Heart-rate complexity (HRC) has been proposed as a new vital sign for critical care medicine. The purpose of this research was to develop a reliable method for determining HRC continuously in real time in critically ill patients using multiple waveform channels that also compensates for noisy and unreliable data. Using simultaneously acquired electrocardiogram (Leads I, II, V) and arterial blood pressure waveforms sampled at 360 Hz from 250 patients (over 375 h of patient data), we evaluated a new data fusion framework for computing HRC in real time. The framework employs two algorithms as well as signal quality indices. HRC was calculated (via the method of sample entropy), and equivalence tests were then performed. Bland–Altman plots and box plots of differences between mean HRC values were also obtained. Finally, HRC differences were analyzed by paired t tests. The gold standard for obtaining true means was manual verification of R waves and subsequent entropy calculations. Equivalence tests between mean HRC values derived from manually verified sequences and those derived from automatically detected peaks showed that the “Fusion” values were the least statistically different from the gold standard. Furthermore, the fusion of waveform sources produced better error density distributions than those derived from individual waveforms. The data fusion framework was shown to provide in real-time a reliable continuously streamed HRC value, derived from multiple waveforms in the presence of noise and artifacts. This approach will be validated and tested for assessment of HRC in critically ill patients.
