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Current sleep analyses have used electroencephalography (EEG) to establish sleep intensity through linear and nonlinear measures. Slow wave activity (SWA) and entropy are the most commonly used markers of sleep depth. The purpose of this study is to evaluate changes in brain EEG connectivity during sleep in healthy subjects and compare them with SWA and entropy. Four different connectivity metrics: coherence (MSC), synchronization likelihood (SL), cross mutual information function (CMIF), and phase locking value (PLV), were computed focusing on their correlation with sleep depth. These measures provide different information and perspectives about functional connectivity. All connectivity measures revealed to have functional changes between the different sleep stages. The averaged CMIF seemed to be a more robust connectivity metric to measure sleep depth (correlations of 0.78 and 0.84 with SWA and entropy, respectively), translating greater linear and nonlinear interdependences between brain regions especially during slow wave sleep. Potential changes of brain connectivity were also assessed throughout the night. Connectivity measures indicated a reduction of functional connectivity in N2 as sleep progresses. The validation of connectivity indexes is necessary because they can reveal the interaction between different brain regions in physiological and pathological conditions and help understand the different functions of deep sleep in humans.

The respiratory control system is one of the most complex physiological systems, whose controller has been extensively modeled based on the criterion of minimizing work of breathing (WOB). However, cost functions and their related parameters are still an open topic. The aim of this paper is to evaluate different estimates of WOB for given ventilatory demands. Two known computations of mechanical work are compared with regard to an estimate proposed in this paper. Appropriate values for these parameters were found based on the best fitting of the breathing pattern obtained from experimental data during increased hypercapnia. The comparison among them was carried out from their breathing pattern predictability and the physiological viewpoint. For this purpose, two nested optimization processes were performed: minimization of prediction error, with regard to the experimental response, in order to obtain the best estimating parameters; identification of a breathing pattern that minimizes the mechanical WOB. The former was performed using Co-variance Matrix Adaptation Evolution Strategy and the latter using sequential quadratic programming. Results show that the estimate proposed in this paper is less sensitive to their parameters and reaches a significantly lower prediction error of breathing pattern during increased ventilatory effort and a better interpretation from a physiological viewpoint.

Objective. We propose a novel automated method called the S-Transform Gaussian Mixture detection algorithm (SGM) to detect high-frequency oscillations (HFO) combining the strengths of different families of previously published detectors. Approach. This algorithm does not depend on parameter tuning on a subject (or database) basis, uses time-frequency characteristics, and relies on non-supervised classification to determine if the events standing out from the baseline activity are HFO or not. SGM consists of three steps: the first stage computes the signal baseline using the entropy of the autocorrelation; the second uses the S-Transform to obtain several time-frequency features (area, entropy, and time and frequency widths); and in the third stage Gaussian mixture models cluster time-frequency features to decide if events correspond to HFO-like activity. To validate the SGM algorithm we tested its performance in simulated and real environments. Main results. We assessed the algorithm on a publicly available simulated stereoelectroencephalographic (SEEG) database with varying signal-to-noise ratios (SNR), obtaining very good results for medium and high SNR signals. We further tested the SGM algorithm on real signals from patients with focal epilepsy, in which HFO detection was performed visually by experts, yielding a high agreement between experts and SGM. Significance. The SGM algorithm displayed proper performance in simulated and real environments and therefore can be used for non-supervised detection of HFO. This non-supervised algorithm does not require previous labelling by experts or parameter adjustment depending on the subject or database considered. SGM is not a computationally intensive algorithm, making it suitable to detect and characterize HFO in long-term SEEG recordings.

Rationale: Recent evidence suggests that obstructive sleep apnea (OSA) may be a risk factor for developing mild cognitive impairment and Alzheimer's disease. However, how sleep apnea affects longitudinal risk for Alzheimer's disease is less well understood. Objectives: To test the hypothesis that there is an association between severity of OSA and longitudinal increase in amyloid burden in cognitively normal elderly. Methods: Data were derived from a 2-year prospective longitudinal study that sampled community-dwelling healthy cognitively normal elderly. Subjects were healthy volunteers between the ages of 55 and 90, were nondepressed, and had a consensus clinical diagnosis of cognitively normal. Cerebrospinal fluid amyloid beta was measured using ELISA. Subjects received Pittsburgh compound B positron emission tomography scans following standardized procedures. Monitoring of OSA was completed using a home sleep recording device. Measurements and Main Results: We found that severity of OSA indices (AHIall [F-1,F-88 = 4.26; P < 0.05] and AHI4%[F-1,F-87 = 4.36; P < 0.05]) were associated with annual rate of change of cerebrospinal fluid amyloid beta(42) using linear regression after adjusting for age, sex, body mass index, and apolipoprotein E4 status. AHIall and AHI4% were not associated with increases in AD(PiB)-mask (Alzheimer's disease vulnerable regions of interest Pittsburg compound B positron emission tomography mask) most likely because of the small sample size, although there was a trend for AHIall (F-1,F-28 = 2.96, P = 0.09; and F-1,F-28 = 2.32, not significant, respectively). Conclusions: In a sample of cognitively normal elderly, OSA was associated with markers of increased amyloid burden over the 2-year follow-up. Sleep fragmentation and/or intermittent hypoxia from OSA are likely candidate mechanisms. If confirmed, clinical interventions for OSA may be useful inpreventing amyloid build-up in cognitively normal elderly.

Background The study of cerebral underpinnings of schizophrenia may benefit from the high temporal resolution of electromagnetic techniques, but its spatial resolution is low. However, source imaging approaches such as low-resolution brain electromagnetic tomography (LORETA) allow for an acceptable compromise between spatial and temporal resolutions. Methods We combined LORETA with 32 channels and 3-Tesla diffusion magnetic resonance (Dmr) to study cerebral dysfunction in 38 schizophrenia patients (17 first episodes, FE), compared to 53 healthy controls. The EEG was acquired with subjects performing an odd-ball task. Analyses included an adaptive window of interest to take into account the interindividual variability of P300 latency. We compared source activation patters to distractor (P3a) and target (P3b) tones within- and between-groups. Results Patients showed a reduced activation in anterior cingulate and lateral and medial prefrontal cortices, as well as inferior/orbital frontal regions. This was also found in the FE patients alone. The activation was directly related to IQ in the patients and controls and to working memory performance in controls. Symptoms were unrelated to source activation. Fractional anisotropy in the tracts connecting lateral prefrontal and anterior cingulate regions predicted source activation in these regions in the patients. Conclusions These results replicate the source activation deficit found in a previous study with smaller sample size and a lower number of sensors and suggest an association between structural connectivity deficits and functional alterations.

The modeling and simulation of the cardiorespiratory system has allowed a better understanding of the physiological system behavior to different stimuli and pathologies. Among its main applications stands out the computational tools for diagnosis and clinical treatment support, context in which the knowledge of the computational cost is fundamental for its implementation. The purpose of this work is to develop a comparative evaluation of computational cost of the cardiorespiratory models proposed by Cheng et al. and Albanese et al. under physical activity simulation. The evaluation consisted in the comparison of the computational cost and the prediction error of the physiological variables for each model. Variations in carbon dioxide production between 0.2 and 2.0 l/min were implemented, under the conditions and nominal parameters proposed for each model. The variables compared were mean arterial blood pressure, heart rate, frequency of breathing, tidal volume, arterial partial pressure of oxygen and arterial partial pressure of carbon dioxide. The results evidence that the model proposed by Cheng et al. is the most appropriate for the exercise simulation, due to both the best prediction of variables and the best relationship between complexity and simulation time required. © 2018 IEEE.

The detection of ischemic changes is a primary task in the interpretation of brain Computer Tomography (CT) of patients suffering from neurological disorders. Although CT can easily show these lesions, their interpretation may be difficult when the lesion is not easily recognizable. The gold standard for the detection of acute stroke is highly variable and depends on the experience of physicians. This research proposes a new method of automatic detection of parenchymal changes of ischemic stroke in Non-Contrast CT. The method identifies non-pathological cases (94 cases, 40 training, 54 test) based on the analysis of cerebral symmetry. Parenchymal changes in cases with abnormalities (20 cases) are detected by means of a contralateral analysis of brain regions. In order to facilitate the evaluation of abnormal regions, non-pathological tissues in Hounsfield Units were characterized using fuzzy logic techniques. Cases of non-pathological and stroke patients were used to discard/confirm abnormality with a sensitivity (TPR) of 91% and specificity (SPC) of 100%. Abnormal regions were evaluated and the presence of parenchymal changes was detected with a TPR of 96% and SPC of 100%. The presence of parenchymal changes of ischemic stroke was detected by the identification of tissues using fuzzy logic techniques. Because of abnormal regions are identified, the expert can prioritize the examination to a previously delimited region, decreasing the diagnostic time. The identification of tissues allows a better visualization of the region to be evaluated, helping to discard or confirm a stroke.

