Dimitri Giovanna Maria, Agrawal Shruti, Young Adam, Donnelly Joseph, Liu Xiuyun, Smielewski Peter, Hutchinson Peter, Czosnyka Marek, Lió Pietro, Haubrich Christina
Computer Laboratory, University of Cambridge, Thomson Avenue, Cambridge, UK.
Appl Netw Sci. 2017;2(1):29. doi: 10.1007/s41109-017-0050-3. Epub 2017 Aug 30.
We present a multiplex network model for the analysis of Intracranial Pressure (ICP) and Heart Rate (HR) behaviour after severe brain traumatic injuries in pediatric patients. The ICP monitoring is of vital importance for checking life threathening conditions, and understanding the behaviour of these parameters is crucial for a successful intervention of the clinician. Our own observations, exhibit cross-talks interaction events happening between HR and ICP, i.e. transients in which both the ICP and the HR showed an increase of 20% with respect to their baseline value in the window considered. We used a complex event processing methodology, to investigate the relationship between HR and ICP, after traumatic brain injuries (TBI). In particular our goal has been to analyse events of simultaneous increase by HR and ICP (i.e. cross-talks), modelling the two time series as a unique multiplex network system (Lacasa et al., Sci Rep 5:15508-15508, 2014).
We used a complex network approach based on visibility graphs (Lacasa et al., Sci Rep 5:15508-15508, 2014) to model and study the behaviour of our system and to investigate how and if network topological measures can give information on the possible detection of crosstalks events taking place in the system. Each time series was converted as a layer in a multiplex network. We therefore studied the network structure, focusing on the behaviour of the two time series in the cross-talks events windows detected. We used a dataset of 27 TBI pediatric patients, admitted to Addenbrooke's Hospital, Cambridge, Pediatric Intensive Care Unit (PICU) between August 2012 and December 2014.
Following a preliminary statistical exploration of the two time series of ICP and HR, we analysed the multiplex network proposed, focusing on two standard topological network metrics: the mutual interaction, and the average edge overlap (Lacasa et al., Sci Rep 5:15508-15508, 2014). We compared results obtained for these two indicators, considering windows in which a cross talks event between HR and ICP was detected with windows in which cross talks events were not present. The analysis of such metrics gave us interesting insights on the time series behaviour. More specifically we observed an increase in the value of the mutual interaction in the case of cross talk as compared to non cross talk. This seems to suggest that mutual interaction could be a potentially interesting "marker" for cross talks events.
我们提出了一种多重网络模型，用于分析小儿严重脑外伤后颅内压（ICP）和心率（HR）的行为。ICP监测对于检查危及生命的情况至关重要，了解这些参数的行为对于临床医生的成功干预至关重要。我们自己的观察结果显示，HR和ICP之间存在串扰相互作用事件，即在考虑的窗口中，ICP和HR相对于其基线值均增加20%的瞬变情况。我们使用复杂事件处理方法，研究创伤性脑损伤（TBI）后HR和ICP之间的关系。特别是，我们的目标是分析HR和ICP同时增加的事件（即串扰），将这两个时间序列建模为一个独特的多重网络系统（Lacasa等人，《科学报告》5:15508 - 15508，2014）。
我们使用基于可见性图的复杂网络方法（Lacasa等人，《科学报告》5:15508 - 15508，2014）对我们的系统行为进行建模和研究，并调查网络拓扑度量如何以及是否能够提供有关系统中可能发生的串扰事件检测的信息。每个时间序列都被转换为多重网络中的一层。因此，我们研究了网络结构，重点关注在检测到的串扰事件窗口中两个时间序列的行为。我们使用了2012年8月至2014年12月期间入住剑桥阿登布鲁克医院儿科重症监护病房（PICU）的27名TBI小儿患者的数据集。
在对ICP和HR的两个时间序列进行初步统计探索之后，我们分析了所提出的多重网络，重点关注两个标准的拓扑网络度量：相互作用和平均边重叠（Lacasa等人，《科学报告》5:15508 - 15508，2014）。我们比较了这两个指标的结果，考虑了检测到HR和ICP之间串扰事件的窗口与未出现串扰事件的窗口。对这些度量的分析为我们提供了关于时间序列行为的有趣见解。更具体地说，我们观察到与非串扰情况相比，串扰情况下相互作用的值有所增加。这似乎表明相互作用可能是串扰事件的一个潜在有趣的“标志”。
