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Acta Neurochir Suppl. 2016;122:75-80. doi: 10.1007/978-3-319-22533-3_15.

Real-Time Processing of Continuous Physiological Signals in a Neurocritical Care Unit on a Stream Data Analytics Platform.

Bai Y1, Sow D2, Vespa P3, Hu X4,5,6.

Author information

1
Biomedical Engineering Graduate Program, Henry Samueli School of Engineering and Applied Science, University of California-Los Angeles, Los Angeles, CA, USA.
2
IBM T. J. Watson Research Center, Yorktown, NY, USA.
3
Department of Neurosurgery, David Geffen School of Medicine, University of California-Los Angeles, Los Angeles, CA, USA.
4
Departments of Physiological Nursing/Neurosurgery, University of California-San Francisco, San Francisco, CA, USA. xiao.hu@ucsf.edu.
5
Institute for Computational Health Sciences, University of California-San Francisco, San Francisco, CA, USA. xiao.hu@ucsf.edu.
6
Affiliate, UCB/UCSF Graduate Group in Bioengineering, University of California-San Francisco, San Francisco, CA, USA. xiao.hu@ucsf.edu.

Abstract

Continuous high-volume and high-frequency brain signals such as intracranial pressure (ICP) and electroencephalographic (EEG) waveforms are commonly collected by bedside monitors in neurocritical care. While such signals often carry early signs of neurological deterioration, detecting these signs in real time with conventional data processing methods mainly designed for retrospective analysis has been extremely challenging. Such methods are not designed to handle the large volumes of waveform data produced by bedside monitors. In this pilot study, we address this challenge by building a prototype system using the IBM InfoSphere Streams platform, a scalable stream computing platform, to detect unstable ICP dynamics in real time. The system continuously receives electrocardiographic and ICP signals and analyzes ICP pulse morphology looking for deviations from a steady state. We also designed a Web interface to display in real time the result of this analysis in a Web browser. With this interface, physicians are able to ubiquitously check on the status of their patients and gain direct insight into and interpretation of the patient's state in real time. The prototype system has been successfully tested prospectively on live hospitalized patients.

KEYWORDS:

InfoSphere streams; Intracranial pressure (ICP) steady state; Real-time data processing and analysis; Stream computing

PMID:
27165881
DOI:
10.1007/978-3-319-22533-3_15
[Indexed for MEDLINE]

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