Copyright © 2018 Serna, Mañanas, Hernández and Rabinovich. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. Respiratory system modeling has been extensively studied in steady-state conditions to simulate sleep disorders, to predict its behavior under ventilatory diseases or stimuli and to simulate its interaction with mechanical ventilation. Nevertheless, the studies focused on the instantaneous response are limited, which restricts its application in clinical practice. The aim of this study is double: firstly, to analyze both dynamic and static responses of two known respiratory models under exercise stimuli by using an incremental exercise stimulus sequence (to analyze the model responses when step inputs are applied) and experimental data (to assess prediction capability of each model). Secondly, to propose changes in the models' structures to improve their transient and stationary responses. The versatility of the resulting model vs. the other two is shown according to the ability to simulate ventilatory stimuli, like exercise, with a proper regulation of the arterial blood gases, suitable constant times and a better adjustment to experimental data. The proposed model adjusts the breathing pattern every respiratory cycle using an optimization criterion based on minimization of work of breathing through regulation of respiratory frequency.

STUDY OBJECTIVES: Sleep problems are often undetected in adults with Down syndrome (DS). Our objective was to determine the prevalence of sleep disorders in adults with DS through self-reported and objective sleep measures. METHODS: We performed a community-based cross-sectional study of 54 adults with DS not referred for sleep disorders. Two polysomnography (PSG) sleep studies were performed. Sleep quality was evaluated using the Pittsburgh Sleep Quality Index (PSQI); daytime sleepiness was evaluated using the Epworth Sleepiness Scale (ESS) and the risk for the sleep apnea syndrome (OSA) was identified using the Berlin Questionnaire (BQ). Participants' sleep/wake pattern was assessed from sleep diaries and by wrist actigraphy. PSQI, ESS, and PSG measures were compared with 35 sex-, age-, and body mass index-matched patients in the control groups. RESULTS: In PSG measures, adults with DS showed lower sleep efficiency (69 ± 17.7 versus 81.6 ± 11; P < .001), less rapid eye movement sleep (9.4 ± 5.8 versus 19.4 ± 5.1; P < .001), a higher prevalence of OSA (78% versus 14%; P < .001), and a higher apnea-hypopnea index (23.5 ± 24.5 versus 3.8 ± 10.5; P < .001) than patients in the control group. In the DS group, the questionnaires (mean PSQI 3.7 ± 2.9; mean ESS 6.3 ± 4.5 and mean BQ 1 ± 0) did not reflect the sleep disturbances detected on the PSG. Actigraphy data recorded daytime sleep that was not self-reported (118.2 ± 104.2 minutes). CONCLUSIONS: Adults with DS show severe sleep disruption and a high prevalence of OSA, undetected by self-reported sleep measures. Actigraphy, PSG, and validated simplified devices for screening OSA should be routinely recommended for this population because treatment of sleep disorders can contribute to healthy aging. CITATION: Giménez S, Videla L, Romero S, Benejam B, Clos S, Fernández S, Martínez M, Carmona-Iragui M, Antonijoan RM, Mayos M, Fortuna A, Peñacoba P, Plaza V, Osorio RS, Sharma RA, Bardés I, Rebillat AS, Lleó A, Blesa R, Videla S, Fortea J. Prevalence of sleep disorders in adults with down syndrome: a comparative study of self-reported, actigraphic, and polysomnographic findings. J Clin Sleep Med. 2018;14(10):1725–1733.

In neuroimaging, brain tissue segmentation is a fundamental part of the techniques that seek to automate the detection of pathologies, the quantification of tissues or the evaluation of the progress of a treatment. Because of its wide availability, lower cost than other imaging techniques, fast execution and proven efficacy, Non-contrast Cerebral Computerized Tomography (NCCT) is the most used technique in emergency room for neuroradiology examination, however, most research on brain segmentation focuses on MRI due to the inherent difficulty of brain tissue segmentation in NCCT. In this work, three brain tissues were characterized: white matter, gray matter and cerebrospinal fluid in NCCT images. Feature extraction of these structures was made based on the radiological attenuation index denoted by the Hounsfield Units using fuzzy logic techniques. We evaluated the classification of each tissue in NCCT images and quantified the feature extraction technique in images from real tissues with a sensitivity of 92% and a specificity of 96% for images from cases with slice thickness of 1 mm, and 96% and 98% respectively for those of 1.5 mm, demonstrating the ability of the method as feature extractor of brain tissues.

This paper describes a method to grade gliomas examined by magnetic resonance imaging. It is a preliminary study focused only on the features extracted from susceptibility-weighted images. The proposed method involves fusion of classifiers based on decision trees designed using fuzzy techniques. The favorable results indicate that the fuzzy approach may be of particular value for grading gliomas.

Introduction and hypothesis Urinary incontinence is a common condition in women, with a reported prevalence ranging from 25% to 51%. Of these women, an estimated 38% suffer from stress urinary incontinence (SUI). A European research consortium is investigating an innovative system based on information and communication technology for the conservative treatment of women with SUI. When introducing a new intervention, implementation barriers arise and need to be identified. Therefore, we investigated healthcare providers’ experience with and attitude towards innovative care options. Methods We performed an online survey to assess (1) the characteristics and practice of healthcare providers, (2) current protocols for SUI, (3) current use of biofeedback, and (4) knowledge about serious gaming. The survey was sent to members of professional societies in Europe (EUGA), UK (BSUG) and The Netherlands (DPFS). Results Of 341 questionnaires analyzed (response rate between 18% and 30%), 64% of the respondents had access to a protocol for the treatment of SUI, and 31% used biofeedback when treating patients with SUI. However, 92% considered that biofeedback has a clear or probable added value, and 97% of those who did not use biofeedback would change their practice if research evidence supported its use. Finally, 89% of respondents indicated that they had no experience of serious gaming, but 92% considered that it could be useful. Conclusions Although inexperienced, European urogynecologists and physical therapists welcome innovative treatment options for the conservative treatment of SUI such as portable wireless biofeedback and serious gaming. Scientific evidence is considered a prerequisite to incorporate such innovations into clinical practice., published version, peerReviewed

The aim of this pilot study was to evaluate the feasibility of surface electromyographic signal derived indexes for the prediction of weaning outcomes among mechanically ventilated subjects after cardiac surgery.A sample of 10 postsurgical adult subjects who received cardiovascular surgery that did not meet the criteria for early extubation were included. Surface electromyographic signals from diaphragm and ventilatory variables were recorded during the weaning process, with the moment determined by the medical staff according to their expertise. Several indexes of respiratory muscle expenditure from surface electromyography using linear and non-linear processing techniques were evaluated. Two groups were compared: successfully and unsuccessfully weaned patients.The obtained indexes allow estimation of the diaphragm activity of each subject, showing a correlation between high expenditure and weaning test failure.Surface electromyography is becoming a promising procedure for assessing the state of mechanically ventilated patients, even in complex situations such as those that involve a patient after cardiovascular surgery.Avaliar a viabilidade do uso de índices derivados do sinal de eletromiografia de superfície para predizer desfechos do processo de desmame em pacientes mecanicamente ventilados após cirurgia cardíaca.Foram incluídos dez pacientes em pós-operatório de cirurgia cardiovascular que não cumpriram os critérios para extubação precoce. Os sinais da eletromiografia de superfície foram registrados, assim como as variáveis ventilatórias durante o processo de desmame, sendo o momento do procedimento determinado pela equipe médica, segundo sua experiência. Avaliaram-se diversos índices da atividade dos músculos respiratórios obtidos a partir da eletromiografia de superfície com uso de técnicas de processamento lineares e não lineares. Compararam-se dois grupos: pacientes com e sem sucesso no desmame.Os índices obtidos permitiram estimar a atividade diafragmática de cada paciente, demonstrando uma correlação entre atividade elevada e falha do teste de desmame.A eletromiografia de superfície está se tornando um procedimento promissor para avaliar as condições de pacientes ventilados mecanicamente, mesmo em condições complexas, como as que envolvem aqueles após cirurgia cardiovascular.

