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Circadian rhythms (CR) have been well known in normal physiology and cardiac diseases. Although the nocturnal predominance of atrial fibrillation (AF) episodes has been reported, circadian changes were rarely used to detect AF. In this study, we developed a novel method to detect paroxysmal AF (PAF) subjects based on circadian changes of heart rate variability (HRV). HRV features were calculated from 1 hour long normal to normal (NN) heartbeat intervals. CR parameters such as amplitude, phase, and shift were obtained by a least square fitting of 24 hour HRV data of PAF (n=18) and normal subjects (n=21). Compared to normal subjects, CR amplitudes of heart rates were reduced but those of root mean square of successive difference (RMSSD), autocorrelation of NN intervals (rRR), high frequency (HF) and low frequency components (LF) were increased in PAF subjects (Mann Whitney W test, p<;0.05). Using a simple logistic regression analysis, PAF subjects were identified at the accuracy of 84%. These results suggest the CR amplitudes might be useful to predict PAF subjects.