Electrocardiomatrix facilitates qualitative identification of diminished heart rate variability in critically ill patients shortly before cardiac arrest

Abstract Background Although heart rate variability (HRV) has diagnostic and prognostic value for the assessment of cardiac risk, HRV analysis is not routinely performed in a hospital setting. Current HRV analysis methods are primarily quantitative; such methods are sensitive to signal contamination and require extensive post hoc processing. Methods and results Raw electrocardiogram (ECG) data from the Sleep Heart Health Study was transformed into electrocardiomatrix (ECM), in which sequential cardiac cycles are aligned, in parallel, along a shared axis. Such juxtaposition facilitates the visual evaluation of beat-to-beat changes in the R-R interval without sacrificing the morphology of the native ECG signal. Diminished HRV, verified by traditional methods, was readily identifiable. We also examined data from a cohort of hospitalized patients who suffered cardiac arrest within 24 h of data acquisition, all of whom exhibited severely diminished HRV that were visually apparent on ECM display. Conclusions ECM streamlines the identification of depressed HRV, which may signal deteriorating patient condition.