This study was designed to develop a risk assessment chart for the clinical management and prevention of the risk of cardiovascular disease (CVD) in Iranian population, which is vital for developing national prevention programs. The Isfahan Cohort Study (ICS) is a popu- lation-based prospective study of 6504 Iranian adults 35 years old, followed-up for ten years, from 2001 to 2010. Behavioral and cardiometabolic risk factors were examined every five years, while biennial follow-ups for the occurrence of the events was performed by phone calls or by verbal autopsy. Among these participants, 5432 (2784 women, 51.3%) were CVD free at baseline examination and had at least one follow-up. Cox proportional hazard regression was used to predict the risk of ischemic CVD events, including sudden cardiac death due to unstable angina, myocardial infarction, and stroke. The model fit statis- tics such as area under the receiver-operating characteristic (AUROC), calibration chi- square and the overall bias were used to assess the model performance. We also tested the Framingham model for comparison. Seven hundred and five CVD events occurred during 49452.8 person-years of follow-up. The event probabilities were calculated and presented color-coded on each gender-specific PARS chart. The AUROC and Harrell’s C indices were 0.74 (95% CI, 0.72–0.76) and 0.73, respectively. In the calibration, the Nam-D’Ago stino ¿ 2 was 10.82 (p = 0.29). The overall bias of the proposed model was 95.60%. PARS model was also internally validated using cross-validation. The Android app and the Web-based risk assessment tool were also developed as to have an impact on public health. In compari- son, the refitted and recalibrated Framingham models, estimated the CVD incidence with the overall bias of 149.60% and 128.23% for men, and 222.70% and 176.07% for women, respectively. In conclusion, the PARS risk assessment chart is a simple, accurate, and well- calibrated tool for predicting a 10-year risk of CVD occurrence in Iranian population and can be used in an attempt to develop national guidelines for the CVD management .

The neural command from motor neurons to muscles — sometimes referred to as the neural drive to muscle — can be identified by decomposition of electromyographic (EMG) signals. This approach can be used for inferring the voluntary commands in neural interfaces in patients with limb amputations. This paper proposes for the first time an innovative method for fully automatic and real-time intramuscular EMG (iEMG) decomposition. The method is based on online single-pass density-based clustering and adaptive classification of bivariate features, using the concept of potential measure. No attempt was made to resolve superimposed motor unit action potentials. The proposed algorithm was validated on sets of simulated and experimental iEMG signals. Signals were recorded from the biceps femoris long-head, vastus medialis and lateralis and tibialis anterior muscles during low-to-moderate isometric constant-force and linearly-varying force contractions. The average number of missed, duplicated and erroneous clusters for the examined signals was [Formula: see text], [Formula: see text], and [Formula: see text], respectively. The average decomposition accuracy (defined similar to signal detection theory but without using True Negatives in the denominator) and coefficient of determination (variance accounted for) for the cumulative discharge rate estimation were [Formula: see text], and [Formula: see text], respectively. The time cost for processing each 200[Formula: see text]ms iEMG interval was [Formula: see text] (21–97)[Formula: see text]ms. However, computational time generally increases over time as a function of frames/signal epochs. Meanwhile, the incremental accuracy defined as the accuracy of real-time analysis of each signal epoch, was [Formula: see text]% for epochs recorded after initial one second. The proposed algorithm is thus a promising new tool for neural decoding in the next-generation of prosthetic control.

This chapter presents a sensor placement method for the classifier-based approaches for leak localization in water distribution networks introduced in the previous chapter. The proposed approach formulates the sensor placement problem as a binary optimization problem. Because of the complexity of the problem, it is solved by applying genetic algorithms. In order to reduce the number of sensor configurations to test, a binary matrix that identifies pairs of sensors providing similar information is added as a constraint. The sensors are placed in an optimal way, which maximizes the accuracy of the leak localization . The proposed approach is first illustrated by means of the application to an academic example based on the reduced version of the Hanoi water distribution network . A more realistic case study is then proposed based on the Limassol district metered area.

This study aimed to explore the usefulness of measuring respiratory muscle activity in mechanically ventilated patients suffering from acute organophosphate poisoning, with a view towards providing complementary information to determine the best time to suspend ventilatory support. Surface electromyography in respiratory muscles (diaphragm, external intercostal and sternocleidomastoid muscles) was recorded in a young man affected by selfpoisoning with an unknown amount of parathion to determine the muscle activity level during several weaning attempts from mechanical ventilation. The energy distribution of each surface electromyography signal frequency, the synchronization between machine and patient and between muscles, acetylcholinesterase enzyme activity, and work of breathing and rapid shallow breathing indices were calculated in each weaning attempt. The work of breathing and rapid shallow breathing indices were not correlated with the failure/success of the weaning attempt. The diaphragm gradually increased its engagement with ventilation, achieving a maximal response that correlated with successful weaning and maximal acetylcholinesterase enzyme activity; in contrast, the activity of accessory respiratory muscles showed an opposite trend.

© 2015 Springer Science+Business Media Dordrecht The assessment and management of sleep are increasingly recommended in the clinical practice. Polysomnography (PSG) is considered the gold standard test to monitor sleep objectively, but some practical and technical constraints exist due to environmental and patient considerations. Bispectral index (BIS) monitoring is commonly used in clinical practice for guiding anesthetic administration and provides an index based on relationships between EEG components. Due to similarities in EEG synchronization between anesthesia and sleep, several studies have assessed BIS as a sleep monitor with contradictory results. The aim of this study was to evaluate objectively both the feasibility and reliability of BIS for sleep monitoring through a robust methodology, which included full PSG recordings at a baseline situation and after 40 h of sleep deprivation. Results confirmed that the BIS index was highly correlated with the hypnogram (0.89 ± 0.02), showing a progressive decrease as sleep deepened, and an increase during REM sleep (awake: 91.77 ± 8.42; stage N1: 83.95 ± 11.05; stage N2: 71.71 ± 11.99; stage N3: 42.41 ± 9.14; REM: 80.11 ± 8.73). Mean and median BIS values were lower in the post-deprivation night than in the baseline night, showing statistical differences for the slow wave sleep (baseline: 42.41 ± 9.14 vs. post-deprivation: 39.49 ± 10.27; p = 0.02). BIS scores were able to discriminate properly between deep (N3) and light (N1, N2) sleep. BIS values during REM overlapped those of other sleep stages, although EMG activity provided by the BIS monitor could help to identify REM sleep if needed. In conclusion, BIS monitors could provide a useful measure of sleep depth in especially particular situations such as intensive care units, and they could be used as an alternative for sleep monitoring in order to reduce PSG-derived costs and to increase capacity in ambulatory care.

Graphical abstractDisplay Omitted HighlightsTwo respiratory control models based on minimizing work of breathing are analyzed.Optimization algorithms and hypercapnic data are used to adjust model parameters.Algorithms are assessed in terms of convergence rate, solution accuracy and precision.Models are evaluated in terms of prediction error and physiological meaning. One of the most complex physiological systems whose modeling is still an open study is the respiratory control system where different models have been proposed based on the criterion of minimizing the work of breathing (WOB). The aim of this study is twofold: to compare two known models of the respiratory control system which set the breathing pattern based on quantifying the respiratory work; and to assess the influence of using direct-search or evolutionary optimization algorithms on adjustment of model parameters. This study was carried out using experimental data from a group of healthy volunteers under CO2 incremental inhalation, which were used to adjust the model parameters and to evaluate how much the equations of WOB follow a real breathing pattern. This breathing pattern was characterized by the following variables: tidal volume, inspiratory and expiratory time duration and total minute ventilation. Different optimization algorithms were considered to determine the most appropriate model from physiological viewpoint. Algorithms were used for a double optimization: firstly, to minimize the WOB and secondly to adjust model parameters. The performance of optimization algorithms was also evaluated in terms of convergence rate, solution accuracy and precision. Results showed strong differences in the performance of optimization algorithms according to constraints and topological features of the function to be optimized. In breathing pattern optimization, the sequential quadratic programming technique (SQP) showed the best performance and convergence speed when respiratory work was low. In addition, SQP allowed to implement multiple non-linear constraints through mathematical expressions in the easiest way. Regarding parameter adjustment of the model to experimental data, the evolutionary strategy with covariance matrix and adaptation (CMA-ES) provided the best quality solutions with fast convergence and the best accuracy and precision in both models. CMAES reached the best adjustment because of its good performance on noise and multi-peaked fitness functions. Although one of the studied models has been much more commonly used to simulate respiratory response to CO2 inhalation, results showed that an alternative model has a more appropriate cost function to minimize WOB from a physiological viewpoint according to experimental data.

