  We consider the use of a running measure of power spectrum disorder to
distinguish between the normal sinus rhythm of the heart and two forms of
cardiac arrhythmia: atrial fibrillation and atrial flutter. This spectral
entropy measure is motivated by characteristic differences in the spectra of
beat timings during the three rhythms. We plot patient data derived from
ten-beat windows on a "disorder map" and identify rhythm-defining ranges in the
level and variance of spectral entropy values. Employing the spectral entropy
within an automatic arrhythmia detection algorithm enables the classification
of periods of atrial fibrillation from the time series of patients' beats. When
the algorithm is set to identify abnormal rhythms within 6 s it agrees with
85.7% of the annotations of professional rhythm assessors; for a response time
of 30 s this becomes 89.5%, and with 60 s it is 90.3%. The algorithm provides a
rapid way to detect atrial fibrillation, demonstrating usable response times as
low as 6 s. Measures of disorder in the frequency domain have practical
significance in a range of biological signals: the techniques described in this
paper have potential application for the rapid identification of disorder in
other rhythmic signals.
