Jesús Poza

Retinal image analysis to detect and quantify lesions associated with diabetic retinopathy

An automatic method to detect hard exudates, a lesion associated with diabetic retinopathy, is proposed. The algorithm found on their color, using a statistical classification, and their sharp edges, applying an edge detector, to localize them. A sensitivity of 79.62% with a mean number of 3 false positives per image is obtained in a database of 20 retinal image with variable color, brightness and quality. In that way, we evaluate the robustness of the method in order to make adequate to a clinical environment. Further efforts will be done to improve its performance.

Variability, regularity, and complexity of time series generated by schizophrenic patients and control subjects

We analyzed time series generated by 20 schizophrenic patients and 20 sex- and age-matched control subjects using three nonlinear methods of time series analysis as test statistics: central tendency measure (CTM) from the scatter plots of first differences of data, approximate entropy (ApEn), and Lempel-Ziv (LZ) complexity. We divided our data into a training set (10 patients and 10 control subjects) and a test set (10 patients and 10 control subjects). The training set was used for algorithm development and optimum threshold selection. Each method was assessed prospectively using the test dataset. We obtained 80% sensitivity and 90% specificity with LZ complexity, 90% sensitivity, and 60% specificity with ApEn, and 70% sensitivity and 70% specificity with CTM. Our results indicate that there exist differences in the ability to generate random time series between schizophrenic subjects and controls, as estimated by the CTM, ApEn, and LZ. This finding agrees with most previous results showing that schizophrenic patients are characterized by less complex neurobehavioral and neuropsychologic measurements.

Spectral and Nonlinear Analyses of MEG Background Activity in Patients With Alzheimer's Disease

The aim of the present study is to analyze the magnetoencephalogram (MEG) background activity from patients with Alzheimers disease (AD) and elderly control subjects. MEG recordings from 20 AD patients and 21 controls were analyzed by means of two spectral [median frequency (MF) and spectral entropy (SpecEn)] and two nonlinear parameters [approximate entropy (ApEn) and Lempel-Ziv complexity (LZC)]. In the AD diagnosis, the highest accuracy of 75.6% (80% sensitivity, 71.4% specificity) was obtained with the MF according to a linear discriminant analysis (LDA) with a leave-one-out cross-validation procedure. Moreover, we wanted to assess whether these spectral and nonlinear analyses could provide complementary information to improve the AD diagnosis. After a forward stepwise LDA with a leave-one-out cross-validation procedure, one spectral (MF) and one nonlinear parameter (ApEn) were automatically selected. In this model, an accuracy of 80.5% (80.0% sensitivity, 81.0% specificity) was achieved. We conclude that spectral and nonlinear analyses from spontaneous MEG activity could be complementary methods to help in AD detection.

Detection of Hard Exudates in Retinal Images Using a Radial Basis Function Classifier

Diabetic retinopathy (DR) is one of the most important causes of visual impairment. Automatic recognition of DR lesions, like hard exudates (EXs), in retinal images can contribute to the diagnosis and screening of the disease. The aim of this study was to automatically detect these lesions in fundus images. To achieve this goal, each image was normalized and the candidate EX regions were segmented by a combination of global and adaptive thresholding. Then, a group of features was extracted from image regions and the subset which best discriminated between EXs and retinal background was selected by means of logistic regression (LR). This optimal subset was subsequently used as input to a radial basis function (RBF) neural network. To improve the performance of the proposed algorithm, some noisy regions were eliminated by an innovative postprocessing of the image. The main novelty of the paper is the use of LR in conjunction with RBF and the proposed postprocessing technique. Our database was composed of 117 images with variable color, brightness and quality. The database was divided into a training set of 50 images (from DR patients) and a test set of 67 images (40 from DR patients and 27 from healthy retinas). Using a lesion-based criterion (pixel resolution), a mean sensitivity of 92.1% and a mean positive predictive value of 86.4% were obtained. With an image-based criterion, a mean sensitivity of 100%, mean specificity of 70.4% and mean accuracy of 88.1% were achieved. These results suggest that the proposed method could be a diagnostic aid for ophthalmologists in the screening for DR.

