How can we measure the slowing down of healthy and ischemic stroke individuals?

Document Type : Article


1 Department of Biomedical Engineering, Amirkabir University of Technology, Tehran, 15875-4413, Iran

2 Neuroscience Research Center, Baqiyatallah University of Medical Sciences, Tehran, Iran

3 Nonlinear Systems and Applications, Faculty of Electrical and Electronics Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam



Recently, resilience has attracted lots of attention in the study of biological systems. The goal of this paper is investigating the slowness in ischemic stroke patients. A Trier Social Stress Test (TSST) is used to reveal the slowness of the biological system. The slowness of dynamics is calculated for the ECG of healthy individuals and patients with ischemic stroke. The ECG is investigated in four stages: before stress, right after stress, 20 minutes after stress, and 40 minutes after stress. Ten healthy individuals and nine ischemic stroke patients are studied. Six early warning indicators based on slowness and variability are used in this study. The indicators are applied to the RR interval of individuals in four stages. Also, the results were normalized with the rest state of each individual. Heart rate variations were studied as another measure of the slowness of the dynamics. The results reveal that there is no significant difference in the slowness of healthy and patient cases. So, in this case, resilience cannot be used in predicting health problems.


References    1. Pirzad Jahromi, G., Shabanzadeh Pirsaraei, A., Sadr,    S.S., et al. Multipotent bone marrow stromal cell    therapy promotes endogenous cell proliferation following    ischemic stroke", Clin. Exp. Pharmacol. Physiol.,    42, pp. 1158{1167 (2015).    2. Jahromi, G.P., Shabanzadeh, A.P., Hashtjini, M.M., et    al. Bone marrow-derived mesenchymal stem cell and    simvastatin treatment leads to improved functional    recovery and modi_ed c-Fos expression levels in the    brain following ischemic stroke", Iran. J. Basic Med.    Sci., 21, p. 1004 (2018).    3. Mitchell, A.J., Sheth, B., Gill, J., et al. Prevalence    and predictors of post-stroke mood disorders: a metaanalysis    and meta-regression of depression, anxiety and    adjustment disorder", Gen. Hosp. Psychiatry, 47, pp.    48{60 (2017).    4. Bao, A.-M., Meynen, G., and Swaab, D. The stress    system in depression and neurodegeneration: focus on    the human hypothalamus", Brain Res. Rev., 57, pp.    531{553 (2008).    5. Yaribeygi, H., Panahi, Y., Sahraei, H., et al. The    impact of stress on body function: A review", Exp.    Clin. Sci. J., 16, p. 1057 (2017).    6.  Ori, Z., Monir, G., Weiss, J., et al. Heart rate    variability: frequency domain analysis", Cardiol. Clin.,    10, pp. 499{533 (1992).    7. Dutsch, M., Burger, M., Dorer, C., et al. Cardiovascular    autonomic function in poststroke patients",    Neurology, 69, pp. 2249{2255 (2007).    8. Mirzaee, O., Saneian, M., Vani, J.R., et al. The    psychophysiological responses of the chronic ischemic    stroke patients to the acute stress were changed", Braz.    Arch. Biol. Technol., 62, p. e19180494 (2019).    9. Narin, A., Isler, Y., Ozer, M., et al. Early prediction    of paroxysmal atrial _brillation based on short-term    heart rate variability", Physica A, 509, pp. 56{65    (2018).    10. Jagric, T., Marhl, M., stajer, D., et al. Irregularity    test for very short electrocardiogram (ECG) signals    as a method for predicting a successful de_brillation    in patients with ventricular _brillation", Transl. Res.,    149, pp. 145{151 (2007).    11. Isler, Y., Narin, A., Ozer, M., et al. Multi-stage    classi_cation of congestive heart failure based on shortterm    heart rate variability", Chaos, Solitons Fractals,    118, pp. 145{151 (2019).    