Cancer is a collection of diseases that involves growing abnormal cells with the potential to invade or spread to the body. Breast cancer is the second leading cause of cancer death among women. A method for 5-year breast cancer recurrence prediction is presented in this manuscript. Clinicopathologic characteristics of 579 breast cancer patients (recurrence prevalence of 19.3%) were analyzed and discriminative features were selected using statistical feature selection methods. They were further refined by Particle Swarm Optimization (PSO) as the inputs of the classification system with ensemble learning (Bagged Decision Tree: BDT). The proper combination of selected categorical features and also the weight (importance) of the selected interval-measurement-scale features were identified by the PSO algorithm. The performance of HPBCR (hybrid predictor of breast cancer recurrence) was assessed using the holdout and 4-fold cross-validation. Three other classifiers namely as supported vector machines, DT, and multilayer perceptron neural network were used for comparison. The selected features were diagnosis age, tumor size, lymph node involvement ratio, number of involved axillary lymph nodes, progesterone receptor expression, having hormone therapy and type of surgery. The minimum sensitivity, specificity, precision and accuracy of HPBCR were 77%, 93%, 95% and 85%, respectively in the entire cross-validation folds and the hold-out test fold. HPBCR outperformed the other tested classifiers. It showed excellent agreement with the gold standard (i.e. the oncologist opinion after blood tumor marker and imaging tests, and tissue biopsy). This algorithm is thus a promising online tool for the prediction of breast cancer recurrence., Highlights • Breast cancer is the second leading cause of cancer death among women. • Recurrence prediction could be used by clinicians to make proper treatment plan. • A hybrid prognosis tool with optimized ensemble learning is proposed in this paper. • 579 breast cancer patients (with the recurrence prevalence of 19.3%) were analyzed. • The average accuracy and precision were 89.2 ± 0.6% and 96.5 ± 1.0%, respectively.

Trabajo presentado a la 15th European Control Conference (ECC) celebrada en Aalborg (Dinamarca) del 29 de junio al 1 de julio de 2016., This paper presents a new method for leak localization in Water Distribution Networks that uses a model-based approach combined with Bayesian reasoning. Probability density functions in model-based pressure residuals are calibrated off-line for all the possible leak scenarios by using a hydraulic simulator, being leak size uncertainty, demand uncertainty and sensor noise considered. A Bayesian reasoning is applied online to the available residuals to determine the location of leaks present in the Water Distribution Network. A time horizon method combined with the Bayesian reasoning is also proposed to improve the accuracy of the leak localization method. The Hanoi District Metered Area case study is used to illustrate the performance of the proposed approach., This work has been partially funded by Spanish Government (Ministerio de Economia y Competitividad) and FEDER through the Project ECOCIS (Ref. DPI2013-48243-C2-1-R) and Project HARCRICS (Ref. DPI2014-58104-R), and by EFFINET grant FP7-ICT-2012-318556 of the European Commission.

Objective. The development of modern assistive and rehabilitation devices requires reliable and easy-to-use methods to extract neural information for control of devices. Group-specific pattern recognition identifiers are influenced by inter-subject variability. Based on high-density EMG (HD-EMG) maps, our research group has already shown that inter-subject muscle activation patterns exist in a population of healthy subjects. The aim of this paper is to analyze muscle activation patterns associated with four tasks (flexion/extension of the elbow, and supination/pronation of the forearm) at three different effort levels in a group of patients with incomplete Spinal Cord Injury (iSCI). Approach. Muscle activation patterns were evaluated by the automatic identification of these four isometric tasks along with the identification of levels of voluntary contractions. Two types of classifiers were considered in the identification: linear discriminant analysis and support vector machine. Main results. Results show that performance of classification increases when combining features extracted from intensity and spatial information of HD-EMG maps (accuracy = 97.5%). Moreover, when compared to a population with injuries at different levels, a lower variability between activation maps was obtained within a group of patients with similar injury suggesting stronger task-specific and effort-level-specific co-activation patterns, which enable better prediction results. Significance. Despite the challenge of identifying both the four tasks and the three effort levels in patients with iSCI, promising results were obtained which support the use of HD-EMG features for providing useful information regarding motion and force intention

Background: Recent studies show that spatial distribution of High Density surface EMG maps (HD-EMG) improves the identification of tasks and their corresponding contraction levels. However, in patients with incomplete spinal cord injury (iSCI), some nerves that control muscles are damaged, leaving some muscle parts without an innervation. Therefore, HD-EMG maps in patients with iSCI are affected by the injury and they can be different for every patient. The objective of this study is to investigate the spatial distribution of intensity in HD-EMG recordings to distinguish co-activation patterns for different tasks and effort levels in patients with iSCI. These patterns are evaluated to be used for extraction of motion intention.; Method: HD-EMG was recorded in patients during four isometric tasks of the forearm at three different effort levels. A linear discriminant classifier based on intensity and spatial features of HD-EMG maps of five upper-limb muscles was used to identify the attempted tasks. Task and force identification were evaluated for each patient individually, and the reliability of the identification was tested with respect to muscle fatigue and time interval between training and identification. Results: Three feature sets were analyzed in the identification: 1) intensity of the HD-EMG map, 2) intensity and center of gravity of HD-EMG maps and 3) intensity of a single differential EMG channel (gold standard).; Results show that the combination of intensity and spatial features in classification identifies tasks and effort levels properly (Acc = 98.8 %; S = 92.5 %; P = 93.2 %; SP = 99.4 %) and outperforms significantly the other two feature sets (p < 0.05).; Conclusion: In spite of the limited motor functionality, a specific co-activation pattern for each patient exists for both intensity, and spatial distribution of myoelectric activity. The spatial distribution is less sensitive than intensity to myoelectric changes that occur due to fatigue, and other time-dependent influences.

Background: Recent studies report increases in neural activity in brain regions critical to episodic memory at preclinical stages of Alzheimer’s disease (AD). Although electroencephalography (EEG) is widely used in AD studies, given its non-invasiveness and low cost, there is a need to translate the findings in other neuroimaging methods to EEG. Objective: To examine how the previous findings using functional magnetic resonance imaging (fMRI) at preclinical stage in presenilin-1 E280A mutation carriers could be assessed and extended, using EEG and a connectivity approach. Methods: EEG signals were acquired during resting and encoding in 30 normal cognitive young subjects, from an autosomal dominant early-onset AD kindred from Antioquia, Colombia. Regions of the brain previously reported as hyperactive were used for connectivity analysis. Results: Mutation carriers exhibited increasing connectivity at analyzed regions. Among them, the right precuneus exhibited the highest changes in connectivity. Conclusion: Increased connectivity in hyperactive cerebral regions is seen in individuals, genetically-determined to develop AD, at preclinical stage. The use of a connectivity approach and a widely available neuroimaging technique opens the possibility to increase the use of EEG in early detection of preclinical AD. COL0007551 COL0010744 COL0054963

Ayahuasca is an Amazonian psychotropic plant tea typically obtained from two plants, Banisteriopsis caapi and Psychotria viridis. It contains the psychedelic 5-HT2A and sigma-1 agonist N,N-dimethyltryptamine (DMT) plus beta-carboline alkaloids with monoamine-oxidase (MAO)-inhibiting properties. Although the psychoactive effects of ayahuasca have commonly been attributed solely to agonism at the 5-HT2A receptor, the molecular target of classical psychedelics, this has not been tested experimentally. Here we wished to study the contribution of the 5-HT2A receptor to the neurophysiological and psychological effects of ayahuasca in humans. We measured drug-induced changes in spontaneous brain oscillations and subjective effects in a double-blind randomized placebo-controlled study involving the oral administration of ayahuasca (0.75 mg DMT/kg body weight) and the 5-HT2A antagonist ketanserin (40 mg). Twelve healthy, experienced psychedelic users (5 females) participated in four experimental sessions in which they received the following drug combinations: placebo + placebo, placebo +ayahuasca, ketanserin +placebo and ketanserin +ayahuasca. Ayahuasca induced EEG power decreases in the delta, theta and alpha frequency bands. Current density in alpha-band oscillations in parietal and occipital cortex was inversely correlated with the intensity of visual imagery induced by ayahuasca. Pretreatment with ketanserin inhibited neurophysiological modifications, reduced the correlation between alpha and visual effects, and attenuated the intensity of the subjective experience. These findings suggest that despite the chemical complexity of ayahuasca, 5-HT2A activation plays a key role in the neurophysiological and visual effects of ayahuasca in humans. (C) 2016 Elsevier B.V. and ECNP. All rights reserved.

