Discrimination of secondary hypsarrhythmias to Zika virus congenital syndrome and west syndrome based on joint moments and entropy measurements.

Hypsarrhythmia is a specific chaotic morphology, present in the interictal period of the electroencephalogram (EEG) signal in patients with West Syndrome (WS), a severe form of childhood epilepsy and that, recently, was also identified in the examinations of patients with Zika Virus Congenital Syndrome (ZVCS). This innovative work proposes the development of a computational methodology for analysis and differentiation, based on the time-frequency domain, between the chaotic pattern of WS and ZVCS hypsarrhythmia. The EEG signal time-frequency analysis is carried out from the Continuous Wavelet Transform (CWT). Four joint moments-joint mean-[Formula: see text], joint variance-[Formula: see text], joint skewness-[Formula: see text], and joint kurtosis-[Formula: see text]-and four entropy measurements-Shannon, Log Energy, Norm, and Sure-are obtained from the CWT to compose the representative feature vector of the EEG hypsarrhythmic signals under analysis. The performance of eight classical types of machine learning algorithms are verified in classification using the k-fold cross validation and leave-one-patient-out cross validation methods. Discrimination results provided 78.08% accuracy, 85.55% sensitivity, 73.21% specificity, and AUC = 0.89 for the ANN classifier.

Interdigitated Electrode for Electrical Characterization of Commercial Pseudo-Binary Biodiesel-Diesel Blends.

Non-standard diesel blends can be harmful to the environment and human health. In this context, a simple analytical method to estimate the biodiesel mixture ratio in diesel was developed based on impedance spectroscopy (IS) associated with interdigitated sensors. In this article, four different interdigitated sensors with varied comb spacing (G) were simulated using the COMSOL Multiphysics software. Based on finite element simulations, four interdigitated electrode architectures were manufactured and evaluated. The best geometry was chosen according to theoretical data simulations, and its interdigitated electrodes were manufactured for the compositional evaluation of pseudo-binary biodiesel-diesel mixtures. According to the X-ray powder diffraction technique, the deposition of the conductive layer (Au0) over the surface of the dielectric substrate (SiO2) did not alter its phase composition. In the analysis of AFM and SEM, it was possible to observe irregular edges on the electrodes, possibly related to the manufacturing process of the thin layers and mechanical stability. Another characteristic observed in the AFM images was the height of the step of the gold layer of the sensor. Several cross sections were obtained, and the mean step value was 225.71 ± 0.0032 nm. Although there were differences in the roughness, the whole sensor had nanometric roughness. Based on the finite element method simulation performed, it can be assumed that the geometric parameters more suitable for the manufacturing of the electrode are W = 20 µm, L = 1000 µm, G = 50 µm, and N = 40 digits. The electrical characterization performed by impedance spectroscopy showed that we could differentiate between biodiesel and diesel fuels and their pseudo-binary mixtures in the low-frequency region.

Autofocus Entropy Repositioning Method Bioinspired in the Magnetic Field Memory of the Bees Applied to Pollination.

In this paper, a bioinspired method in the magnetic field memory of the bees, applied in a rover of precision pollination, is presented. The method calculates sharpness features by entropy and variance of the Laplacian of images segmented by color in the HSV system in real-time. A complementary positioning method based on area feature extraction between active markers was developed, analyzing color characteristics, noise, and vibrations of the probe in time and frequency, through the lateral image of the probe. From the observed results, it can be seen that the unsupervised method does not require previous calibration of target dimensions, histogram, and distances involved in positioning. The algorithm showed less sensitivity in the extraction of sharpness characteristics regarding the number of edges and greater sensitivity to the gradient, allowing unforeseen operation scenarios, even in small sharpness variations, and robust response to variance local, temporal, and geophysical of the magnetic declination, not needing luminosity after scanning, with the two freedom of degrees of the rotation.

Zebrafish automatic monitoring system for conditioning and behavioral analysis.

