Open AccessVital sign normalisation for improving performance of multi‐parameter patient monitors
Author(s) -
Kumar C.S.,
Ramachandran K.I.,
Kumar A.A.
Publication year - 2015
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
eISSN - 1350-911X
pISSN - 0013-5194
DOI - 10.1049/el.2015.2636
Subject(s) - covariance , support vector machine , remote patient monitoring , vital signs , sensitivity (control systems) , pattern recognition (psychology) , computer science , artificial intelligence , mathematics , statistics , engineering , medicine , anesthesia , electronic engineering , radiology
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 (SpO 2 ) 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.