Integrative Analysis of Open Datasets for Stress Prediction

Purpose Stress is the physiological or psychological response to internal or external stressors. By causing physiological and behavioral changes, persistent stress affects psychological and physiological functionality, potentially reducing the quality of life. Therefore, it is of great interest to s...

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Vydáno v:	Journal of medical and biological engineering Ročník 45; číslo 3; s. 385 - 399
Hlavní autoři:	Ladakis, Ioannis, Fotopoulos, Dimitris, Chouvarda, Ioanna
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Berlin/Heidelberg Springer Berlin Heidelberg 01.06.2025 Springer Nature B.V
Témata:	Accuracy Biological Techniques Biomedical and Life Sciences Biomedical Engineering and Bioengineering Biomedical Engineering/Biotechnology Biomedicine Classification Data collection Datasets Deep learning Electrocardiography Electromyography Feature selection Heart rate Machine learning Open data Original Article Physiology Predictions Quality of life Regenerative Medicine/Tissue Engineering Sensors Signal processing Stress Support vector machines Wearable computers Deep learning Stress detection Physiological data Machine learning Biosignals Stress assessment Supervised models Open datasets
ISSN:	1609-0985, 2199-4757
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Abstract	Purpose Stress is the physiological or psychological response to internal or external stressors. By causing physiological and behavioral changes, persistent stress affects psychological and physiological functionality, potentially reducing the quality of life. Therefore, it is of great interest to systematically monitor individuals daily to investigate their physiological reactions to potential stressors. The aim of this study is to investigate the potential of training reliable and accurate ML and DL models for the prediction of acute stress based on available open datasets. Methods The open datasets include physiological signals collected using wearable sensors during stress characterization experiments and made available to the public. Various machine and deep learning (ML/DL) models were tested and compared based on their accuracy (ACC). Harmonizing techniques were also included to increase the generalizability of the models and reduce their dependence on sensors and experimental protocols variations. Results The models achieved test-set accuracies over 80% for different classification problems, while the combination of the available data led to more accurate models for the binary classification problem (over 90%). Harmonizing differences between participants and datasets led to more generalizable models with encouraging initial results (around 70% accuracy) when evaluated with unseen subjects. Conclusion The extracted models can all be used depending on the classification problem and the experimental settings/protocols. All models could play a significant role towards forming and developing a stress prediction ‘service’, taking into account the impact of using different sensors, and, thus, having to proceed to required adjustments.
AbstractList	PurposeStress is the physiological or psychological response to internal or external stressors. By causing physiological and behavioral changes, persistent stress affects psychological and physiological functionality, potentially reducing the quality of life. Therefore, it is of great interest to systematically monitor individuals daily to investigate their physiological reactions to potential stressors. The aim of this study is to investigate the potential of training reliable and accurate ML and DL models for the prediction of acute stress based on available open datasets.MethodsThe open datasets include physiological signals collected using wearable sensors during stress characterization experiments and made available to the public. Various machine and deep learning (ML/DL) models were tested and compared based on their accuracy (ACC). Harmonizing techniques were also included to increase the generalizability of the models and reduce their dependence on sensors and experimental protocols variations.ResultsThe models achieved test-set accuracies over 80% for different classification problems, while the combination of the available data led to more accurate models for the binary classification problem (over 90%). Harmonizing differences between participants and datasets led to more generalizable models with encouraging initial results (around 70% accuracy) when evaluated with unseen subjects.ConclusionThe extracted models can all be used depending on the classification problem and the experimental settings/protocols. All models could play a significant role towards forming and developing a stress prediction ‘service’, taking into account the impact of using different sensors, and, thus, having to proceed to required adjustments. Purpose Stress is the physiological or psychological response to internal or external stressors. By causing physiological and behavioral changes, persistent stress affects psychological and physiological functionality, potentially reducing the quality of life. Therefore, it is of great interest to systematically monitor individuals daily to investigate their physiological reactions to potential stressors. The aim of this study is to investigate the potential of training reliable and accurate ML and DL models for the prediction of acute stress based on available open datasets. Methods The open datasets include physiological signals collected using wearable sensors during stress characterization experiments and made available to the public. Various machine and deep learning (ML/DL) models were tested and compared based on their accuracy (ACC). Harmonizing techniques were also included to increase the generalizability of the models and reduce their dependence on sensors and experimental protocols variations. Results The models achieved test-set accuracies over 80% for different classification problems, while the combination of the available data led to more accurate models for the binary classification problem (over 90%). Harmonizing differences between participants and datasets led to more generalizable models with encouraging initial results (around 70% accuracy) when evaluated with unseen subjects. Conclusion The extracted models can all be used depending on the classification problem and the experimental settings/protocols. All models could play a significant role towards forming and developing a stress prediction ‘service’, taking into account the impact of using different sensors, and, thus, having to proceed to required adjustments.
Author	Fotopoulos, Dimitris Ladakis, Ioannis Chouvarda, Ioanna
Author_xml	– sequence: 1 givenname: Ioannis orcidid: 0000-0002-3457-1333 surname: Ladakis fullname: Ladakis, Ioannis email: iladakig@auth.gr organization: Laboratory of Computing, Medical Informatics and Biomedical—Imaging Technologies, School of Medicine, Aristotle University of Thessaloniki – sequence: 2 givenname: Dimitris surname: Fotopoulos fullname: Fotopoulos, Dimitris organization: Laboratory of Computing, Medical Informatics and Biomedical—Imaging Technologies, School of Medicine, Aristotle University of Thessaloniki – sequence: 3 givenname: Ioanna surname: Chouvarda fullname: Chouvarda, Ioanna organization: Laboratory of Computing, Medical Informatics and Biomedical—Imaging Technologies, School of Medicine, Aristotle University of Thessaloniki
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Snippet	Purpose Stress is the physiological or psychological response to internal or external stressors. By causing physiological and behavioral changes, persistent... PurposeStress is the physiological or psychological response to internal or external stressors. By causing physiological and behavioral changes, persistent...
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SubjectTerms	Accuracy Biological Techniques Biomedical and Life Sciences Biomedical Engineering and Bioengineering Biomedical Engineering/Biotechnology Biomedicine Classification Data collection Datasets Deep learning Electrocardiography Electromyography Feature selection Heart rate Machine learning Open data Original Article Physiology Predictions Quality of life Regenerative Medicine/Tissue Engineering Sensors Signal processing Stress Support vector machines Wearable computers
Title	Integrative Analysis of Open Datasets for Stress Prediction
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