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Ultra-short term HRV features as surrogates of short term HRV : a case study on mental stress detection in real life
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Castaldo, Rossana, Montesinos-Silva, Luis, Melillo, Paolo, James, Christopher J. and Pecchia, Leandro (2019) Ultra-short term HRV features as surrogates of short term HRV : a case study on mental stress detection in real life. BMC Medical Informatics and Decision Making, 19 (12). doi:10.1186/s12911-019-0742-y
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WRAP-ultra-short-term-Surogates-HRV-mental-stress-Castaldo-2019.pdf - Published Version - Requires a PDF viewer. Available under License Creative Commons Attribution 4.0. Download (803Kb) | Preview |
Official URL: https://doi.org/10.1186/s12911-019-0742-y
Abstract
Background
This paper suggests a method to assess the extent to which ultra-short Heart Rate Variability (HRV) features (less than 5 min) can be considered as valid surrogates of short HRV features (nominally 5 min). Short term HRV analysis has been widely investigated for mental stress assessment, whereas the validity of ultra-short HRV features remains unclear. Therefore, this study proposes a method to explore the extent to which HRV excerpts can be shortened without losing their ability to automatically detect mental stress.
Methods
ECGs were acquired from 42 healthy subjects during a university examination and resting condition. 23 features were extracted from HRV excerpts of different lengths (i.e., 30 s, 1 min, 2 min, 3 min, and 5 min). Significant differences between rest and stress phases were investigated using non-parametric statistical tests at different time-scales. Features extracted from each ultra-short length were compared with the standard short HRV features, assumed as the benchmark, via Spearman’s rank correlation analysis and Bland-Altman plots during rest and stress phases. Using data-driven machine learning approaches, a model aiming to detect mental stress was trained, validated and tested using short HRV features, and assessed on the ultra-short HRV features.
Results
Six out of 23 ultra-short HRV features (MeanNN, StdNN, MeanHR, StdHR, HF, and SD2) displayed consistency across all of the excerpt lengths (i.e., from 5 to 1 min) and 3 out of those 6 ultra-short HRV features (MeanNN, StdHR, and HF) achieved good performance (accuracy above 88%) when employed in a well-dimensioned automatic classifier.
Conclusion
This study concluded that 6 ultra-short HRV features are valid surrogates of short HRV features for mental stress investigation.
| Item Type: | Journal Article | |||||||||
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| Subjects: | Q Science > QP Physiology | |||||||||
| Divisions: | Other > Institute of Advanced Study Faculty of Science, Engineering and Medicine > Engineering > Engineering |
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| Library of Congress Subject Headings (LCSH): | Heart beat, Stress (Psychology) -- Diagnosis | |||||||||
| Journal or Publication Title: | BMC Medical Informatics and Decision Making | |||||||||
| Publisher: | BioMed Central Ltd. | |||||||||
| ISSN: | 1472-6947 | |||||||||
| Official Date: | 17 January 2019 | |||||||||
| Dates: |
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| Volume: | 19 | |||||||||
| Number: | 12 | |||||||||
| DOI: | 10.1186/s12911-019-0742-y | |||||||||
| Status: | Peer Reviewed | |||||||||
| Publication Status: | Published | |||||||||
| Access rights to Published version: | Open Access | |||||||||
| Copyright Holders: | Authors | |||||||||
| RIOXX Funder/Project Grant: |
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