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A continuous electrocardiogram, whether from a Holter recording, an intensive care unit monitor, an overnight polysomnogram, or even a short-term recording, provides a signal that can yield information about the morphology and time of onset of each heartbeat. This information, exported as a "beat file" provides the basis for multiple ways of quantifying and categorizing heart rate variability (HRV), in most cases based on intervals between normal to normal (N-N) heartbeats only. The various methods for quantifying HRV (eg, time domain, frequency domain, nonlinear) and their relationship to cardiac autonomic function have been described in multiple excellent reviews elsewhere.1-3 Less appreciated is the power of using graphical images, also derived from beat files, to obtain information about normal and abnormal cardiac autonomic function, sinus node function, and sleep-disordered breathing.

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The periods between successive heartbeats can be converted to a time series of instantaneous heart rates (60,000 milliseconds in a minute/time between beats in ms). Heart rate (HR) patterns can be examined on multiple scales, each providing both unique and overlapping information. HR itself can be plotted on a beat-by-beat basis, or HR averages and ranges over longer periods can be plotted. Power spectral analysis can mathematically deconstruct heartbeat patterns into their underlying rhythmic components using fast Fourier transforms (FFTs).3 The structure of heartbeat patterns can be examined using Poincaré plots, which are plots of the interval between every pair of successive beats versus the next pair.

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The current review will illustrate the types of information potentially available from these aspects of graphical HRV analysis (ie, 5-minute averaged HR patterns, hourly power spectral analysis, hourly Poincaré plots, and beat-by-beat HR tachograms). To accomplish this, we will primarily use representative plots from recordings selected from our database of subjects with and without known cardiovascular disease. From among the healthy subjects, we will examine graphical HRV in a younger adult with high HRV (the standard deviation of all normal-to-normal interbeat intervals [SDNN] = 198), an older adult with high HRV (SDNN = 167), and an older adult with low HRV (SDNN = 65). From among those with known cardiovascular disease, we will examine graphical HRV in a subject with very low HRV but normal HR patterns (SDNN = 63), a subject with periods of abnormal HR patterns (SDNN = 99), a subject with significant sleep-disordered breathing HR patterns (SDNN = 49), and a subject with atrial fibrillation (SDNN = 211).

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Commercial Holter scanner reports often show plots of 5-minute averaged HR patterns. The presence or relative absence of a circadian rhythm of HR is clearly visible on these plots. Under normal circumstances, a clear decrease in HR during the night, a distinct rise in HR on awakening, and a relatively higher HR during the daytime are seen. Lack of circadian rhythm of HR is the primary determinant of low values for total HRV and suggests severe autonomic dysfunction and/or a complete lack of physical activity. In Fig. 16–1, ...

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