Abstract
In this work we perform a study of various unsupervised methods to identify mental stress in firefighter trainees based on unlabeled heart rate variability data. We collect RR interval time series data from nearly 100 firefighter trainees that participated in a drill. We explore and compare three methods in order to perform unsupervised stress detection: (1) traditional K-Means clustering with engineered time and frequency domain features (2) convolutional autoencoders and (3) long short-term memory (LSTM) autoencoders, both trained on the raw RR data combined with DBSCAN clustering and K-Nearest-Neighbors classification. We demonstrate that K-Means combined with engineered features is unable to capture meaningful structure within the data. On the other hand, convolutional and LSTM autoencoders tend to extract varying structure from the data pointing to different clusters with different sizes of clusters. We attempt at identifying the true stressed and normal clusters using the HRV markers of mental stress reported in the literature. We demonstrate that the clusters produced by the convolutional autoencoders consistently and successfully stratify stressed versus normal samples, as validated by several established physiological stress markers such as RMSSD, Max-HR, Mean-HR and LF-HF ratio.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Aggarwal, C.C., Hinneburg, A., Keim, D.A.: On the surprising behavior of distance metrics in high dimensional space. Presented at the International Conference on Database Theory, pp. 420–434. Springer (2001)
Beaton, R., Murphy, S., Pike, K., Jarrett, M.: Stress-symptom factors in firefighters and paramedics (1995)
Bhardwaj, R., Natrajan, P., Balasubramanian, V.: Study to determine the effectiveness of deep learning classifiers for ECG based driver fatigue classification. Presented at the 2018 IEEE 13th International Conference on Industrial and Information Systems (ICIIS), pp. 98–102. IEEE (2018)
Blásquez, J.C.C., Font, G.R., Ortís, L.C.: Heart-rate variability and precompetitive anxiety in swimmers. Psicothema 21, 531–536 (2009)
Camm, A.J., Malik, M., Bigger, J.T., Breithardt, G., Cerutti, S., Cohen, R.J., Coumel, P., Fallen, E.L., Kennedy, H.L., Kleiger, R.: Heart rate variability: standards of measurement, physiological interpretation and clinical use. Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology (1996)
Clevert, D.A., Unterthiner, T., Hochreiter, S.: Fast and accurate deep network learning by exponential linear units (elus). arXiv preprint arXiv:1511.07289 (2015)
Giannakakis, G., Grigoriadis, D., Giannakaki, K., Simantiraki, O., Roniotis, A., Tsiknakis, M.: Review on psychological stress detection using biosignals. IEEE Trans. Affect. Comput., 1 (2019)
Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press, Cambridge (2016)
Hannun, A.Y., Rajpurkar, P., Haghpanahi, M., Tison, G.H., Bourn, C., Turakhia, M.P., Ng, A.Y.: Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network. Nat. Med. 25, 65 (2019)
Harris, M.B., Baloğlu, M., Stacks, J.R.: Mental health of trauma-exposed firefighters and critical incident stress debriefing. J. Loss Trauma 7, 223–238 (2002)
Huysmans, D., Smets, E., De Raedt, W., Van Hoof, C., Bogaerts, K., Van Diest, I., Helic, D.: Unsupervised learning for mental stress detection. Presented at the Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies, pp. 26–35 (2018)
Hwang, B., You, J., Vaessen, T., Myin-Germeys, I., Park, C., Zhang, B.-T.: Deep ECGNet: an optimal deep learning framework for monitoring mental stress using ultra short-term ECG signals. Telemed. E-Health 24, 753–772 (2018)
Järvelin-Pasanen, S., Sinikallio, S., Tarvainen, M.P.: Heart rate variability and occupational stress-systematic review. Ind. Health 56, 500–511 (2018)
Kanungo, T., Mount, D.M., Netanyahu, N.S., Piatko, C.D., Silverman, R., Wu, A.Y.: An efficient k-means clustering algorithm: analysis and implementation. IEEE Trans. Pattern Anal. Mach. Intell. 24, 881–892 (2002)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
