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Applications of Heartbeat Complexity Analysis to Depression and Bipolar Disorder

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Complexity and Nonlinearity in Cardiovascular Signals

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Abstract

Nonlinear digital signal processing methods addressing system complexity have provided useful computational tools for helping in the diagnosis and treatment monitoring of a wide range of pathologies. In particular, heartbeat complexity measures have been successful in characterizing patients with mental disorders such as Major Depression and Bipolar Disorder. In this chapter, we describe the use of standard complexity measures such as sample entropy and multiscale entropy, as well as instantaneous measures of entropy to characterize pathological mood states when patients undergo affective elicitation or long-term monitoring. Results demonstrate that complexity measures of cardiovascular dynamics can be promising and viable tools to support clinical decision in mental health, improving on the diagnosis and management of psychiatric disorders.

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References

  1. Marmarelis, V.Z.: Nonlinear Dynamic Modeling of Physiological Systems, vol. 10. Wiley, New York (2004)

    Book  Google Scholar 

  2. Valenza, G., Scilingo, E.P.: Autonomic Nervous System Dynamics for Mood and Emotional-State Recognition: Significant Advances in Data Acquisition, Signal Processing and Classification. Springer, Cham (2014). http://www.springer.com/gp/book/9783319026381

    Book  Google Scholar 

  3. Huikuri, H.V., Mäkikallio, T.H., Peng, C.-K., Goldberger, A.L., Hintze, U., Møller, M., Group, D.S., et al.: Fractal correlation properties of RR interval dynamics and mortality in patients with depressed left ventricular function after an acute myocardial infarction. Circulation 101(1), 47–53 (2000)

    Article  CAS  Google Scholar 

  4. Sassi, R., Signorini, M.G., Cerutti, S.: Multifractality and heart rate variability. Chaos: Interdiscip. J. Nonlinear Sci. 19(2), 028507 (2009)

    Article  Google Scholar 

  5. Valenza, G., Allegrini, P., Lanatà, A., Scilingo, E.P.: Dominant lyapunov exponent and approximate entropy in heart rate variability during emotional visual elicitation. Front. Neuroeng. 5, 3 (2012)

    Article  PubMed  PubMed Central  Google Scholar 

  6. Lanata, A., Valenza, G., Scilingo, E.P.: A novel EDA glove based on textile-integrated electrodes for affective computing. Med. Biol. Eng. Comput. 50(11), 1163–1172 (2012)

    Article  PubMed  Google Scholar 

  7. Valenza, G., Lanata, A., Scilingo, E.P.: The role of nonlinear dynamics in affective valence and arousal recognition. IEEE Trans. Affect. Comput. 3(2), 237–249 (2012)

    Article  Google Scholar 

  8. Valenza, G., Citi, L., Scilingo, E.P., Barbieri, R.: Point-process nonlinear models with Laguerre and Volterra expansions: instantaneous assessment of heartbeat dynamics. IEEE Trans. Signal Process. 61(11), 2914–2926 (2013)

    Article  Google Scholar 

  9. Valenza, G., Lanatá, A., Scilingo, E.P.: Improving emotion recognition systems by embedding cardiorespiratory coupling. Physiol. Meas. 34(4), 449 (2013)

    Article  PubMed  Google Scholar 

  10. Valenza, G., Lanata, A., Scilingo, E.P.: Oscillations of heart rate and respiration synchronize during affective visual stimulation. IEEE Trans. Inf. Technol. Biomed. 16(4), 683–690 (2012)

    Article  PubMed  Google Scholar 

  11. Sunagawa, K., Kawada, T., Nakahara, T.: Dynamic nonlinear vago-sympathetic interaction in regulating heart rate. Heart Vessels 13(4), 157–174 (1998)

    Article  CAS  PubMed  Google Scholar 

  12. Carney, R.M., Freedland, K.E., Miller, G.E., Jaffe, A.S.: Depression as a risk factor for cardiac mortality and morbidity: a review of potential mechanisms. J. Psychosom. Res. 53(4), 897–902 (2002)

