Publication Type:

Journal Article


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



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


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.


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Cite this Research Publication

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