id: 06075354
dt: j
an: 06075354
au: Längkvist, Martin; Karlsson, Lars; Loutfi, Amy
ti: Sleep stage classification using unsupervised feature learning.
so: Adv. Artif. Neural Syst. 2012, Article ID 107046, 9 p. (2012).
py: 2012
pu: Hindawi Publishing Corporation, New York, NY
la: EN
cc: 
ut: 
ci: 
li: doi:10.1155/2012/107046
ab: Summary: 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.
rv: