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.

Detection of subjects with higher self-reporting stress scores using heart rate variability patterns during the day.

Heart rate variability (HRV) has been well established to measure instantaneous levels of mental stress. Circadian patterns of HRV features have been reported but their use to estimate levels of mental stress were not studied thoroughly. In this study, we investigated time dependent variations of HRV features to detect subjects under chronic mental stress. Sixty eight subjects were divided into high (n=10) and low stress group (n=43) depending on their self-reporting stress scores. HRV features were calculated during three different time periods of the day. High stress group showed decreased patterns of HRV features compared to low stress group. When logistic regression analysis was performed with raw multiple HRV features, the classification was 63.2% accurate. A new % deviance score reflecting the degree of difference from normal reference patterns increased the accuracy to 66.1%. Our data suggested that HRV patterns obtained at multiple time points of the day could provide useful data to monitor subjects under chronic stress.