Open AccessTowards enhancing the performance of multi‐parameter patient monitors
Author(s) -
Vaijeyanthi V.,
Vishnuprasad K.,
Kumar C. Santhosh,
Ramachandran K.I.,
Gopinath R.,
Kumar A. Anand,
Yadav Praveen Kumar
Publication year - 2014
Publication title -
healthcare technology letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.45
H-Index - 19
ISSN - 2053-3713
DOI - 10.1049/htl.2013.0041
Subject(s) - vital signs , alarm , computer science , sensitivity (control systems) , heart rate , oxygen saturation , blood pressure , machine learning , reliability engineering , artificial intelligence , medicine , surgery , engineering , chemistry , organic chemistry , electronic engineering , oxygen , aerospace engineering
Multi‐parameter patient monitors (MPMs) have become increasingly important in providing quality healthcare to patients. It is well known in the medical community that there exists an intrinsic relationship between different vital parameters in a healthy person, these include heart rate, blood pressure, respiration rate and oxygen saturation. For example, an increase in blood pressure would lead to a decrease in the heart rate, and vice versa. Although it is likely to improve the performance of MPM systems, this fact is not explored in engineering research. In this work, experiments show that deriving additional features to capture the intrinsic relationship between the vital parameters, the alarm accuracy (sensitivity), no‐alarm accuracy (specificity) and the overall performance of MPMs can be improved. The geometric mean of the product of all the vital parameters taken in pairs of two was used to capture the intrinsic relationship between the different parameters. An improvement of 10.55% for sensitivity, 0.32% for specificity and an overall performance improvement of 1.03% was obtained, compared to the baseline system using classification and regression tree with the four vital parameters.