Frequency-Based Sleep Stage Detections by Single EEG Derivation in Healthy Human Subjects

A need for sleep monitoring is increasing in modern society. However, sleep stage scoring is time consuming, and large inconsistencies may exist among scorers. The settings for the recordings are also complicated and usually need to be professionally prepared. If simple small equipment could record human EEG and detect sleep stages, it would bring significant benefits to a large population. We thus developed a simple frequency-based sleep stage classifier by single EEG derivation, and evaluated the performance of the classifier. It showed a potential to work as well as the other known automated classifiers. The classifier was not based on specific frequency bands or EEG patterns. It could perform as well with poor quality signals and could easily be adopted to score any other biological signals.