only when studying functional images but also when comparing MRI scans given that they fit better to a reference template. However, the amount of bias introduced by the nonlinear transformations can potentially alter the final outcome of a diagnosis especially when studying functional scans for neurological disorders like Parkinson's Disease. In this context, we have tried to quantify the bias introduced by the affine and the nonlinear spatial registration of FP-CIT SPECT volumes of healthy control subjects and patients with PD. For that purpose, we calculated the deformation fields of each participant and applied these deformation fields to a 3D-grid. As the space between the edges of small cubes comprising the grid change, we can quantify which parts from the brain have been enlarged, compressed or just remain the same. When the nonlinear approach is applied, scans from PD patients show a region near their striatum very similar in shape to that of healthy subjects. This artificially increases the interclass separation between patients with PD and healthy subjects as the local intensity is decreased in the latter region, and leads machine learning systems to biased results due to the artificial information introduced by these deformations.

There remains an open question about the usefulness and the interpretation of machine learning (ML) approaches for discrimination of spatial patterns of brain images between samples or activation states. In the last few decades, these approaches have limited their operation to feature extraction and linear classification tasks for between-group inference. In this context, statistical inference is assessed by randomly permuting image labels or by the use of random effect models that consider between-subject variability. These multivariate ML-based statistical pipelines, whilst potentially more effective for detecting activations than hypotheses-driven methods, have lost their mathematical elegance, ease of interpretation, and spatial localization of the ubiquitous General linear Model (GLM). Recently, the estimation of the conventional GLM parameters has been demonstrated to be connected to an univariate classification task when the design matrix in the GLM is expressed as a binary indicator matrix. In this paper we explore the complete connection between the univariate GLM and ML-based regressions. To this purpose we derive a refined statistical test with the GLM based on the parameters obtained by a linear Support Vector Regression (SVR) in the inverse problem (SVR-iGLM). Subsequently, random field theory (RFT) is employed for assessing statistical significance following a conventional GLM benchmark. Experimental results demonstrate how parameter estimations derived from each model (mainly GLM and SVR) result in different experimental design estimates that are significantly related to the predefined functional task. Moreover, using real data from a multisite initiative the proposed ML-based inference demonstrates statistical power and the control of false positives, outperforming the regular GLM.

A connection between the general linear model (GLM) with frequentist statistical testing and machine learning (MLE) inference is derived and illustrated. Initially, the estimation of GLM parameters is expressed as a Linear Regression Model (LRM) of an indicator matrix; that is, in terms of the inverse problem of regressing the observations. Both approaches, i.e. GLM and LRM, apply to different domains, the observation and the label domains, and are linked by a normalization value in the least-squares solution. Subsequently, we derive a more refined predictive statistical test: the linear Support Vector Machine (SVM), that maximizes the class margin of separation within a permutation analysis. This MLE-based inference employs a residual score and associated upper bound to compute a better estimation of the actual (real) error. Experimental results demonstrate how parameter estimations derived from each model result in different classification performance in the equivalent inverse problem. Moreover, using real data, the MLE-based inference including model-free estimators demonstrates an efficient trade-off between type I errors and statistical power.

In recent years, several computer-aided diagnosis (CAD) systems have been proposed for an early identification of dementia. Although these approaches have mostly used the transformation of data into a different feature space, more precise information can be gained from a Searchlight strategy. The current study presents a data fusion classification system that employs magnetic resonance imaging (MRI) and neuropsychological tests to distinguish between Mild-Cognitive Impairment (MCI) patients that convert to Alzheimer’s disease (AD) and those that remain stable. Specifically, this method uses a nested cross-validation procedure to compute the optimum contribution of each data modality in the final decision. The model employs Support-Vector Machine (SVM) classifiers for both data modalities and is combined with Searchlight when applied to neuroimaging. We compared the performance of our system with an alternative based on Principal Component Analysis (PCA) for dimensionality reduction. Results show that Searchlight outperformed PCA both for uni/multimodal classification, obtaining a maximum accuracy of 80.9% when combining data from six and twelve months before patients converted to AD. Moreover, Searchlight allowed the identification of the most informative regions at different stages of the longitudinal study, which can be crucial for a better understanding of the development of AD. Additionally, results do not depend on the parcellations provided by a specific brain atlas, which manifests the robustness and the spatial precision of the method proposed.