Quantitative magnetoencephalography of spontaneous brain activity in Alzheimer disease : An exhaustive frequency analysis

Quantitative magnetoencephalography (qMEG) was used to investigate differences in the 2 to 60 Hz spectral power, between Alzheimer disease (AD) patients and control subjects. Twenty-two AD patients and 21 age-matched control subjects participated in this study. MEG signal analysis comprised the division of the entire 2 to 60 Hz spectrum in 2 Hz-width subbands. Both the relative power and the contribution of each subband to the correct classification of AD patients and controls were calculated. The relative power in 2 bands comprised between 2 to 4 Hz and 16 to 28 Hz was selected by a restrictive multiple-comparison test, among the entire 2 to 60 Hz spectrum. Using 2 to 4 Hz values it is possible to choose a classification rule with an estimate sensitivity and specificity given by 68% and 76% respectively. Alternatively, when 16 to 28 Hz values are used, it is possible to obtain a better classification rule with an estimate sensitivity and specificity given by 81% and 80%, respectively. To the best of our knowledge, this is the first electroencephalography or MEG study where a so exhaustive analysis of the magneto-electric spectrum has been performed. This study supports the notion that more attention should be devoted to the study of β band in AD.

Analysis of spontaneous MEG activity in mild cognitive impairment and Alzheimer's disease using spectral entropies and statistical complexity measures

Alzheimers disease (AD) is the most common cause of dementia. Over the last few years, a considerable effort has been devoted to exploring new biomarkers. Nevertheless, a better understanding of brain dynamics is still required to optimize therapeutic strategies. In this regard, the characterization of mild cognitive impairment (MCI) is crucial, due to the high conversion rate from MCI to AD. However, only a few studies have focused on the analysis of magnetoencephalographic (MEG) rhythms to characterize AD and MCI. In this study, we assess the ability of several parameters derived from information theory to describe spontaneous MEG activity from 36 AD patients, 18 MCI subjects and 26 controls. Three entropies (Shannon, Tsallis and Rényi entropies), one disequilibrium measure (based on Euclidean distance ED) and three statistical complexities (based on Lopez Ruiz-Mancini-Calbet complexity LMC) were used to estimate the irregularity and statistical complexity of MEG activity. Statistically significant differences between AD patients and controls were obtained with all parameters (p < 0.01). In addition, statistically significant differences between MCI subjects and controls were achieved by ED and LMC (p < 0.05). In order to assess the diagnostic ability of the parameters, a linear discriminant analysis with a leave-one-out cross-validation procedure was applied. The accuracies reached 83.9% and 65.9% to discriminate AD and MCI subjects from controls, respectively. Our findings suggest that MCI subjects exhibit an intermediate pattern of abnormalities between normal aging and AD. Furthermore, the proposed parameters provide a new description of brain dynamics in AD and MCI.

A comparative study of event-related coupling patterns during an auditory oddball task in schizophrenia

OBJECTIVE The aim of this research is to explore the coupling patterns of brain dynamics during an auditory oddball task in schizophrenia (SCH). APPROACH Event-related electroencephalographic (ERP) activity was recorded from 20 SCH patients and 20 healthy controls. The coupling changes between auditory response and pre-stimulus baseline were calculated in conventional EEG frequency bands (theta, alpha, beta-1, beta-2 and gamma), using three coupling measures: coherence, phase-locking value and Euclidean distance. MAIN RESULTS Our results showed a statistically significant increase from baseline to response in theta coupling and a statistically significant decrease in beta-2 coupling in controls. No statistically significant changes were observed in SCH patients. SIGNIFICANCE Our findings support the aberrant salience hypothesis, since SCH patients failed to change their coupling dynamics between stimulus response and baseline when performing an auditory cognitive task. This result may reflect an impaired communication among neural areas, which may be related to abnormal cognitive functions.

