Year: 2024
Type: conference-paper
Abstract: This study use of Approximate Entropy (ApEn) for classification of sleep stages using R-R intervals derived from ECG signals.The results demonstrate that ApEn can effectively differentiate between sleep stages and wakefulness across varying embedding dimensions (m) and time windows.In lower dimensions, higher entropy values correspond to deeper sleep stages, while in higher dimensions, the relationship reverses, associating higher entropy with REM, light sleep, and wakefulness.The analysis also reveals that specific time windows (e.g., 5 minutes) and embedding dimensions (e.g., m = 5) improve the discrimination between sleep and wake states.These findings suggest that ApEn is a valuable tool for non-invasive sleep monitoring and classification.Further studies are recommended to explore its applicability in broader and more diverse populations.
Source: Computing in cardiology
Institution: Universidad Nacional de San Agustin de Arequipa
Language: English
Subfield: Cardiology and Cardiovascular Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: bronze
OpenAlex ID: W4405434928