1.

Akselrod S, Gordon D, Ubel FA, Shannon DC, Berger AC, Cohen RJ. Power spectrum analysis of heart rate fluctuation: a quantitative probe of beat-to-beat cardiovascular control. Science 1981;213:220–2.Google Scholar

2.

Malik M, Bigger JT, Cam AJ, Kleiger RE, Malliani A, Moss AJ, et al. Task force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology. Heart rate variability. Standards of measurement, physiological interpretation, and clinical use. Eur Heart J 1996;17:354–81.CrossrefGoogle Scholar

3.

Parin VV, Baevskii RM, Gazenko OG. Achievements and problems of modern space cardiology. Kardiologiia 1965;22:3–11.Google Scholar

4.

Parin VV, Baevskii RM, Gazenko OG. Heart and circulation under space conditions. Cor Vasa 1965;32:165–84.Google Scholar

5.

Zhemaitite D. Statistical analysis of the sinusal activity in normal and pathological states. In: Methods of mathematical analysis of the heart rhythm. Nauka: Moscow, Russia, 1967.Google Scholar

6.

Baeovskii RM, Zamotaev IP, Nidekker IG. Mathematical analysis of sinus automatism for prognosis of rhythm disorders. Kardiologiia 1971;11:65–8.Google Scholar

7.

Baevskii RM, Barsukova ZhV, Tazetdinov IG. Cybernetic analysis of the heart rhythm in the measured physical loading test with crew members of the Saliut-6 orbital station. Kardiologiia 1981;21:100–4.Google Scholar

8.

Baevskii RM. Analysis of heart rate variability in space medicine. Fiziol Cheloveka 2002;28:70–82.Google Scholar

9.

Nidekker IG. Method of spectral analysis for long-term recordings of physiological curves. Kosm Biol Aviakosm Med 1981;15:78–82.Google Scholar

10.

Estévez Báez M, Casanova-Sotolongo P, Peñalver JC, Zayas-López F. El electroencefalograma en la evaluación del estado funcional del piloto de caza después de los vuelos. Revista de Medicina Militar 1983;2:95–104.Google Scholar

11.

Estévez Báez M, Peñalver JC, Trápaga Ortega M, Cintas González E. Estudio de la excitabilidad cortical del cerebro del hombre en condiciones de hipodinamia y antiortostasis. Boletín Academia de Ciencias de Cuba Suppl Especial 1983;141–3.Google Scholar

12.

Estévez Báez M, Asyamolova NM, Brodyetskaya L, Rodríguez F, Peñalver JC, Grachev VA, et al. Investigación de la actividad bioeléctrica cerebral de los cosmonautas en estado de impesantez. Experimento ‘Córtex’. Orbita Extraordinario, 1985;54–76.Google Scholar

13.

Estévez Báez M, Matveev AD, Kornilova LN, Tarasov IK, Borunov NL, Peñalver JC, et al. Estudio comparativo de la enfermedad del movimiento y la estabilidad vestíbulo-vegetativa en condiciones terrestres y del vuelo cósmico. Experimento ‘Encuesta,’ órbita Extraordinario 1985;82–91.Google Scholar

14.

Gazenko OG, Papenfuss W, Hideg J, Estévez Báez M. Results of the medical experiments during the spatial flights of the international crews. Rev Med Lotnicza 1988;1:6–14.Google Scholar

15.

Estévez Báez M. A medicina espacial cubana e o sistema neuromega. Saude para Todos 1993;20–6.Google Scholar

16.

Estévez-Báez M, Iglesias-Alfonso J, Villar-Olivera C, Cabana-González J, Fernández-Pérez L, Pujol-García J. El sistema neuromega en la evaluación de la influencia del estrés. Rev Cubana de Med Militar 1994;23:42–55.Google Scholar

17.

Berger RD, Akselrod S, Gordon D, Cohen RJ. An efficient algorithm for spectral analysis of heart rate variability. IEEE Trans Biomed Eng 1986;33:900–4.CrossrefGoogle Scholar

18.