Comparison of logistic regression and neural network classifiers in the detection of hard exudates in retinal images

Diabetic Retinopathy (DR) is a common cause of visual impairment in industrialized countries. Automatic recognition of DR lesions in retinal images can contribute to the diagnosis and screening of this disease. The aim of this study is to automatically detect one of these lesions: hard exudates (EXs). Based on their properties, we extracted a set of features from image regions and selected the subset that best discriminated between EXs and the retinal background using logistic regression (LR). The LR model obtained, a multilayer perceptron (MLP) classifier and a radial basis function (RBF) classifier were subsequently used to obtain the final segmentation of EXs. Our database contained 130 images with variable color, brightness, and quality. Fifty of them were used to obtain the training examples. The remaining 80 images were used to test the performance of the method. The highest statistics were achieved for MLP or RBF. Using a lesion based criterion, our results reached a mean sensitivity of 95.9% (MLP) and a mean positive predictive value of 85.7% (RBF). With an image-based criterion, we achieved a 100% mean sensitivity, 87.5% mean specificity and 93.8% mean accuracy (MLP and RBF).

Assessment of classification improvement in patients with Alzheimer's disease based on magnetoencephalogram blind source separation

OBJECTIVES In this pilot study, we intended to assess whether a procedure based on blind source separation (BSS) and subsequent partial reconstruction of magnetoencephalogram (MEG) recordings might enhance the differences between MEGs from Alzheimers disease (AD) patients and elderly control subjects. MATERIALS AND METHODS We analysed MEG background activity recordings acquired with a 148-channel whole-head magnetometer from 21 AD patients and 21 control subjects. Artefact-free epochs of 20 s were blindly decomposed using the algorithm for multiple unknown signals extraction (AMUSE), which arranges the extracted components by decreasing linear predictability. Thus, the components of diverse epochs and subjects could be easily compared. Every component was characterised with its median frequency and spectral entropy (denoted by fmedian and SpecEn, respectively). The differences between subject groups in these variables were statistically evaluated to find out which components could improve the subject classification. Then, these significant components were used to partially reconstruct the MEG recordings. RESULTS The statistical analysis showed that the AMUSE components which provided the largest differences between demented patients and control subjects were ordered together. Considering this analysis, we defined two subsets, denoted by BSS-{15,35} and BSS-{20,30}, which included 21 components (15-35) and 11 components (20-30), respectively. We partially reconstructed the MEGs with these subsets. Then, the classification performance was computed with a leave-one-out cross-validation procedure for the case where no BSS was applied and for the partial reconstructions BSS-{15,35} and BSS-{20,30}. The BSS and component selection procedure improved the classification accuracy from 69.05% to 83.33% using f(median) with BSS-{15,35} and from 61.91% to 73.81% using SpecEn with BSS-{20,30}. CONCLUSION These preliminary results lead us to think that the proposed procedure based on BSS and selection of significant components may improve the classification of AD patients using straightforward features from MEG recordings.

Neural Network Reorganization Analysis During an Auditory Oddball Task in Schizophrenia Using Wavelet Entropy

The aim of the present study was to characterize the neural network reorganization during a cognitive task in schizophrenia (SCH) by means of wavelet entropy (WE). Previous studies suggest that the cognitive impairment in patients with SCH could be related to the disrupted integrative functions of neural circuits. Nevertheless, further characterization of this effect is needed, especially in the time-frequency domain. This characterization is sensitive to fast neuronal dynamics and their synchronization that may be an important component of distributed neuronal interactions; especially in light of the disconnection hypothesis for SCH and its electrophysiological correlates. In this work, the irregularity dynamics elicited by an auditory oddball paradigm were analyzed through synchronized-averaging (SA) and single-trial (ST) analyses. They provide complementary information on the spatial patterns involved in the neural network reorganization. Our results from 20 healthy controls and 20 SCH patients showed a WE decrease from baseline to response both in controls and SCH subjects. These changes were significantly more pronounced for healthy controls after ST analysis, mainly in central and frontopolar areas. On the other hand, SA analysis showed more widespread spatial differences than ST results. These findings suggest that the activation response is weakly phase-locked to stimulus onset in SCH and related to the default mode and salience networks. Furthermore, the less pronounced changes in WE from baseline to response for SCH patients suggest an impaired ability to reorganize neural dynamics during an oddball task.