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Cirrhosis is a liver disease that could impair the functionality of the neural system, limiting cognitive processes and reduce mobility, possibly because alterations on dynamics of neural communications. In this study the dynamics of band power fluctuations on ß and γ bands were studied on three groups: control, patients with cirrhosis and patients diagnosed with minimal hepatic encephalopathy. EEG signals were recorded on an oddball paradigm and analyzed to obtain the latency and amplitude of the P300 wave and Detrended Fluctuation Analysis (DFA) exponent values over ß and γ band power time series for each channel. As expected, the latency of the P300 wave was significantly different for control subjects ( ). Amplitudes were not as different, but they tend to decrease for cirrhosis and minimal hepatic encephalopathy groups. DFA exponent values also tends to describe a more regular process and seems to be related with presence of symptoms of hepatic encephalopathy. These subtle changes could be produced by the mechanisms of a mild neurological impairment and be used, if it is confirmed, as an index to evaluate such deterioration.

Parkinson's disease (PD) is a neuropathy characterized by motor disorders, but it has also been associated with the presence of autonomic alterations as a result of degradation of the dopaminergic system. Studying the relation between Band Power time series (BPts) and Heart Rate Variability (HRV), has been proposed as a tool to explore the bidirectional communication pathways between cortex and autonomic control. This work presents a primer analysis on study brain ↔ heart interaction on a databse of PD patients under two conditions: without and after levadopa (L-dopa) intake. Additionally a healthy control population was also analyzed, and used as comparison level between both conditions. Results show PD affects pathways by reducing the number of connections, specially association of beta and power and the second faster component of HRV seems to be more sensitive to L-dopa administration.

Parkinson's Disease is a neuropathy that produces changes in several biomarkers, these changes could be used to evaluate even sub-clinical conditions. This paper presents an evaluation of indices extracted from electroencephalography and Heart Rate Variability (HRV), when used to classify a sample of subjects from three groups: control (healthy), medicated and non medicated subjects diagnosed with Parkinson's disease. Classification performance was measured using accuracy over these classes and a cross validation scheme was used to assess repeatability for the classification process. Results tend to prove that inclusion of an autonomic index derived from HRV analysis enhances classification, suggesting that Parkinson's disease could be related with unperceptible to mild alterations of the Autonomic Nervous System.

Asynchronous Brain Computer Interfaces (BCI) al-low system activation at free will. This scenario would be desirable for potential users such as patients with motor disabilities, however, there are still several limitations such as long calibration times and overall performance. Different approaches to improve these systems explore diverse linear electroencephalography (EEG) features in order to describe cognitive states. Alternatively, the present study proposes the use of recurrence quantification analysis (RQA), which provides indices about the dynamical behaviour of non-linear systems. We evaluated the accuracy of continuous detection of mental calculation and idle state using RQA features from EEG recordings. Among the different classification methods that were tested, random forest (RF) classifier resulted in the best performance. From 35 selected RQA features, 12 healthy subjects achieved mean accuracy of 0.924 ±0.048, whereas with 150 selected features mean accuracy was 0.977 ±0.020. These results suggest that RQA features are adequate for continuous task episode detection in asynchronous BCI. However, a restriction in the number of these features, conceived for improving computational costs and time, does impact detection performance.

Most methods for sudden cardiac death (SCD) prediction require long-term (24 h) electrocardiogram recordings to measure heart rate variability (HRV) indices or premature ventricular complex indices (with the heartprint method). This work aimed to identify the best combinations of HRV and heartprint indices for predicting SCD based on short-term recordings (1000 heartbeats) through a support vector machine (SVM). Eleven HRV indices and five heartprint indices were measured in 135 pairs of recordings (one before an SCD episode and another without SCD as control). SVMs (defined with a radial basis function kernel with hyperparameter optimization) were trained with this dataset to identify the 13 best combinations of indices systematically. Through 10-fold cross-validation, the best area under the curve (AUC) value as a function of &gamma, (gamma) and cost was identified. The predictive value of the identified combinations had AUCs between 0.80 and 0.86 and accuracies between 80 and 86%. Further SVM performance tests on a different dataset of 68 recordings (33 before SCD and 35 as control) showed AUC = 0.68 and accuracy = 67% for the best combination. The developed SVM may be useful for preventing imminent SCD through early warning based on electrocardiogram (ECG) or heart rate monitoring.

Brainwaves amplitude obtained from electroencephalography (EEG) has been well-recognized as a basis for cognitive capacity, memory, and learning on animals and humans. Adult neurogenesis mechanism is also linked to memory and learning improvement. Traditionally, researchers used to assess learning and memory parameters in rodent models by behavioral tasks. Therefore, the simultaneous monitoring of behavioral changes and EEG is particularly interesting in correlating data between brain activity and task-related behaviors. However, most of the equipment required to perform both studies are either complex, expensive, or uses a wired setup network that hinders the natural animals' movement. In this study, EEG was recorded with a wireless electrophysiology device along with the execution of a novel object recognition task (NORT). The animal's behavior was monitored simultaneously by a video tracking system. Both recordings were analyzed offline by their timestamps which were synched to link EEG signals with the animal's actions. Subjects consist of adult Wistar rats after medium-term environmental enrichment treatment. Six skull screw electrodes were fixed in pairs on both hemispheres over frontal, central, and parietal regions and were referenced to an electrode located posterior of the nasal bone. NORT protocol consists of exposing the animal to two identical objects for 10 min. After 2 h and 24 h, one of the objects was replaced with a novel one. Exploration time for each object was monitored by a behavioral tracking software (BTS) and EEG data recording. The analysis of the EEG synced with behavioral data consists of estimations of alpha and beta relative band power and comparisons between novel object recognition versus familiar object exploration, between three experimental stages. In this manuscript, we have discussed electrodes manufacturing process, epidural electrodes implantation surgery, environmental enrichment protocol, NORT protocol, BTS setup, EEG - BTS coupling for simultaneous monitoring in real-time, and EEG data analysis based on automatic events detection.