No abstract available.

This work was partly supported by the MINECO/ FEDER under the RTI2018-098913-B100, CV20-45250 and A-TIC-080-UGR18 projects.

Finding new biomarkers to model Parkinson's Disease (PD) is a challenge not only to help discerning between Healthy Control (HC) subjects and patients with potential PD but also as a way to measure quantitatively the loss of dopaminergic neurons mainly concentrated at substantia nigra. Within this context, this work presented here tries to provide a set of imaging features based on morphological characteristics extracted from I[Formula: see text]-Ioflupane SPECT scans to discern between HC and PD participants in a balanced set of [Formula: see text] scans from Parkinson's Progression Markers Initiative (PPMI) database. These features, obtained from isosurfaces of each scan at different intensity levels, have been classified through the use of classical Machine Learning classifiers such as Support-Vector-Machines (SVM) or Naïve Bayesian and compared with the results obtained using a Multi-Layer Perceptron (MLP). The proposed system, based on a Mann-Whitney-Wilcoxon U-Test for feature selection and the SVM approach, yielded a [Formula: see text] balanced accuracy when the performance was evaluated using a [Formula: see text]-fold cross-validation. This proves the reliability of these biomarkers, especially those related to sphericity, center of mass, number of vertices, 2D-projected perimeter or the 2D-projected eccentricity, among others, but including both internal and external isosurfaces.

Deep learning models for MRI classification face two recurring problems: they are typically limited by low sample size, and are abstracted by their own complexity (the "black box problem"). In this paper, we train a convolutional neural network (CNN) with the largest multi-source, functional MRI (fMRI) connectomic dataset ever compiled, consisting of 43,858 datapoints. We apply this model to a cross-sectional comparison of autism spectrum disorder (ASD) versus typically developing (TD) controls that has proved difficult to characterize with inferential statistics. To contextualize these findings, we additionally perform classifications of gender and task versus rest. Employing class-balancing to build a training set, we trained [Formula: see text] modified CNNs in an ensemble model to classify fMRI connectivity matrices with overall AUROCs of 0.6774, 0.7680, and 0.9222 for ASD versus TD, gender, and task versus rest, respectively. Additionally, we aim to address the black box problem in this context using two visualization methods. First, class activation maps show which functional connections of the brain our models focus on when performing classification. Second, by analyzing maximal activations of the hidden layers, we were also able to explore how the model organizes a large and mixed-center dataset, finding that it dedicates specific areas of its hidden layers to processing different covariates of data (depending on the independent variable analyzed), and other areas to mix data from different sources. Our study finds that deep learning models that distinguish ASD from TD controls focus broadly on temporal and cerebellar connections, with a particularly high focus on the right caudate nucleus and paracentral sulcus.

A central challenge in cognitive neuroscience is to understand the neural mechanisms that underlie the capacity to control our behavior according to internal goals. Flanker tasks, which require responding to stimuli surrounded by distracters that trigger incompatible action tendencies, are frequently used to measure this conflict. Even though the interference generated in these situations has been broadly studied, multivariate analysis techniques can shed new light into the underlying neural mechanisms. The current study is an initial approximation to adapt an interference Flanker paradigm embedded in a Demand-Selection Task (DST) to a format that allows measuring concurrent high-density electroencephalography (EEG). We used multivariate pattern analysis (MVPA) to decode conflict-related electrophysiological markers associated with congruent or incongruent target events in a time-frequency resolved way. Our results replicate findings obtained with other analysis approaches and offer new information regarding the dynamics of the underlying mechanisms, which show signs of reinstantiation. Our findings, some of which could not have been obtained with classic analytical strategies, open novel avenues of research.

