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 a...
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Published in | Data in brief Vol. 49; p. 109421 |
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Main Authors | , , , , |
Format | Journal Article |
Language | English |
Published |
Netherlands
Elsevier Inc
01.08.2023
Elsevier |
Subjects | |
Online Access | Get full text |
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Summary: | 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. |
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Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 Xingran Cui is the first corresponding author. |
ISSN: | 2352-3409 2352-3409 |
DOI: | 10.1016/j.dib.2023.109421 |