Abstract:

Permutation entropy is computationally efficient, robust to noise, 
and effective to measure complexity. 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. With early vital signs data, 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 biomedical time series for early prediction of mortality 
and long-term patient outcomes.