Physiologically-Informed Gaussian Processes for Interpretable Modelling
of Psycho-Physiological States
Abstract
This work has been submitted to the IEEE for possible publication.
Copyright may be transfered without notice, after which this version may
no longer be accessible.
We introduce the Physiologically-informed Gaussian Process (PhGP) model,
a novel Bayesian probabilistic approach to integrate and interpret prior
physiological knowledge in machine
learning models. Existing recognition algorithms often consider either
end-end-to models and/or alongside feature extraction techniques.
Conversely, our model based on Gaussian Processes
(GP) which are grounded on Bayesian statistics proposes a principled and
interpretable integration of these techniques for recognition problems
in biomedical engineering realm.
This paper builds upon but significantly extends our previous conference
paper presented at EMBC 2020 [1]. In this paper, we develop a new
model that considers both the raw physiological signals and the prior
expert knowledge for training GP models. Moreover, unlike our previous
paper by relying on the explicit formula of the GP inference equations,
we developed an interpretability framework for our proposed recognition
model. Finally, we thoroughly validated our new model on two different
public datasets.
Our paper brings novelties in the field of biomedical engineering,
affective computing , machine learning and computer sicence.
[1] Ghiasi, S., Patane, A., Greco, A., Laurenti, L., Scilingo, E.P.
and Kwiatkowska, M., 2020, July. Gaussian
Processes with Physiologically-Inspired Priors for Physical Arousal
Recognition. In 2020 42nd Annual International
Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)
(pp. 54-57). IEEE.