  We focus on various measures of the fluctuations of the sequence of intervals
between beats of the human heart, and how such fluctuations can be used to
assess the presence or likelihood of cardiovascular disease. We examine sixteen
such measures and their suitability for correctly classifying heartbeat records
of various lengths as normal or revealing the presence of cardiac dysfunction,
particularly congestive heart failure. Using receiver-operating-characteristic
analysis we demonstrate that scale-dependent measures prove substantially
superior to scale-independent ones. The wavelet-transform standard deviation at
a scale near 32 heartbeat intervals, and its spectral counterpart near 1/32
cycles/interval, turn out to provide reliable results using heartbeat records
just minutes long. We further establish for all subjects that the human
heartbeat has an underlying stochastic origin rather than arising from a
chaotic attractor. Finally, we develop a mathematical point process that
emulates the human heartbeat time series for both normal subjects and
heart-failure patients.
