Detection of atrial fibrillation episodes using multiple heart rate variability features in different time periods

Circadian variations of cardiac diseases have been well known. For example, atrial fibrillation (AF) episodes show nocturnal predominance. In this study, we have developed multiple formulas that detect AF episodes in different times of the day. Heart rate variability features were calculated from randomly sampled three min ECG data. Logistic regression analyses were performed to generate three formulas for the entire day, daytime, and evening time. Compared to the first formula that disregarded the time of the day, the second formula for the daytime detection detected AF episodes more accurately (95.2% vs. 99.3%), whereas third formula for the evening time detection did less accurately (93.8%). These results suggest the detection of AF episodes might become more accurate by considering the time-dependent changes of HRV features. In addition, the detection method for the evening time requires further investigation.