    Article  PubMed  Google Scholar 

  13. Agelink, M.W., Boz, C., Ullrich, H., Andrich, J.: Relationship between major depression and heart rate variability: clinical consequences and implications for antidepressive treatment. Psychiat. Res. 113(1), 139–149 (2002)

    Article  Google Scholar 

  14. Iverson, G., et al.: A new potential marker for abnormal cardiac physiology in depression. J. Behav. Med. 28(6), 507–511 (2005)

    Article  PubMed  Google Scholar 

  15. Sponheim, S., Allen, J., Iacono, W.: Selected psychophysiological measures in depression: the significance of electrodermal activity, electroencephalographic asymmetries, and contingent negative variation to behavioral and neurobiological aspects of depression. In: Behavioral High Risk Paradigm in Psychopathology, pp. 222–249 (1995)

    Google Scholar 

  16. Taillard, J., et al.: Heart rate orcadian rhythm as a biological marker of desynchronization in major depression: a methodological and preliminary report. Chronobiol. Int. 7(4), 305–316 (1990)

    Article  CAS  PubMed  Google Scholar 

  17. Valenza, G., Nardelli, M., Lanata, A., Gentili, C., Bertschy, G., Paradiso, R., Scilingo, E.P.: Wearable monitoring for mood recognition in bipolar disorder based on history-dependent long-term heart rate variability analysis. IEEE J. Biomed. Health Informatics 18(5), 1625–1635 (2014)

    Article  Google Scholar 

  18. Valenza, G., Gentili, C., Lanatà, A., Scilingo, E.P.: Mood recognition in bipolar patients through the psyche platform: preliminary evaluations and perspectives. Artif. Intell. Med. 57(1), 49–58 (2013)

    Article  PubMed  Google Scholar 

  19. Voss, A., et al.: Linear and nonlinear methods for analyses of cardiovascular variability in bipolar disorders. Bipolar Disord. 8(5p1), 441–452 (2006)

    Google Scholar 

  20. Levy, B.: Autonomic nervous system arousal and cognitive functioning in bipolar disorder. Bipolar Disord. 15(1), 70–79 (2013)

    Article  PubMed  Google Scholar 

  21. Latalova, K., et al.: Autonomic nervous system in euthymic patients with bipolar affective disorder. Neuro Endocrinol. Lett. 31(6), 829 (2010)

    PubMed  Google Scholar 

  22. Schulz, S., Koschke, M., Bär, K.-J., Voss, A.: The altered complexity of cardiovascular regulation in depressed patients. Physiol. Meas. 31(3), p. 303 (2010)

    Article  PubMed  Google Scholar 

  23. Leistedt, S.J., Linkowski, P., Lanquart, J., Mietus, J., Davis, R.B., Goldberger, A.L., Costa, M.D.: Decreased neuroautonomic complexity in men during an acute major depressive episode: analysis of heart rate dynamics. Transl. Psychiatry 1(7), e27 (2011)

    Article  PubMed  PubMed Central  Google Scholar 

  24. Pincus, S.: Approximate entropy as a measure of system complexity. Proc. Natl. Acad. Sci. 88(6), 2297 (1991)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Pincus, S.: Approximate entropy (ApEn) as a complexity measure. Chaos: Interdiscip. J. Nonlinear Sci. 5(1), 110–117 (1995)

    Article  Google Scholar 

  26. Richman, J., Moorman, J.: Physiological time-series analysis using approximate entropy and sample entropy. Am. J. Physiol.- Heart Circul. Physiol. 278(6), H2039 (2000)

    Google Scholar 

  27. Srinivasan, V., Eswaran, C., Sriraam, N.: Approximate entropy-based epileptic EEG detection using artificial neural networks. IEEE Trans. Inf. Technol. Biomed. 11(3), 288–295 (2007)

