Multi-Class Stress Detection Through Heart Rate Variability: A Deep Neural Network Based Study

Author:

Mortensen Jon Andreas1,Mollov Martin Efremov2,Chatterjee Ayan1ORCID,Ghose Debasish1ORCID,Li Frank Y.1ORCID

Affiliation:

1. Department of Information and Communication Technology, University of Agder, Grimstad, Norway

2. Department of Holistic Systems, Simula Metropolitan Center for Digital Engineering, Oslo, Norway

Funder

Research Council of Norway through the Orchestrating Internet of Things and Machine Learning for Early Risk Detection to Ensure Inpatients Safety (StaySafe) Program

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Cited by 5 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. A machine-learning approach for stress detection using wearable sensors in free-living environments;Computers in Biology and Medicine;2024-09

2. Breaking Barriers in Stress Detection: An Inter-Subject Approach Using ECG Signals;2024 IEEE 48th Annual Computers, Software, and Applications Conference (COMPSAC);2024-07-02

3. Explaining Predicted Stress Levels in employed Individuals;2024 9th International Conference on Machine Learning Technologies (ICMLT);2024-05-24

4. Elicitation of Anxiety Without Time Pressure and Its Detection Using Physiological Signals and Artificial Intelligence: A Proof of Concept;IEEE Access;2024

5. Predictive Modeling for Heart Rate: A Comparative Analysis of LSTM, XGBoost, and LightGBM;2023 26th International Conference on Computer and Information Technology (ICCIT);2023-12-13

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