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Image Credit: Arxiv

LG-Sleep: Local and Global Temporal Dependencies for Mice Sleep Scoring

  • 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.

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