Background: Sleep spindles are a marker of stage 2 NREM sleep that are linked to learning & memory and are altered by many neurological diseases. Although visual inspection of the EEG is considered the gold standard for spindle detection, it is time-consuming, costly and can introduce inter/ra-scorer bias.
New Method: Our goal was to develop a simple and efficient sleep-spindle detector (algorithm #7, or 'A7') that emulates human scoring.