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 Naïve
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
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: email@example.com, firstname.lastname@example.org).
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.