NDLI: An Intelligent Decision System for Intraoperative Somatosensory Evoked Potential Monitoring

Content Provider	IEEE Xplore Digital Library
Author	Fan, B. Li, H.-X. Hu, Y.
Copyright Year	2001
Abstract	Somatosensory evoked potential (SEP) is a useful, noninvasive technique widely used for spinal cord monitoring during surgery. One of the main indicators of a spinal cord injury is the drop in amplitude of the SEP signal in comparison to the nominal baseline that is assumed to be constant during the surgery. However, in practice, the real-time baseline is not constant and may vary during the operation due to nonsurgical factors, such as blood pressure, anaesthesia, etc. Thus, a false warning is often generated if the nominal baseline is used for SEP monitoring. In current practice, human experts must be used to prevent this false warning. However, these well-trained human experts are expensive and may not be reliable and consistent due to various reasons like fatigue and emotion. In this paper, an intelligent decision system is proposed to improve SEP monitoring. First, the least squares support vector regression and multi-support vector regression models are trained to construct the dynamic baseline from historical data. Then a control chart is applied to detect abnormalities during surgery. The effectiveness of the intelligent decision system is evaluated by comparing its performance against the nominal baseline model by using the real experimental datasets derived from clinical conditions.
Sponsorship	IEEE Engineering in Medicine and Biology Society
Starting Page	300
Ending Page	307
Page Count	8
File Size	1149790
File Format	PDF
ISSN	15344320
Volume Number	24
Issue Number	2
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2016-01-01
Publisher Place	U.S.A.
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Monitoring Surgery Control charts Biomedical monitoring Predictive models Reliability somatosensory evoked potential (SEP) Control chart intelligent decision system least squares support vector regression (LS-SVR) multi-support vector regression (M-SVR)
Content Type	Text
Resource Type	Article
Subject	Neuroscience Rehabilitation Internal Medicine Biomedical Engineering Computer Science Applications

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