Skip to main content

To What Extent Can We Shorten HRV Analysis in Wearable Sensing? A Case Study on Mental Stress Detection.

Part of the IFMBE Proceedings book series (IFMBE,volume 65)


Mental stress is one of the first causes of cognitive dysfunctions, cardiovascular disorders and depression. In addition, it reduces performances, on the work place and in daily life. The diffusion of wearablesensors (embedded in smart-watches, phones, etc.) has opened up the potential to assess mental stress detection through ultra-shortterm Heart Rate Variability (HRV) analysis (i.e., less than 5 min).Although informative analyses of features coming from short HRV (i.e., 5 min) have already been performed, the reliability of ultra-short HRVremains unclear. This study aims to tackle this gap by departing from a systematic review of the existing literature and investigating, in healthy subjects, the associations between acute mental stress and short/ultra-short term HRV features in time, frequency, and non-linear domains. Building on these findings, three experiments were carried outto empirically assess the usefulness of HRV for mental stress detection using ultra-short term analysis and wearable devices. Experiment 1 detected mental stress in a real life situation by exploring to which extent HRV excerpts can be shortened without losing their ability to detect mental stress. This allowed us to advance a method to explore to what extentultra-short HRV features can be consideredas good surrogates of 5 min HRV features. Experiment 2 and 3 sought todevelop automatic classifiers to detect mental stress through 2 min HRV excerpts, by usinga Stroop Color Word Test(CWT) and a highly pacedvideo game, which are two common laboratory-based stressors.

Results from experiment 1 demonstrated that7ultra-short HRV features can be considered as good surrogates ofshort HRV features in detecting mental stress in real life.By leveraging these 7 features,experiment 2 and 3 offered an automatic classifier detecting mental stress with ultra-short features (2min), achieving sensitivity, specificity and accuracy rate above 60%.


  • Mental stress
  • HRV
  • real-life stressor
  • CWT
  • video game

This is a preview of subscription content, access via your institution.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  • [1] R. Castaldo, P. Melillo, U. Bracale, M. Caserta, M. Triassi, and L. Pecchia, “Acute mental stress assessment via short term HRV analysis in healthy adults: A systematic review with metaanalysis,” Biomedical Signal Processing and Control, vol. 18, pp. 370–377, 2015.

    Google Scholar 

  • [2] R. Castaldo, L. Montesinos, P. Melillo, and L. Pecchia, “Ultra-short term HRV Features as Surrogate of Short term HRV. A Case Study on Mental Stress Detection in Real Life,” IEEE journal of biomedical and health informatics (under review) 2017.

    Google Scholar 

  • [3] P. Melillo, M. Bracale, and L. Pecchia, “Nonlinear Heart Rate Variability features for real-life stress detection. Case study: students under stress due to university examination,” Biomedical engineering online, vol. 10, p. 1, 2011.

    Google Scholar 

  • [4] A. L. Goldberger, L. A. Amaral, L. Glass, J. M. Hausdorff, P. C. Ivanov, R. G. Mark, et al., “Physiobank, physiotoolkit, and physionet components of a new research resource for complex physiologic signals,” Circulation, vol. 101, pp. e215–e220, 2000.

    Google Scholar 

  • [5] M. P. Tarvainen and J.-P. Niskanen, “Kubios HRV User’s Guide,” Biosignal Analysis and Medical Imaging Group (BSAMIG), Department of Physics University of Kuopio, 2013.

    Google Scholar 

  • [6] T. Force, “Heart rate variability guidelines: Standards of measurement, physiological interpretation, and clinical use,” European Heart Journal, vol. 17, pp. 354–381, 1996.

    Google Scholar 

  • [7] S. Massaro and L. Pecchia, “Heart Rate Variability (HRV) Analysis A Methodology for Organizational Neuroscience,” Organizational Research Methods, p. 1094428116681072, 2016.

    Google Scholar 

  • [8] R. Castaldo, P. Melillo, R. Izzo, N. D. Luca, and L. Pecchia, “Fall Prediction in Hypertensive Patients via Short-Term HRV Analysis,” IEEE Journal of Biomedical and Health Informatics, vol. 21, pp. 399–406, 2017.

    Google Scholar 

Download references

Author information

Authors and Affiliations


Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2018 Springer Nature Singapore Pte Ltd.

About this paper

Cite this paper

Castaldo, R., Montesinos, L., Melillo, P., Massaro, S., Pecchia, L. (2018). To What Extent Can We Shorten HRV Analysis in Wearable Sensing? A Case Study on Mental Stress Detection.. In: Eskola, H., Väisänen, O., Viik, J., Hyttinen, J. (eds) EMBEC & NBC 2017. EMBEC NBC 2017 2017. IFMBE Proceedings, vol 65. Springer, Singapore.

Download citation

  • DOI:

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-5121-0

  • Online ISBN: 978-981-10-5122-7

  • eBook Packages: EngineeringEngineering (R0)