Abstract
The paper presents algorithms for automatic detection of non-stationary periods of cardiac rhythm during professional activity. While working and subsequent rest operator passes through the phases of mobilization, stabilization, work, recovery and the rest. The amplitude and frequency of non-stationary periods of cardiac rhythm indicates the human resistance to stressful conditions. We introduce and analyze a number of algorithms for non-stationary phase extraction: the different approaches to phase preliminary detection, thresholds extraction and final phases extraction are studied experimentally. These algorithms are based on local extremum computation and analysis of linear regression coefficient histograms. The algorithms do not need any labeled datasets for training and could be applied to any person individually. The suggested algorithms were experimentally compared and evaluated by human experts.
A. Dubatovka—The work of the first author is partially supported by the Google Anita Borg Memorial Scholarship 2015.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Adams, R.P., MacKay, D.J.: Bayesian online changepoint detection. Cambridge, UK (2007)
Balandina, E., Balandin, S., Koucheryavy, Y., Mouromtsev, D.: Iot use cases in healthcare and tourism. In: 2015 IEEE 17th Conference on Business Informatics (CBI), vol. 2, pp. 37–44, July 2015
Cinaz, B., Arnrich, B., Marca, R.L., Troster, G.: Monitoring of mental workload levels during an everyday life office-work scenario. Pers. Ubiquit. Comput. 17(2), 229–239 (2013). http://dblp.uni-trier.de/db/journals/puc/puc17.html
Comstock, J.: The Multi-attribute Task Battery for Human Operator Workload and Strategic Behavior Research. NASA Langley Research Center, Hampton (1992). https://books.google.ru/books?id=JlY3AQAAMAAJ
Driskell, J., Salas, E., Johnston, J.: Making and performance under stress. In: Military Life: The Psychology of Serving in Peace and Comba, vol. 1, pp. 128–154 (2006). Military Performance
Gupta, A., Agrawal, R.K., Kaur, B.: A three phase approach for mental task classification using EEG. In: Gopalan, K., Thampi, S.M. (eds.) ICACCI, pp. 898–904. ACM (2012). http://dblp.uni-trier.de/db/conf/icacci/icacci2012.html
Hartigan, J.A., Wong, M.A.: A k-means clustering algorithm. JSTOR Appl. Stat. 28(1), 100–108 (1979)
Inclan, C., Tiao, G.C.: Use of cumulative sums of squares for retrospective detection of changes of variance. J. Am. Stat. Assoc. 89(427), 913–923 (1994). http://www.jstor.org/stable/2290916
Killick, R., Eckley, I.A.: Changepoint: an R package for changepoint analysis. J. Stat. Softw. 58(3), 1–19 (2014). http://www.jstatsoft.org/v58/i03/
Korzun, D.G., Borodin, A.V., Timofeev, I.A., Paramonov, I.V., Balandin, S.I.: Digital assistance services for emergency situations in personalized mobile healthcare: smart space based approach. In: 2015 International Conference on Biomedical Engineering and Computational Technologies (SIBIRCON), pp. 62–67, October 2015
Malhotra, V., Patil, M.K.: Mental stress assessment of ECG signal using statistical analysis of bio-orthogonal wavelet coefficients: part-2. Int. J. Sci. Res. (IJSR) 2(12) (2013). http://www.ijsr.net/archive/v2i12/MjYxMjEzMDE=.pdf
Malhotra, V., Patil, M.K.: Mental stress assessment of ECG signal using statistical analysis of bio-orthogonal wavelet coefficients: part-2. Int. J. Sci. Res. (IJSR) 3(2) (2014). http://www.ijsr.net/archive/v3i2/MDUwMjE0MDE=.pdf
Mulder, L., de Waard, D., Brookhuis, K.: Estimating mental effort using heart rate and heart rate variability. In: Stanton, N., Hedge, A., Brookhuis, K., Salas, E., Hendrick, H. (eds.) Handbook of Human Factors and Ergonomics Methods. CRC Press, Boca Raton (2004)
Novikov, V.S., Stupakov, G.P., Lustin, S.I., et al.: In: Novikov, V.S. (ed.) Physiology of Flight Work. Nauka, St. Petersburg (1997)
Petrukovich, V.: Technology for assessing the flight navigator’s capacity to operate with numerical information in the spatial pattern structure. Vestnik Baltiyskoi pedagogicheskoi akademii (Bull. Baltic Pedagogical Acad.) 34, 83–90 (2000)
Sapova, N.: Complex evaluation of heart rhythm regulation during measured functional loads. Fiziologicheskii zhurnal SSSR imeni I. M. Sechenova (Sechenov Physiol. J. USSR) 68(8), 1159–1164 (1982)
Task Force of the European Society of Cardiology the North American Society of Pacing Electrophysiology: Heart rate variability standards of measurement, physiological interpretation, and clinical use. Circulation 93(5), 1043–1065 (1996). http://circ.ahajournals.org/content/93/5/1043.full. hRV autonomic risk factors
Veltman, J., Gaillard, A.: Physiological workload reactions to increasing levels of task difficulty. Ergonomics 41(5), 656–669 (1998)
Zotov, M., Forsythe, J., Petrukovich, V., Akhmedova, I.: Physiological-based assessment of the resilience of training to stressful conditions. In: Schmorrow, D.D., Estabrooke, I.V., Grootjen, M. (eds.) FAC 2009. LNCS, vol. 5638, pp. 563–571. Springer, Heidelberg (2009). http://dblp.uni-trier.de/db/conf/hci/hci2009-16.html
Zotov, M.V., Petrukovich, V.M., Akhmedova, I.S., Palamarchuk, N: Optimization factors of regulation of cognitive activity during training. Vestnik Sankt-Peterbugskogo universiteta (Saint Petersburg State Univ. Bull.) 12(2), 17–31 (2011)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Dubatovka, A., Mikhailova, E., Zotov, M., Novikov, B. (2016). Algorithms for Extracting Mental Activity Phases from Heart Beat Rate Streams. In: Arnicans, G., Arnicane, V., Borzovs, J., Niedrite, L. (eds) Databases and Information Systems. DB&IS 2016. Communications in Computer and Information Science, vol 615. Springer, Cham. https://doi.org/10.1007/978-3-319-40180-5_8
Download citation
DOI: https://doi.org/10.1007/978-3-319-40180-5_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-40179-9
Online ISBN: 978-3-319-40180-5
eBook Packages: Computer ScienceComputer Science (R0)