  Sleep signals from a polysomnographic database are sequences in nature.
Commonly employed analysis and classification methods, however, ignored this
fact and treated the sleep signals as non-sequence data. Treating the sleep
signals as sequences, this paper compared two powerful unsupervised feature
extractors and three sequence-based classifiers regarding accuracy and
computational (training and testing) time after 10-folds cross-validation. The
compared feature extractors are Deep Belief Networks (DBN) and Fuzzy C-Means
(FCM) clustering. Whereas the compared sequence-based classifiers are Hidden
Markov Models (HMM), Conditional Random Fields (CRF) and its variants, i.e.,
Hidden-state CRF (HCRF) and Latent-Dynamic CRF (LDCRF); and Conditional Neural
Fields (CNF) and its variant (LDCNF). In this study, we use two datasets. The
first dataset is an open (public) polysomnographic dataset downloadable from
the Internet, while the second dataset is our polysomnographic dataset (also
available for download). For the first dataset, the combination of FCM and CNF
gives the highest accuracy (96.75\%) with relatively short training time (0.33
hours). For the second dataset, the combination of DBN and CRF gives the
accuracy of 99.96\% but with 1.02 hours training time, whereas the combination
of DBN and CNF gives slightly less accuracy (99.69\%) but also less computation
time (0.89 hours).