12. Sche_er, M., Carpenter, S.R., Lenton, T.M., et al.    Anticipating critical transitions", Science, 338, pp.    344{348 (2012).    13. Sche_er, M., Bascompte, J., Brock, W.A., et al.    Early-warning signals for critical transitions", Nature,    461, pp. 53{59 (2009).    14. Moghadam, N.N., Nazarimehr, F., Jafari, S., et al.    Studying the performance of critical slowing down indicators    in a biological system with a period-doubling    route to chaos", Physica A, 544, p. 123396 (2020).    15. Dakos, V., Carpenter, S.R., van Nes, E.H., et al.    Resilience indicators: prospects and limitations for    early warnings of regime shifts", Philos. Trans. R. Soc.    B, 370, p. 20130263 (2015).    16. Dakos, V., Carpenter, S.R., Brock, W.A., et al. Methods    for detecting early warnings of critical transitions    in time series illustrated using simulated ecological    data", PLoS One, 7, p. e41010 (2012).    17. Nazarimehr, F., Gha_ari, A., Jafari, S., et al. Sparse    recovery and dictionary learning to identify the nonlinear    dynamical systems: One step toward _nding    bifurcation points in real systems", Int. J. Bifurcation    Chaos, 29, p. 1950030 (2019).    18. Nazarimehr, F., Jafari, S., Golpayegani, S.M.R.H., et    al. Can Lyapunov exponent predict critical transitions    in biological systems?", Nonlinear Dyn., 88, pp.    1493{1500 (2017).    19. Nazarimehr, F., Jafari, S., Golpayegani, S.M.R.H., et    al. Predicting tipping points of dynamical systems    during a period-doubling route to chaos", Chaos, 28,    p. 073102 (2018).    20. Olde Rikkert, M.G., Dakos, V., Buchman, T.G., et    al. Slowing down of recovery as generic risk marker    for acute severity transitions in chronic diseases", Crit.    Care Med., 44, pp. 601{606 (2016).    21. Sche_er, M., Bolhuis, J.E., Borsboom, D., et al.    Quantifying resilience of humans and other animals",    Proc. Natl. Acad. Sci., U.S.A., 115, pp. 11883{11890    (2018).    22. Nazarimehr, F., Golpayegani, S.M.R.H., and Hatef,    B. Does the onset of epileptic seizure start from a    bifurcation point?", Eur. Phys. J. Spec. Top., 227, pp.    697{705 (2018).    23. Wichers, M., Groot, P.C., Psychosystems, E., et al.    Critical slowing down as a personalized early warning    signal for depression", Psychother. Psychosom., 85,    pp. 114{116 (2016).    1660 F. Nazarimehr et al./Scientia Iranica, Transactions D: Computer Science & ... 28 (2021) 1653{1660    24. Gijzel, S.M., van de Leemput, I.A., Sche_er, M., et    al. Dynamical resilience indicators in time series of    self-rated health correspond to frailty levels in older    adults", J. Gerontol. Ser. A, 72, pp. 991{996 (2017).    25. Gijzel, S.M., van de Leemput, I.A., Sche_er, M., et al.    Dynamical indicators of resilience in postural balance    time series are related to successful aging in highfunctioning    older adults", J. Gerontol. Ser. A, 74, pp.    1119{1126 (2019).    26. Le, V.-V., Mitiku, T., Sungar, G., et al. The blood    pressure response to dynamic exercise testing: a systematic    review", Prog. Cardiovasc. Dis., 51, pp. 135{    160 (2008).    27. Saal, D., Thijs, R., and Van Dijk, J. Tilt table    testing in neurology and clinical neurophysiology",    Clin. Neurophysiol., 127, pp. 1022{1030 (2016).    28. Carpenter, S.R. and Brock, W.A. Rising variance: a    leading indicator of ecological transition", Ecol. Lett.,    9, pp. 311{318 (2006).    29. Qi, J. and Yang, H. Hurst exponents for short time    series", Phys. Rev. E, 84, p. 066114 (2011).    30. Grassberger, P. and Procaccia, I. Characterization of    strange attractors", Phys. Rev. Lett., 50, pp. 346{349    (1983).    31. Biggs, R., Carpenter, S.R., and Brock, W.A. Turning    back from the brink: detecting an impending regime    shift in time to avert it", Proc. Natl. Acad. Sci., U.S.A.,    106, pp. 826{831 (2009).