Explainable Artificial Intelligence (XAI) in Pain Research: Understanding the Role of Electrodermal Activity for Automated Pain Recognition

Philip Gouverneur*, Frédéric Li, Kimiaki Shirahama, Luisa Luebke, Wacław M. Adamczyk, Tibor M. Szikszay, Kerstin Luedtke, Marcin Grzegorzek

*Corresponding author for this work
4 Citations (Scopus)


Artificial intelligence and especially deep learning methods have achieved outstanding results for various applications in the past few years. Pain recognition is one of them, as various models have been proposed to replace the previous gold standard with an automated and objective assessment. While the accuracy of such models could be increased incrementally, the understandability and transparency of these systems have not been the main focus of the research community thus far. Thus, in this work, several outcomes and insights of explainable artificial intelligence applied to the electrodermal activity sensor data of the PainMonit and BioVid Heat Pain Database are presented. For this purpose, the importance of hand-crafted features is evaluated using recursive feature elimination based on impurity scores in Random Forest (RF) models. Additionally, Gradient-weighted class activation mapping is applied to highlight the most impactful features learned by deep learning models. Our studies highlight the following insights: (1) Very simple hand-crafted features can yield comparative performances to deep learning models for pain recognition, especially when properly selected with recursive feature elimination. Thus, the use of complex neural networks should be questioned in pain recognition, especially considering their computational costs; and (2) both traditional feature engineering and deep feature learning approaches rely on simple characteristics of the input time-series data to make their decision in the context of automated pain recognition.

Original languageEnglish
Article number1959
Issue number4
Publication statusPublished - 02.2023

Research Areas and Centers

  • Health Sciences
  • Centers: Center for Artificial Intelligence Luebeck (ZKIL)

DFG Research Classification Scheme

  • 206-02 Molecular Biology and Physiology of Nerve and Glial Cells
  • 409-05 Interactive and Intelligent Systems, Image and Language Processing, Computer Graphics and Visualisation

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