Sleep apnoea analysis from neural network post-processing

This paper presents methods of analysis of electroencephalogram (EEG) signals using artificial neural networks, and subsequent methods of detection of obstructive sleep apnoea (OSA) from the neural network outputs. EEG signals are measurements of scalp potential differences arising from the brain's electrical activity. Gross changes in the human EEG occur between different types of sleep. Traditionally these have been categorised into several sleep stages by a visual scoring system, each stage defined by a set of rules based on the EEG and on muscle tone and eye movement.