In this paper, the problem of robust fault diagnosis of proton exchange membrane (PEM) fuel cells is addressed by introducing the Takagi-Sugeno (TS) interval observers that consider uncertainty in a bounded context, adapting TS observers to the so-called interval approach. Design conditions for the TS interval observer based on regional pole placement are also introduced to guarantee the fault detection and isolation (FDI) performance. The fault detection test is based on checking the consistency between the measurements and the output estimations provided by the TS observers. In presence of bounded uncertainty, this check relies on determining if all the measurements lie inside their corresponding estimated interval bounds. When a fault is detected, the measurements that are inconsistent with their corresponding estimations are annotated and a fault isolation procedure is triggered. By using the theoretical fault signature matrix (FSM), which summarizes the effects of the different faults on the available residuals, the fault is isolated by means of a logic reasoning that takes into account the bounded uncertainty, and if the number of candidate faults is more than one, a correlation analysis is used to obtain the most likely fault candidate. Finally, the proposed approach is tested using a PEM fuel cell case study proposed in the literature., This work has been funded by the Spanish Government (MINECO) through the project CICYT ECOCIS (ref. DPI2013-48243-C2-1-R), by MINECO and FEDER through the project CICYT HARCRICS (ref. DPI2014-58104-R), by AGAUR through the contracts FI-DGR 2014 (ref. 2014FI_B1 00172) and FI-DGR 2015 (ref. 2015FI_B2 00171) and by the DGR of Generalitat de Catalunya (SAC group Ref. 2014/SGR/374).

Knowledge of the location of muscle Innervation Zones (IZs) is important in many applications, e.g. for minimizing the quantity of injected botulinum toxin for the treatment of spasticity or for deciding on the type of episiotomy during child delivery. Surface EMG (sEMG) can be noninvasively recorded to assess physiological and morphological characteristics of contracting muscles. However, it is not often possible to record signals of high quality. Moreover, muscles could have multiple IZs, which should all be identified. We designed a fully-automatic algorithm based on the enhanced image Graph-Cut segmentation and morphological image processing methods to identify up to five IZs in 60-ms intervals of very-low to moderate quality sEMG signal detected with multi-channel electrodes (20 bipolar channels with Inter Electrode Distance (IED) of 5 mm). An anisotropic multilayered cylinder model was used to simulate 750 sEMG signals with signal-to-noise ratio ranging from -5 to 15 dB (using Gaussian noise) and in each 60-ms signal frame, 1 to 5 IZs were included. The micro- and macro- averaged performance indices were then reported for the proposed IZ detection algorithm. In the micro-averaging procedure, the number of True Positives, False Positives and False Negatives in each frame were summed up to generate cumulative measures. In the macro-averaging, on the other hand, precision and recall were calculated for each frame and their averages are used to determine F1-score. Overall, the micro (macro)-averaged sensitivity, precision and F1-score of the algorithm for IZ channel identification were 82.7% (87.5%), 92.9% (94.0%) and 87.5% (90.6%), respectively. For the correctly identified IZ locations, the average bias error was of 0.02±0.10 IED ratio. Also, the average absolute conduction velocity estimation error was 0.41±0.40 m/s for such frames. The sensitivity analysis including increasing IED and reducing interpolation coefficient for time samples was performed. Meanwhile, the effect of adding power-line interference and using other image interpolation methods on the deterioration of the performance of the proposed algorithm was investigated. The average running time of the proposed algorithm on each 60-ms sEMG frame was 25.5±8.9 (s) on an Intel dual-core 1.83 GHz CPU with 2 GB of RAM. The proposed algorithm correctly and precisely identified multiple IZs in each signal epoch in a wide range of signal quality and is thus a promising new offline tool for electrophysiological studies. The research leading to these results has received funding from the People Programme (Marie Curie Actions) of the Seventh Framework Programme of the European Union (FP7/2007-2013) under REA grant agreement no. 600388 (TECNIOspring programme), from the Agency for Business Competitiveness of the Government of Catalonia, ACCIÓ, and from Spanish Ministry of Economy and Competitiveness- Spain (project DPI2014-59049-R).

Sleep deprivation (SD) has adverse effects on mental and physical health, affecting the cognitive abilities and emotional states. Specifically, cognitive functions and alertness are known to decrease after SD. The aim of this work was to identify the directional information transfer after SD on scalp EEG signals using transfer entropy (TE). Using a robust methodology based on EEG recordings of 18 volunteers deprived from sleep for 36 h, TE and spectral analysis were performed to characterize EEG data acquired every 2 h. Correlation between connectivity measures and subjective somnolence was assessed. In general, TE showed medium- and long-range significant decreases originated at the occipital areas and directed towards different regions, which could be interpreted as the transfer of predictive information from parieto-occipital activity to the rest of the head. Simultaneously, short-range increases were obtained for the frontal areas, following a consistent and robust time course with significant maps after 20 h of sleep deprivation. Changes during sleep deprivation in brain network were measured effectively by TE, which showed increased local connectivity and diminished global integration. TE is an objective measure that could be used as a potential measure of sleep pressure and somnolence with the additional property of directed relationships.

This paper addresses the problem of fault detection and isolation of wind turbines using a mixed Bayesian/Set-membership approach. Modeling errors are assumed to be unknown but bounded, following the set-membership approach. On the other hand, measurement noise is also assumed to be bounded, but following a statistical distribution inside the bounds. To avoid false alarms, the fault detection problem is formulated in a set-membership context. Regarding fault isolation, a new fault isolation scheme that is inspired on the Bayesian fault isolation framework is developed. Faults are isolated by matching the fault detection test results, enhanced by a complementary consistency index that measures the certainty of not being in a fault situation, with the structural information about the faults stored in the theoretical fault signature matrix. The main difference with respect to the classical Bayesian approach is that only models of fault-free behavior are used. Finally, the proposed FDI method is assessed against the wind turbine FDI benchmark proposed in the literature, where a set of realistic fault scenarios in wind turbines are proposed.

Background: Psychedelics induce intense modifications in the sensorium, the sense of "self," and the experience of reality. Despite advances in our understanding of the molecular and cellular level mechanisms of these drugs, knowledge of their actions on global brain dynamics is still incomplete. Recent imaging studies have found changes in functional coupling between frontal and parietal brain structures, suggesting a modification in information flow between brain regions during acute effects. Methods: Here we assessed the psychedelic-induced changes in directionality of information flow during the acute effects of a psychedelic in humans. We measured modifications in connectivity of brain oscillations using transfer entropy, a nonlinear measure of directed functional connectivity based on information theory. Ten healthy male volunteers with prior experience with psychedelics participated in 2 experimental sessions. They received a placebo or a dose of ayahuasca, a psychedelic preparation containing the serotonergic 5-HT2A agonist N,N-dimethyltryptamine. Results: The analysis showed significant changes in the coupling of brain oscillations between anterior and posterior recording sites. Transfer entropy analysis showed that frontal sources decreased their influence over central, parietal, and occipital sites. Conversely, sources in posterior locations increased their influence over signals measured at anterior locations. Exploratory correlations found that anterior-to-posterior transfer entropy decreases were correlated with the intensity of subjective effects, while the imbalance between anterior-to-posterior and posterior-to-anterior transfer entropy correlated with the degree of incapacitation experienced. Conclusions: These results suggest that psychedelics induce a temporary disruption of neural hierarchies by reducing top-down control and increasing bottom-up information transfer in the human brain.

The biological response to stress originates in the brain but involves different biochemical and physiological effects. Many common clinical methods to assess stress are based on the presence of specific hormones and on features extracted from different signals, including electrocardiogram, blood pressure, skin temperature, or galvanic skin response. The aim of this paper was to assess stress using EEG-based variables obtained from univariate analysis and functional connectivity evaluation. Two different stressors, the Stroop test and sleep deprivation, were applied to 30 volunteers to find common EEG patterns related to stress effects. Results showed a decrease of the high alpha power (11 to 12 Hz), an increase in the high beta band (23 to 36 Hz, considered a busy brain indicator), and a decrease in the approximate entropy. Moreover, connectivity showed that the high beta coherence and the interhemispheric nonlinear couplings, measured by the cross mutual information function, increased significantly for both stressors, suggesting that useful stress indexes may be obtained from EEG-based features.

Multiple sclerosis (MS) is a chronic central nervous system disorder characterized by white matter inflammation, demyelination and neurodegeneration. Although cognitive dysfunction is a common manifestation, it may go unnoticed in recently-diagnosed patients. Prior studies suggest MS patients develop compensatory mechanisms potentially involving enhanced performance monitoring. Here we assessed the performance monitoring system in early-stage MS patients using the error-related negativity (ERN), an event-related brain potential (ERP) observed following behavioral errors. Twenty-seven early-stage MS patients and 31 controls were neuropsychologically assessed. Electroencephalography recordings were obtained while participants performed: a) a stop task and b) an auditory oddball task. Behavior and ERP measures were assessed. No differences in performance were found between groups in most neuropsychological tests or in behavior or ERP components in the auditory oddball task. However, the amplitude of the ERN associated with stop errors in the stop task was significantly higher in patients. ERN amplitude correlated positively with scores on the Expanded Disability Status Scale and the Multiple Sclerosis Severity Score, and negatively with the time since last relapse. Patients showed higher neuronal recruitment in tasks involving performance monitoring. Results suggest the development of compensatory brain mechanisms in early-stage MS and reflect the sensitivity of the ERN to detect these changes.