The Temporal Sampling Framework (TSF) theorizes that the characteristic phonological difficulties of dyslexia are caused by an atypical oscillatory sampling at one or more temporal rates. The LEEDUCA study conducted a series of Electroencephalography (EEG) experiments on children listening to amplitude modulated (AM) noise with slow-rythmic prosodic (0.5-1[Formula: see text]Hz), syllabic (4-8[Formula: see text]Hz) or the phoneme (12-40[Formula: see text]Hz) rates, aimed at detecting differences in perception of oscillatory sampling that could be associated with dyslexia. The purpose of this work is to check whether these differences exist and how they are related to children's performance in different language and cognitive tasks commonly used to detect dyslexia. To this purpose, temporal and spectral inter-channel EEG connectivity was estimated, and a denoising autoencoder (DAE) was trained to learn a low-dimensional representation of the connectivity matrices. This representation was studied via correlation and classification analysis, which revealed ability in detecting dyslexic subjects with an accuracy higher than 0.8, and balanced accuracy around 0.7. Some features of the DAE representation were significantly correlated ([Formula: see text]) with children's performance in language and cognitive tasks of the phonological hypothesis category such as phonological awareness and rapid symbolic naming, as well as reading efficiency and reading comprehension. Finally, a deeper analysis of the adjacency matrix revealed a reduced bilateral connection between electrodes of the temporal lobe (roughly the primary auditory cortex) in DD subjects, as well as an increased connectivity of the F7 electrode, placed roughly on Broca's area. These results pave the way for a complementary assessment of dyslexia using more objective methodologies such as EEG.

Artificial intelligence and all its supporting tools, e.g. machine and deep learning in computational intelligence-based systems, are rebuilding our society (economy, education, life-style, etc.) and promising a new era for the social welfare state. In this paper we summarize recent advances in data science and artificial intelligence within the interplay between natural and artificial computation. A review of recent works published in the latter field and the state the art are summarized in a comprehensive and self-contained way to provide a baseline framework for the international community in artificial intelligence. Moreover, this paper aims to provide a complete analysis and some relevant discussions of the current trends and insights within several theoretical and application fields covered in the essay, from theoretical models in artificial intelligence and machine learning to the most prospective applications in robotics, neuroscience, brain computer interfaces, medicine and society, in general.

Many classical machine learning techniques have been used to explore Alzheimer's disease (AD), evolving from image decomposition techniques such as principal component analysis toward higher complexity, non-linear decomposition algorithms. With the arrival of the deep learning paradigm, it has become possible to extract high-level abstract features directly from MRI images that internally describe the distribution of data in low-dimensional manifolds. In this work, we try a new exploratory data analysis of AD based on deep convolutional autoencoders. We aim at finding links between cognitive symptoms and the underlying neurodegeneration process by fusing the information of neuropsychological test outcomes, diagnoses, and other clinical data with the imaging features extracted solely via a data-driven decomposition of MRI. The distribution of the extracted features in different combinations is then analyzed and visualized using regression and classification analysis, and the influence of each coordinate of the autoencoder manifold over the brain is estimated. The imaging-derived markers could then predict clinical variables with correlations above 0.6 in the case of neuropsychological evaluation variables such as the MMSE or the ADAS11 scores, achieving a classification accuracy over 80% for the diagnosis of AD.

Although much research has been undertaken, the spatial patterns, developmental course, and sexual dimorphism of brain structure associated with autism remains enigmatic. One of the difficulties in investigating differences between the sexes in autism is the small sample sizes of available imaging datasets with mixed sex. Thus, the majority of the investigations have involved male samples, with females somewhat overlooked. This paper deploys machine learning on partial least squares feature extraction to reveal differences in regional brain structure between individuals with autism and typically developing participants. A four-class classification problem (sex and condition) is specified, with theoretical restrictions based on the evaluation of a novel upper bound in the resubstitution estimate. These conditions were imposed on the classifier complexity and feature space dimension to assure generalizable results from the training set to test samples. Accuracies above [Formula: see text] on gray and white matter tissues estimated from voxel-based morphometry (VBM) features are obtained in a sample of equal-sized high-functioning male and female adults with and without autism ([Formula: see text], [Formula: see text]/group). The proposed learning machine revealed how autism is modulated by biological sex using a low-dimensional feature space extracted from VBM. In addition, a spatial overlap analysis on reference maps partially corroborated predictions of the “extreme male brain” theory of autism, in sexual dimorphic areas.

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.