Studies using zebrafish (Danio rerio) in neuro-behavioural research are growing. Measuring fish behavior by computational methods is one of the most efficient ways to avoid human bias in experimental analyses, extending them to various approaches. Sometimes, thorough analyses are difficult to do, as fish can behave unpredictably during an experimental strategy. However, the analyses can be implemented in an automated way, using an online strategy and video processing for a complete assessment of the zebrafish behavior, based on the detection and tracking of fish during an activity. Here, a fully automatic conditioning and detailed analysis of zebrafish behavior is presented. Microcontrolled components were used to control the delivery of visual and sound stimuli, in addition to the concise amounts of food after conditioned stimuli for adult zebrafish groups in a conventional tank. The images were captured and processed for automatic detection of the fish, and the training of the fish was done in two evaluation strategies: simple and complex. In simple conditioning, the zebrafish showed significant responses from the second attempt, learning that the conditioned stimulus was a predictor of food presentation in a specific space of the tank, where the food was dumped. When the fish were subjected to two stimuli for decision-making in the food reward, the zebrafish obtained better responses to red light stimuli in relation to vibration. The behavior change was clear in stimulated fish in relation to the control group, thus, the distances traveled and the speed were greater, while the polarization was lower in stimulated fish. This automated system allows for the conditioning and assessment of zebrafish behavior online, with greater stability in experiments, and in the analysis of the behavior of individual fish or fish schools, including learning and memory studies.

Zebrafish tracking using YOLOv2 and Kalman filter.

Fish show rapid movements in various behavioral activities or associated with the presence of food. However, in periods of rapid movement, the rate at which occlusion occurs among the fish is quite high, causing inconsistency in the detection and tracking of fish, hindering the fish's identity and behavioral trajectory over a long period of time. Although some algorithms have been proposed to solve these problems, most of their applications were made in groups of fish that swim in shallow water and calm behavior, with few sudden movements. To solve these problems, a convolutional network of object recognition, YOLOv2, was used to delimit the region of the fish heads to optimize individual fish detection. In the tracking phase, the Kalman filter was used to estimate the best state of the fish's head position in each frame and, subsequently, the trajectories of each fish were connected among the frames. The results of the algorithm show adequate performances in the trajectories of groups of zebrafish that exhibited rapid movements.

Association between brain morphology and electrophysiological features in Congenital Zika Virus Syndrome: A cross-sectional, observational study.

BACKGROUND: Intrauterine infection with the Zika virus (ZIKV) has been connected to severe brain malformations, microcephaly, and abnormal electrophysiological activity. METHODS: We describe the interictal electroencephalographic (EEG) recordings of 47 children born with ZIKV-derived microcephaly. EEGs were recorded in the first year of life and correlated with brain morphology. In 31 subjects, we tested the association between computed tomography (CT) findings and interictal epileptiform discharges (IED). In eighteen, CTs were used for correlating volumetric measurements of the brainstem, cerebellum, and prosencephalon with the rate of IED. FINDINGS: Twenty-nine out of 47 (62%) subjects were diagnosed as having epilepsy. Those subjects presented epileptiform discharges, including unilateral interictal spikes (26/29, 90%), bilateral synchronous and asynchronous interictal spikes (21/29, 72%), and hypsarrhythmia (12/29, 41%). Interestingly, 58% of subjects with clinical epilepsy were born with rhombencephalon malformations, while none of the subjects without epilepsy showed macroscopic abnormalities in this region. The presence of rhombencephalon malformation was associated with epilepsy (odds ratio of 34; 95% CI: 2 - 654). Also, the presence of IED was associated with smaller brain volumes. Age-corrected total brain volume was inversely correlated with the rate of IED during sleep. Finally, 11 of 44 (25%) subjects presented sleep spindles. We observed an odds ratio of 0·25 (95% CI: 0·06 - 1·04) for having sleep spindles given the IED presence. INTERPRETATION: The findings suggest that certain CT imaging features are associated with an increased likelihood of developing epilepsy, including higher rates of IED and impaired development of sleep spindles, in the first year of life of CZVS subjects. FUNDING: This work was supported by the Brazilian Federal Government through a postdoctoral fellowship for EBS (Talented Youth, Science without Borders), an undergraduate scholarship for AJR (Institutional Program of Science Initiation Scholarships, Federal University of Rio Grande do Norte, Brazil), by International Centre for Genetic Engineering and Biotechnology (CRP/BRA18-05_EC) and by CAPES (Grant number 440893/2016-0), and CNPq (Grant number 88881.130729/2016-01).

The 'Heart Kuznets Curve'? Understanding the relations between economic development and cardiac conditions.

