Intracranial pressure model in intensive care unit using a simple recurrent neural network through time ?
This paper aims to establish a patient's intracranial pressure (ICP) model in neurosurgical intensive care unit (NICU) using neural network. Non-invasive physiological signals from patients including mean arterial pressure (MAP), heart rate (HR), end-tidal of carbon dioxide (EtCO2) and regional cerebral oxygenation (rSO2) were measured. However, ICP remains ill-defined, complicated and non-linear because it is affected by many predictable and unpredictable factors. Our study employs the structure of recurrent network to develop a modified neural network algorithm called a simple recurrent neural network through time (SRNNTT). The proposed recurrent neural network combines Elman architecture of the simple recurrent network structure and backpropagation through time. In order to demonstrate the performance of the proposed model, four kinds of neural-network classifiers have been tested on MackeyGlass differential-delay equation which is a chaotic time series signal. Finally, we used this SRNNTT model to build the ICP model using data from six head-injured patients. Although the accuracy of the ICP model is still far from ideal, the methodology used non-invasive vital signs (i.e., MAP, HR, EtCO2, and rSO2) to predict an invasive, dangerous and expensive signal (i.e., ICP) has achieved this monitoring system more safely and flexibly in NICU.
intracranial pressure simple recurrent neural network through time neurosurgical intensive care unit elman