Intracranial Pressure Forecasting in Children Using Dynamic Averaging of Time Series Data

Increased Intracranial Pressure (ICP) is a serious and often life-threatening condition. If the increased pressure pushes on critical brain structures and blood vessels, it can lead to serious permanent problems or even death. In this study, we propose a novel regression model to forecast ICP episodes in children, 30 min in advance, by using the dynamic characteristics of continuous intracranial pressure, vitals and medications during the last two hours. The correlation between physiological parameters, including blood pressure, respiratory rate, heart rate and the ICP, is analyzed. Linear regression, Lasso regression, support vector machine and random forest algorithms are used to forecast the next 30 min of the recorded ICP. Finally, dynamic features are created based on vitals, medications and the ICP. The weak correlation between blood pressure and the ICP (0.2) is reported. The Root-Mean-Square Error (RMSE) of the random forest model decreased from 1.6 to 0.89% by using the given medication variables in the last two hours. The random forest regression gave an accurate model for the ICP forecast with 0.99 correlation between the forecast and experimental values.

[1]  T. Lo,et al.  Critical thresholds of intracranial pressure and cerebral perfusion pressure related to age in paediatric head injury , 2005, Journal of Neurology, Neurosurgery & Psychiatry.

[2]  Hyun Kang.  The prevention and handling of the missing data , 2013, Korean journal of anesthesiology.

[3]  Manish Kumar,et al.  Forecasting Stock Index Movement: A Comparison of Support Vector Machines and Random Forest , 2006 .

[4]  P. Hutchinson,et al.  The Role of Surgical Intervention in Traumatic Brain Injury. , 2016, Neurosurgery clinics of North America.

[5]  Bhawna Mallick,et al.  A Study of Time Series Models ARIMA and ETS , 2017 .

[6]  Xiao Hu,et al.  Forecasting ICP Elevation Based on Prescient Changes of Intracranial Pressure Waveform Morphology , 2010, IEEE Transactions on Biomedical Engineering.

[7]  G. Van den Berghe,et al.  Novel Methods to Predict Increased Intracranial Pressure During Intensive Care and Long-Term Neurologic Outcome After Traumatic Brain Injury: Development and Validation in a Multicenter Dataset* , 2013, Critical care medicine.

[8]  Intracranial Pressure Monitors. , 2015, Atlas of the oral and maxillofacial surgery clinics of North America.

[9]  M. Ursino,et al.  A simple mathematical model of the interaction between intracranial pressure and cerebral hemodynamics. , 1997, Journal of applied physiology.

[10]  Suvasini Sharma,et al.  Management of Raised Intracranial Pressure , 2010, Indian journal of pediatrics.

[11]  D. Evans,et al.  Dynamics and Bifurcations in Low-Dimensional Models of Intracranial Pressure , 2016 .

[12]  S. Mayer,et al.  Management of Increased Intracranial Pressure , 2021, Decision-Making in Adult Neurology.

[13]  Gert R. G. Lanckriet,et al.  A random forest classifier for the prediction of energy expenditure and type of physical activity from wrist and hip accelerometers , 2014, Physiological measurement.

[14]  Mina Mahmoudi,et al.  The Transmission of the US Stock Market Crash of 2008 to the European Stock Markets: An Applied Time Series Investigation , 2016 .

[15]  Kwong-Sak Leung,et al.  Substituting random forest for multiple linear regression improves binding affinity prediction of scoring functions: Cyscore as a case study , 2014, BMC Bioinformatics.

[16]  Young-Chan Lee,et al.  Bankruptcy prediction using support vector machine with optimal choice of kernel function parameters , 2005, Expert Syst. Appl..

[17]  F. Fang,et al.  Using Multivariate Regression Model with Least Absolute Shrinkage and Selection Operator (LASSO) to Predict the Incidence of Xerostomia after Intensity-Modulated Radiotherapy for Head and Neck Cancer , 2014, PloS one.

[18]  Emmanuel Sirimal Silva,et al.  Forecasting U.S. Tourist arrivals using optimal Singular Spectrum Analysis , 2015 .

[19]  Martin Smith.  Monitoring Intracranial Pressure in Traumatic Brain Injury , 2008, Anesthesia and analgesia.

[20]  R. J. Hunt,et al.  Percent Agreement, Pearson's Correlation, and Kappa as Measures of Inter-examiner Reliability , 1986, Journal of dental research.

[21]  Maryam Ahmadi,et al.  The Information Security Needs in Radiological Information Systems—an Insight on State Hospitals of Iran, 2012 , 2013, Journal of Digital Imaging.

[22]  Xiao Hu,et al.  Morphological Clustering and Analysis of Continuous Intracranial Pressure , 2009, IEEE Transactions on Biomedical Engineering.

[23]  J. Palmer.  Management of raised intracranial pressure in children. , 2000, Intensive & critical care nursing.

[24]  Jelena Mirkovic,et al.  iDECIDE: A Mobile Application for Pre-Meal Insulin Dosing Using an Evidence Based Equation to Account for Patient Preferences , 2014, AMIA.

[25]  R. Bellomo,et al.  The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3). , 2016, JAMA.

[26]  Xue Qiao.  Three essays in macroeconomics , 2007 .

[27]  M. Kosteljanetz,et al.  Prevalence and predictive factors of post‐traumatic hypopituitarism , 2007, Clinical endocrinology.

[28]  J. Ogutu,et al.  Genomic selection using regularized linear regression models: ridge regression, lasso, elastic net and their extensions , 2012, BMC Proceedings.

[29]  M. Swiercz,et al.  Intracranial Pressure Processing with Artificial Neural Networks: Prediction of ICP Trends , 2000, Acta Neurochirurgica.

[30]  Wei-Yin Loh,et al.  Classification and regression trees , 2011, WIREs Data Mining Knowl. Discov..

[31]  Bradley Efron,et al.  Missing Data, Imputation, and the Bootstrap , 1994 .

[32]  Chengcheng Hu,et al.  Least absolute shrinkage and selection operator type methods for the identification of serum biomarkers of overweight and obesity: simulation and application , 2016, BMC Medical Research Methodology.