On the Explainable Detection of Stress Levels Using Heart Rate Variability Based Deep Neural Networks

Yuan Lin; Debasish Ghose; Jari Korhonen; Junyong You; Soumya P. Dash

doi:10.1109/Healthcom56612.2023.10472396

On the Explainable Detection of Stress Levels Using Heart Rate Variability Based Deep Neural Networks

Yuan Lin, Debasish Ghose, Jari Korhonen, Junyong You, Soumya P. Dash

Research output: Chapter in Book/Report/Conference proceeding › Published conference contribution

1 Citation (Scopus)

Abstract

This paper presents one of the first explorations of transparency and explainability of Heart Rate Variability (HRV) based deep learning models designed for stress detection. We employed Shapley additive explanations (SHAP) as an explainable AI (XAI) method, and cross-validated the results with saliency maps, which provides valuable insights into the main contributing factors for decision-making process of these deep models.

Original language	English
Title of host publication	2023 IEEE International Conference on E-Health Networking, Application and Services, Healthcom 2023
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	333-335
Number of pages	3
ISBN (Electronic)	9798350302301
DOIs	https://doi.org/10.1109/Healthcom56612.2023.10472396
Publication status	Published - 25 Mar 2024
Event	2023 IEEE International Conference on E-Health Networking, Application and Services, Healthcom 2023 - Chongqing, China Duration: 15 Dec 2023 → 17 Dec 2023

Publication series

Name	2023 IEEE International Conference on E-Health Networking, Application and Services, Healthcom 2023

Conference

Conference	2023 IEEE International Conference on E-Health Networking, Application and Services, Healthcom 2023
Country/Territory	China
City	Chongqing
Period	15/12/23 → 17/12/23

Keywords

Heart Rate Variability
Stress Detection
XAI

Access to Document

10.1109/Healthcom56612.2023.10472396Licence: Unspecified

Cite this

Lin, Y., Ghose, D., Korhonen, J., You, J., & Dash, S. P. (2024). On the Explainable Detection of Stress Levels Using Heart Rate Variability Based Deep Neural Networks. In 2023 IEEE International Conference on E-Health Networking, Application and Services, Healthcom 2023 (pp. 333-335). (2023 IEEE International Conference on E-Health Networking, Application and Services, Healthcom 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/Healthcom56612.2023.10472396

On the Explainable Detection of Stress Levels Using Heart Rate Variability Based Deep Neural Networks. / Lin, Yuan; Ghose, Debasish; Korhonen, Jari et al.
2023 IEEE International Conference on E-Health Networking, Application and Services, Healthcom 2023. Institute of Electrical and Electronics Engineers Inc., 2024. p. 333-335 (2023 IEEE International Conference on E-Health Networking, Application and Services, Healthcom 2023).

Research output: Chapter in Book/Report/Conference proceeding › Published conference contribution

Lin, Y, Ghose, D, Korhonen, J, You, J & Dash, SP 2024, On the Explainable Detection of Stress Levels Using Heart Rate Variability Based Deep Neural Networks. in 2023 IEEE International Conference on E-Health Networking, Application and Services, Healthcom 2023. 2023 IEEE International Conference on E-Health Networking, Application and Services, Healthcom 2023, Institute of Electrical and Electronics Engineers Inc., pp. 333-335, 2023 IEEE International Conference on E-Health Networking, Application and Services, Healthcom 2023, Chongqing, China, 15/12/23. https://doi.org/10.1109/Healthcom56612.2023.10472396

Lin Y, Ghose D, Korhonen J, You J, Dash SP. On the Explainable Detection of Stress Levels Using Heart Rate Variability Based Deep Neural Networks. In 2023 IEEE International Conference on E-Health Networking, Application and Services, Healthcom 2023. Institute of Electrical and Electronics Engineers Inc. 2024. p. 333-335. (2023 IEEE International Conference on E-Health Networking, Application and Services, Healthcom 2023). doi: 10.1109/Healthcom56612.2023.10472396

Lin, Yuan ; Ghose, Debasish ; Korhonen, Jari et al. / On the Explainable Detection of Stress Levels Using Heart Rate Variability Based Deep Neural Networks. 2023 IEEE International Conference on E-Health Networking, Application and Services, Healthcom 2023. Institute of Electrical and Electronics Engineers Inc., 2024. pp. 333-335 (2023 IEEE International Conference on E-Health Networking, Application and Services, Healthcom 2023).

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abstract = "This paper presents one of the first explorations of transparency and explainability of Heart Rate Variability (HRV) based deep learning models designed for stress detection. We employed Shapley additive explanations (SHAP) as an explainable AI (XAI) method, and cross-validated the results with saliency maps, which provides valuable insights into the main contributing factors for decision-making process of these deep models.",

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On the Explainable Detection of Stress Levels Using Heart Rate Variability Based Deep Neural Networks

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