    Article  PubMed  Google Scholar 

  28. Fusheng, Y., Bo, H., Qingyu, T.: Approximate entropy and its application to biosignal analysis. In: Nonlinear Biomedical Signal Processing: Dynamic Analysis and Modeling, vol. 2, pp. 72–91 (2001)

    Google Scholar 

  29. Chen, X., Solomon, I., Chon, K.: Comparison of the use of approximate entropy and sample entropy: applications to neural respiratory signal. In: Conference Proceedings: … Annual International Conference of the IEEE Engineering in Medicine and Biology Society, vol. 4, pp. 4212–4215 (2004). IEEE Engineering in Medicine and Biology Society, Piscataway

    Google Scholar 

  30. Takens, F.: Detecting strange attractors in turbulence. In: Dynamical Systems and Turbulence, Warwick 1980, pp. 366–381. Springer, Berlin (1981)

    Google Scholar 

  31. Schouten, J.C., Takens, F., van den Bleek, C.M.: Estimation of the dimension of a noisy attractor. Phys. Rev. E 50(3), 1851 (1994)

    Article  CAS  Google Scholar 

  32. Costa, M., Goldberger, A.L., Peng, C.-K.: Multiscale entropy analysis of complex physiologic time series. Phys. Rev. Lett. 89(6), 068102 (2002)

    Article  PubMed  Google Scholar 

  33. Costa, M., Peng, C.-K., Goldberger, A.L., Hausdorff, J.M.: Multiscale entropy analysis of human gait dynamics. Phys. A: Stat. Mech. Appl. 330(1), 53–60 (2003)

    Article  Google Scholar 

  34. Park, J.-H., Kim, S., Kim, C.-H., Cichocki, A., Kim, K.: Multiscale entropy analysis of EEG from patients under different pathological conditions. Fractals 15(04), 399–404 (2007)

    Article  Google Scholar 

  35. Takahashi, T., Cho, R.Y., Mizuno, T., Kikuchi, M., Murata, T., Takahashi, K., Wada, Y.: Antipsychotics reverse abnormal EEG complexity in drug-naive schizophrenia: a multiscale entropy analysis. Neuroimage 51(1), 173–182 (2010)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Catarino, A., Churches, O., Baron-Cohen, S., Andrade, A., Ring, H.: Atypical EEG complexity in autism spectrum conditions: a multiscale entropy analysis. Clin. Neurophysiol. 122(12), 2375–2383 (2011)

    Article  PubMed  Google Scholar 

  37. Trunkvalterova, Z., Javorka, M., Tonhajzerova, I., Javorkova, J., Lazarova, Z., Javorka, K., Baumert, M.: Reduced short-term complexity of heart rate and blood pressure dynamics in patients with diabetes mellitus type 1: multiscale entropy analysis. Physiol. Meas. 29(7), 817 (2008)

    Article  CAS  PubMed  Google Scholar 

  38. Valencia, J.F., Porta, A., Vallverdú, M., Claria, F., Baranowski, R., Orlowska-Baranowska, E., Caminal, P.: Refined multiscale entropy: application to 24-h holter recordings of heart period variability in healthy and aortic stenosis subjects. IEEE Trans. Biomed. Eng. 56(9), 2202–2213 (2009)

    Article  PubMed  Google Scholar 

  39. Turianikova, Z., Javorka, K., Baumert, M., Calkovska, A., Javorka, M.: The effect of orthostatic stress on multiscale entropy of heart rate and blood pressure. Physiol. Meas. 32(9), 1425 (2011)

    Article  PubMed  Google Scholar 

  40. Castiglioni, P., Di Rienzo, M.: How the threshold “r” influences approximate entropy analysis of heart-rate variability. In: 2008 Computers in Cardiology, pp. 561–564. IEEE, New York (2008)

