You are not logged in.

A real-time abnormality detection system for intensive care management

Huang, Guangyan, He, Jing, Cao, Jie, Qiao, Zhi, Steyn, Michael and Taraporewalla, Kersi 2013, A real-time abnormality detection system for intensive care management, in ICDE 2013: Proceedings of the 29th International Conference on Data Engineering, IEEE, Piscataway, N.J., pp. 1376-1379, doi: 10.1109/ICDE.2013.6544948.

Attached Files
Name Description MIMEType Size Downloads

Title A real-time abnormality detection system for intensive care management
Author(s) Huang, GuangyanORCID iD for Huang, Guangyan orcid.org/0000-0002-1821-8644
He, Jing
Cao, Jie
Qiao, Zhi
Steyn, Michael
Taraporewalla, Kersi
Conference name IEEE International Conference on Data Engineering (29th : 2013 : Brisbane, Qld.)
Conference location Brisbane, QLD
Conference dates 8-11 Apr. 2013
Title of proceedings ICDE 2013: Proceedings of the 29th International Conference on Data Engineering
Publication date 2013
Start page 1376
End page 1379
Total pages 4
Publisher IEEE
Place of publication Piscataway, N.J.
Summary Detecting abnormalities from multiple correlated time series is valuable to those applications where a credible realtime event prediction system will minimize economic losses (e.g. stock market crash) and save lives (e.g. medical surveillance in the operating theatre). For example, in an intensive care scenario, anesthetists perform a vital role in monitoring the patient and adjusting the flow and type of anesthetics to the patient during an operation. An early awareness of possible complications is vital for an anesthetist to correctly react to a given situation. In this demonstration, we provide a comprehensive medical surveillance system to effectively detect abnormalities from multiple physiological data streams for assisting online intensive care management. Particularly, a novel online support vector regression (OSVR) algorithm is developed to approach the problem of discovering the abnormalities from multiple correlated time series for accuracy and real-time efficiency. We also utilize historical data streams to optimize the precision of the OSVR algorithm. Moreover, this system comprises a friendly user interface by integrating multiple physiological data streams and visualizing alarms of abnormalities. © 2013 IEEE.
ISBN 9781467349109
ISSN 1084-4627
1063-6382
Language eng
DOI 10.1109/ICDE.2013.6544948
Field of Research 080109 Pattern Recognition and Data Mining
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1.1 Full written paper - refereed
ERA Research output type E Conference publication
Copyright notice ©2013, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30083694

Document type: Conference Paper
Collection: School of Information Technology
Connect to link resolver
 
Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved.

Versions
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 0 times in TR Web of Science
Scopus Citation Count Cited 1 times in Scopus
Google Scholar Search Google Scholar
Access Statistics: 88 Abstract Views, 2 File Downloads  -  Detailed Statistics
Created: Mon, 30 May 2016, 14:38:36 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.