NDLI: Study on predicting method for acute hypotensive episodes based on wavelet transform and support vector machine

Content Provider	IEEE Xplore Digital Library
Author	Zhigang Wang Lijuan Lai Dongsheng Xiong Xiaoming Wu
Copyright Year	2010
Description	Author affiliation: Department of Biomedical Engineering, South China University of Technology, Guangzhou, China (Zhigang Wang; Lijuan Lai; Dongsheng Xiong; Xiaoming Wu)
Abstract	The occurrence of acute hypotensive episodes (AHE) in intensive care units (ICU) seriously endanger the lives of patients, and are depended mainly on the expert experience of doctors to treat currently. How to detect and predict AHE in advance has become a clinical problem which is highly paid attention to by the medical world. In this paper, the theory of medical Informatics has been applied to achieve the prediction of the occurrence of AHE. In our study, the mean arterial blood pressure (MAP) signals of those who experienced AHE and those who not are both described on different scales by using wavelet transform. Through the extraction of the median and maximum from the wavelet coefficients for learning and training based on support vector machine (SVM), a predicting model with a predictive accuracy of 90% has developed. The experiment demonstrates that the proposed approach has better prediction accuracy comparing with the one which extracts statistical parameters from the MAP signals directly, and is beneficial to early prediction of AHE and intervention.
Starting Page	1041
Ending Page	1045
File Size	264120
Page Count	5
File Format	PDF
ISBN	9781424464951
e-ISBN	9781424464982
DOI	10.1109/BMEI.2010.5639747
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-10-16
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Training Wavelet transforms Accuracy wavelet transform Predictive models SVM Kernel parameters classification and prediction AHE
Content Type	Text
Resource Type	Article

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