Oppenheim AV, Schafer RW. Discrete-time signal processing. Upper Saddle River, New Jersey: Prentice-Hall, 1999.Google Scholar

19.

Marple SL. Digital spectral analysis. Engelwood Cliffs, NJ: Prentice Hall, 1987.Google Scholar

20.

Welch PD. The use of fast Fourier transform for the estimation of power spectra: a method based on time averaging over short, modified periodograms. IEEE Trans Audio Electroacoustics 1967;AU-15:70–3.CrossrefGoogle Scholar

21.

The MathWorks. MATLAB signal processing toolbox – help. Availble at: http://www.mathworks.com/help/releases/R2011a/pdf_doc/allpdf.html#signal. Accessed 4 February, 2014.

22.

Semmlow JL. Biosignal and biomedical image processing: MATLAB-based applications. New York: Marcel Dekker, Inc., 2014.Google Scholar

23.

Laguna P, Moody GB, Mark RG. Power spectral density of unevenly sampled data by least-square analysis: performance and application to heart rate signals. IEEE Trans Biomed Eng 1998;45:698–715.CrossrefGoogle Scholar

24.

Rudiger H, Klinghammer L, Scheuch K. The trigonometric regressive spectral analysis--a method for mapping of beat-to-beat recorded cardiovascular parameters on to frequency domain in comparison with Fourier transformation. Comp Meth Prog Biomed 1999;58:1–15.CrossrefGoogle Scholar

25.

Clifford GD, Tarassenko L. Segmenting cardiac-related data using sleep stages increases separation between normal subjects and apnoeic patients. Physiol Meas 2004;25:N27–35.CrossrefGoogle Scholar

26.

Laude D, Elghozi JL, Girard A, Bellard E, Bouhaddi M, Castiglioni P, et al. Comparison of various techniques used to estimate spontaneous baroreflex sensitivity (the EuroBaVar study). Am J Physiol Regul Integr Comp Physiol 2004;286:R226–31.Google Scholar

27.

Rudiger H, Seibt R, Scheuch K, Krause M, Alam S. Sympathetic and parasympathetic activation in heart rate variability in male hypertensive patients under mental stress. J Hum Hypertens 2004;18:307–15.CrossrefGoogle Scholar

28.

Barbieri R, Matten EC, Abdul Rasheed AA, Brown EM. A point-process model of human heartbeat intervals: new definitions of heart rate and heart rate variability. Am J Physiol Heart Circ Physiol 2005;288:H424–35.Google Scholar

29.

Clifford GD, Tarassenko L. Quantifying errors in spectral estimates of HRV due to beat replacement and resampling. IEEE Trans Biomed Eng 2005;52:630–8.CrossrefGoogle Scholar

30.

Holland A, Aboy M. A novel recursive Fourier transform for nonuniform sampled signals: application to heart rate variability spectrum estimation. Med Biol Eng Comput 2009;47:697–707.CrossrefGoogle Scholar

31.

Krause M, Rudiger H, Bald M, Nake A, Paditz E. Autonomic blood pressure control in children and adolescents with type 1 diabetes mellitus. Pediatric Diabetes 2009;10:255–63.Google Scholar

32.

Reimann M, Friedrich C, Gasch J, Reichmann H, Rudiger H, Ziemssen T. Trigonometric regressive spectral analysis reliably maps dynamic changes in baroreflex sensitivity and autonomic tone: the effect of gender and age. PloS One 2010;5:e12187.CrossrefGoogle Scholar

33.

Tank J, Baevsky RM, Funtova II, Diedrich A, Slepchenkova IN, Jordan J. Orthostatic heart rate responses after prolonged space flights. Clin Auton Res 2011;21:121–4.CrossrefGoogle Scholar

34.

Schaffer T, Hensel B, Weigand C, Schuttler J, Jeleazcov C. Evaluation of techniques for estimating the power spectral density of RR-intervals under paced respiration conditions. J Clin Monit Comput 2014;28:481–6.CrossrefGoogle Scholar

35.

