Online mining abnormal period patterns from multiple medical sensor data streams

Huang, Guangyan, Zhang, Yanchun, Cao, Jie, Steyn, Michael and Taraporewalla, Kersi (2013) Online mining abnormal period patterns from multiple medical sensor data streams. World Wide Web: internet and web information systems, 17 4: 569-587. doi:10.1007/s11280-013-0203-y

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Author Huang, Guangyan
Zhang, Yanchun
Cao, Jie
Steyn, Michael
Taraporewalla, Kersi
Title Online mining abnormal period patterns from multiple medical sensor data streams
Journal name World Wide Web: internet and web information systems   Check publisher's open access policy
ISSN 1386-145X
Publication date 2013-01-01
Year available 2014
Sub-type Article (original research)
DOI 10.1007/s11280-013-0203-y
Open Access Status Not yet assessed
Volume 17
Issue 4
Start page 569
End page 587
Total pages 19
Place of publication New York, NY, U.S.A.
Publisher Springer New York LLC
Language eng
Abstract With the advanced technology of medical devices and sensors, an abundance of medical data streams are available. However, data analysis techniques are very limited, especially for processing massive multiple physiological streams that may only be understood by medical experts. The state-of-the-art techniques only allow multiple medical devices to independently monitor different physiological parameters for the patient's status, thus they signal too many false alarms, creating unnecessary noise, especially in the Intensive Care Unit (ICU). An effective solution which has been recently studied is to integrate information from multiple physiologic parameters to reduce alarms. But it is a challenge to detect abnormalities from high frequently changed physiological streams data, since abnormalities occur gradually due to the complex situation of patients. An analysis of ICU physiological data streams shows that many vital physiological parameters are changed periodically (such as heart rate, arterial pressure, and respiratory impedance) and thus abnormalities are generally abnormal period patterns. In this paper, we develop a Mining Abnormal Period Patterns from Multiple Physiological Streams (MAPPMPS) method to detect and rank abnormalities in medical sensor streams. The efficiency and effectiveness of the MAPPMPS method is demonstrated by a real-world massive database of multiple physiological streams sampled in ICU, comprising 250 patients' streams (each stream involving over 1.3 million data points) with a total size of 28 GB data.
Keyword Computer Science, Information Systems
Computer Science, Software Engineering
Computer Science
Q-Index Code C1
Q-Index Status Confirmed Code
Grant ID LP100200682
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: Official 2014 Collection
School of Medicine Publications
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Citation counts: TR Web of Science Citation Count  Cited 6 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 9 times in Scopus Article | Citations
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Created: Sun, 02 Feb 2014, 23:49:30 EST by Matthew Lamb on behalf of School of Medicine