Vital sign normalisation for improving performance of multi-parameter patient monitors

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.