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Open AccessArticle

Exploring Deep Physiological Models for Nociceptive Pain Recognition

1
Institute of Medical Systems Biology, Ulm University, Albert-Einstein-Allee 11, 89081 Ulm, Germany
2
Institute of Neural Information Processing, Ulm University, James-Franck-Ring, 89081 Ulm, Germany
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(20), 4503; https://doi.org/10.3390/s19204503
Received: 5 September 2019 / Revised: 7 October 2019 / Accepted: 14 October 2019 / Published: 17 October 2019
(This article belongs to the Special Issue Multi-Sensor Fusion in Body Sensor Networks)
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 outperform previous methods reported in the literature, with respective average performances of 84.57 % and 84.40 % for the binary classification experiment consisting of the discrimination between the baseline and the pain tolerance level ( T 0 vs. T 4 ) in a Leave-One-Subject-Out (LOSO) cross-validation evaluation setting. Moreover, the experimental results clearly show the relevance of the proposed approaches, which also offer more flexibility in the case of transfer learning due to the modular nature of deep neural networks. View Full-Text
Keywords: convolutional neural networks; signal processing; information fusion; pain intensity classification convolutional neural networks; signal processing; information fusion; pain intensity classification
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MDPI and ACS Style

Thiam, P.; Bellmann, P.; Kestler, H.A.; Schwenker, F. Exploring Deep Physiological Models for Nociceptive Pain Recognition. Sensors 2019, 19, 4503.

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