Some hypotheses relate oscillations of EEG band power with autonomic processes derived from homeostatic control modulated by structures like the Central Autonomic Network and the Autonomic Nervous System. This research project studies the causal relationships between fluctuations of an autonomic process marker like the Heart Rate Variability (HRV) and the proposed EEG band power time series (BPts). To verify the existence of directional causal relationships, using Granger Causality (GC) test between HRV and BPts. Analyses were performed using two databases, of 9 and 14 subjects respectively. Experiments consisted of spontaneous breathing and a controlled breathing task (CBT). GC was tested over Intrinsinc Mode Functions of HRV derived from Empirical Mode Decomposition and BPts computed over α, β and γ bands. Positive GC tests were observed through each experimental task, channels, IMFs, EEG band, and direction. The largest number of positive GC relationships were found from BPts to HRV when testing, higher EEG band and IMF with lower spectral content. Opposite direction achieves lower total counts, but more related with IMFs of higher spectral content. Its presence also suggests that some homeostatic condition alters the BPts course given its increment under the CBT. It is important to notice that in both cases γ band achieves larger values for almost all of the studied conditions. Suggesting that such band has an important influence over HRV, but alterations on breathing condition also produce changes on BPts evolution, suggesting that the closed loop for homeostatic control alters neural dynamics at cortical level.

This work presents an evaluation of two machine learning schemes for lid segmentation on meibography images. Both schemes use same input features based on pixel gray levels, laplacian and entropy filters, and also distances to anatomical landmarks like pupil and eye lashes. The methods evaluated were support vector machines (SVM) with 4th degree polynomial kernel, a Neural Network (NN) with 60 neurons distributed on three layers and the intersection of both. Performance was evaluated with AUC on a bootstrapped cross validation (CV) tests of 20 folds. Dataset is conformed by 465 images for each fold entire dataset was split on 70% training and remainder for testing. Results of CV: SVM 0.851 ± 0.103, NN 0.713 ± 0.144 and SVM & NN 0.835 ± 0.118, suggest that SVM is a suitable model to be used for this task.

Chronic kidney disease impacts the cognitive abilities of patients, and yet few works have analyzed functional electroencephalographic changes on event-related potentials before and after an hemodialysis session. This work shows that, as a consequence of hemodialysis, cognitive potential waveforms suffer changes, occurring with reduced latency $( \sim 50$ ms) and with larger amplitude $( \sim 0.128\mathrm {a}$.u.) after the treatment session. While only a limited sample of five patients is reported herein, the observed changes immediately after hemodialysis could be a sign of central nervous system alterations that are not clinically evaluated. Signs like these could entail an early indicator of possible evolution into some neuropathology.

To search for possible interactions between the autonomic heart control and the brain activity we have evaluatedthe relation between heart rate variability (HRV) and the alpha and beta EEG band power dynamics. Theexperiments consisted in the alteration of HRV induced by respiratory rhythm changes. Modi cations in thetime series of the spectral density in each band {causally related to the HRV{ were recognized in the centraland occipital regions using a Granger causality approach. The number of subjects and distribution of channelsin which there was a causal relation changed with the di erent tasks assigned to the subjects and the analyzedbands. The causal relation between HRV and beta power series was observed in at least one channel, in eightout of nine subjects for a resting condition. On the other hand, in the HRV and alpha power series analysis thechange in distribution was less pronounced, and detected in a smaller number of subjects. The results suggestthat the HRV may be associated with the spectral band power time series dynamics, and this relationship isaltered by the respiration.Keywords: HRV, RSA, EEG band power time series, Granger causality

During the first trimester of pregnancy fetal health assessment is especially important. In the clinical practice, the gestational sac (GS) volume estimation is manually done using a tedious procedure which is prone to physicians' subjectivity. The method proposed in this paper consists on a semiautomatic delimitation of the GS and a segmentation of its content with minimal expert intervention. It is based on spreading active contours (SAC), following a planimetric strategy to define the GS' edges. Additionally, an optimal thresholding method was used to separate solid matter and amniotic fluid. The comparison between manual GS segmentations and those obtained with the proposed SAC method, shows Dice similarities of 90% and a mean Hausdor distance of 5.63 ± 1.94 mm, while the correlation index between SAC and the clinical reference (VOCAL) is 0.997. However, with statistical tests (t-paired) a value of p < 0.05 was obtained, which suggests a difference in the measured volume by the compared methods. The proposed method (SAC) has shown to be reliable, besides of being easy to implement.

Steady state visual evoked potentials represent the least training-dependent brain computer interface paradigm, a condition that significantly improves the user experience with this kind of interfaces. On the other hand, the open-source DASHER writing system is an unconventional machine interface paradigm on its own, for which their developers encourage the search of new controlling mechanisms. In this paper, we report the use of a single steady-state visual stimulus approach to control the navigation vector of the DASHER writing system. Fourier-based power estimation was used to compute the power around the fundamental band of the evoked response detected in occipital electrodes, and user-dependent threshold was used as the directional control. In an online trial, a short word was successfully -albeit inefficiently- written by a naive user, which suggests the feasibility of the application of such a paradigm for performing this task.