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Mobile Platform for Fatigue Evaluation: HRV Analysis

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1078))

Abstract

Recent studies report that heart rate variability (HRV) is related to increased self-control abilities, greater social skills, and fatigue where the decision support systems often use RR interval signal data directly. The RR interval data filtering is compared by using different data sets and artefact removal methods when data were recorded during various training intensity. The results of artefact analysis indicate the intensity of the exercise which increases as the amount of artefact RR interval data increase. Furthermore, for artefact evaluation and fatigue estimation the Poincare plots were selected. The SD1 and SD2 indexes (standard deviation in two orthogonal directions of the Poincare plot) carry similar information to the spectral analysis and simple statistical means (like average pulse and standard error) but are easier calculated and lesser stationarity dependence. In this article Poincare diagram parameters were used to analyze HRV parameters during five stage training sessions for stages with less signal artefacts and evaluate the training impact on human fatigue. The process of HRV signal analysis described in this article is a basis for RR data processing which is important in parameter expertise and method repeatability. Systematic comparison of multiple signal sources enables different RR interval sensor evaluation based on their signal artefacts by using current findings. This research contains different methods of HRV analysis that will be used to support development of a mobile fatigue evaluation system.

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References

  1. Raudonis, V., Maskeliunas, R., Stankevicius, K., Damasevicius, R.: Gender, age, colour, position and stress: how they influence attention at workplace? ICCSA 5, 248–264 (2017)

    Google Scholar 

  2. Vandecasteele, K., et al.: Automated epileptic seizure detection based on wearable ECG and PPG in a hospital environment. Sensors (Basel, Switzerland) 17(10), 2338 (2017)

    Article  Google Scholar 

  3. Wen, C., Yeh, M.-F., Chang, K.-C., Lee, R.-G.: Real-time ECG telemonitoring system design with mobile phone platform. Measurement 41(4), 463–470 (2008)

    Article  Google Scholar 

  4. Damasevicius, R., Vasiljevas, M., Salkevicius, J., Wozniak, M.: Human activity recognition in AAL environments using random projections. Comput. Math. Methods Med. 4073584:1–4073584:17 (2016)

    Article  MathSciNet  Google Scholar 

  5. Maskeliunas, R., Blazauskas, T., Damasevicius, R.: Depression behavior detection model based on participation in serious games. IJCRS 2, 423–434 (2017)

    Google Scholar 

  6. Ulinskas, M., Wozniak, M., Damasevicius, R.: Analysis of keystroke dynamics for fatigue recognition. ICCSA 5, 235–247 (2017)

    Google Scholar 

  7. Elgendi, M., Mohamed, E., Ward, R.: Efficient ECG compression and QRS detection for E-health applications. Sci. Rep. 7, 1–16 (2017)

    Article  Google Scholar 

  8. Peritz, D.C., Howard, A., Ciocca, M., Chung, E.H.: Smartphone ECG aids real time diagnosis of palpitations in the competitive college athlete. J. Electrocardiol. 48, 896–899 (2015)

    Article  Google Scholar 

  9. Susan Torres-Harding, L.: What is fatigue? History and epidemiology. In: Susan Torres-Harding, L.A. (ed.) Fatigue as a Window to the Brain. The MIT Press, Cambridge (2005)

    Google Scholar 

  10. Sherwood, L.: Human Physiology From Cells to Systems, 5th edn. Thomson Learning, Belmont (2005)

    Google Scholar 

  11. Berntson, G.G., Bigger, J.T.: Heart rate variability: origins, methods, and interpretive caveats. Psychophysiology 34, 623–648 (1997)

    Article  Google Scholar 

  12. Braun, C., Kowallik, P., Freking, A., Hadeler, D., Kniffki, K.D., Meesmann, M.: Demonstration of nonlinear components in heart rate variability of healthy persons. Am. J. Physiol. 275, H1577–H1584 (1998)

    Google Scholar 

  13. Hautala, A., Tulppo, M.P., Makikallio, T.H., Laukkanen, R., Nissila, S., Huikuri, H.V.: Changes in cardiac autonomic regulation after prolonged maximal exercise. Clin. Physiol. 21, 238–245 (2001)

    Article  Google Scholar 

  14. Mourot, L., et al.: Decrease in heart rate variability with overtraining: assessment by the Poincare plot analysis. Clin. Physiol. Funct. Imaging 24, 10–18 (2004)

    Article  Google Scholar 

  15. Stirenko, S., et al.: Parallel statistical and machine learning methods for estimation of physical load. In: Vaidya, J., Li, J. (eds.) ICA3PP 2018. LNCS, vol. 11334, pp. 483–497. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-05051-1_33

    Chapter  Google Scholar 

  16. Tokoro, K., Ito, Y., Emori, Y., et al.: Relationship between fatigue and heart rate variability in mothers up to three months postpartum. MOJ Womens Health 6(3), 391–395 (2017)