Parkinsonism is a clinical syndrome characterized by the progressive loss of striatal dopamine. Its diagnosis is usually corroborated by neuroimaging data such as DaTSCAN neuroimages that allow visualizing the possible dopamine deficiency. During the last decade, a number of computer systems have been proposed to automatically analyze DaTSCAN neuroimages, eliminating the subjectivity inherent to the visual examination of the data. In this work, we propose a computer system based on machine learning to separate Parkinsonian patients and control subjects using the size and shape of the striatal region, modeled from DaTSCAN data. First, an algorithm based on adaptative thresholding is used to parcel the striatum. This region is then divided into two according to the brain hemisphere division and characterized with 152 measures, extracted from the volume and its three possible 2-dimensional projections. Afterwards, the Bhattacharyya distance is used to discard the least discriminative measures and, finally, the neuroimage category is estimated by means of a Support Vector Machine classifier. This method was evaluated using a dataset with 189 DaTSCAN neuroimages, obtaining an accuracy rate over 94%. This rate outperforms those obtained by previous approaches that use the intensity of each striatal voxel as a feature.

Spatial and intensity normalizations are nowadays a prerequisite for neuroimaging analysis. Influenced by voxel-wise and other univariate comparisons, where these corrections are key, they are commonly applied to any type of analysis and imaging modalities. Nuclear imaging modalities such as PET-FDG or FP-CIT SPECT, a common modality used in Parkinson's disease diagnosis, are especially dependent on intensity normalization. However, these steps are computationally expensive and furthermore, they may introduce deformations in the images, altering the information contained in them. Convolutional neural networks (CNNs), for their part, introduce position invariance to pattern recognition, and have been proven to classify objects regardless of their orientation, size, angle, etc. Therefore, a question arises: how well can CNNs account for spatial and intensity differences when analyzing nuclear brain imaging? Are spatial and intensity normalizations still needed? To answer this question, we have trained four different CNN models based on well-established architectures, using or not different spatial and intensity normalization preprocessings. The results show that a sufficiently complex model such as our three-dimensional version of the ALEXNET can effectively account for spatial differences, achieving a diagnosis accuracy of 94.1% with an area under the ROC curve of 0.984. The visualization of the differences via saliency maps shows that these models are correctly finding patterns that match those found in the literature, without the need of applying any complex spatial normalization procedure. However, the intensity normalization - and its type - is revealed as very influential in the results and accuracy of the trained model, and therefore must be well accounted.

Computer-aided diagnosis (CAD) systems constitute a powerful tool for early diagnosis of Alzheimer's disease (AD), but limitations on interpretability and performance exist. In this work, a fully automatic CAD system based on supervised learning methods is proposed to be applied on segmented brain magnetic resonance imaging (MRI) from Alzheimer's disease neuroimaging initiative (ADNI) participants for automatic classification. The proposed CAD system possesses two relevant characteristics: optimal performance and visual support for decision making. The CAD is built in two stages: a first feature extraction based on independent component analysis (ICA) on class mean images and, secondly, a support vector machine (SVM) training and classification. The obtained features for classification offer a full graphical representation of the images, giving an understandable logic in the CAD output, that can increase confidence in the CAD support. The proposed method yields classification results up to 89% of accuracy (with 92% of sensitivity and 86% of specificity) for normal controls (NC) and AD patients, 79% of accuracy (with 82% of sensitivity and 76% of specificity) for NC and mild cognitive impairment (MCI), and 85% of accuracy (with 85% of sensitivity and 86% of specificity) for MCI and AD patients.

This paper shows a magnetic resonance image (MRI) classification technique based on nonnegative matrix factorization (NNMF) and ensemble tree learning methods. The system consists of a feature extraction process that applies NNMF to gray matter (GM) MRI first-order statistics of a number of sub-cortical structures and a learning process of an ensemble of decision trees. The ensembles are trained by means of boosting and bagging while their performance is compared in terms of the classification error and the received operating characteristics curve (ROC) using k-fold cross validation. The results show that NNMF is well suited for reducing the dimensionality of the input data without a penalty on the performance of the ensembles. The best performance was obtained by bagging in terms of convergence rate and minimum residual loss, especially for high complexity classification tasks (i.e. NC vs. MCI and MCI vs. AD.

