Sleep Stage Classification Using Unsupervised Feature Learning

المؤلفون المشاركون

Karlsson, Lars
Loutfi, Amy
Längkvist, Martin

المصدر

Advances in Artificial Neural Systems

العدد

المجلد 2012، العدد 2012 (31 ديسمبر/كانون الأول 2012)، ص ص. 1-9، 9ص.

الناشر

Hindawi Publishing Corporation

تاريخ النشر

2012-07-24

دولة النشر

مصر

عدد الصفحات

9

التخصصات الرئيسية

تكنولوجيا المعلومات وعلم الحاسوب

الملخص EN

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.

نمط استشهاد جمعية علماء النفس الأمريكية (APA)

Längkvist, Martin& Karlsson, Lars& Loutfi, Amy. 2012. Sleep Stage Classification Using Unsupervised Feature Learning. Advances in Artificial Neural Systems،Vol. 2012, no. 2012, pp.1-9.
https://search.emarefa.net/detail/BIM-446919

نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)

Längkvist, Martin…[et al.]. Sleep Stage Classification Using Unsupervised Feature Learning. Advances in Artificial Neural Systems No. 2012 (2012), pp.1-9.
https://search.emarefa.net/detail/BIM-446919

نمط استشهاد الجمعية الطبية الأمريكية (AMA)

Längkvist, Martin& Karlsson, Lars& Loutfi, Amy. Sleep Stage Classification Using Unsupervised Feature Learning. Advances in Artificial Neural Systems. 2012. Vol. 2012, no. 2012, pp.1-9.
https://search.emarefa.net/detail/BIM-446919

نوع البيانات

مقالات

لغة النص

الإنجليزية

الملاحظات

Includes bibliographical references

رقم السجل

BIM-446919