Home > Archive > 2013 > Volume 3 Number 5 (Oct. 2013) >
IJIET 2013 Vol.3(5): 505-511 ISSN: 2010-3689
DOI: 10.7763/IJIET.2013.V3.326

Modeling Physiological Data with Deep Belief Networks

Dan Wang and Yi Shang

Abstract—Feature extraction is key in understanding and modeling of physiological data. Traditionally hand-crafted features are chosen based on expert knowledge and then used for classification or regression. To determine important features and pick the effective ones to handle a new task may be labor-intensive and time-consuming. Moreover, the manual process does not scale well with new or large-size tasks. In this work, we present a system based on Deep Belief Networks (DBNs) that can automatically extract features from raw physiological data of 4 channels in an unsupervised fashion and then build 3 classifiers to predict the levels of arousal, valance, and liking based on the learned features. The classification accuracies are 60.9%, 51.2%, and 68.4%, respectively, which are comparable with the results obtained by Gaussian Na├»ve Bayes classifier on the state-of-the-art expert designed features. These results suggest that DBNs can be applied to raw physiological data to effectively learn relevant features and predict emotions.

Index Terms—Deep belief networks, emotion classification, feature learning, physiological data.

Dan Wang and Yi Shang are with the Department of Computer Science, University of Missouri, Columbia, MO 65211 USA (e-mail: dwdy8@mail.missouri.edu, shangy@missouri.edu).


Cite:Dan Wang and Yi Shang, "Modeling Physiological Data with Deep Belief Networks," International Journal of Information and Education Technology vol. 3, no. 5, pp. 505-511, 2013.

General Information

  • ISSN: 2010-3689 (Online)
  • Abbreviated Title: Int. J. Inf. Educ. Technol.
  • Frequency: Monthly
  • DOI: 10.18178/IJIET
  • Editor-in-Chief: Prof. Dr. Steve Thatcher
  • Executive Editor: Ms. Nancy Y. Liu
  • Abstracting/ Indexing: Scopus (CiteScore 2022: 2.0), INSPEC (IET), UGC-CARE List (India), CNKI, EBSCO, Google Scholar
  • E-mail: ijiet@ejournal.net


Article Metrics in Dimensions