The usage of biomedical imaging in the diagnosis of dementia is increasingly widespread. A number of works explore the possibilities of computational techniques and algorithms in what is called computed aided diagnosis. Our work presents an automatic parametrization of the brain structure by means of a path generation algorithm based on hidden Markov models (HMMs). The path is traced using information of intensity and spatial orientation in each node, adapting to the structure of the brain. Each path is itself a useful way to characterize the distribution of the tissue inside the magnetic resonance imaging (MRI) image by, for example, extracting the intensity levels at each node or generating statistical information of the tissue distribution. Additionally, a further processing consisting of a modification of the grey level co-occurrence matrix (GLCM) can be used to characterize the textural changes that occur throughout the path, yielding more meaningful values that could be associated to Alzheimer's disease (AD), as well as providing a significant feature reduction. This methodology achieves moderate performance, up to 80.3% of accuracy using a single path in differential diagnosis involving Alzheimer-affected subjects versus controls belonging to the Alzheimer's disease neuroimaging initiative (ADNI).

Computer Aided Diagnosis (CAD) constitutes an important tool for the early diagnosis of Alzheimer's Disease (AD), which, in turn, allows the application of treatments that can be simpler and more likely to be effective. This paper explores the construction of classification methods based on deep learning architectures applied on brain regions defined by the Automated Anatomical Labeling (AAL). Gray Matter (GM) images from each brain area have been split into 3D patches according to the regions defined by the AAL atlas and these patches are used to train different deep belief networks. An ensemble of deep belief networks is then composed where the final prediction is determined by a voting scheme. Two deep learning based structures and four different voting schemes are implemented and compared, giving as a result a potent classification architecture where discriminative features are computed in an unsupervised fashion. The resulting method has been evaluated using a large dataset from the Alzheimer's disease Neuroimaging Initiative (ADNI). Classification results assessed by cross-validation prove that the proposed method is not only valid for differentiate between controls (NC) and AD images, but it also provides good performances when tested for the more challenging case of classifying Mild Cognitive Impairment (MCI) Subjects. In particular, the classification architecture provides accuracy values up to 0.90 and AUC of 0.95 for NC/AD classification, 0.84 and AUC of 0.91 for stable MCI/AD classification and 0.83 and AUC of 0.95 for NC/MCI converters classification.

This work highlights the exploitation of Gaussian Mixture Model (GMM) and Mean squared Error (MSE) in DaTSCAN SPECT brain images for intensity normalization purposes over two proposed approaches. The first proposed methodology is based on a nonlinear image filtering by means of GMM, which considers not only the intensity levels of each voxel but also its coordinates inside the so-defined spatial Gaussian functions. It is achieved according to a probability threshold that measures the weight of each kernel or cluster on the striatum area, the voxels in the non-specific regions are intensity normalized by removing clusters whose likelihood is negligible. The second normalization method based on MSE which is performed by a linear intensity transformation in each voxel. This approach is based on predicting jointly different intensity normalization parameters that leads to the joint minimization of the squared sum errors between the template image and the optimal linear estimated image (normalized image). We compare these methods of normalization together with another approach widely used based on specific-to-non-specific binding ratio. This comparison is based on DaTSCAN image analysis and classification for the development of a computer aided diagnosis (CAD) system for Parkinsonian syndrome detection.

This work presents a novel feature selection method for Positron Emission Tomography (PET) images for the development of a computer aided diagnosis (CAD) system aiming to improve the early detection of the Alzheimer’s Disease (AD). Voxels are selected by means of the combination of the antecedents and consequents of Association Rules (ARs) which are mined from the Regions of Interest (ROIs) of controls by Apriori algorithm. In order to reduce the input space to the classifier, features are extracted using Principal Component Analysis (PCA) or Partial Least Squares (PLS) techniques. Finally, classification is performed by using a kernel Support Vector Machine (SVM) reaching accuracy rates of 90% outperforming other reported methods.

