Assessment of Outliers and Detection of Artifactual Network Segments using Univariate and Multivariate Dispersion Entropy on Physiological Signals

Network physiology has emerged as a promising paradigm for the extraction of clinically relevant information from physiological signals by moving from univariate to multivariate analysis, allowing for the inspection of interdependencies between organ systems. However, for its successful implementation, the disruptive effects of artifactual outliers, which are a common occurrence in physiological recordings, have to be studied, quantified, and addressed. Within the scope of this study, we utilize Dispersion Entropy (DisEn) to initially quantify the capacity of outlier samples to disrupt the values of univariate and multivariate features extracted with DisEn from physiological network segments consisting of synchronised, electroencephalogram, nasal respiratory, blood pressure, and electrocardiogram signals. The DisEn algorithm is selected due to its efficient computation and good performance in the detection of changes in signals for both univariate and multivariate time-series. The extracted features are then utilised for the training and testing of a logistic regression classifier in univariate and multivariate configurations in an effort to partially automate the detection of artifactual network segments. Our results indicate that outlier samples cause significant disruption in the values of extracted features with multivariate features displaying a certain level of robustness based on the number of signals formulating the network segments from which they are extracted. Furthermore, the deployed classifiers achieve noteworthy performance, where the percentage of correct network segment classification surpasses 95% in a number of experimental setups, with the effectiveness of each configuration being affected by the signal in which outliers are located. Finally, due to the increase in the number of features extracted within the framework of network physiology and the observed impact of artifactual samples in the accuracy of their values, the implementation of algorithmic steps capable of effective feature selection is highlighted as an important area for future research

Stratified Multivariate Multiscale Dispersion Entropy for Physiological Signal Analysis

Multivariate Entropy quantification algorithms are becoming a prominent tool for the extraction of information from multi-channel physiological time-series. However, in the analysis of physiological signals from heterogeneous organ systems, certain channels may overshadow the patterns of others, resulting in information loss. Here, we introduce the framework of Stratified Entropy to prioritize each channels' dynamics based on their allocation to respective strata, leading to a richer description of the multi-channel time-series. As an implementation of the framework, three algorithmic variations of the Stratified Multivariate Multiscale Dispersion Entropy are introduced. These variations and the original algorithm are applied to synthetic time-series, waveform physiological time-series, and derivative physiological data . Based on the synthetic time-series experiments, the variations successfully prioritize channels following their strata allocation while maintaining the low computation time of the original algorithm. In experiments on waveform physiological time-series and derivative physiological data, increased discrimination capacity was noted for multiple strata allocations in the variations when benchmarked to the original algorithm. This suggests improved physiological state monitoring by the variations. Furthermore, our variations can be modified to utilize a priori knowledge for the stratification of channels. Thus, our research provides a novel approach for the extraction of previously inaccessible information from multi-channel time series acquired from heterogeneous systems

Asthma in paediatric intensive care in England residents:observational study

Despite high prevalence of asthma in children in the UK, there were no prior report on asthma admissions in paediatric intensive care units (PICU). We investigated the epidemiology and healthcare resource utilisation in children with asthma presenting to PICUs in England. PICANet, a UK national PICU database, was queried for asthma as the primary reason for admission, of children resident in England from April 2006 until March 2013. There were 2195 admissions to PICU for a median stay of 1.4 days. 59% were males and 51% aged 0–4 years. The fourth and fifth most deprived quintiles represented 61% (1329) admissions and 73% (11) of the 15 deaths. Deaths were most frequent in 10–14 years age (n = 11, 73%), with no deaths in less than 5 years age. 38% of admissions (828/2193) received invasive ventilation, which was more frequent with increasing deprivation (13% (108/828) in least deprived to 31% (260/828) in most deprived) and with decreasing age (0–4-year-olds: 49%, 409/828). This first multi-centre PICU study in England found that children from more deprived neighbourhoods represented the majority of asthma admissions, invasive ventilation and deaths in PICU. Children experiencing socioeconomic deprivation could benefit from enhanced asthma support in the community

Continuous intracranial pressure monitoring in severe traumatic brain injury in children

