PhysioNet 2012 Challenge: Predicting mortality of ICU patients using a cascaded SVM-GLM paradigm

The focus of the PhysioNet/CinC Challenge 2012 is to develop methods for patient-specific prediction of inhospital mortality using general descriptors recorded at the time of admission to the ICU and up to 37 time-series measurements collected during the first 48 hours after admission. We developed an algorithm that uses both general descriptors and time-series measurements to predict the in-hospital death (IHD) of ICU patients in Event 1, and to provide a probability estimate of IHD in Event 2. Both aggregated variables and general descriptors were used as features of quadratic Support Vector Machine (SVM) classifiers. Six SVMs were trained using, for each one, all the positive examples plus, in turn, one sixth of the negative examples in the training set. Finally, a Generalized Linear Model with probit link was used to predict the probability of IHD for Event 2 using the raw outputs of the six SVMs as regressors. A positive binary prediction of IHD for Event 1 was made when the probability estimate was higher than an optimized threshold. Official final results of the challenge reported that our entry achieved an Event 2 score of 17.88, which is the best score out of the total 23 submissions, and Event 1 score of 0.5345 (second best score). © 2012 CCAL

Seizing political opportunity: how the European Commission becomes a ‘policy entrepreneur’

Political actors need to be nimble and respond to the opportunity to reform old policies and initiate new ones. Manuele Citi and Mogens K Justesen look at how the European Commission takes advantage of politically opportune moments (the ‘gridlock interval’) in the European Parliament to put forward new legislation. As a ‘policy entrepreneur’, it is therefore able to navigate European institutions and bring about change

Analogue P300-based BCI pointing device

We propose a P300-based BCI mouse. The system is analogue: the pointer is controlled by directly combining the amplitudes of the outputs produced by a filter in the presence of different stimuli. The system is optimised by a genetic algorithm

Tight junction formation in early Xenopus laevis embryos: identification and ultrastructural characterization of junctional crests and junctional vesicles

How tight junctions (TJ) form during early amphibian embryogenesis is still an open question. We used time-lapse video microscopy, scanning electron microscopy (SEM), TEM and freeze-fracture to gain new insight into TJ biogenesis in early clevages of Xenopus laevis. Video analysis suggests three phases in junction formation between blastomeres. A first "waiting” phase, where new unpigmented lateral membranes are generated. A second "mixing” phase, where the unpigmented lateral membrane is separated from the pigmented apical membrane by an area showing a limited degree of intermingling of cortical pigment. And a third "sealing” phase, characterized by the formation of cingulin-containing boundaries between membrane domains, and their rapid directional adhesion in a zipper-like fashion. By SEM, we characterized these boundaries ("junctional crests”, JC) as arrays of villiform protrusions at the border between old and new membranes. In the 2-cell embryo, JC are deeply located, and thus not visible at the surface, but they become increasingly more superficial as cleavages progress. After adjacent blastomeres have adhered to each other, fractured JC display linear arrays of junctional vesicles (JV) of 1-3μm diameter. TEM analysis shows that JV are symmetrically located near the apposed membranes of adjacent blastomeres, and that the membranes near the JV display focal sites of intimate contact, typical of TJ. Freeze-fracture analysis confirms that intramembrane fibrils, typical of TJ, are present at adhesion sites. We conclude that TJ are formed following the sealing of JC, through the recruitment, sorting and assembly of membrane and cytoplasmic proteins at or near J

Estimation of instantaneous complex dynamics through Lyapunov exponents: a study on heartbeat dynamics

Measures of nonlinearity and complexity, and in particular the study of Lyapunov exponents, have been increasingly used to characterize dynamical properties of a wide range of biological nonlinear systems, including cardiovascular control. In this work, we present a novel methodology able to effectively estimate the Lyapunov spectrum of a series of stochastic events in an instantaneous fashion. The paradigm relies on a novel point-process high-order nonlinear model of the event series dynamics. The long-term information is taken into account by expanding the linear, quadratic, and cubic Wiener-Volterra kernels with the orthonormal Laguerre basis functions. Applications to synthetic data such as the H�non map and R�ssler attractor, as well as two experimental heartbeat interval datasets (i.e., healthy subjects undergoing postural changes and patients with severe cardiac heart failure), focus on estimation and tracking of the Instantaneous Dominant Lyapunov Exponent (IDLE). The novel cardiovascular assessment demonstrates that our method is able to effectively and instantaneously track the nonlinear autonomic control dynamics, allowing for complexity variability estimations

Possible sources of perceptual errors in P300-based speller paradigm

Some perceptual phenomena can interfere with character identification in Farwell and Donchin's P300-based speller paradigm: attentional blink, repetition blindness and other effects caused by attentional limits. In the paper we discuss these and provide empirical evidence for one class of perceptual errors

Tetravariate point-process model for the continuous characterization of cardiovascular-respiratory dynamics during passive postural changes

In this study, we present a new methodology for time-varying characterization of cardiovascular (CV) control, which includes RR interval (RRI), systolic arterial pressure (SAP), respiration (RSP) and pulse transit time (PTT). Within a multivariate model, CV dynamics are represented as stochastic point processes whose means has a tetravariate autoregressive structure. Such framework provides the simultaneous time-frequency assessment of: (i) both arms of the SAP-RRI loop, along baroreflex and mechanical feedforward pathways; (ii) Respiratory sinus arrhythmia (RSA), through the direct evaluation of the interactions between RSP and the RRI; (iii) the coupling between cardio-respiratory activity and vascular tone through quantification of the interactions between PTT and the other CV variables. We validated the model by characterizing CV control in 16 healthy subjects during a tilt table test, and we were able to confirm a satisfactory model's goodness-of-ft. We further estimated transfer function gains, instantaneous powers and directed coherences, and observed that RSP strongly drove respiratory-related oscillations in all the other CV variables, and that PTT depended on RRI dynamics rather than blood pressure variations. During head-up tilt, baroreflex sensitivity and RSA decreased, while the gain from RRI to SAP increased, thus confirming previous physiological characterizations. © 2012 CCAL

Clyde: A deep reinforcement learning DOOM playing agent

In this paper we present the use of deep reinforcement learn-ing techniques in the context of playing partially observablemulti-agent 3D games. These techniques have traditionallybeen applied to fully observable 2D environments, or navigation tasks in 3D environments. We show the performanceof Clyde in comparison to other competitors within the con-text of the ViZDOOM competition that saw 9 bots competeagainst each other in DOOM death matches. Clyde managedto achieve 3rd place in the ViZDOOM competition held at theIEEE Conference on Computational Intelligence and Games2016. Clyde performed very well considering its relative sim-plicity and the fact that we deliberately avoided a high levelof customisation to keep the algorithm generic

An evolutionary approach to feature selection and classification in P300-based BCI

We explore the use of evolutionary algorithms in the selection of features and the classification of P300 signals in BCI. As a result we have found new ways to process and combine EEG signals to improve detection