Algorithms for Extracting Mental Activity Phases from Heart Beat Rate Streams

  • Alina Dubatovka
  • Elena Mikhailova
  • Mikhail Zotov
  • Boris Novikov
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 615)


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.


Pattern recognition Signal processing Mental activity phases Data stream Linear regression Phase extraction 


  1. 1.
    Adams, R.P., MacKay, D.J.: Bayesian online changepoint detection. Cambridge, UK (2007)Google Scholar
  2. 2.
    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 2015Google Scholar
  3. 3.
    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). CrossRefGoogle Scholar
  4. 4.
    Comstock, J.: The Multi-attribute Task Battery for Human Operator Workload and Strategic Behavior Research. NASA Langley Research Center, Hampton (1992). Google Scholar
  5. 5.
    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 PerformanceGoogle Scholar
  6. 6.
    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).
  7. 7.
    Hartigan, J.A., Wong, M.A.: A k-means clustering algorithm. JSTOR Appl. Stat. 28(1), 100–108 (1979)CrossRefzbMATHGoogle Scholar
  8. 8.
    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). MathSciNetzbMATHGoogle Scholar
  9. 9.
    Killick, R., Eckley, I.A.: Changepoint: an R package for changepoint analysis. J. Stat. Softw. 58(3), 1–19 (2014). CrossRefGoogle Scholar
  10. 10.
    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 2015Google Scholar
  11. 11.
    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).
  12. 12.
    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).
  13. 13.
    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)Google Scholar
  14. 14.
    Novikov, V.S., Stupakov, G.P., Lustin, S.I., et al.: In: Novikov, V.S. (ed.) Physiology of Flight Work. Nauka, St. Petersburg (1997)Google Scholar
  15. 15.
    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)Google Scholar
  16. 16.
    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)Google Scholar
  17. 17.
    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). hRV autonomic risk factors
  18. 18.
    Veltman, J., Gaillard, A.: Physiological workload reactions to increasing levels of task difficulty. Ergonomics 41(5), 656–669 (1998)CrossRefGoogle Scholar
  19. 19.
    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). Google Scholar
  20. 20.
    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)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Alina Dubatovka
    • 1
  • Elena Mikhailova
    • 1
  • Mikhail Zotov
    • 1
  • Boris Novikov
    • 1
  1. 1.Saint Petersburg State UniversitySt. PetersburgRussia

Personalised recommendations