• Konstantinos Kalpakis, Shiming Yang, Peter F Hu, Colin F Mackenzie, Lynn G Stansbury, Deborah M Stein, and Thomas M Scalea.
• Department of Computer Science and Electric Engineering, University of Maryland, Baltimore County, MD 21250, United States. Electronic address: kalpakis@umbc.edu.
• Comput. Biol. Med. 2015 Jan 1;56:167-74.

AbstractPermutation entropy is computationally efficient, robust to outliers, and effective to measure complexity of time series. We used this technique to quantify the complexity of continuous vital signs recorded from patients with traumatic brain injury (TBI). Using permutation entropy calculated from early vital signs (initial 10-20% of patient hospital stay time), we built classifiers to predict in-hospital mortality and mobility, measured by 3-month Extended Glasgow Outcome Score (GOSE). Sixty patients with severe TBI produced a skewed dataset that we evaluated for accuracy, sensitivity and specificity. The overall prediction accuracy achieved 91.67% for mortality, and 76.67% for 3-month GOSE in testing datasets, using the leave-one-out cross validation. We also applied Receiver Operating Characteristic analysis to compare classifiers built from different learning methods. Those results support the applicability of permutation entropy in analyzing the dynamic behavior of TBI vital signs for early prediction of mortality and long-term patient outcomes.Copyright © 2014 Elsevier Ltd. All rights reserved.

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