Spindle Annotation

Spindle annotation focuses on the automated identification and characterization of sleep spindles in electroencephalographic (EEG) data and other signals like video, aiming to improve the accuracy and efficiency of sleep research and clinical diagnostics. Current research employs diverse approaches, including advanced signal processing techniques like non-linear time-frequency analysis and deep learning architectures such as U-Nets and transformers, to achieve high-performance annotation. These advancements address the inherent variability in manual annotation, enabling more reliable quantification of spindle characteristics for studying their relationship to cognitive functions like memory consolidation and for improving sleep disorder diagnosis.