Software Open Access
Cerebral Haemodynamic Autoregulatory Information System GUI
Published: Dec. 16, 2016. Version: 1.0.0
New Toolkit Added: CHARIS GUI (Dec. 16, 2016, midnight)
The Cerebral Haemodynamic Autoregulatory Information System (CHARIS) GUI toolkit contains a Matlab GUI used to display and process intracranial pressure features and relationships with other physiological signals.
Kim N, Krasner A, Kosinski C, Wininger M, Qadri M, Kappus Z, Danish S, Craelius W. Trending autoregulatory indices during treatment for traumatic brain injury. J Clin Monit Comput. 2016 Dec;30(6):821-831. Epub 2015 Oct 7.
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Goldberger, A., Amaral, L., Glass, L., Hausdorff, J., Ivanov, P. C., Mark, R., ... & Stanley, H. E. (2000). PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Circulation [Online]. 101 (23), pp. e215–e220.
Acute Brain injury (ABI) is a devastating event requiring intensive acute treatment and post-injury rehabilitation, both delivered for indeterminate periods of time. For severe ABIs, acute treatment is aimed at stabilizing the patient to prevent secondary brain injury from ischemia and swelling. This requires a balance between adequate levels of cerebral blood flow and safely low intracranial pressure (ICP) - a task normally done by autoregulatory (AR) processes of the brain.
In the absence of normal AR, hemodynamic stability is difficult to maintain because there are no reliable predictors to guide treatment. Thus, guidelines for triage and discharge are somewhat arbitrary for ABI and the need for a Clinical Decision Support (CDS) system for neurotrauma is widely recognized. Prerequisite to a CDS is a large database of patient records and efficient means to extract meaningful information from them.
CHARIS will systematize the analysis of relevant physiological signals, and will embody data-driven algorithms to search for potential predictors of acute clinical events.
The CHARIS GUI provides an integrated platform for evaluating data necessary for developing predictive models of intracranial hyptertension (IH).
The chosen test index which the underlying algorithms use is the well-established PRx, a moving correlation index between mean arterial blood pressure and intracranial pressure. This is based on the assumption that normal intracranial pressure (ICP) should not directly correlate with arterial blood pressure (ABP).
After loading the data, the algorithm packet averages the data to reduce information volume and calculates the PRx of the input waveforms. It will then search through the data to detect noteable events based on the set threshold parameters. Upon the detection of each potential IH location, the user will be prompted to state whether or not it is an artifact, ultimately resulting in a set of semi-supervised detected events.
The GUI provides a convenient interface to load, process, label, graph, and save the waveforms, features, and events. Open the GUI by running
CHARIS-GUI-Instructions.pdf file contains detailed instructions on how to implement each functionality.
sample-input-data.zip file contains a set of waveform
.mat files (ABP, ECG, ICP and Time) that can be fed into the GUI and analyzed. The
sample-output.zip file contains an example set of analysis results output by the GUI.
The code and GUI are implemented in Matlab. Tests were successfully performed on Windows 10 and Ubuntu 16.04 for Matlab versions 2015b, 2016a, and 2016b.
This software package was contributed by William Craelius, professor of Biomedical Engineering at Rutgers University.
Anyone can access the files, as long as they conform to the terms of the specified license.
License (for files):
Open Data Commons Attribution License v1.0
intracranial pressure brain injury
Total uncompressed size: 0 B.
Access the files
Download the files using your terminal:
wget -r -N -c -np https://physionet.org/files/charis-gui/1.0.0/
|CHARIS-GUI-Instructions.pdf (download)||1.9 MB||2019-04-12|
|CHARIS_GUI_CODE.zip (download)||343.2 KB||2019-04-12|
|gui-image.png (download)||98.2 KB||2019-04-12|
|sample-input-data.zip (download)||184.5 MB||2019-04-12|
|sample-output.zip (download)||128.1 MB||2019-04-12|