Cerebral Haemodynamic Autoregulatory Information System GUI

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A description of the algorithms and their applications can be found in:

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.

Please cite the above publication when referencing this material, and also include the standard citation for PhysioNet:

Goldberger AL, Amaral LAN, Glass L, Hausdorff JM, Ivanov PCh, Mark RG, Mietus JE, Moody GB, Peng C-K, Stanley HE. "PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals," Circulation 101(23):e215-e220 [Circulation Electronic Pages; http://circ.ahajournals.org/content/101/23/e215.full]; 2000 (June 13).


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.

Software Description

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.m. The CHARIS-GUI-Instructions.pdf file contains detailed instructions on how to implement each functionality.

[Charis GUI]

Example Data

The 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.

Software Requirements

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.

Icon  Name                        Last modified      Size  Description
[PARENTDIR] Parent Directory - [   ] CHARIS-GUI-Instructions.pdf 2016-12-16 16:50 1.9M [   ] CHARIS_GUI_CODE.zip 2016-12-14 16:46 343K [DIR] CHARIS_GUI_CODE/ 2016-12-14 16:46 - [IMG] gui-image.png 2016-12-14 16:46 98K [   ] sample-input-data.zip 2016-12-14 16:46 185M [   ] sample-output.zip 2016-12-14 16:46 128M

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Updated Friday, 28 October 2016 at 16:58 EDT

PhysioNet is supported by the National Institute of General Medical Sciences (NIGMS) and the National Institute of Biomedical Imaging and Bioengineering (NIBIB) under NIH grant number 2R01GM104987-09.