Efficiently identifying sleep stages is crucial for unraveling the intricacies of sleep in both preclinical and clinical research.
This study introduces LG-Sleep, a novel subject-independent deep neural network architecture designed for mice sleep scoring through electroencephalogram (EEG) signals.
LG-Sleep extracts local and global temporal transitions within EEG signals to categorize sleep data into three stages: wake, rapid eye movement (REM) sleep, and non-rapid eye movement (NREM) sleep.
Experimental findings demonstrate superior performance of LG-Sleep compared to conventional deep neural networks, even with limited training samples.