In this paper we propose a novel method for brain SPECT image feature extraction based on the Empirical Mode Decomposition (EMD). The proposed method applied to assist the diagnosis of Alzheimer Disease (AD) selects the most discriminant voxels for classification from the transformed EMD feature space. In particular, high-frequency components of the EMD transformation are found to retain regional differences in functional activity which is characteristic of AD. The EMD represents a fully data-driven, unsupervised and additive signal decomposition and does not need any a priori defined basis system. Several experiments were carried out on a balanced SPECT database collected from the “Virgen de las Nieves” Hospital in Granada (Spain), containing 96 recordings and yielding up to 100% accuracy in separating AD and normal controls, and then outperforming recently proposed CAD systems.

Image segmentation aims to partition an image into its constituent parts. In Magnetic Resonance Imaging (MRI), image segmentation groups the voxels belonging to a specific tissue or fluids. Currently, this is an essential step in MRI analysis and provides a way to diagnose many neurological disorders such as the Alzheimer disease. In this paper we present a segmentation method for MRI based on the Growing Hierarchical Self-Organizing Map and multiobjective-based feature selection to optimize the performance of the segmentation process. Since the features extracted from the image result crucial for the final performance of the segmentation process, the optimized features which maximize the performance of the segmentation process on each plane are used. The experiments performed have been carried out using the Internet Brain Segmentation Repository (IBSR) as it has been widely used in previous works. Thus, the comparisons with other methods carried out using the IBSR database, show that our method outperforms other algorithms.

Imaging of dopamine transporters (DaT) has been introduced as a valuable tool to evaluate patients with several neuropsychiatrie disorders, such as Parkinson’s disease (PD). Over the last decade, several computer applications have been developed in order to facilitate the exploration of DaT images to clinicians, however they require a high interaction with the user to delimit regions of interest (ROIs) and to obtain a diagnosis. In this paper we show a full automatic computed aided diagnosis (CAD) system for Parkinson’s disease. We use a Partial Least Square scheme to decompose DaT images into scores and loading. Then, the scores with highest Fisher Discriminant Ratio are used as feature for a Support Vector Machine classifier that determines the state (normal of pathological) of a given DaT image. The evaluation of the system using a database with 178 images from controls and PD patients has produced accuracy rates of nearly 93%.

This paper presents a novel computer-aided diagnosis (CAD) technique for the early diagnosis of the Alzheimer's disease (AD) based on nonnegative matrix factorization (NMF) and support vector machines (SVM) with bounds of confidence. The CAD tool is designed for the study and classification of functional brain images. For this purpose, two different brain image databases are selected: a single photon emission computed tomography (SPECT) database and positron emission tomography (PET) images, both of them containing data for both Alzheimer's disease (AD) patients and healthy controls as a reference. These databases are analyzed by applying the Fisher discriminant ratio (FDR) and nonnegative matrix factorization (NMF) for feature selection and extraction of the most relevant features. The resulting NMF-transformed sets of data, which contain a reduced number of features, are classified by means of a SVM-based classifier with bounds of confidence for decision. The proposed NMF-SVM method yields up to 91% classification accuracy with high sensitivity and specificity rates (upper than 90%). This NMF-SVM CAD tool becomes an accurate method for SPECT and PET AD image classification.

This paper presents a computer-aided diagnosis technique for improving the accuracy of the early diagnosis of the Alzheimer type dementia. The proposed methodology is based on the calculation of the skewness to each m-by-m sliding block of the transaxial slices of the SPECT brain images. We replace the center pixel in the m-by-m block by the skewness value and build a new 3-D brain image which will be used for classification purposes. After that, we select the voxels which present a Welch's t-statistic between both classes, Normal and Alzheimer images, higher (or lower) than a given threshold. The mean, standard deviation, skewness and kurtosis are calculated for selected voxels and they are chosen as feature vectors for three different classifiers: support vector machines with linear kernel, classification trees and multivariate normal model. The proposed methodology reaches an accuracy higher than 98% in the classification task.