We present the results of the Romanian team for the multi-center grant “Paediatric Brain Monitoring with Information Technology (KidsBrainIT). Using IT Innovations to Improve Childhood Traumatic Brain Injury Intensive Care Management, Outcome, and Patient Safety”, acronym KidsBrainIT. Children aged 2 to 16 years who require intensive care management after sustaining traumatic severe brain injury are included in this study in three neurosurgical hospital: "Prof. Dr. N. Oblu" Clinical Emergency Hospital Iasi, "Sf. Maria" Children Clinical Emergency Hospital Iasi and "Bagdasar-Arseni" Clinical Emergency Hospital Bucharest. Continuous real-time intracranial pressure monitoring became a "gold standard" in TBI intensive-care management and ICP-lowering therapy is recommended when ICP is elevated above 20 mmHg or more. Continuous ICP and mean arterial blood pressure (MAP) monitoring allow calculation of cerebral perfusion pressure (CPP) and to establish of an optimal CPP. This study aims to improve the treatments and the outcomes in severe traumatic brain injury in children

Mid-term results in continuous intracranial pressure monitoring in severe traumatic brain injury in children: ERA-NET NEURON Grant

This article presents the mid-term results of the multi-center grant “Paediatric Brain Monitoring with Information Technology (KidsBrainIT). Using IT Innovations to Improve Childhood Traumatic Brain Injury Intensive Care Management, Outcome, and Patient Safety”, acronym KidsBrainIT, of the Romanian team. Continuous real-time intracranial pressure monitoring is a standard in TBI intensive-care management and ICP-lowering therapy is recommended when ICP is elevated above 20 mmHg or more. Paediatric TBI patients requiring intensive care are recruited from more contributing centres in 4 different countries and the Romanian team includes doctors CA Apetrei, C Gheorghita and A Tascu as principal investigators. Children aged 2 to 16 years who require intensive care management after sustaining traumatic severe brain injury are included in this study in three neurosurgical hospital: "Prof. Dr. N. Oblu" Clinical Emergency Hospital Iasi, "Sf. Maria" Children Clinical Emergency Hospital Iasi and "Bagdasar-Arseni" Clinical Emergency Hospital Bucharest. Continuous ICP and mean arterial blood pressure (MAP) monitoring allow calculation of cerebral perfusion pressure (CPP) and establish of an optimal CPP. The aim of this study is to improve the treatments in severe traumatic brain injury in children

Paediatric Brain Monitoring with Information Technology (KidsBrainIT): ERA-NET NEURON Grant

The complete name of this ERA-NET NEURON Grant is “Paediatric Brain Monitoring with Information Technology (KidsBrainIT). Using IT Innovations to Improve Childhood Traumatic Brain Injury Intensive Care Management, Outcome, and Patient Safety”. The Project Coordinators are Ms. Dr. Tsz-Yan Milly Lo (Consultant Paediatric Intensivist and Research Lead in Paediatric Critical Care Medicine ) and Ian Piper from University of Edinburgh, UK and the partners are: Prof. Bart Depreitere and his team from Neurosurgery & Intensive Care Research Group, University Hospitals Leuven, Belgium; Prof. Juan Sahuquillo and his team from Department of Neurosurgery, Vall d’Hebron University Hospital, Barcelona, Spain and the Romanian team with doctors CA Apetrei, C Gheorghita and A Tascu as principal investigators in three different hospitals. This material is based on the scientific project proposal with the basic project data. The aim of this grant is to test two clinically relevant hypotheses: after sustaining traumatic brain injury (TBI), paediatric patients with a longer period of measured cerebral perfusion pressure (CPP) maintained within the calculated optimal CPP (CPPopt) have an improved global clinical outcome and better tolerance against raised intracranial pressure (ICP). Paediatric TBI patients requiring intensive care are recruited from more contributing centres in 4 different countries. Their anonymised routinely collected bedside physiological monitoring data in minute-resolutions linking with anonmyised clinical and outcome data are exported and archived in the central KidsBrainIT data-bank. CPPopt is calculated and ICP dose-response analyses are performed on the KidsBrainIT dataset and their correlations with global outcome at 6 months are determined. The final aim of this study is to improve the treatments of the abnormal physiology insults: increase pressure from brain swelling (raised ICP) and brain perfusion pressure (CPP)