Publication Type:

Journal Article

Source:

Electronics Letters, Institution of Engineering and Technology, Volume 51, Number 25, p.2089-2090 (2015)

URL:

http://www.scopus.com/inward/record.url?eid=2-s2.0-84949883457&partnerID=40&md5=18e455092468fbd2468d4c2146a7a72f

Keywords:

Arterial blood pressure, Baseline systems, Blood pressure, Classification accuracy, Improving performance, Intelligent monitoring, oxygen saturation, Patient monitoring, Radial basis function kernels, Radial basis function networks, Support vector machine classifiers

Abstract:

Using covariance normalisation (CVN) of vital signs is explored to improve the performance of multi-parameter patient monitors with heart rate, arterial blood pressure, respiration rate, and oxygen saturation (SpO2) as its input. The baseline system for the experiments is a support vector machine classifier with a radial basis function kernel. Although an improvement in the overall classification accuracy with the use of CVN is obtained, there was a deterioration in sensitivity. Furthermore, it is noted that the estimate of the covariance is often noisy, and therefore the covariance estimates is smoothed to obtain a performance improvement of 0.23% absolute for sensitivity, 1.34% absolute for specificity, and 1.08% absolute for the overall classification accuracy. Multi-parameter intelligent monitoring in intensive care II database for all the experiments is used.

Notes:

cited By 0

Cite this Research Publication

K. C.S., K.I., R., and A.A., K., “Vital sign normalisation for improving performance of multi-parameter patient monitors”, Electronics Letters, vol. 51, pp. 2089-2090, 2015.