In this letter, we propose a novel least-mean-square (LMS) algorithm for filtering speech sounds in the adaptive noise cancellation (ANC) problem. It is based on the minimization of the squared Euclidean norm of the difference weight vector under a stability constraint defined over theaposterioriestimation error. To this purpose, the Lagrangian methodology has been used in order to propose a nonlinear adaptation rule defined in terms of the product of differential inputs and errors which means a generalization of the normalized (N)LMS algorithm. The proposed method yields better tracking ability in this context as shown in the experiments which are carried out on the AURORA 2 and 3 speech databases. They provide an extensive performance evaluation along with an exhaustive comparison to standard LMS algorithms with almost the same computational load, including the NLMS and other recently reported LMS algorithms such as the modified (M)-NLMS, the error nonlinearity (EN)-LMS, or the normalized data nonlinearity (NDN)-LMS adaptation.

This paper presents a novel voice activity detector (VAD) for improving speech detection robustness in noisy environments and the performance of speech recognition systems in real-time applications. The algorithm is based on a generalized complex Gaussian (GCG) observation model and defines an optimal likelihood ratio test (LRT) involving multiple and correlated observations (MCO) based on jointly Gaussian probability distribution functions (jGpdf). An extensive analysis of the proposed methodology for a low dimensional observation model demonstrates 1) the improved robustness of the proposed approach by means of a clear reduction of the classification error as the number of observations is increased, and 2) the tradeoff between the number of observations and the detection performance. The proposed strategy is also compared to different VAD methods including the G.729, AMR, and AFE standards, as well as other recently reported algorithms showing a sustained advantage in speech/nonspeech detection accuracy and speech recognition performance.

This paper shows a revised statistical test for voice activity detection in noise adverse environments. The method is based on a revised contextual likelihood ratio test (LRT) defined over a multiple observation window. The new approach not only evaluates the two hypothesis consisting on all the observations to be speech or nonspeech but all the possible hypothesis defined over the individual observations. The implicit hangover mechanism artificially added by the original method was not found in the revised method so its design can be further improved. With these and other innovations the proposed method showed a high speech/non-speech discrimination over a wide range of SNR conditions. The experimental framework showed that the revised method yields significant improvements over standardized VADs for discontinuous voice transmission and distributed speech recognition, as well as over recently reported methods.

This paper shows an improved statistical test for voice activity detection in noise adverse environments. The method is based on a revised contextual likelihood ratio test (LRT) defined over a multiple observation window. The motivations for revising the original multiple observation LRT (MO-LRT) are found in its artificially added hangover mechanism that exhibits an incorrect behavior under different signal-to-noise ratio (SNR) conditions. The new approach defines a maximum a posteriori (MAP) statistical test in which all the global hypotheses on the multiple observation window containing up to one speech-to-nonspeech or nonspeech-to-speech transitions are considered. Thus, the implicit hangover mechanism artificially added by the original method was not found in the revised method so its design can be further improved. With these and other innovations, the proposed method showed a higher speech/nonspeech discrimination accuracy over a wide range of SNR conditions when compared to the original MO-LRT voice activity detector (VAD). Experiments conducted on the AURORA databases and tasks showed that the revised method yields significant improvements in speech recognition performance over standardized VADs such as ITU T G.729 and ETSI AMR for discontinuous voice transmission and the ETSI AFE for distributed speech recognition (DSR), as well as over recently reported methods.

This paper shows an effective speech/pause discrimination method combining spectral noise filtering and fuzzy logic rules. The fuzzy system is based on a Sugeno inference engine with membership functions defined as combination of two Gaussian functions. Its operation is optimized by means of a hybrid training algorithm combining the least-squares method and the backpropagation gradient descent method for training membership function parameters. The fuzzy classifier consists of ten fuzzy rules defined in terms of the denoised subband signal-to-noise ratios (SNRs) and the zero crossing rate (ZCRs). An exhaustive analysis conducted on the Spanish SpeechDat-Car databases is conducted in order to assess the performance of the proposed method and to compare it to existing standard VAD methods. The results show improvements in detection accuracy over standard VADs and a representative set of recently reported VAD algorithms

This paper shows an effective voice activity detector based on a statistical likelihood ratio test defined on the integrated bispectrum of the signal. It inherits the ability of higher order statistics to detect signals in noise with many other additional advantages: i) its computation as a cross spectrum leads to significant computational savings, and ii) the variance of the estimator is of the same order as that of the power spectrum estimator. The proposed method incorporates contextual information to the decision rule, a strategy that has reported significant improvements in speech detection accuracy and robust speech recognition applications. The experimental analysis conducted on the well-known AURORA databases has reported significant improvements over standardized techniques such as ITU G.729, AMRI, AMR2 and ESTI AFE VADs, as well as over recently published VADs

