Ultra short term analysis of heart rate variability using normal sinus rhythm and atrial fibrillation ECG data

Heart rate variability (HRV) analysis is well established as a quantitative predictor of clinical cardiac events. To provide reliable assessment of HRV in mobile settings, short duration ECG recordings may be analyzed since the conventional five min measurement might be inadequately long. In this study, HRV features were calculated in variable time lengths of long term mobile ECG data. The ultra short term HRV features as reliable as five min measurement were found using Kruskal-Wallis test (p > 0.05). However, these HRV features may not have any clinical relevance. Thus, two sets of HRV features were calculated from varying lengths of normal sinus rhythm and atrial fibrillation ECG data. Finally, ultra short term HRV features were obtained from the shortest ECG data segments that produced significant differences between the two sets of HRV features. These results suggested that we could assess the cardiac activity of individuals accurately and conveniently by ultra short term ECG recordings from mobile sensors.