Subject-Adaptive Real-Time Sleep Stage Classification Based on Conditional Random Field

Sleep staging is the pattern recognition task of classifying sleep recordings into sleep stages. This task is one of the most important steps in sleep analysis. It is crucial for the diagnosis and treatment of various sleep disorders, and also relates closely to brain-machine interfaces. We report an automatic, online sleep stager using electroencephalogram (EEG) signal based on a recently-developed statistical pattern recognition method, conditional random field, and novel potential functions that have explicit physical meanings. Using sleep recordings from human subjects, we show that the average classification accuracy of our sleep stager almost approaches the theoretical limit and is about 8% higher than that of existing systems. Moreover, for a new subject S(new) with limited training data D(new), we perform subject adaptation to improve classification accuracy. Our idea is to use the knowledge learned from old subjects to obtain from D(new) a regulated estimate of CRF's parameters. Using sleep recordings from human subjects, we show that even without any D(new), our sleep stager can achieve an average classification accuracy of 70% on S(new). This accuracy increases with the size of D(new) and eventually becomes close to the theoretical limit.