Cerebral Haemodynamic Autoregulatory Information System Database (CHARIS DB)

First Edition, January 2017

When referencing this material, please cite:

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) 30: 821. doi:10.1007/s10877-015-9779-3. [bib]
@article{CHARISDB,
  author  = {Nam Kim, Alex Krasner, Colin Kosinski, Michael Wininger, Maria Qadri, Zachary Kappus, Shabbar Danish, and William Craelius},
  title   = {Trending autoregulatory indices during treatment for traumatic brain injury},
  journal = {Journal of clinical monitoring and computing},
  volume  = {30},
  number  = {6},
  pages   = {821-831},
  year    = {2016}
}

Please 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]; 2000 (June 13). [bib]
@article{PhysioNet,
  author    = {Goldberger, Ary L. and Amaral, Luis A. N.
               and Glass, Leon and Hausdorff, Jeffrey M.
               and Ivanov, Plamen Ch. and Mark, Roger G.
               and Mietus, Joseph E. and Moody, George B.
               and Peng, Chung-Kang and Stanley, H. Eugene},
  title     = {{PhysioBank}, {PhysioToolkit}, and {PhysioNet}:
               Components of a New Research Resource for Complex
               Physiologic Signals},
  journal   = {Circulation},
  publisher = {American Heart Association, Inc.},
  volume    = {101},
  number    = {23},
  year      = {2000},
  month     = {June},
  pages     = {e215--e220},
  doi       = {10.1161/01.CIR.101.23.e215},
  issn      = {0009-7322},
  url       = {http://circ.ahajournals.org/content/101/23/e215}
}

The CHARIS database contains multi-channel patient recordings of ECG, arterial blood pressure (ABP), and intracranial pressure (ICP). The data is contributed by members of the CHARIS project which aims to systematize the analysis of relevant physiological signals, and create data-driven algorithms to search for potential predictors of acute clinical events for patients with acute brain injury.

Data Collection

Data acquisition units were connected to patient monitors installed in surgical intensive care unit (SICU) rooms of Robert Wood Johnson Medical Center of Rutgers University, and were activated upon arrival of a patient with diagnosis of brain injury and requiring an ICP bolt/ventriculostomy. In most cases, patients were sedated and ventilated. Arterial blood pressure (ABP) was continuously monitored with an indwelling catheter, and ICP was continuously monitored with either a subarachnoid bolt or ventriculostomy. Elevations of ICP above 20 mmHg were treated with boluses of mannitol and mild hyperventilation.

Signals were acquired from the outputs of clinical monitors routinely employed in the SICU, via isolated and filtered (25 Hz cutoff) outputs from a General Electric TRAM-rac 4A. The sampling rate was 50 Hz with a resolution of 1.41 mV at ±5 V analog input range, which is equivalent to a pressure resolution of 0.14 mmHg and a dynamic range of ±500 mmHg. ICP was continuously monitored with micro transducers (Camino Direct Pressure Monitor, Camino Laboratories, San Diego, CA) that were inserted intra-parenchymally into the frontal cranium. In some patients, ICP was only available intermittently from a ventriculostomy. ABP was registered with a fluid-filled catheter in the radial artery (Arterial Line, Edwards LifeSciences Inc.). For more details, see the referenced paper.

Data Files

The data files are provided in standard WFDB format, numbered by patient number. The digitized ABP channel is given in volts. Multiply the signal value by 100 to get the pressure in mmHg. Additional patient information is contained in the header files.

Contributors

This data was contributed by William Craelius, professor of Biomedical Engineering at Rutgers University. See also the CHARIS-GUI physiotoolkit contribution, an integrated platform for detecting intracranial hypertension events.

Icon  Name                    Last modified      Size  Description
[DIR] Parent Directory - [   ] DOI 23-Jan-2017 15:50 20 [   ] RECORDS 09-Jan-2017 16:21 108 list of record names [   ] charis10.hea 09-Jan-2017 16:20 222 header file [   ] charis8.hea 09-Jan-2017 16:21 223 header file [   ] charis7.hea 09-Jan-2017 16:21 264 header file [   ] charis4.hea 09-Jan-2017 16:21 274 header file [   ] charis6.hea 09-Jan-2017 16:21 275 header file [   ] charis11.hea 09-Jan-2017 16:20 278 header file [   ] charis5.hea 09-Jan-2017 16:21 279 header file [   ] charis3.hea 09-Jan-2017 16:21 280 header file [   ] charis2.hea 09-Jan-2017 16:21 281 header file [   ] charis12.hea 09-Jan-2017 16:21 286 header file [   ] charis13.hea 09-Jan-2017 16:21 286 header file [   ] charis1.hea 09-Jan-2017 16:21 287 header file [   ] charis9.hea 09-Jan-2017 16:21 302 header file [   ] MD5SUMS 23-Jan-2017 13:24 1.3K [   ] SHA1SUMS 23-Jan-2017 13:25 1.5K [   ] SHA256SUMS 23-Jan-2017 13:25 2.1K [   ] charis4.dat 09-Jan-2017 16:21 41M digitized signal(s) [   ] charis6.dat 09-Jan-2017 16:21 47M digitized signal(s) [   ] charis1.dat 09-Jan-2017 16:21 70M digitized signal(s) [   ] charis13.dat 09-Jan-2017 16:21 81M digitized signal(s) [   ] charis3.dat 09-Jan-2017 16:21 98M digitized signal(s) [   ] charis8.dat 09-Jan-2017 16:21 98M digitized signal(s) [   ] charis11.dat 09-Jan-2017 16:20 99M digitized signal(s) [   ] charis12.dat 09-Jan-2017 16:21 118M digitized signal(s) [   ] charis10.dat 09-Jan-2017 16:20 122M digitized signal(s) [   ] charis7.dat 09-Jan-2017 16:21 138M digitized signal(s) [   ] charis9.dat 09-Jan-2017 16:21 176M digitized signal(s) [   ] charis5.dat 09-Jan-2017 16:21 206M digitized signal(s) [   ] charis2.dat 09-Jan-2017 16:21 386M digitized signal(s)

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Updated Friday, 28 October 2016 at 16:58 EDT

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