Towards enhancing the performance of multi-parameter patient monitors

• Published in: Healthcare technology letters Vol. 1; no. 1; pp. 19 - 20
• Main Authors: Vaijeyanthi, V, Vishnuprasad, K, Kumar, C. Santhosh, Ramachandran, K.I, Gopinath, R, Kumar, A. Anand, Yadav, Praveen Kumar
• Format: Journal Article
• Language: English
• Published: England The Institution of Engineering and Technology 01-01-2014
• Subjects: alarm accuracy; baseline system; blood pressure; cardiology; classification tree; decision trees; health care; heart rate; medical community; MPM systems; multiparameter patient monitoring performance; oxygen saturation; patient monitoring; quality health care; regression analysis; regression tree; respiration rate; vital parameters; quality health care; regression tree; blood pressure; vital parameters; heart rate; respiration rate; regression analysis; MPM systems; classification tree; oxygen saturation; patient monitoring; medical community; cardiology; alarm accuracy; decision trees; multiparameter patient monitoring performance; baseline system; health care
• ISSN: 2053-3713; 2053-3713
• DOI: 10.1049/htl.2013.0041

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.