Intracranial Pressure (ICP) monitoring signal collected in Neuro Intensive Care Units often contains large amount of artifacts. The artifacts not only directly lead to false alarms in automatic Intracranial Hypertension (IH) alert systems, and they also severely contaminate the characteristics of the underlying signal, which makes accurate forecasting of impending IH impossible. Therefore, in this paper, we propose a novel solution to effectively remove artifacts from ICP monitoring signals. The proposed method effectively detects artifacts by decomposing the ICP monitoring signal with Empirical Mode Decomposition (EMD) method. An iterative filtering method is also proposed to extract artifacts from the decomposed components of ICP signals. The proposed filter is robust. That is, the parameters of the iterative filter are estimated with robust statistics, which ensures the performance of the proposed filter will not be unduly affected by artifacts. The detected artifacts are then imputed based on the Auto-Regressive Moving Average (ARMA) model to preserve the original characteristics of the ICP signal. The effectiveness of the proposed artifact removal method is experimentally justified based on the ICP monitoring signals of 59 patients.

To prevent Traumatic Brain Injury (TBI) patients from secondary brain injuries, patients' physiological readings are continuously monitored. However, the visualization dashboards of most existing monitoring devices cannot effectively present all physiological information of TBI patients and are also ineffective in facilitating neuro-clinicians for fast and accurate diagnosis. To address these shortcomings, we proposed a new visualization dashboard, namely the Multi-signal Visualization of Physiology (MVP). MVP makes use of multi-signal polygram to collate various physiological signals, and it also utilizes colors and the concept of "safe/danger zones" to assist neuro-clinicians to achieve fast and accurate diagnosis. Moreover, MVP allows neuro-clinicians to review historical physiological statuses of TBI patients, which can guide and optimize clinicians' diagnosis and prognosis decisions. The performance of MVP is tested and justified with an actual Philips monitoring device.

Although the future mean of intracranial pressure (ICP) is of critical concern of many clinicians for timely medical treatment, the problem of forecasting the future ICP mean has not been addressed yet. In this paper, we present a nonlinear autoregressive with exogenous input artificial neural network based mean forecast algorithm (ANN(NARX)-MFA) to predict the ICP mean of the future windows based on features extracted from past windows and segmented sub-windows. We compare its performance with nonlinear autoregressive artificial neural network algorithm (ANN(NAR)) without features extracted from window segmentation. Experimental results showed that, ANN(NARX)-MFA algorithm outperforms ANN(NAR) algorithm in prediction accuracy, because additional features extracted from finer segmented sub-windows help to catch the subtle changes of ICP trends. This verifies the effectiveness of decomposing the whole window into sub-windows to obtain features in making predictions on future windows. Based on the forecast of ICP mean, medical treatments can be planned in advance to control ICP elevation, in order to maximize recovery and optimize outcome.