    Google Scholar 

  41. Chon, K.H., Scully, C.G., Lu, S.: Approximate entropy for all signals. IEEE Eng. Med. Biol. Mag. 28(6), 18–23 (2009)

    Article  PubMed  Google Scholar 

  42. Magalhaes, F., de Sá, J.M., Bernardes, J., Ayres-de Campos, D.: Characterization of fetal heart rate irregularity using approximate entropy and wavelet filtering. In: 2006 Computers in Cardiology, pp. 933–936. IEEE, New York (2006)

    Google Scholar 

  43. Lu, S., Chen, X., Kanters, J.K., Solomon, I.C., Chon, K.H.: Automatic selection of the threshold value for approximate entropy. IEEE Trans. Biomed. Eng. 55(8), 1966–1972 (2008)

    Article  PubMed  Google Scholar 

  44. Alcaraz, R., Abásolo, D., Hornero, R., Rieta, J.J.: Optimal parameters study for sample entropy-based atrial fibrillation organization analysis. Comput. Methods Progr. Biomed. 99(1), 124–132 (2010)

    Article  Google Scholar 

  45. Boskovic, A., Loncar-Turukalo, T., Sarenac, O., Japundzic-Zigon, N., Bajic, D.: Unbiased entropy estimates in stress: a parameter study. Comput. Biol. Med. 42(6), 667–679 (2012)

    Article  PubMed  Google Scholar 

  46. Zurek, S., Guzik, P., Pawlak, S., Kosmider, M., Piskorski, J.: On the relation between correlation dimension, approximate entropy and sample entropy parameters, and a fast algorithm for their calculation. Phys. A: Stat. Mech. Appl. 391(24), 6601–6610 (2012)

    Article  Google Scholar 

  47. Acharya, U.R., Joseph, K.P., Kannathal, N., Lim, C.M., Suri, J.S.: Heart rate variability: a review. Med. Biol. Eng. Comput. 44(12), 1031–1051 (2006)

    Article  Google Scholar 

  48. Barbieri, R., Matten, E., Alabi, A., Brown, E.: A point-process model of human heartbeat intervals: new definitions of heart rate and heart rate variability. Am. J. Physiol.-Heart Circul. Physiol. 288(1), H424 (2005)

    Article  CAS  Google Scholar 

  49. Citi, L., Valenza, G., Barbieri, R.: Instantaneous estimation of high-order nonlinear heartbeat dynamics by lyapunov exponents. In: Proceedings of IEEE-EMBC, pp. 13–16. IEEE, New York (2012)

    Google Scholar 

  50. Merikangas, K., et al.: Prevalence and correlates of bipolar spectrum disorder in the world mental health survey initiative. Arch. Gen. Psychiatry 68(3), 241 (2011)

    Article  PubMed  PubMed Central  Google Scholar 

  51. A. P. Association: Diagnostic and statistical manual of mental disorders: DSM-IV-TR®;. American Psychiatric Publishing, Arlington (2000)

    Google Scholar 

  52. Revicki, D.A., Matza, L.S., Flood, E., Lloyd, A.: Bipolar disorder and health-related quality of life. Pharmacoeconomics 23(6), 583–594 (2005)

    Article  PubMed  Google Scholar 

  53. Michalak, E.E., Yatham, L.N., Lam, R.W.: Quality of life in bipolar disorder: a review of the literature. Health Qual. Life Outcomes 3(1), 72 (2005)

    Article  PubMed  PubMed Central  Google Scholar 

  54. Brissos, S., Dias, V.V., Kapczinski, F.: Cognitive performance and quality of life in bipolar disorder. Can. J. Psychiatry: Rev. Can. Psychiatr. 53(8), 517–524 (2008)

    Article  Google Scholar 

  55. Kauer-Sant’Anna, M., Frey, B.N., Andreazza, A.C., Cereser, K.M., Gazalle, F.K., Tramontina, J., da Costa, S.C., Santin, A., Kapczinski, F.: Anxiety comorbidity and quality of life in bipolar disorder patients. Can. J. Psychiatry: Rev. Can. Psychiatr. 52(3), 175–181 (2007)

