NONRANDOMNESS INDEX APPLIED FOR HEART RATE VARIABILITY IN SURGICAL INTENSIVE CARE UNITS USING FREQUENCY AND RANK ORDER STATISTICS

The complexity of physiologic signals may carry hidden dynamical structures that are related to their underlying mechanisms. Based on rank order statistics of symbolic sequences, we applied this method to heart rate variability (HRV) in surgical intensive care units (SICU) in order to determine a nonrandomness index to help doctors diagnose patients more rapidly in a SICU in the future. Twenty one patients with 47 cases undergoing different types of neurosurgery were studied as group A. From this group, electrocardiograph (ECG) signals were collected. They lasting around 60 min for 29 cases selected from 16 patients with brain injury, 12 cases selected from 2 patients with septicemia, and 6 cases selected from 3 patients with mechanical ventilator. Ten college student volunteers as group B also provided ECG signals lasting around 60 min. Finally, ten randomized surrogate signals generated from a computer as group C were used as baseline for comparison with healthy volunteers and pathologic states in the SICU. The nonrandomness indexes of groups A, B, and C were 0.160 ± 0.100, 0.237 ± 0.051, and 0.030 ± 0.003, respectively. It was found that this index of patients in the SICU was significantly lower (P < 0.05) than healthy volunteers and significantly higher (P < 0.05) than randomized surrogate signals. These results demonstrate that the nonrandomness index based on rank order statistics concept can clearly distinguish pathologic states in SICU from the healthy group and the randomized surrogate signals.