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鈥痟 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.