    Article  Google Scholar 

  56. Kessler, R., McGonagle, K., Zhao, S., Nelson, C., Hughes, M., Eshleman, S., et al.: Lifetime and 12-month prevalence of DSM-III-R psychiatric disorders in the United States: results from the national comorbidity survey. Arch. Gen. Psychiatry 51(1), 8 (1994)

    Article  CAS  PubMed  Google Scholar 

  57. Wittchen, H., Jacobi, F.: Size and burden of mental disorders in Europe–a critical review and appraisal of 27 studies. Eur. Neuropsychopharmacol. 15(4), 357–376 (2005)

    Article  CAS  PubMed  Google Scholar 

  58. Pini, S., de Queiroz, V., et al.: Prevalence and burden of bipolar disorders in European countries. Eur. Neuropsychopharmacol. 15(4), 425–434 (2005)

    Article  CAS  PubMed  Google Scholar 

  59. Chen, Y.-W., Dilsaver, S.C.: Lifetime rates of suicide attempts among subjects with bipolar and unipolar disorders relative to subjects with other axis I disorders. Biol. Psychiatry 39(10), 896–899 (1996)

    Article  CAS  PubMed  Google Scholar 

  60. Vieta, E., Reinares, M., Rosa, A.: Staging bipolar disorder. Neurotox. Res. 19(2), 279–285 (2011)

    Article  PubMed  Google Scholar 

  61. Greco, A., Valenza, G., Lanata, A., Rota, G., Scilingo, E.P.: Electrodermal activity in bipolar patients during affective elicitation. IEEE J. Biomed. Health Inform. 18(6), 1865–1873 (2014)

    Article  PubMed  Google Scholar 

  62. Greco, A., Lanata, A., Valenza, G., Rota, G., Vanello, N., Scilingo, E.: On the deconvolution analysis of electrodermal activity in bipolar patients. In: Proceeding of IEEE-EMBC, pp. 6691–6694. IEEE, New York (2012)

    Google Scholar 

  63. Watkins, L., et al.: Association of anxiety with reduced baroreflex cardiac control in patients after acute myocardial infarction. Am. Heart J. 143(3), 460–466 (2002)

    Article  PubMed  Google Scholar 

  64. Taillard, J., et al.: Sleep and heart rate circadian rhythm in depression: the necessity to separate. Chronobiol. Int. 10(1), 63–72 (1993)

    Article  CAS  PubMed  Google Scholar 

  65. Scilingo, E., Gemignani, A., Paradiso, R., Taccini, N., Ghelarducci, B., De Rossi, D.: Performance evaluation of sensing fabrics for monitoring physiological and biomechanical variables. IEEE Trans. Inf. Technol. Biomed. 9(3), 345–352 (2005)

    Article  PubMed  Google Scholar 

  66. Rajendra Acharya, U., Paul Joseph, K., Kannathal, N., Lim, C., Suri, J.: Heart rate variability: a review. Med. Biol. Eng. Comput. 44(12), 1031–1051 (2006)

    Article  CAS  PubMed  Google Scholar 

  67. Karasik, R., Sapir, N., Ashkenazy, Y., Ivanov, P.C., Dvir, I., Lavie, P., Havlin, S.: Correlation differences in heartbeat fluctuations during rest and exercise. Phys. Rev. E 66(6), 062902 (2002)

    Article  Google Scholar 

  68. Ivanov, P.C., Bunde, A., Amaral, L.N., Havlin, S., Fritsch-Yelle, J., Baevsky, R.M., Stanley, H.E., Goldberger, A.L.: Sleep-wake differences in scaling behavior of the human heartbeat: analysis of terrestrial and long-term space flight data. Europhys. Lett. 48(5), 594 (1999)

    Article  CAS  PubMed  Google Scholar 

  69. Kantelhardt, J.W., Ashkenazy, Y., Ivanov, P.C., Bunde, A., Havlin, S., Penzel, T., Peter, J.-H., Stanley, H.E.: Characterization of sleep stages by correlations in the magnitude and sign of heartbeat increments. Phys. Rev. E 65(5), 051908 (2002)

