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Authors: Patrick Thiam 1 ; Hans Kestler 2 and Friedhelm Schwenker 3

Affiliations: 1 Institute of Medical Systems Biology, Ulm University, Albert-Einstein-Allee 11, 89081 Ulm, Germany, Institute of Neural Information Processing, Ulm University, James-Franck-Ring, 89081 Ulm, Germany ; 2 Institute of Medical Systems Biology, Ulm University, Albert-Einstein-Allee 11, 89081 Ulm, Germany ; 3 Institute of Neural Information Processing, Ulm University, James-Franck-Ring, 89081 Ulm, Germany

ISBN: 978-989-758-397-1

ISSN: 2184-4313

Keyword(s): Pain Intensity Classification, Information Fusion, Autoencoder, Convolutional Neural Networks.

Abstract: The performance of a conventional information fusion architecture is greatly affected by its ability to detect and combine useful and complementary information from heterogeneous representations stemming from a set of distinctive modalities. Moreover, manually designing a set of relevant and complementary features for a specific pattern recognition task is a complex and tedious endeavour. Therefore, enabling pattern recognition architectures to autonomously generate and select relevant descriptors directly from the set of preprocessed raw data is a favourable alternative to the more conventional manual feature engineering. In the following work, multimodal information fusion approaches based on Deep Denoising Convolutional Autoencoders (DDCAEs) are proposed for the classification of pain intensities based on physiological signals (electrodermal activity (EDA), electromyogram (EMG) and electrocardiogram (ECG)). The approaches are characterized by the simultaneous optimization of both t he joint representation of the input channels generated by the multimodal DDCAE and the feed-forward neural network performing the classification of the pain intensities. The assessment performed on the BioVid Heat Pain Database (Part A) points at the relevance of the proposed approaches. In particular, the introduction of trainable weighting parameters for the generation of an aggregated latent representation outperforms most of the previously proposed methods in related works, each based on a set of carefully selected hand-crafted features. (More)

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Paper citation in several formats:
Thiam, P.; Kestler, H. and Schwenker, F. (2020). Multimodal Deep Denoising Convolutional Autoencoders for Pain Intensity Classification based on Physiological Signals. In Proceedings of the 9th International Conference on Pattern Recognition Applications and Methods - ICPRAM, ISBN 978-989-758-397-1 ISSN 2184-4313, pages 289-296. DOI: 10.5220/0008896102890296

@conference{icpram20,
author={Patrick Thiam. and Hans Kestler. and Friedhelm Schwenker.},
title={Multimodal Deep Denoising Convolutional Autoencoders for Pain Intensity Classification based on Physiological Signals},
booktitle={Proceedings of the 9th International Conference on Pattern Recognition Applications and Methods - ICPRAM,},
year={2020},
pages={289-296},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008896102890296},
isbn={978-989-758-397-1},
issn={2184-4313},
}

TY - CONF

JO - Proceedings of the 9th International Conference on Pattern Recognition Applications and Methods - ICPRAM,
TI - Multimodal Deep Denoising Convolutional Autoencoders for Pain Intensity Classification based on Physiological Signals
SN - 978-989-758-397-1
IS - 2184-4313
AU - Thiam, P.
AU - Kestler, H.
AU - Schwenker, F.
PY - 2020
SP - 289
EP - 296
DO - 10.5220/0008896102890296

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