1. A method of subject-adaptive, real-time sleep stage classification to classify electroencephalogram sleep recordings into sleep stages to determine whether a subject exhibits a sleep disorder, comprising: performing subject adaptation to improve classification accuracy for a new subject with limited training data, said performing subject adaptation comprises using linear-chain conditional random fields and potential functions; training said linear-chain conditional random fields using said training data; continuously receiving said electroencephalogram waves; continuously extracting features from said electroencephalogram waves, said extracting features comprising transforming each of said electroencephalogram waves to capture information embedded in said electroencephalogram waves, said extracting features comprises extracting features from four disjoint frequency bands, said four disjoint frequency bands consisting of a first band within a range of 1 H z -5 H z , a second band within a range of 5.5 H z -10 H z , a third band with a range of 10.5 H z -20 H z , and a fourth band within a range of 20.5 H z -30 H z ; and continuously classifying the sleep stages according to extracted features and learned parameters from said linear-chain conditional random fields, wherein said performing subject adaptation comprises: using training data from a plurality of old subjects to obtain a prior distribution of conditional random field parameters; using said prior distribution of conditional random field parameters in combination with training data for at least one new subject to obtain a regulated estimate of conditional random field parameters; and using said regulated estimate of conditional random field parameters to classify said electroencephalogram waves of said at least one new subject to determine whether a subject exhibits a sleep disorder, wherein said continuously extracting features comprises obtaining a localized power spectral density of said electroencephalogram waves.