Prediction of Stress Coping Capabilities from Nightly Heart Rate Patterns using Machine Learning

Vorberg L, Pflüger S, Richer R, Jäger K, Küderle A, Rohleder N, Eskofier B (2023)

Publication Language: English

Publication Type: Conference contribution, Original article

Publication year: 2023

Publisher: IEEE

Conference Proceedings Title: 2023 IEEE EMBS International Conference on Biomedical and Health Informatics (BHI)

Event location: Pittsburgh US

URI: https://www.mad.tf.fau.de/files/2023/12/vorberg_pflueger23_stress_coping_nightly_hr.pdf

DOI: 10.1109/BHI58575.2023.10313401

Abstract

Stress is related to short- and long-term alterations in stress systems, including the hypothalamic-pituitary-adrenal (HPA) axis and the sympathetic nervous system (SNS). While it is well established that stress experienced during the day can affect sleep quality, less is known about how it affects stress systems during the night. We assume that stress coping strategies can have an impact on how stress carries over into the night and that individuals with bad coping mechanisms show elevated activation of stress systems during sleep. For that reason, we recorded the heart rate (HR) and heart rate variability (HRV) of 21 healthy participants on two consecutive nights during sleep and the first hour after awakening and extracted cortisol and alpha-amylase from saliva samples collected in the first hour after awakening. Stress coping capabilities were assessed using self-reports. We performed backward stepwise multiple regression models to analyze the relationship between HR(V) and stress coping and trained different machine learning-based regression algorithms to predict positive (SVF Pos ) and negative (SVF Neg ) stress coping capabilities, respectively. Our results show that individuals with higher SVF Neg scores showed higher SNS activity during the night, whereas higher SVF Pos scores indicated lower SNS activity. SVF Pos was predicted with a mean absolute error (MAE) of 1.51 ± 0.73 and SVF Neg with an MAE of 2.79 ± 1.53. Our findings indicate an association between nightly HR(V) and the individual’s capability of coping with stress. This provides further information about how stress influences sleep and might be used for tailored intervention and feedback on successful stress coping.

Authors with CRIS profile

Linda Vorberg Lehrstuhl für Informatik 5 (Mustererkennung) Robert Richer Machine Learning and Data Analytics Lab Katharina Jäger Machine Learning and Data Analytics Lab Arne Küderle Machine Learning and Data Analytics Lab Nicolas Rohleder Lehrstuhl für Gesundheitspsychologie Björn Eskofier Machine Learning and Data Analytics Lab

Additional Organisation(s)

Sonderforschungsbereich 1483/1 Empathokinästhetische Sensorik – Sensortechniken und Datenanalyseverfahren zur empathokinästhetischen Modellbildung und Zustandsbestimmung

Related research project(s)

Berührungslose Messung von Stress, seiner Determinanten und Konsequenzen (SFB 1483 EmpkinS D03) Empatho-Kinaesthetic Sensor Technology (SFB 1483 EmpkinS) July 1, 2021 - June 30, 2025

How to cite

APA:

Vorberg, L., Pflüger, S., Richer, R., Jäger, K., Küderle, A., Rohleder, N., & Eskofier, B. (2023). Prediction of Stress Coping Capabilities from Nightly Heart Rate Patterns using Machine Learning. In 2023 IEEE EMBS International Conference on Biomedical and Health Informatics (BHI). Pittsburgh, US: IEEE.

MLA:

Vorberg, Linda, et al. "Prediction of Stress Coping Capabilities from Nightly Heart Rate Patterns using Machine Learning." Proceedings of the 2023 IEEE EMBS International Conference on Biomedical and Health Informatics (BHI), Pittsburgh IEEE, 2023.

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