Multi-signal Visualization of Physiology (MVP): a novel visualization dashboard for physiological monitoring of Traumatic Brain Injury 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).

Go green! Reusing brain monitoring data containing missing values: a feasibility study with traumatic brain injury patients.

Background: Despite the wealth of information carried, periodic brain monitoring data are often incomplete with a significant amount of missing values. Incomplete monitoring data are usually discarded to ensure purity of data. However, this approach leads to the loss of statistical power, potentially biased study and a great waste of resources.

Artificial neural network based intracranial pressure mean forecast algorithm for medical decision support.

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.

iSyNCC: an intelligent system for patient monitoring & clinical decision support in Neuro-Critical-Care.

Close monitoring and timely treatment are extremely crucial in Neuro Intensive/Critical Care Units (NICUs) to prevent patients from secondary brain damages. However, the current clinical practice is labor-intensive, prone to human errors and ineffective. To address this, we developed an integrated and intelligent system, namely iSyNCC, to enhance the effectiveness of patient monitoring and clinical decision makings in NICUs.

Artifact removal for intracranial pressure monitoring signals: a robust solution with signal decomposition.

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.