Online mining abnormal period patterns from multiple medical sensor data streams

Huang, Guangyan, Zhang, Yanchun, Cao, Jie, Steyn, Michael and Taraporewalla, Kersi 2014, Online mining abnormal period patterns from multiple medical sensor data streams, World wide web, vol. 17, no. 4, pp. 569-587, doi: 10.1007/s11280-013-0203-y.

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Title Online mining abnormal period patterns from multiple medical sensor data streams
Author(s) Huang, GuangyanORCID iD for Huang, Guangyan
Zhang, Yanchun
Cao, Jie
Steyn, Michael
Taraporewalla, Kersi
Journal name World wide web
Volume number 17
Issue number 4
Start page 569
End page 587
Total pages 19
Publisher Springer
Place of publication Berlin, Germany
Publication date 2014-07
ISSN 1386-145X
Keyword(s) abnormal period patterns
data mining
multiple data streams
medical sensor data streams
Summary 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.
Language eng
DOI 10.1007/s11280-013-0203-y
Field of Research 080109 Pattern Recognition and Data Mining
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category C1.1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2014, Springer
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Collection: School of Information Technology
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