As countries turn wealthier, some health indicators, such as child mortality, seem to have well-defined trends. However, others, including cardiovascular conditions, do not follow clear linear patterns of change with economic development. Abnormal blood pressure is a serious health risk factor with consequences for population growth and longevity as well as public and private expenditure in health care and labor productivity. This also increases the risk of the population in certain pandemics, such as COVID-19. To determine the correlation of income and blood pressure, we analyzed time-series for the mean systolic blood pressure (SBP) of men's population (mmHg) and nominal Gross Domestic Product per capita (GDPPC) for 136 countries from 1980 to 2008 using regression and statistical analysis by Pearson's correlation (r). Our study finds a trend similar to an inverted-U shaped curve, or a 'Heart Kuznets Curve'. There is a positive correlation (increase GDPPC, increase SBP) in low-income countries, and a negative correlation in high-income countries (increase GDPPC, decrease SBP). As country income rises people tend to change their diets and habits and have better access to health services and education, which affects blood pressure. However, the latter two may not offset the rise in blood pressure until countries reach a certain income. Investing early in health education and preventive health care could avoid the sharp increase in blood pressure as countries develop, and therefore, avoiding the 'Heart Kuznets Curve' and its economic and human impacts.

Characterization of HIV-1 genetic diversity and antiretroviral resistance in the state of Maranhão, Northeast Brazil.

The HIV-1 epidemic in Brazil has been growing in northeast and north regions, particularly an increase in AIDS cases among the younger male population has been observed. This study aims to characterize the HIV-1 genetic diversity and to evaluate its antiretroviral resistance profile among individuals presenting virological failure in the state of Maranhão-Brazil. HIV-1 pol gene sequences from 633 patients on antiretroviral therapy were obtained from the Department of Surveillance, Prevention and Control of Sexually Transmitted Infections, HIV/AIDS and Viral Hepatitis of the Brazilian Ministry of Health. Phylogenetic and recombination analyses were performed to characterize viral genetic diversity. The presence of antiretroviral resistance mutations was assessed using the HIV Drug Resistance Database online platform of Stanford University. A predominance of subtype B (84.5%) was observed, followed by recombinant BF (9.5%), where more than half of the sequences were dispersed in 3 clusters. Antiretroviral resistance was detected in 74.1% of the sequences, and it was significantly higher for nucleoside analogue reverse-transcriptase inhibitors (NRTIs) than for non-nucleoside analogue reverse-transcriptase inhibitors (NNRTIs) and protease inhibitors (PIs). Inference of putative transmissions clusters identified 11 clusters with 22 query sequences (22/633, 3.5%). Thus, we conclude that continuous monitoring of the molecular epidemiology of HIV-1 is essential for prevention strategies, epidemic control, and treatment adequacy.

Development of a Computational Model to Predict Excess Body Fat in Adolescents through Low Cost Variables.

BACKGROUND: Excess body fat has been growing alarmingly among adolescents, especially in low income and middle income countries where access to health services is scarce. Currently, the main method for assessing overweight in adolescents is the body mass index, but its use is criticized for its low sensitivity and high specificity, which may lead to a late diagnosis of comorbidities associated with excess body fat, such as cardiovascular diseases. Thus, the aim of this study was to develop a computational model using linear regression to predict obesity in adolescents and compare it with commonly used anthropometric methods. To improve the performance of our model, we estimated the percentage of fat and then classified the nutritional status of these adolescents. METHODS: The model was developed using easily measurable socio-demographic and clinical variables from a database of 772 adolescents of both genders, aged 10-19 years. The predictive performance was evaluated by the following metrics: accuracy, sensitivity, specificity, and area under ROC curve. The performance of the method was compared to the anthropometric parameters: body mass index and waist-to-height ratio. RESULTS: Our model showed a high correlation (R = 0.80) with the body fat percentage value obtained through bioimpedance. In addition, regarding discrimination, our model obtained better results compared to BMI and WHtR: AUROC = 0.80, 0.64, and 0.55, respectively. It also presented a high sensitivity of 92% and low false negative rate (6%), while BMI and WHtR showed low sensitivity (27% and 9.9%) and a high false negative rate (65% and 53%), respectively. CONCLUSIONS: The computational model of this study obtained a better performance in the evaluation of excess body fat in adolescents, compared to the usual anthropometric indicators presenting itself as a low cost alternative for screening obesity in adolescents living in Brazilian regions where financial resources are scarce.

Identification of Hypsarrhythmia in Children with Microcephaly Infected by Zika Virus.

Hypsarrhythmia is an electroencephalographic pattern specific to some epileptic syndromes that affect children under one year of age. The identification of this pattern, in some cases, causes disagreements between experts, which is worrisome since an inaccurate diagnosis can bring complications to the infant. Despite the difficulties in visually identifying hypsarrhythmia, options of computerized assistance are scarce. Aiming to collaborate with the recognition of this electropathological pattern, we propose in this paper a mathematical index that can help electroencephalography experts to identify hypsarrhythmia. We performed hypothesis tests that indicated significant differences in the groups under analysis, where the p-values were found to be extremely small.

