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Article

Comparison of Feature Extraction Methods for Physiological Signals for Heat-Based Pain Recognition

1
Institute of Medical Informatics, University of Lübeck, Ratzeburger Allee 160, 23562 Lübeck, Germany
2
Institute of Health Sciences, Department of Physiotherapy, Pain and Exercise Research Lübeck (P.E.R.L.), University of Lübeck, 23562 Lübeck, Germany
3
Laboratory of Pain Research, Institute of Physiotherapy and Health Sciences, The Jerzy Kukuczka Academy of Physical Education, 40-959 Katowice, Poland
*
Author to whom correspondence should be addressed.
Academic Editors: Christian Baumgartner and Ki H. Chon
Sensors 2021, 21(14), 4838; https://doi.org/10.3390/s21144838
Received: 20 May 2021 / Revised: 10 July 2021 / Accepted: 13 July 2021 / Published: 15 July 2021
(This article belongs to the Section Biomedical Sensors)
While even the most common definition of pain is under debate, pain assessment has remained the same for decades. But the paramount importance of precise pain management for successful healthcare has encouraged initiatives to improve the way pain is assessed. Recent approaches have proposed automatic pain evaluation systems using machine learning models trained with data coming from behavioural or physiological sensors. Although yielding promising results, machine learning studies for sensor-based pain recognition remain scattered and not necessarily easy to compare to each other. In particular, the important process of extracting features is usually optimised towards specific datasets. We thus introduce a comparison of feature extraction methods for pain recognition based on physiological sensors in this paper. In addition, the PainMonit Database (PMDB), a new dataset including both objective and subjective annotations for heat-induced pain in 52 subjects, is introduced. In total, five different approaches including techniques based on feature engineering and feature learning with deep learning are evaluated on the BioVid and PMDB datasets. Our studies highlight the following insights: (1) Simple feature engineering approaches can still compete with deep learning approaches in terms of performance. (2) More complex deep learning architectures do not yield better performance compared to simpler ones. (3) Subjective self-reports by subjects can be used instead of objective temperature-based annotations to build a robust pain recognition system. View Full-Text
Keywords: pain recognition; machine learning; deep learning; physiological signals; pain perception pain recognition; machine learning; deep learning; physiological signals; pain perception
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MDPI and ACS Style

Gouverneur, P.; Li, F.; Adamczyk, W.M.; Szikszay, T.M.; Luedtke, K.; Grzegorzek, M. Comparison of Feature Extraction Methods for Physiological Signals for Heat-Based Pain Recognition. Sensors 2021, 21, 4838. https://doi.org/10.3390/s21144838

AMA Style

Gouverneur P, Li F, Adamczyk WM, Szikszay TM, Luedtke K, Grzegorzek M. Comparison of Feature Extraction Methods for Physiological Signals for Heat-Based Pain Recognition. Sensors. 2021; 21(14):4838. https://doi.org/10.3390/s21144838

Chicago/Turabian Style

Gouverneur, Philip, Frédéric Li, Wacław M. Adamczyk, Tibor M. Szikszay, Kerstin Luedtke, and Marcin Grzegorzek. 2021. "Comparison of Feature Extraction Methods for Physiological Signals for Heat-Based Pain Recognition" Sensors 21, no. 14: 4838. https://doi.org/10.3390/s21144838

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