Automatic sleep onset detection using single EEG sensor.

Sleep has been shown to be imperative for the health and well-being of an individual. To design intelligent sleep management tools, such as the music-induce sleep-aid device, automatic detection of sleep onset is critical. In this work, we propose a simple yet accurate method for sleep onset prediction, which merely relies on Electroencephalogram (EEG) signal acquired from a single frontal electrode in a wireless headband. The proposed method first extracts energy power ratio of theta (4-8Hz) and alpha (8-12Hz) bands along a 3-second shifting window, then calculates the slow wave of each frequency band along the time domain. The resulting slow waves are then fed to a rule-based engine for sleep onset detection. To evaluate the effectiveness of the approach, polysomnographic (PSG) and headband EEG signals were obtained from 20 healthy adults, each of which underwent 2 sessions of sleep events. In total, data from 40 sleep events were collected. Each recording was then analyzed offline by a PSG technologist via visual observation of PSG waveforms, who annotated sleep stages N1 and N2 by using the American Academy of Sleep Medicine (AASM) scoring rules. Using this as the gold standard, our approach achieved a 87.5% accuracy for sleep onset detection. The result is better or at least comparable to the other state of the art methods which use either multi-or single- channel based data. The approach has laid down the foundations for our future work on developing intelligent sleep aid devices.