    Article  Google Scholar 

  70. Ivanov, P.C., Hu, K., Hilton, M.F., Shea, S.A., Stanley, H.E.: Endogenous circadian rhythm in human motor activity uncoupled from circadian influences on cardiac dynamics. Proc. Natl. Acad. Sci. 104(52), 20702–20707 (2007)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Amaral, L.A.N., Ivanov, P.C., Aoyagi, N., Hidaka, I., Tomono, S., Goldberger, A.L., Stanley, H.E., Yamamoto, Y.: Behavioral-independent features of complex heartbeat dynamics. Phys. Rev. Lett. 86(26), 6026 (2001)

    Article  Google Scholar 

  72. Schmitt, D.T., Ivanov, P.C.: Fractal scale-invariant and nonlinear properties of cardiac dynamics remain stable with advanced age: a new mechanistic picture of cardiac control in healthy elderly. Am. J. Physiol.-Regul. Integr. Comp. Physiol. 293(5), R1923–R1937 (2007)

    Article  CAS  PubMed  Google Scholar 

  73. Barnett, A., Wolff, R.: A time-domain test for some types of nonlinearity. IEEE Trans. Signal Process. 53(1), 26–33 (2005)

    Article  Google Scholar 

  74. Ivanov, P.C., Ma, Q.D., Bartsch, R.P., Hausdorff, J.M., Amaral, L.A.N., Schulte-Frohlinde, V., Stanley, H.E., Yoneyama, M.: Levels of complexity in scale-invariant neural signals. Phys. Rev. E 79(4), 041920 (2009)

    Article  Google Scholar 

  75. Ashkenazy, Y., Havlin, S., Ivanov, P.C., Peng, C.-K., Schulte-Frohlinde, V., Stanley, H.E.: Magnitude and sign scaling in power-law correlated time series. Phys. A: Stat. Mech. Appl. 323, 19–41 (2003)

    Article  Google Scholar 

  76. Ivanov, P.C., Amaral, L.A.N., Goldberger, A.L., Havlin, S., Rosenblum, M.G., Struzik, Z.R., Stanley, H.E.: Multifractality in human heartbeat dynamics. Nature 399(6735), 461–465 (1999)

    Article  CAS  PubMed  Google Scholar 

  77. Nierenberg, A.A.: Major depressive disorder: epidemiology, course of illness, and treatment. CNS Spectr. 13(S8), 4–7 (2008)

    Article  PubMed  Google Scholar 

  78. Soleimani, L., Lapidus, K.A., Iosifescu, D.V.: Diagnosis and treatment of major depressive disorder. Neurol. Clin. 29(1), 177–193 (2011)

    Article  PubMed  Google Scholar 

  79. Rakofsky, J.J., Schettler, P.J., Kinkead, B.L., Frank, E., Judd, L.L., Kupfer, D.J., Rush, A.J., Thase, M.E., Yonkers, K.A., Rapaport, M.H.: The prevalence and severity of depressive symptoms along the spectrum of unipolar depressive disorders: a post hoc analysis. J. Clin. Psychiatry 74(11), 1084–1091 (2013)

    Article  PubMed  Google Scholar 

  80. Mathews, A., MacLeod, C.: Cognitive vulnerability to emotional disorders. Annu. Rev. Clin. Psychol. 1, 167–195 (2005)

    Article  PubMed  Google Scholar 

  81. Valenza, G., Citi, L., Scilingo, E.P., Barbieri, R.: Inhomogeneous point-process entropy: an instantaneous measure of complexity in discrete systems. Phys. Rev. E 89(5), 052803 (2014)

    Article  Google Scholar 

  82. Zung, W.W.: A self-rating depression scale. Arch. Gen. Psychiatry 12(1), 63–70 (1965)

    Article  CAS  PubMed  Google Scholar 

  83. Campo-Arias, A., Díaz-Martínez, L.A., Eduardo, G., Jaimes, R., Cadena, L.D.P., Hernández, N.L.: Validation of Zung’s self-rating depression scale among the colombian general population. Soc. Behav. Personal.: Int. J. 34(1), 87–94 (2006)

