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

Rank-based multi-scale entropy analysis of heart rate variability

The method of MultiScale Entropy (MSE) is an invaluable tool to quantify and compare the complexity of physiological time series at different time scales. Although MSE traditionally employs sample entropy to measure the unpredictability of each coarse-grained series, the same framework can be applied to other metrics. Here we investigate the use of a rank-based entropy measure within the MSE framework. Like in the traditional method, the series are studied in an embedding space of dimension m. The novel entropy assesses the unpredictability of the series quantifying the "amount of shuffling" that the ranks of the mutual distances between pairs of m-long vectors undergo when considering the next observation. The algorithm was tested on recordings from the Fantasia database in a time-varying fashion using non-overlapping 300-samples windows. The method was able to find statistically significant differences between young and healthy elderly subjects at 7 scales/time-windows after accounting for multiple comparisons using the Holm-Bonferroni correction. These promising results suggest the possibility of using this measure to perform a time-varying assessment of complexity with increased accuracy and temporal resolution

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

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

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

A Real-Time Automated Point-Process Method for the Detection and Correction of Erroneous and Ectopic Heartbeats

The presence of recurring arrhythmic events (also known as cardiac dysrhythmia or irregular heartbeats), as well as erroneous beat detection due to low signal quality, significantly affects estimation of both time and frequency domain indices of heart rate variability (HRV). A reliable, real-time classification and correction of ECG-derived heartbeats is a necessary prerequisite for an accurate online monitoring of HRV and cardiovascular control. We have developed a novel point-process-based method for real-time R-R interval error detection and correction. Given an R-wave event, we assume that the length of the next R-R interval follows a physiologically motivated, time-varying inverse Gaussian probability distribution. We then devise an instantaneous automated detection and correction procedure for erroneous and arrhythmic beats by using the information on the probability of occurrence of the observed beat provided by the model. We test our algorithm over two datasets from the PhysioNet archive. The Fantasia normal rhythm database is artificially corrupted with known erroneous beats to test both the detection procedure and correction procedure. The benchmark MIT-BIH Arrhythmia database is further considered to test the detection procedure of real arrhythmic events and compare it with results from previously published algorithms. Our automated algorithm represents an improvement over previous procedures, with best specificity for the detection of correct beats, as well as highest sensitivity to missed and extra beats, artificially misplaced beats, and for real arrhythmic events. A near-optimal heartbeat classification and correction, together with the ability to adapt to time-varying changes of heartbeat dynamics in an online fashion, may provide a solid base for building a more reliable real-time HRV monitoring device. © 1964-2012 IEEE

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

Instantaneous Transfer Entropy for the Study of Cardiovascular and Cardio-Respiratory Nonstationary Dynamics

Objective: Measures of Transfer Entropy (TE) quantify the direction and strength of coupling between two complex systems. Standard approaches assume stationarity of the observations, and therefore are unable to track time-varying changes in nonlinear information transfer with high temporal resolution. In this study, we aim to define and validate novel instantaneous measures of transfer entropy to provide an im- proved assessment of complex non-stationary cardio-respiratory interactions

Characterization of autonomic states by complex sympathetic and parasympathetic dynamics*

Assessment of heartbeat dynamics provides a promising framework for non-invasive monitoring of cardiovascular and autonomic states. Nevertheless, the non-specificity of such measurements among clinical populations and healthy conditions associated with different autonomic states severely limits their applicability and exploitation in naturalistic conditions. This limitation arises especially when pathological or postural change-related sympathetic hyperactivity is compared to autonomic changes across age and experimental conditions. In this frame, we investigate the intrinsic irregularity and complexity of cardiac sympathetic and vagal activity series in different populations, which are associated with different cardiac autonomic dynamics. Sample entropy, fuzzy entropy, and distribution entropy are calculated on the recently proposed sympathetic and parasympathetic activity indices (SAI and PAI) series, which are derived from publicly available heartbeat series of congestive heart failure patients, elderly and young subjects watching a movie in the supine position, and healthy subjects undergoing slow postural changes. Results show statistically significant differences between pathological/old subjects and young subjects in the resting state and during slow tilt, with interesting trends in SAI- and PAI-related entropy values. Moreover, while CHF patients and healthy subjects in upright position show the higher cardiac sympathetic activity, elderly and young subjects in resting state showed higher vagal activity. We conclude that quantification of intrinsic cardiac complexity from sympathetic and vagal dynamics may provide new physiology insights and improve on the non-specificity of heartbeat-derived biomarkers