Exploiting the existence of temporal heart-rate patterns for the detection of trauma-induced hemorrhage.

Unattended hemorrhage is a major source of mortality in trauma casualties. In this study, we explore a set of prehospital heart rate (HR) time-series data collected from 358 civilian casualties to examine whether temporal HR patterns can be used for automated hemorrhage identification. Continuous and reliable HR time series are fragmented into overlapping segments of 128 s, with a 118-s overlap between each two neighboring segments, which are projected into a wavelet coefficient space using the Haar wavelet function. A supervised nearest-neighbor clustering algorithm is developed to explore the existence of temporal HR patterns represented by the wavelet coefficients to discriminate casualties with and without (control) major hemorrhage. The clustering algorithm identifies 162 HR patterns. The most frequent pattern is observed in 11 (23%) hemorrhage and 16 (5%) control patients, which is a significant association (p<0.05, chi-square test). When the top 10 patterns are combined for hemorrhage detection, their sensitivity and specificity are 0.68 and 0.79, respectively, and when the top 20 patterns are used sensitivity increases to 0.77 and specificity decreases to 0.71.