Diagnostic decision support systems for atrial fibrillation based on a novel electrocardiogram approach.

BACKGROUND: The electrocardiogram (ECG) is one of the most non-invasive techniques to give support to the atrial fibrillation (AF) diagnosis. Several authors use the temporal difference between two consecutive R waves, a method known as RR interval, to perform the AF diagnosis. However, RR interval-based analysis does not detect distortions on the other ECG waves. PURPOSE: Thus, the present work proposes a diagnostic decision support systems for AF based on higher order spectrum analysis of the voltage variation on the ECG.. METHODS: The proposed method was used aiming AF classifying. The classifier is composed by two screening stages: one based on the average and another on the average deviation of kurtosis of the ECG signals. Heartbeat obtained from the MIT-BIH atrial fibrillation and MIT-BIH normal were used. RESULTS: ECG signal featured by kurtosis outperforms second order statistics based metrics in up to 476 times, and up to 110 times above the RR interval. The screening methods obtained sensitivity equal to 100% and specificity is up to 84.04%. The two screening methods combined provided an AF classifier with an accuracy rate at diagnosis of 100%. The results presented take into account windows of up to five heartbeats and a 99.73% confidence interval. CONCLUSION: The results obtained by the proposed method can be used to support decision-making in clinical practices with a diagnostic accuracy rate of 90.04% to 100%.

Detecting masses in dense breast using independent component analysis.

Breast cancer is the second type of cancer that most affects women in the world, losing only for non-melanoma skin cancer. Breast density can hinder the location of masses, especially in early stages. In this work, the use of independent component analysis for detecting lesions in dense breasts is proposed. Several works suggest the use of computer aided diagnosis (CAD), increasing sensitivity to over 90% in detecting cancer in nondense breasts, however there are few published studies about detecting in dense breasts. To analyse its efficiency in relation to other segmentation techniques, we compare the performance with principal component analysis. To measure the quality of the segmentation obtained by the two methods, an area overlay measure will be used. To verify if there was any difference between the results of the proposed methods in the detection of lesions in nondense breasts and in dense breasts, a statistic test for two proportions was used. Experimental results on the Mini-MIAS and DDSM database showed an accuracy of 92.71% in detecting masses in nondense and 79.17% in dense breasts. All experiments showed that the ICA filters have a better performance for detect lesions in dense breast, compared with PCA. Contrary to previous works, our experiments showed that there is actually a significant difference between the detection of masses in dense and nondense breasts. This study can help specialist to detect lesions in dense breast.

Performance quantification of clustering algorithms for false positive removal in fMRI by ROC curves

Abstract Introduction Functional magnetic resonance imaging (fMRI) is a non-invasive technique that allows the detection of specific cerebral functions in humans based on hemodynamic changes. The contrast changes are about 5%, making visual inspection impossible. Thus, statistic strategies are applied to infer which brain region is engaged in a task. However, the traditional methods like general linear model and cross-correlation utilize voxel-wise calculation, introducing a lot of false-positive data. So, in this work we tested post-processing cluster algorithms to diminish the false-positives. Methods In this study, three clustering algorithms (the hierarchical cluster, k-means and self-organizing maps) were tested and compared for false-positive removal in the post-processing of cross-correlation analyses. Results Our results showed that the hierarchical cluster presented the best performance to remove the false positives in fMRI, being 2.3 times more accurate than k-means, and 1.9 times more accurate than self-organizing maps. Conclusion The hierarchical cluster presented the best performance in false-positive removal because it uses the inconsistency coefficient threshold, while k-means and self-organizing maps utilize a priori cluster number (centroids and neurons number); thus, the hierarchical cluster avoids clustering scattered voxels, as the inconsistency coefficient threshold allows only the voxels to be clustered that are at a minimum distance to some cluster.

The Performance of Short-Term Heart Rate Variability in the Detection of Congestive Heart Failure.

Congestive heart failure (CHF) is a cardiac disease associated with the decreasing capacity of the cardiac output. It has been shown that the CHF is the main cause of the cardiac death around the world. Some works proposed to discriminate CHF subjects from healthy subjects using either electrocardiogram (ECG) or heart rate variability (HRV) from long-term recordings. In this work, we propose an alternative framework to discriminate CHF from healthy subjects by using HRV short-term intervals based on 256 RR continuous samples. Our framework uses a matching pursuit algorithm based on Gabor functions. From the selected Gabor functions, we derived a set of features that are inputted into a hybrid framework which uses a genetic algorithm and k-nearest neighbour classifier to select a subset of features that has the best classification performance. The performance of the framework is analyzed using both Fantasia and CHF database from Physionet archives which are, respectively, composed of 40 healthy volunteers and 29 subjects. From a set of nonstandard 16 features, the proposed framework reaches an overall accuracy of 100% with five features. Our results suggest that the application of hybrid frameworks whose classifier algorithms are based on genetic algorithms has outperformed well-known classifier methods.

