Strategies for Reliable Stress Recognition: A Machine Learning Approach Using Heart Rate Variability Features

Stress recognition, particularly using machine learning (ML) with physiological data such as heart rate variability (HRV), holds promise for mental health interventions. However, limited datasets in affective computing and healthcare research can lead to inaccurate conclusions regarding the ML model...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2024-05, Vol.24 (10), p.3210
Main Authors: Bahameish, Mariam, Stockman, Tony, Requena Carrión, Jesús
Format: Article
Language:English
Subjects:
Accuracy
Adult
Affect (Psychology)
affective computing
Algorithms
Classification
Data integrity
Data mining
Datasets
Electrocardiography - methods
Feature selection
Female
Heart beat
Heart rate
Heart Rate - physiology
heart rate variability
Humans
Machine Learning
Male
Mental disorders
Mental health
Muscle function
Physiology
Questionnaires
Respiration
Stress
stress recognition
Stress, Psychological - physiopathology
Wearable computers
Young Adult
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Summary:Stress recognition, particularly using machine learning (ML) with physiological data such as heart rate variability (HRV), holds promise for mental health interventions. However, limited datasets in affective computing and healthcare research can lead to inaccurate conclusions regarding the ML model performance. This study employed supervised learning algorithms to classify stress and relaxation states using HRV measures. To account for limitations associated with small datasets, robust strategies were implemented based on methodological recommendations for ML with a limited dataset, including data segmentation, feature selection, and model evaluation. Our findings highlight that the random forest model achieved the best performance in distinguishing stress from non-stress states. Notably, it showed higher performance in identifying stress from relaxation (F1-score: 86.3%) compared to neutral states (F1-score: 65.8%). Additionally, the model demonstrated generalizability when tested on independent secondary datasets, showcasing its ability to distinguish between stress and relaxation states. While our performance metrics might be lower than some previous studies, this likely reflects our focus on robust methodologies to enhance the generalizability and interpretability of ML models, which are crucial for real-world applications with limited datasets.
ISSN:1424-8220
1424-8220
DOI:10.3390/s24103210