Fig 1.
(A) Selection and exclusion criteria of electrocardiogram (ECG) signals are summarized. (B) Selection of ECG segments for daytime and sleep periods is also summarized.
Table 1.
Demographic and clinical characteristics of the participants.
Fig 2.
Comparison of frequency-based heart rate variability (HRV) component power between daytime and sleep.
Bars represent the mean frequency prevalence of the very low frequency power (VLFp), low frequency power (LFp), and high frequency power (HFp) components during day (blue) and sleep (orange). Error bars indicate the standard error of the mean. There is a significant increase in HFp during sleep compared to daytime (p = 0.012), while no significant differences are noted in LFp (p = 0.860) or VLFp components (p = 0.400).
Fig 3.
Comparison of motif frequency prevalence (%) between daytime (blue) and sleep (orange) periods.
Motifs are 3-beat symbolic sequences derived from heart rate (HR) transitions using values of: HR deceleration −1, no change 0 and HR acceleration +1. Bars represent mean prevalence across subjects, with error bars indicating the standard error (SD) of the mean. Red asterisks (*) denote statistically significant differences between day and sleep periods (p < 0.05). Several motifs, such as [1, 1, −1] and [−1, 1, 1], show significant differences between day and sleep HR transition dynamics.
Table 2.
Comparison of daytime and sleep heart rate (HR) transitions and high-frequency (HFp) components in relation to their correlation with diabetic complications.
Table 3.
Correlation between HR transition motifs and HRV.