    Google Scholar 

  17. Schmitt, L., Regnard, J., Millet, G.P.: Monitoring fatigue status with HRV measures in elite athletes: an avenue beyond RMSSD? Front. Physiol. 6, 343 (2015)

    Article  Google Scholar 

  18. Boissoneault, J., Letzen, J., Robinson, M., Staud, R.: Cerebral blood flow and heart rate variability predict fatigue severity in patients with chronic fatigue syndrome. Brain Imaging Behav. 13, 789–797 (2018)

    Article  Google Scholar 

  19. Tanaka, M., Mizuno, K., Tajima, S., Sasabe, T., Watanabe, Y.: Central nervous system fatigue alters autonomic nerve activity. Life Sci. 84(7–8), 235–239 (2009)

    Article  Google Scholar 

  20. Gonzalez, K., Sasangohar, F., Ranjana, K.M.: Measuring fatigue through heart rate variability and activity recognition: a scoping literature review of machine learning techniques 61(1), 1748–1752

    Google Scholar 

  21. Kubickova, A., Kozumplik, J., et al.: Heart rate variability analyzed by Poincare plot in patients with metabolic syndrome. J. Electrocardiol. 49, 23–28 (2016)

    Article  Google Scholar 

  22. Sharmila, V., Krishna, E.H., Reddy, K.A.: Cumulant based Teager energy operator for ECG signal modeling. In: IEEE Proceedings of 2013 International Conference on Advances in Computing, pp. 1959–1963 (2013)

    Google Scholar 

  23. Phukpattaranont, P.: QRS detection algorithm based on the quadratic filter. J. Expert Syst. Appl. 42, 4867–4877 (2015)

    Article  Google Scholar 

  24. Rangayan, R.M.: Biomedical Signal Analysis: A Case Study Approach. IEEE Press/Wiley, New York (2002)

    Google Scholar 

  25. Ning, X., Selesnick, I.W.: ECG enhancement and QRS detection based on sparse derivatives. J. Biomed. Signal Process. Control 8, 713–723 (2013)

    Article  Google Scholar 

  26. Saini, I., Singh, D., Khosla, A.: QRS detection using K-Nearest Neighbor algorithm and evaluation on standard ECG databases. J. Adv. Res. 4, 331–344 (2013)

    Article  Google Scholar 

  27. Ramakrishnan, A.G., Prathosh, A.P.: Threshold-independent QRS detection using the dynamic plosion index. IEEE Signal Process. Lett. 21, 554–558 (2014)

    Article  Google Scholar 

  28. Karimui, R.Y., Azadi, S.: Cardiac arrhythmia classification using the phase space sorted by Poincare sections. J. Biocybern. Biomed. Eng. 37, 690–700 (2017)

    Article  Google Scholar 

  29. Nallathambi, G., Principe, J.C.: Integrate and fire pulse train automaton for QRS detection. IEEE Trans. Biomed. Eng. 61(2), 317–326 (2013)

    Article  Google Scholar 

  30. Sedghamiz, H., Santonocito, D.: Unsupervised detection and classification of motor unit action potentials in intramuscular electromyography signals. In: Proceedings of the 5th IEEE International Conference on EHB (2015)

    Google Scholar 

  31. Vollmer, M.: A robust, simple and reliable measure of Heart Rate Variability using relative RR intervals. In: IEEE 2015 Computing in Cardiology Conference (2015)

    Google Scholar 

  32. Beritelli, F., Capizzi, G., Sciuto, L.G., Napoli, C., Wozniak, M.: A novel training method to preserve generalization of RBPNN classifiers applied to ECG signals diagnosis. Neural Networks 108, 331–338 (2018)

    Article  Google Scholar 

  33. Borys, M., Plechawska-Wójcik, M., Wawrzyk, M., Wesołowska, K.: Classifying cognitive workload using eye activity and EEG features in arithmetic tasks. In: Damaševičius, R., Mikašytė, V. (eds.) ICIST 2017. CCIS, vol. 756, pp. 90–105. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-67642-5_8

    Chapter  Google Scholar 

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Acknowledgment

This research was funded by a grant (No. 1.2.2-MITA-K-702) from the Agency for Science, Innovation and Technology (MITA) regarding Eureka project “Non-intrusive human fatigue assessment” (Eureka 11169 Fatigue).

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Correspondence to Eglė Butkevičiūtė .

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Butkevičiūtė, E., Eriņš, M., Bikulčienė, L. (2019). Mobile Platform for Fatigue Evaluation: HRV Analysis. In: Damaševičius, R., Vasiljevienė, G. (eds) Information and Software Technologies. ICIST 2019. Communications in Computer and Information Science, vol 1078. Springer, Cham. https://doi.org/10.1007/978-3-030-30275-7_42

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  • DOI: https://doi.org/10.1007/978-3-030-30275-7_42

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-30274-0

  • Online ISBN: 978-3-030-30275-7

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