The identification of the brain regions involved in the neuropharmacological action is a potential procedure for drug development. These regions are commonly determined by the voxels showing significant statistical differences after comparing placebo-induced effects with drug-elicited effects. LORETA is an electroencephalography (EEG) source imaging technique frequently used to identify brain structures affected by the drug. The aim of the present study was to evaluate different methods for the correction of multiple comparisons in the LORETA maps. These methods which have been commonly used in neuroimaging and also simulated studies have been applied on a real case of pharmaco-EEG study where the effects of increasing benzodiazepine doses on the central nervous system measured by LORETA were investigated. Data consisted of EEG recordings obtained from nine volunteers who received single oral doses of alprazolam 0.25, 0.5, and 1 mg, and placebo in a randomized crossover double-blind design. The identification of active regions was highly dependent on the selected multiple test correction procedure. The combined criteria approach known as cluster mass was useful to reveal that increasing drug doses led to higher intensity and spread of the pharmacologically induced changes in intracerebral current density.

Introduction Electroencephalographic analysis techniques have become a very useful tool to assess brain activity and interactions between cerebral regions, that is, the so-called cerebral connectivity analysis. The effects of some drugs have, so far, been studied using spectral analysis and, to a lesser extent, some linear and nonlinear connectivity techniques. New indexes have recently been designed based on assumptions that make them more robust against volume conduction effects that could yield to spurious connectivity results. These new indexes such as the imaginary coherence (IC) [Nolte et al., 2004], the phase-lag index (PLI) [Stam et al., 2007] and the weighted phase-lag index (WPLI) [Vinck et al., 2011] have proven very useful in several fields, for example in characterizing electroencephalographic (EEG) and magnetoencephalographic (MEG) activity of Alzheimer’s Disease patients compared to healthy controls. However, these techniques have not been applied to study the effect of drugs on the brain. The main purpose of the current work was to assess the suitability and effectiveness of these innovative indexes to study the brain connectivity under psychoactive drug treatment, and concretely, the effects of a single dose of alprazolam, a short-acting drug of the benzodiazepine family. Alprazolam is extensively prescribed for the treatment of anxiety and panic disorders, and peak plasma concentrations are obtained between 0.5 and 2 hours after intake [Greenblat and Wright, 1993]. Being a benzodiazepines, alprazolam induces an enhancement of the inhibitory pathways through their activity on the GABAA receptor complex, favouring the entrance to chloride ions into the neurons [Haefely, 1990]. Due to the enhancement of the inhibitory pathways a weakening or even an impairment of functional connectivity could be hypothesized. Database A single oral dose of alprazolam 1mg was administered to 9 volunteers (aged between 20 and 32 years, mean age 23 years) in a double-blind randomized crossover placebo-controlled design. Volunteers were reported to be in good health and were not allowed to take any psychoactive drugs, caffeine, alcohol and tobacco in the two weeks before the experimental sessions, nor during the one-week washout period between sessions. The study was conducted in accordance with the Declaration of Helsinki and subsequent revisions concerning experimentation in humans, and was approved by the Hospital Ethics Committee and the Spanish Ministry of Health. EEG signals were recorded for 3 minutes several times: before drug intake, and 30 minutes, 45 minutes, 1 hour, 1.5 hours, 2.5 hours, and 4 hours after the administration. Vertical and horizontal electrooculograms and 19 EEG channels (Fp1/2, F7/8, F3/4, Fz T3/4, C3/4, Cz, T5/6, P3/4, Pz, and O1/2, following the international 10/20 system) were acquired, referenced to averaged mastoid electrodes (A1 and A2). Gold plated standard EEG electrodes were used, keeping impedances below 5 kΩ. Signals were analogically band-pass filtered (0.1 to 45 Hz), and recorded at a sampling frequency of 100 Hz. Methodology All EEG recordings underwent ocular artefact reduction following the procedures described by [Romero et al. 2008]. Given that the main effects of alprazolam on cerebral rhythms focus on alpha1 (7.5 to 10.5 Hz) and beta bands (13 to 35 Hz) [Barbanoj et al. 2006], recordings were subsequently filtered using an inverse Chebyshev filter to obtain their corresponding signals. Attenuation in the passband and the stopbands was set to 1 and 20 dB respectively, and forward and reverse filtering was applied to ensure zero-phase distortion. The considered connectivity measures, namely the phase-lag index (PLI), the weighted phase-lag index (WPLI), and the imaginary coherence (IC), were proposed initially as tools able to quantify the amount of synchronization between pairs of signals without taking into account spurious connectivity due to instantaneous propagation, which could be caused by volume conduction. The PLI is based on the concept of phase synchronization, assuming that when two signals are coupled, their phase difference is constant or almost constant for a certain amount of time. This synchronization can be calculated by obtaining the phases of EEG signals via the Hilbert transform and calculating the instantaneous difference over time. If there is synchronization between signals, the distribution of these phase differences should be centred on a relevant value. The PLI quantifies the asymmetry of the phase distribution in a scale from 0 to 1, ensuring that a null value is obtained when phase differences concentrate around 0 and π values, which correspond to instantaneous propagation [Stam et al., 2007]. WPLI represents an improvement over PLI because both the sensitivity to noise and the capacity to detect changes in phase synchronization are hindered by the discontinuity of PLI [Vinck et al., 2011]. In WPLI, the contribution of the observed phase differences is weighted by additionally accounting for the magnitude of the phase differences, reducing the probability of detecting connectivity in the case of volume conducted noise sources. The third considered measure, the IC, is closely related to the standard magnitude squared coherence, but its definition makes it inconsistent with non-interacting sources and that is why it reflects true interaction in contrast to volume conducted self-interaction [Nolte et al., 2004]. Once these indexes were calculated, the pharmacological effects were assessed by means of statistical parametric maps of the head, representing significant differences in connectivity between placebo and drug situations as lines connecting the corresponding scalp locations. Non-parametric Wilcoxon signed-rank tests were used, and an omnibus threshold based on the binomial theorem [Cross-Chaffin, 1982; Alonso et al. 2010] was used to detect significant maps taking into account multiple comparisons. Results Significant changes were found in the alpha1 band (PLI, WPLI, and IC; significant maps in Figure 1 are underlined), but not in beta band (maps shown in Figure 2 do not reach the minimum threshold established by the omnibus test). The obtained changes between 45 minutes and 90 minutes after intake fall within the expected time-window of peak plasma concentrations. The Pearson correlation coefficients obtained when comparing the time course of the values alpha1 PLI, WPLI and IC with drug plasma concentrations were 0.92, 0.85 and 0.96, respectively. For the beta PLI, WPLI and IC the coefficients were 0.87, 0.79 and 0.79, respectively. Although measures in the beta band could not elicit significant differences caused by the pharmacological effects correlation coefficients were high, showing that these measures were able to follow these effects. Conclusions The suitability of three different connectivity measures, not affected by volume-conducted propagation, for the detection of alprazolam-induced effects on the brain was assessed in this work. Analysis was focused on the previously known bands of interest, alpha1 and beta. The results of the three indexes (the phase-lag index, the weighted phase-lag index, and the imaginary coherence) in these bands match with the pharmacological effect measured by blood levels, but only the alpha1 band shows significant changes associated with drug intake. In this sense, previous studies have successfully extracted such information from spectral and nonlinear connectivity measures that might be affected by volume conduction [Alonso et al., 2010; Alonso et al., 2012]. The special design of the experiment, comparing placebo and drug conditions and correcting by the baseline instant, reduces the effects of spurious connectivity and possibly favours the obtention of results using such measures in contrast to the new indexes studied in the current work. PLI and WPLI indicate a reduction of phase synchronization due to alprazolam administration, whereas IC shows an increase of interaction. This can happen because IC depends on no only on the phase synchronitzation but also on the magnitude of the phase difference, so it is essentially quantifying different relationships between signals induced by the drug. This additional information taken into account by this measure provides the best correlation with drug plasma concentrations after intake The fact that the considered indexes were not able to find significant differences in the beta band might indicate that phase-coupling changes induced by the drug are weak or too subtle to be detected, given that all measures are corrected by a baseline recording. This might discourage their use in psychopharmacological studies when assessing low doses, mild effects, or when working with a reduced number of participants. However, correlations with plasma concentrations remained high, indicating that PLI, WPLI and IC should not be totally discarded as means of evaluating pharmacological effects on the brain via EEG recordings.