This paper presents GAIALAB, a Weblab project for digital communications distributed learning developed at the University of Granada (Spain). This initiative has been funded by a University program to improve teaching quality. The project addresses problems of teaching and learning innovatively and effectively by enabling the user interaction with the simulation engines through a Web browser. Our Weblab project provides an interface with JAVA applets that have been developed and integrated in the system. The Weblab project enables simulating digital communication systems and analyzing the results obtained through the Web. Moreover, all the applets that are included in the system have been designed to allow the modification of the simulation parameters, thus enabling a better understanding of the algorithms. The system also permits evaluating the experimental work developed by the students

Abstract In this paper we propose a novel Voice Activity Detection (VAD)algorithm, based on the integrated bispectrum function (IBI), forimproving Automated Speech Recognition (ASR) systems thatwork in noisy environments. In particular we use the combina-tion of two features, IBI magnitude and IBI phase to formulatea robust and smoothed decision rule for speech/pause discrimina-tion. The analysis performed on the new combined feature high-lighted: i) the advantages of each individual feature, while com-pensating the drawback of each other, and ii) the higher ability forendpoint detection given by a lower variance of the decision func-tion in pause/speech frames. The experiments conducted on theSpanish SpeechDat-Car database showed that the proposed algo-rithm outperforms ITU G.729, ETSI AMR1 and AMR2 and ETSIAFE standards as well as other recently reported VAD methods inspeech/non-speech detection performance.IndexTerms: voiceactivitydetection, clusteringanalysis, bispec-trum function, entropy.

This paper shows an effective speech/non-speech discrimination method for improving the performance of speech processing systems working in noisy environment. The proposed method uses a trained support vector machine (SVM) that defines an optimized non-linear decision rule over different sets of speech features. Two alternative feature extraction processes based on: i) subband SNR estimation after denoising, and ii) long-term SNR estimation were compared. Both methods show the ability of the SVM-based classifier to learn how the signal is masked by the acoustic noise and to define an effective non-linear decision rule. However, it is shown that a feature vector incorporating contextual information yielded better speech/non-speech discrimination even when no denoising is applied. The experimental analysis carried out on the Spanish SpeechDat-Car database shows clear improvements over standard VADs including ITU G.729, ETSI AMR and ETSI AFE for distributed speech recognition (DSR), and other recently reported VADs. Index Terms: voice activity detection, support vector machine learning, speech enhancement.

A robust and effective voice activity detection (VAD) algorithm is proposed for improving speech recognition performance in noisy environments. The approach is based on well-known statistical tests based on the determination of the speech/non-speech bispectra by means of third-order auto-cumulants. This algorithm differs from many others in the way the decision rule is formulated being the statistical tests built on a multiple observation (MO) window consisting of averaged bispectrum coefficients of the speech signal. Clear improvements in speech/non-speech discrimination accuracy demonstrate the effectiveness of the proposed VAD. It is shown that application of a statistical detection test leads to a better separation of the speech and noise distributions, thus allowing a more effective discrimination and a tradeoff between complexity and performance. The experimental analysis carried out on the AURORA 3 databases provides an extensive performance evaluation together with an exhaustive comparison to the standard VADs, such as ITU G.729, GSM AMR, and ETSI AFE, for distributed speech recognition (DSR) and other recently reported VADs

This letter shows an effective statistical voice activity detection algorithm based on the integrated bispectrum, which is defined as a cross spectrum between the signal and its square and inherits the ability of higher order statistics to detect signals in noise with many other additional advantages: 1) its computation as a cross spectrum leads to significant computational savings, and 2) the variance of the estimator is of the same order as that of the power spectrum estimator. The decision rule is formulated in terms of an average likelihood ratio test (LRT) involving successive integrated bispectrum speech features. With these and other innovations, the proposed method reports significant improvements in speech/pause discrimination as well as in speech recognition over standardized techniques such as ITU-T G.729, ETSI AMR, and AFE VADs, and over recently published VADs