Automated sleep stage scoring by decision tree learning

We describe a waveform recognition method that extracts characteristic parameters from waveforms and a method of automated sleep stage scoring using decision tree learning that is in practice regarded as one of the most successful machine learning methods. In our method, first characteristics of EEG, EOG and EMG are compared with characteristic features of alpha waves, delta waves, sleep spindles, K-complexes and REMs. Then, several parameters that are necessary for sleep stage scoring are extracted. We transform these extracted parameters into a few discrete variables using canonical discriminant analysis and the discretization method based on a random walk, and then a committee that consists of several small decision trees is formed from a small number of training instances. Furthermore final sleep stages are decided by a majority decision of the committee. Our method was applied to the digitized PSG chart data, provided by the Japan Society of Sleep Research and we carried out an evaluation experiment. The experiment indicated that our method can quickly execute learning and classification and precisely score sleep stages.