This study deals with the problem of set-membership identification of non-linear-in-the-parameters models. To solve this problem, this study illustrates how the Bayesian approach can be used to determine the feasible parameter set (FPS) by assuming uniform distributed estimation error and flat model prior probability distributions. The key point of the methodology is the interval evaluation of the likelihood function and the result is a set of boxes with associated credibility indices. For each box, the credibility index is in the interval (0, 1] and gives information about the amount of consistent models inside the box. The union of the boxes with credibility value equal to one provides an inner approximation of the FPS, whereas the union of all boxes provides an outer estimation. The boxes with credibility value smaller than one are located around the boundary of the FPS and their credibility index can be used to iteratively refine the inner and outer approximations up to a desired precision. The main issues and performance of the developed algorithms are discussed and illustrated by means of examples., This work has been partially grant-funded by CICYT SHERECS DPI-2011-26243 and CICYT ECOCIS (Ref.DPI2013-48243-C2-1-R) DPI-2009-13744 of the Spanish Ministry of Education and by i-Sense grant FP7-ICT-2009-6-270428 of the European Commission.

Objective. In epilepsy, high-frequency oscillations (HFOs) are expressively linked to the seizure onset zone (SOZ). The detection of HFOs in the noninvasive signals from scalp electroencephalography (EEG) and magnetoencephalography (MEG) is still a challenging task. The aim of this study was to automate the detection of ripples in MEG signals by reducing the high-frequency noise using beamformer-based virtual sensors (VSs) and applying an automatic procedure for exploring the time-frequency content of the detected events. Approach. Two-hundred seconds of MEG signal and simultaneous iEEG were selected from nine patients with refractory epilepsy. A two-stage algorithm was implemented. Firstly, beamforming was applied to the whole head to delimitate the region of interest (ROI) within a coarse grid of MEG-VS. Secondly, a beamformer using a finer grid in the ROI was computed. The automatic detection of ripples was performed using the time-frequency response provided by the Stockwell transform. Performance was evaluated through comparisons with simultaneous iEEG signals. Main results. ROIs were located within the seizure-generating lobes in the nine subjects. Precision and sensitivity values were 79.18% and 68.88%, respectively, by considering iEEG-detected events as benchmarks. A higher number of ripples were detected inside the ROI compared to the same region in the contralateral lobe. Significance. The evaluation of interictal ripples using non-invasive techniques can help in the delimitation of the epileptogenic zone and guide placement of intracranial electrodes. This is the first study that automatically detects ripples in MEG in the time domain located within the clinically expected epileptic area taking into account the time-frequency characteristics of the events through the whole signal spectrum. The algorithm was tested against intracranial recordings, the current gold standard. Further studies should explore this approach to enable the localization of noninvasively recorded HFOs to help during pre-surgical planning and to reduce the need for invasive diagnostics.

This paper deals with the problem of setmembership identification of nonlinear-in-the-parameters models. To solve this problem a Bayesian approach is presented. The paper illustrates how the Bayesian approach can be used to approximate the feasible parameter set (FPS) by assuming uniform distributed estimation error and flat model prior probability distributions. The methodology leads to an approximation of the FPS consisting of a set of boxes, where two regions can be identified. The inner region constitutes an inner approximation of the FPS whereas the external region can be viewed as an outer approximation of the FPS. Also, the boxes in the border give information about the percentage of consistent models inside each box and it can be used to iteratively refine the inner and outer approximations.

En el presente trabajo se estudian los efectos de la privación del sueño sobre las señales electroencefalográficas de voluntarios sanos mediante la transferencia de entropía (TE), una técnica no paramétrica que permite detectar relaciones direccionales entre variables y que no ha sido utilizada antes para el estudio de la vigilia prolongada. Los resultados obtenidos concuerdan con la potencia en la banda ϴ, un marcador reconocido relacionado con la presión de sueño. Los efectos máximos de la privación del sueño se producen a partir de las 24 horas sin dormir. Durante este periodo, se producen incrementos de TE localizados en la zona anterior mientras que los decrementos principalmente se originan en la zona occipital y afectan globalmente al resto del cerebro. El promedio de estos decrementos muestra una fuerte correlación con la potencia ϴ, por lo que se podría considerar como otro marcador fiable de la presión de sueño que, además, proporciona información sobre la localización de los cambios producidos, indicando una disminución de la transferencia de información entre las regiones cerebrales.

Snoring, a symptom which may indicate the presence of the obstructive sleep apnoea syndrome (OSA), is also common in the general population. Recent studies have suggested that the acoustic characteristics of snoring sound may differ between simple snorers and OSA patients. We have studied a small number of patients with simple snoring and OSA, analysing the acoustic characteristics of the snoring sound. Seventeen male patients, 10 with OSA (apnoea/hypopnoea index (AHI) 26.2 events x h(-1)) and seven simple snorers (AHI 3.8 events x h(-1)), were studied. Full night polysomnography was performed and the snoring sound power spectrum was analysed. Spectral analysis of snoring sound showed the existence of two different patterns. The first pattern was characterized by the presence of a fundamental frequency and several harmonics. The second pattern was characterized by a low frequency peak with the sound energy scattered on a narrower band of frequencies, but without clearly identified harmonics. The seven simple snorers and two of the 10 patients with OSA (AIH 13 and 14 events x h(-1), respectively) showed the first pattern. The rest of the OSA patients showed the second pattern. The peak frequency of snoring was significantly lower in OSA patients, with all but one OSA patient and only one simple snorer showing a peak frequency below 150 Hz. A significant negative correlation was found between AHI and peak and mean frequencies of the snoring power spectrum (p

Objective. One of the principal drawbacks of magnetoencephalography (MEG) is its high sensitivity to metallic artifacts, which come from implanted intracranial electrodes and dental ferromagnetic prosthesis and produce a high distortion that masks cerebral activity. The aim of this study was to develop an automatic algorithm based on blind source separation (BSS) techniques to remove metallic artifacts from MEG signals. Approach. Three methods were evaluated: AMUSE, a second-order technique; and INFOMAX and FastICA, both based on high-order statistics. Simulated signals consisting of real artifact-free data mixed with real metallic artifacts were generated to objectively evaluate the effectiveness of BSS and the subsequent interference reduction. A completely automatic detection of metallic-related components was proposed, exploiting the known characteristics of the metallic interference: regularity and low frequency content. Main results. The automatic procedure was applied to the simulated datasets and the three methods exhibited different performances. Results indicated that AMUSE preserved and consequently recovered more brain activity than INFOMAX and FastICA. Normalized mean squared error for AMUSE decomposition remained below 2%, allowing an effective removal of artifactual components. Significance. To date, the performance of automatic artifact reduction has not been evaluated in MEG recordings. The proposed methodology is based on an automatic algorithm that provides an effective interference removal. This approach can be applied to any MEG dataset affected by metallic artifacts as a processing step, allowing further analysis of unusable or poor quality data.

[EN] Identification of tasks and estimation of volitional movement based on electromyography (EMG) constitute a known problem that involves different areas in the field of expert systems and particularly pattern recognition, with many possible applications in assistive and rehabilitation devices. The obtained information can be very useful to control exoskeletons or robots used in active rehabilitation processes. The emerging technology of high-density electromyography (HD-EMG) opens up new possibilities to extract neural information, and it has already been reported that the spatial distribution of HD-EMG intensity maps is a valuable feature in the identification of isometric tasks. This study explores the use of the spatial distribution of myoelectric activity and carries out a task identification during dynamic exercises at different velocities which are much closer to the ones commonly used during therapy. To this end, HD-EMG signals were recorded in a group of healthy subjects while performing a set of isometric and dynamic upper limb tasks. The results show that spatial distribution is a very useful feature in the identification not only of isometric contractions but also of dynamic contractions, so it can be very useful to improve the control of rehabilitation devices, making it more natural and permitting to adapt better to the user., [ES] La identificación de tareas y estimación del movimiento voluntario basados en electromiografía (EMG) constituyen un problema conocido que involucra diferentes áreas en sistemas expertos, particularmente la de reconocimiento de patrones, con muchas aplicaciones posibles en dispositivos de asistencia y rehabilitación. La información que proporciona puede resultar útil para el control de exoesqueletos o brazos robóticos utilizados en terapias activas. La tecnología emergente de electromiografía de alta densidad (HD-EMG) abre nuevas posibilidades para extraer información neural y ya ha sido reportado que la distribución espacial de mapas de intensidad HD-EMG es una característica valiosa en la identificación de tareas isométricas (contracciones que no producen cambio en la longitud del músculo). Este estudio explora la utilización de la distribución espacial de la actividad mioeléctrica y lleva a cabo identificación de tareas durante ejercicios dinámicos a diferentes velocidades que son mucho más cercanos a los que se utilizan habitualmente en las terapias de rehabilitación. Con este objetivo, se registraron señales HD-EMG en un grupo de sujetos sanos durante la realización de un conjunto de tareas isométricas y dinámicas del miembro superior. Los resultados indican que la distribución espacial es una característica muy útil en la identificación, no solo de contracciones isométricas sino también de contracciones dinámicas, mejorando la eficiencia y naturalidad del control de dispositivos de rehabilitación para que se adapte mejor al usuario., Este trabajo ha sido realizado en el marco del proyecto intramural ROBERT del CIBER-BBN, y ha sido en parte financiado por el proyecto DPI2014-59049-R del Ministerio de Economía, Industria y Competitividad de España.