Diabetes classification using a redundancy reduction preprocessor

Introduction Diabetes patients can benefit significantly from early diagnosis. Thus, accurate automated screening is becoming increasingly important due to the wide spread of that disease. Previous studies in automated screening have found a maximum accuracy of 92.6%. Methods This work proposes a classification methodology based on efficient coding of the input data, which is carried out by decreasing input data redundancy using well-known ICA algorithms, such as FastICA, JADE and INFOMAX. The classifier used in the task to discriminate diabetics from non-diaibetics is the one class support vector machine. Classification tests were performed using noninvasive and invasive indicators. Results The results suggest that redundancy reduction increases one-class support vector machine performance when discriminating between diabetics and nondiabetics up to an accuracy of 98.47% while using all indicators. By using only noninvasive indicators, an accuracy of 98.28% was obtained. Conclusion The ICA feature extraction improves the performance of the classifier in the data set because it reduces the statistical dependence of the collected data, which increases the ability of the classifier to find accurate class boundaries.

Measuring streetscape complexity based on the statistics of local contrast and spatial frequency.

Streetscapes are basic urban elements which play a major role in the livability of a city. The visual complexity of streetscapes is known to influence how people behave in such built spaces. However, how and which characteristics of a visual scene influence our perception of complexity have yet to be fully understood. This study proposes a method to evaluate the complexity perceived in streetscapes based on the statistics of local contrast and spatial frequency. Here, 74 streetscape images from four cities, including daytime and nighttime scenes, were ranked for complexity by 40 participants. Image processing was then used to locally segment contrast and spatial frequency in the streetscapes. The statistics of these characteristics were extracted and later combined to form a single objective measure. The direct use of statistics revealed structural or morphological patterns in streetscapes related to the perception of complexity. Furthermore, in comparison to conventional measures of visual complexity, the proposed objective measure exhibits a higher correlation with the opinion of the participants. Also, the performance of this method is more robust regarding different time scenarios.

Statistical coding and decoding of heartbeat intervals.

The heart integrates neuroregulatory messages into specific bands of frequency, such that the overall amplitude spectrum of the cardiac output reflects the variations of the autonomic nervous system. This modulatory mechanism seems to be well adjusted to the unpredictability of the cardiac demand, maintaining a proper cardiac regulation. A longstanding theory holds that biological organisms facing an ever-changing environment are likely to evolve adaptive mechanisms to extract essential features in order to adjust their behavior. The key question, however, has been to understand how the neural circuitry self-organizes these feature detectors to select behaviorally relevant information. Previous studies in computational perception suggest that a neural population enhances information that is important for survival by minimizing the statistical redundancy of the stimuli. Herein we investigate whether the cardiac system makes use of a redundancy reduction strategy to regulate the cardiac rhythm. Based on a network of neural filters optimized to code heartbeat intervals, we learn a population code that maximizes the information across the neural ensemble. The emerging population code displays filter tuning proprieties whose characteristics explain diverse aspects of the autonomic cardiac regulation, such as the compromise between fast and slow cardiac responses. We show that the filters yield responses that are quantitatively similar to observed heart rate responses during direct sympathetic or parasympathetic nerve stimulation. Our findings suggest that the heart decodes autonomic stimuli according to information theory principles analogous to how perceptual cues are encoded by sensory systems.

Computational consciousness: building a self-preserving organism.

Consciousness has been a subject of crescent interest among the neuroscience community. However, building machine models of it is quite challenging, as it involves many characteristics and properties of the human brain which are poorly defined or are very abstract. Here I propose to use information theory (IT) to give a mathematical framework to understand consciousness. For this reason, I used the term "computational". This work is grounded on some recent results on the use of IT to understand how the cortex codes information, where redundancy reduction plays a fundamental role. Basically, I propose a system, here called "organism", whose strategy is to extract the maximal amount of information from the environment in order to survive. To highlight the proposed framework, I show a simple organism composed of a single neuron which adapts itself to the outside dynamics by taking into account its internal state, whose perception is understood here to be related to "feelings".