Kivimäki, M., Leino-Arjas, P., Luukkonen, R., Riihimäi, H., Vahtera, J., Kirjonen, J.: Work stress and risk of cardiovascular mortality: prospective cohort study of industrial employees. BMJ 325, 857 (2002)
Salahuddin, L., Cho, J., Jeong, M.G., Kim, D.: Ultra short term analysis of heart rate variability for monitoring mental stress in mobile settings. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Presented at the 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4656–4659 (2007)
Laborde, S., Mosley, E., Thayer, J.F.: Heart rate variability and cardiac vagal tone in psychophysiological research-recommendations for experiment planning, data analysis, and data reporting. Front. Psychol. 8, 213 (2017)
McCraty, R., Shaffer, F.: Heart rate variability: new perspectives on physiological mechanisms, assessment of self-regulatory capacity, and health risk. Glob. Adv. Health Med. 4, 46–61 (2015)
Medina, L.: Identification of stress states from ECG signals using unsupervised learning methods. Presented at the Portuguese Conference on Pattern Recognition-RecPad (2009)
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)
Raykov, Y.P., Boukouvalas, A., Baig, F., Little, M.A.: What to do when k-means clustering fails: a simple yet principled alternative algorithm. PLoS ONE 11, e0162259 (2016). https://doi.org/10.1371/journal.pone.0162259
Salminen, S., Kivimäki, M., Elovainio, M., Vahtera, J.: Stress factors predicting injuries of hospital personnel. Am. J. Ind. Med. 44, 32–36 (2003)
Schubert, E., Sander, J., Ester, M., Kriegel, H.P., Xu, X.: DBSCAN revisited, revisited: why and how you should (still) use DBSCAN. ACM Trans. Database Syst. (TODS) 42, 19 (2017)
Shaffer, F., Ginsberg, J.P.: An overview of heart rate variability metrics and norms. Front. Public Health 5, 258 (2017)
Smets, E., Casale, P., Großekathöfer, U., Lamichhane, B., De Raedt, W., Bogaerts, K., Van Diest, I., Van Hoof, C.: Comparison of machine learning techniques for psychophysiological stress detection. Presented at the International Symposium on Pervasive Computing Paradigms for Mental Health, pp. 13–22. Springer (2015)
Song, S.H., Kim, D.K.: Development of a stress classification model using deep belief networks for stress monitoring. Healthc. Inform. Res. 23, 285–292 (2017)
Soori, H., Rahimi, M., Mohseni, H.: Occupational stress and work-related unintentional injuries among Iranian car manufacturing workers (2008)
Sun, F.T., Kuo, C., Cheng, H.T., Buthpitiya, S., Collins, P., Griss, M.: Activity-aware mental stress detection using physiological sensors. In: Gris, M., Yang, G. (eds.) Mobile Computing, Applications, and Services, pp. 282–301. Springer, Heidelberg (2012)
Taelman, J., Vandeput, S., Spaepen, A., Van Huffel, S.: Influence of mental stress on heart rate and heart rate variability. Presented at the 4th European Conference of the International Federation for Medical and Biological Engineering, pp. 1366–1369. Springer (2009)
von Rosenberg, W., Chanwimalueang, T., Adjei, T., Jaffer, U., Goverdovsky, V., Mandic, D.P.: Resolving ambiguities in the LF/HF ratio: LF-HF scatter plots for the categorization of mental and physical stress from HRV. Front. Physiol. 8, 360 (2017). https://doi.org/10.3389/fphys.2017.00360
Wang, L., Zhou, X.: Detection of congestive heart failure based on LSTM-based deep network via short-term RR intervals. Sensors 19, 1502 (2019)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
Oskooei, A., Chau, S.M., Weiss, J., Sridhar, A., Martínez, M.R., Michel, B. (2021). DeStress: Deep Learning for Unsupervised Identification of Mental Stress in Firefighters from Heart-Rate Variability (HRV) Data. In: Shaban-Nejad, A., Michalowski, M., Buckeridge, D.L. (eds) Explainable AI in Healthcare and Medicine. Studies in Computational Intelligence, vol 914. Springer, Cham. https://doi.org/10.1007/978-3-030-53352-6_9
Download citation
DOI: https://doi.org/10.1007/978-3-030-53352-6_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-53351-9
Online ISBN: 978-3-030-53352-6
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)