Um dos problemas que apresenta o registro de electroencefalogramas (EEG) está relacionado com a referência usada durante a aquisição dos dados. O presente artigo analisa o comportamento de três referências, o promédio mastoides (RPM), o promédio común (RPC) e o método de estandardização do eletrodo de referência (REST), usadas no registro de EEG num estudo farmacológico realizado com alprazolam (medicamento psicoativo). Os resultados foram analisados no dominio frequencial para avaliar a ação do fármaco e através da informação mútua cruzada para investigar a conetividade cerebral. Nenhuma das referências apresenta melhores resultados em todos os casos estudados, REST tem boa atuação nas variáveis espectrais e de conectividade enquanto que as outras duas trabalham melhor só em um dos casos.

Background A comprehensive study on the interaction of cardiovascular disease (CVD) risk factors is critical to prevent cardiovascular events. The main focus of this study is thus to understand direct and indirect relationships between different CVD risk factors. Methods A longitudinal data on adults aged ≥35 years, who were free of CVD at baseline, were used in this study. The endpoints were CVD events, whereas their measurements were demographic, lifestyle components, socio-economics, anthropometric measures, laboratory findings, quality of life status, and psychological factors. A Bayesian structural equation modelling was used to determine the relationships among 21 relevant factors associated with total CVD, stroke, acute coronary syndrome (ACS), and fatal CVDs. Results In this study, a total of 3161 individuals with complete information were involved in the study. A total of 407 CVD events, with an average age of 54.77(10.66) years, occurred during follow-up. The causal associations between six latent variables were identified in the causal network for fatal and non-fatal CVDs. Lipid profile, with the coefficient of 0.26 (0.01), influenced the occurrence of CVD events as the most critical factor, while it was indirectly mediated through risky behaviours and comorbidities. Lipid profile at baseline was influenced by a wide range of other protective factors, such as quality of life and healthy lifestyle components. Conclusions Analysing a causal network of risk factors revealed the flow of information in direct and indirect paths. It also determined predictors and demonstrated the utility of integrating multi-factor data in a complex framework to identify novel preventable pathways to reduce the risk of CVDs.

© 2016. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/ The electromyography (EMG) signal is the summation of traveling motor unit (MU) action potentials that propagate along the fibers from the neuromuscular junction (Innervation Zone, IZ) to the tendons with a certain conduction velocity (CV) (Merletti and Parker, 2005). EMG signals can be detected using either intramuscular or surface electrodes. Intramuscular EMG (iEMG) signals involve the insertion of needles or fine wire electrodes into a muscle (Merletti et al., 2008). Surface EMG (sEMG) signals from the underlying muscles can be detected and unobtrusively on the skin all over the human body and they can be used in modeling movement intentions and in monitoring muscle function during rehabilitation processes (Zwarts and Stegeman, 2003).

Producción Científica, The present study investigates the neural substrates underlying cognitive processing in schizophrenia (Sz) patients. To this end, an auditory 3-stimulus oddball paradigm was used to identify P3a and P3b components, elicited by rare-distractor and rare-target tones, respectively. Event-related potentials (ERP) were recorded from 31 Sz patients and 38 healthy controls. The P3a and P3b brain-source generators were identified by timeaveraging of low-resolution brain electromagnetic tomography (LORETA) current density images. In contrast with the commonly used fixed window of interest (WOI), we proposed to apply an adaptive WOI, which takes into account subjects' P300 latency variability. Our results showed different P3a and P3b source activation patterns in both groups. P3b sources included frontal, parietal and limbic lobes,whereas P3a response generators were localized over bilateral frontal and superior temporal regions. These areas have been related to the discrimination of auditory stimulus and to the inhibition (P3a) or the initiation (P3b) of motor response in a cognitive task. In addition, differences in source localization between Sz and control groups were observed. Sz patients showed lower P3b source activity in bilateral frontal structures and the cingulate. P3a generators were less widespread for Sz patients than for controls in right superior, medial and middle frontal gyrus. Our findings suggest that target and distractor processing involves distinct attentional subsystems, both being altered in Sz. Hence, the study of neuroelectric brain information can provide further insights to understand cognitive processes and underlying mechanisms in Sz., Ministerio de Economía y Competitividad (projects TEC2014-53196-R, DPI2011-22680 and DPI2014-59049-R), Junta de Castilla y León (VA059U13), Instituto de Salud Carlos III’, ISCIII (FIS PI1102303), Gerencia Regional de Salud de Castilla y León (GRS 613/A/11; GRS 932/A/14)

Trabajo presentado a la 3rd Conference on Control and Fault Tolerant Systems, celebrada en Barcelona (España) del 7 al 9 de septiembre de 2016., This paper presents a sensor placement method for classifier-based leak localization in Water Distribution Networks. The proposed approach consists in applying a Genetic Algorithm to decide the sensors to be used by a classifier (based on the k-Nearest Neighbor approach). The sensors are placed in an optimal way maximizing the accuracy of the leak localization. The results are illustrated by means of the application to the Hanoi District Metered Area and they are compared to the ones obtained by the Exhaustive Search Algorithm. A comparison with the results of a previous optimal sensor placement method is provided as well., This work has been partially funded by the Spanish Government (MINECO) through the project CICYT ECOCIS (ref. DPI2013-48243-C2-1-R), by MINECO and FEDER through the project CICYT HARCRICS (ref. DPI2014-58104-R), by MINECO through the grant IJCI-2014-2081, by EFFINET grant FP7-ICT-2012-318556 of the European Commission, by the DGR of Generalitat de Catalunya (SAC group Ref. 2014/SGR/374) and by the AGAUR of Generalitat de Catalunya through the grants FI-DGR 2015 (ref. 2015 FI B 00591) and 2014PDJ00102.

Las señales corticales del cerebro permiten una visión más clara de la actividad neuronal que el estudio clásico de las señales registradas en el cuero cabelludo y son de gran interés en el estudio de enfermedades neurodegenerativas como la enfermedad de Alzheimer. Con las señales adquiridas sobre el córtex prefrontal y somatosensorial de ratones con la mutación 3xTg-AD, se han estudiado los estados espontáneos UP y DOWN de las oscilaciones lentas del cerebro anestesiado. En primer lugar, se han aplicado técnicas de filtrado para eliminar interferencias y seleccionar la banda de interés entre 0.1 y 200 Hz, en la cual se aplicaron medidas de análisis espectral. Tras un filtrado adicional entre 30 y 100 Hz para seleccionar la banda gamma asociada a la actividad UP y DOWN, se calcularon variables de conectividad. Entre los resultados obtenidos, se escogieron aquellas variables que presentaban una mayor significación estadística mediante las pruebas T-Student y Wilcoxon. Finalmente, con estas variables se diseñaron dos procedimientos de clasificación por análisis discriminante lineal (Stepwise y Leave-one-out) que consiguen diferenciar entre sujetos enfermos de Alzheimer y sujetos control con un 88% y un 82% de precisión, respectivamente.

This paper proposes a new method for leak localization in water distribution networks (WDNs). In a first stage, residuals are obtained by comparing pressure measurements with the estimations provided by a WDN model. In a second stage, a classifier is applied to the residuals with the aim of determining the leak location. The classifier is trained with data generated by simulation of the WDN under different leak scenarios and uncertainty conditions. The proposed method is tested both by using synthetic and experimental data with real WDNs of different sizes. The comparison with the current existing approaches shows a performance improvement., This work has been funded by the Spanish Government (MINECO) through the project CICYT ECOCIS (ref. DPI2013-48243-C2-1-R), by MINECO and FEDER through the project CICYT HARCRICS (ref. DPI2014-58104-R), by MINECO through the grant IJCI-2014-2081, by the DGR of Generalitat de Catalunya (SAC group Ref. 2014/SGR/374), by the AGAUR of Generalitat de Catalunya through the grant 2014PDJ00102 and by EFFINET grant FP7-ICT-2012-318556 of the European Commission.

In this chapter, we discuss how intracranial or scalp electroencephalographic and magnetoencephalographic recordings could be used for epileptic seizure prediction and onset zone localization. The signal processing methods and the challenges of the state-of-the-art are discussed.