We consider the use of a running measure of power spectrum disorder todistinguish between the normal sinus rhythm of the heart and two forms ofcardiac arrhythmia: atrial fibrillation and atrial flutter. This spectralentropy measure is motivated by characteristic differences in the spectra ofbeat timings during the three rhythms. We plot patient data derived fromten-beat windows on a "disorder map" and identify rhythm-defining ranges in thelevel and variance of spectral entropy values. Employing the spectral entropywithin an automatic arrhythmia detection algorithm enables the classificationof periods of atrial fibrillation from the time series of patients' beats. Whenthe algorithm is set to identify abnormal rhythms within 6 s it agrees with85.7% of the annotations of professional rhythm assessors; for a response timeof 30 s this becomes 89.5%, and with 60 s it is 90.3%. The algorithm provides arapid way to detect atrial fibrillation, demonstrating usable response times aslow as 6 s. Measures of disorder in the frequency domain have practicalsignificance in a range of biological signals: the techniques described in thispaper have potential application for the rapid identification of disorder inother rhythmic signals.
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