An online approach for intracranial pressure forecasting based on signal decomposition and robust statistics

Intracranial pressure (ICP) is an important physiological signal for patients with traumatic brain injuries. Accurate ICP forecasting enables active and early interventions for more effective control of ICP levels. To achieve high accuracy, most existing methods require a high sampling rate (100 Hz), which is infeasible for online medical applications. Therefore, we propose an online ICP forecasting method requiring only low rate signal sampling (0.1 Hz). Our ARIMA based forecasting method applies empirical mode decomposition (EMD) to remove non-stationarities from the ICP signal, and robust estimation to mitigate the influence of motion induced artifacts. Experimental performance assessment with simulated and clinically collected data demonstrate that the proposed method is more accurate compared to previously proposed and standard methods.