Intracranial Pressure Prediction with a Recurrent Neural Network Model

Abstract

Abnormal elevation of intracranial pressure (ICP) can cause dangerous or even fatal outcomes. The early detection of high intracranial pressure events can be crucial in saving patients' life in an intensive care unit (ICU). This study proposes an efficient artificial recurrent neural network to predict intracranial pressure evaluation for thirteen patients. The learning model is generated uniquely for each patient to predict the occurrence of the ICP event (classified into high ICP or low ICP) for the upcoming 10 minutes by inputting the previous 20-minutes signal. The results showed that the minimal accuracy of predicting intracranial pressure events was 90% for 11 patients, whereas a minimum of 95% accuracy was obtained among five patients. This study introduces an efficient artificial recurrent neural network model on the early prediction of intracranial pressure evaluation supported by the high adaptive performance of the LSTM model.