    Article  Google Scholar 

  84. Cahill, L., McGaugh, J.L.: A novel demonstration of enhanced memory associated with emotional arousal. Conscious. Cogn. 4(4), 410–421 (1995)

    Article  CAS  PubMed  Google Scholar 

  85. Yang, A.C., Tsai, S.-J.: Is mental illness complex? From behavior to brain. Progr. Neuro-Psychopharmacol. Biol. Psychiatry 45, 253–257 (2013)

    Article  Google Scholar 

  86. Valenza, G., Nardelli, M., Bertschy, G., Lanata, A., Scilingo, E.: Mood states modulate complexity in heartbeat dynamics: a multiscale entropy analysis. Europhys. Lett. 107(1), 18003 (2014)

    Article  Google Scholar 

  87. Thayer, J.F., Friedman, B.H., Borkovec, T.D.: Autonomic characteristics of generalized anxiety disorder and worry. Biol. Psychiatry 39(4), 255–266 (1996)

    Article  CAS  PubMed  Google Scholar 

  88. LeMoult, J., Yoon, K.L., Joormann, J.: Affective priming in major depressive disorder. Front. Integr. Neurosci. 6, 76 (2012)

    Article  PubMed  PubMed Central  Google Scholar 

  89. Beckers, F., Verheyden, B., Ramaekers, D., Swynghedauw, B., Aubert, A.E.: Effects of autonomic blockade on non-linear cardiovascular variability indices in rats. Clin. Exp. Pharmacol. Physiol. 33(5–6), 431–439 (2006)

    Article  CAS  PubMed  Google Scholar 

  90. Valenza, G., Citi, L., Barbieri, R.: Estimation of instantaneous complex dynamics through lyapunov exponents: a study on heartbeat dynamics. PLoS One 9(8), e105622 (2014)

    Article  PubMed  PubMed Central  Google Scholar 

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Authors and Affiliations

  1. Department of Information Engineering & Bioengineering and Robotics Research Center “E. Piaggio”, University of Pisa, School of Engineering, Pisa, Italy

    Gaetano Valenza & Enzo Pasquale Scilingo

  2. School of Computer Science and Electronic Engineering, University of Essex, Colchester, UK

    Luca Citi

  3. Bioengineering and Robotics Research Centre “E. Piaggio” and Department of Information Engineering, Pisa, University of Pisa, Italy

    Antonio Lanata

  4. University of Padua, Padua, Italy

    Claudio Gentili

  5. Department of Electronics, Informatics and Bioengineering, Politecnico di Milano, Milan, Italy

    Riccardo Barbieri

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  1. Gaetano Valenza
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  3. Antonio Lanata
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  4. Claudio Gentili
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  6. Enzo Pasquale Scilingo
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Correspondence to Gaetano Valenza .

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Editors and Affiliations

  1. Department of Electronics, Informatics and Bioengineering, Politecnico di Milano, Milan, Italy

    Riccardo Barbieri

  2. Department of Information Engineering & Bioengineering and Robotics Research Center “E. Piaggio”, University of Pisa, School of Engineering, Pisa, Italy

    Enzo Pasquale Scilingo

  3. Department of Information Engineering & Bioengineering and Robotics Research Center “E. Piaggio”, University of Pisa, School of Engineering, Pisa, Italy

    Gaetano Valenza

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Valenza, G., Citi, L., Lanata, A., Gentili, C., Barbieri, R., Scilingo, E.P. (2017). Applications of Heartbeat Complexity Analysis to Depression and Bipolar Disorder. In: Barbieri, R., Scilingo, E., Valenza, G. (eds) Complexity and Nonlinearity in Cardiovascular Signals. Springer, Cham. https://doi.org/10.1007/978-3-319-58709-7_13

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