Heart rate variability in different sleep stages is associated with metabolic function and glycemic control in type 2 diabetes mellitus

Slope Entropy Characterisation: An Asymmetric Approach to Threshold Parameters Role Analysis

Slope Entropy (SlpEn) is a novel method recently proposed in the field of time series entropy estimation. In addition to the well-known embedded dimension parameter, m, used in other methods, it applies two additional thresholds, denoted as δ and γ, to derive a symbolic representation of a data subsequence. The original paper introducing SlpEn provided some guidelines for recommended specific values of these two parameters, which have been successfully followed in subsequent studies. However, a deeper understanding of the role of these thresholds is necessary to explore the potential for further SlpEn optimisations. Some works have already addressed the role of δ, but in this paper, we extend this investigation to include the role of γ and explore the impact of using an asymmetric scheme to select threshold values. We conduct a comparative analysis between the standard SlpEn method as initially proposed and an optimised version obtained through a grid search to maximise signal classification performance based on SlpEn. The results confirm that the optimised version achieves higher time series classification accuracy, albeit at the cost of significantly increased computational complexity.

A dataset on 24-h electrocardiograph, sleep and metabolic function of male type 2 diabetes mellitus

This dataset provides a collection of 24 h electrocardiograph (ECG) signals, ECG analysis results based on circadian rhythm and R-peak detection, results of sleep quality assessment and clinical indicators of metabolic function acquired from 60 male type 2 diabetes mellitus (T2DM) inpatients. Upon admission, a fasting blood draw and urinary sample were obtained the next morning for routine glucose, lipid and renal panels. Subjects were also involved in investigation for diabetic complications. On the second day of hospitalization, subjects were monitored in hospital for 24-h ECG starting at 10 pm. Subjective sleep quality was assessed by Pittsburgh Sleep Quality Index and a brief sleep log was used to record sleep duration for the studied night. Objective sleep quality and sleep staging were assessed by cardiopulmonary coupling analysis. This dataset could be utilized to conduct conjoint research on the relationships among sleep, metabolic function, and function of cardiovascular system and autonomic nervous system derived from ECG analysis in T2DM, and further investigate the information in ECG signals based on circadian rhythm and physiological status, providing new insights into long term physiological signal processing.