Most attempts at training computers for the difficult and time-consuming task of sleep stage classification involve a feature extraction step. Due to the complexity of multimodal sleep data, the size of the feature space can grow to the extent that it is also necessary to include a feature selection step. In this paper, we propose the use of an unsupervised feature learning architecture called deep belief nets (DBNs) and show how to apply it to sleep data in order to eliminate the use of handmade features. Using a postprocessing step of hidden Markov model (HMM) to accurately capture sleep stage switching, we compare our results to a feature-based approach. A study of anomaly detection with the application to home environment data collection is also presented. The results using raw data with a deep architecture, such as the DBN, were comparable to a feature-based approach when validated on clinical datasets. 1. Introduction One of the main challenges in sleep stage classification is to isolate features in multivariate time-series data which can be used to correctly identify and thereby automate the annotation process to generate sleep hypnograms. In the current absence of a set of universally applicable features, typically a two-stage process is required before training a sleep stage algorithm, namely, feature extraction and feature selection [1–9]. In other domains which share similar challenges, an alternative to using hand-tailored feature representations derived from expert knowledge is to apply unsupervised feature learning techniques, where the feature representations are learned from unlabeled data. This not only enables the discovery of new useful feature representations that a human expert might not be aware of, which in turn could lead to a better understanding of the sleep process and present a way of exploiting massive amounts of unlabeled data. Unsupervised feature learning and in particular deep learning [10–15] propose ways for training the weight matrices in each layer in an unsupervised fashion as a preprocessing step before training the whole network. This has proven to give good results in other areas such as vision tasks [10], object recognition [16], motion capture data [17], speech recognition [18], and bacteria identification [19]. This work presents a new approach to the automatic sleep staging problem. The main focus is to learn meaningful feature representations from unlabeled sleep data. A dataset of 25 subjects consisting of electroencephalography (EEG) of brain activity, electrooculography (EOG) of eye movements, and
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