Sensors, Vol. 19, Pages 4503: Exploring Deep Physiological Models for Nociceptive Pain Recognition

Sensors, Vol. 19, Pages 4503: Exploring Deep Physiological Models for Nociceptive Pain Recognition Sensors doi: 10.3390/s19204503 Authors: Patrick Thiam Peter Bellmann Hans A. Kestler Friedhelm Schwenker Standard feature engineering involves manually designing measurable descriptors based on some expert knowledge in the domain of application, followed by the selection of the best performing set of designed features for the subsequent optimisation of an inference model. Several studies have shown that this whole manual process can be efficiently replaced by deep learning approaches which are characterised by the integration of feature engineering, feature selection and inference model optimisation into a single learning process. In the following work, deep learning architectures are designed for the assessment of measurable physiological channels in order to perform an accurate classification of different levels of artificially induced nociceptive pain. In contrast to previous works, which rely on carefully designed sets of hand-crafted features, the current work aims at building competitive pain intensity inference models through autonomous feature learning, based on deep neural networks. The assessment of the designed deep learning architectures is based on the BioVid Heat Pain Database (Part A) and experimental validation demonstrates that the proposed uni-modal architecture for the electrodermal activity (EDA) and the deep fusion approaches significantly outp...
Source: Sensors - Category: Biotechnology Authors: Tags: Article Source Type: research