Smith AL, Owen H, Reynolds KJ. Heart rate variability indices for very shorterm (30 beat) analysis. Part 1: survey and toolbox. J Clin Monit Comput 2013;27:569–76.CrossrefGoogle Scholar

36.

Smith AL, Owen H, Reynolds KJ. Heart rate variability indices for very short-term (30 beat) analysis. Part 2: validation. J Clin Monit Comput 2013;27:577–85.CrossrefGoogle Scholar

37.

Halberg F. Chronobiology. Annu Rev Physiol 1968;31:675–725.CrossrefGoogle Scholar

38.

Lomb NR. Least-squares frequency analysis of unequally spaced data. Astrophys Space Sci 1976;39:447–62.CrossrefGoogle Scholar

39.

Scargle JD. Studies in astronomical time series analysis. II. Statistical aspects of spectral analysis of unevenly spaced data. Astrophys J 1982;263:835–53.CrossrefGoogle Scholar

40.

Eleuteri A, Fisher AC, Groves D, Dewhurst CJ. An efficient time-varying filter for detrending and bandwidth limiting the heart rate variability tachogram without resampling: MATLAB open-source code and Internet web-based implementation. Comput Math Methods Med 2012:578–785.Google Scholar

41.

Chang KI, Monahan KJ, Griffin MP, Lake D, Moorman JR. Comparison and clinical application of frequency domain methods in analysis of neonatal heart rate time series. Ann Biomed Eng 2001;29:764–74.CrossrefGoogle Scholar

42.

Dantas EM, Sant’anna ML, Varejao AR, Goncalves CP, Morra EA, Baldo MP, et al. Spectral analysis of heart rate variability with the autoregressive method: What model order to choose? Comput Biol Med, 2012;42:164–70.CrossrefGoogle Scholar

43.

Kuusela TA, Kaila TJ, Kahonen M. Fine structure of the low-frequency spectra of heart rate and blood pressure. BMC Physiol 2003;13:1–11.Google Scholar

44.

Piskorski J, Guzik P, Krauze T, Zurek S. Cardiopulmonary resonance at 0.1 Hz demonstrated by averaged Lomb-Scargle periodogram. Cent Eur J Phys 2010;8:386–92.Google Scholar

45.

Montes-Brown J, Machado A, Estevez M, Carricarte C, Velazquez-Perez L. Autonomic dysfunction in presymptomatic spinocerebellar ataxia type-2. Acta Neurol Scand 2012;125: 24–9.Google Scholar

46.

Montes-Brown J, Sanchez-Cruz G, Garcia AM, Baez ME, Velazquez-Perez L. Heart rate variability in type 2 spinocerebellar ataxia. Acta Neurol Scand 2010;122:329–35.Google Scholar

47.

Malik M, Farrell T, Cripps T, Camm AJ. Heart rate variability in relation to prognosis after myocardial infarction: selection of optimal processing techniques. Eur Heart J 1989;10:1060–74.Google Scholar

48.

Tatsuoka MM. Multivariate analysis. New York, NY: Wily, 1971.Google Scholar

49.

Finn JD. A general model for multivariate analysis. New York: Holt, Rinehart & Winston, 1974.Google Scholar

50.

Scheffé H. A method for judging all possible contrasts in the analysis of variance. Biometrica 1953;40:87–104.Google Scholar

51.

Abhishekh HA, Nisarga P, Kisan R, Meghana A, Chandran S, Trichur R, Sathyaprabha TN. Influence of age and gender on autonomic regulation of heart. J Clin Monit Comput 2013;27:259–64.CrossrefGoogle Scholar

52.

Machado-Ferrer Y, Estevez M, Machado C, Hernandez-Cruz A, Carrick FR, Leisman G, et al. Heart rate variability for assessing comatose patients with different Glasgow Coma Scale scores. Clin Neurophysiol 2013;124:589–97.CrossrefGoogle Scholar

53.

Thong T, McNames J, Aboy M. Lomb-Wech periodogram for non-uniform sampling. Conf Proc IEEE Eng Med Biol Soc 2004;1:271–74.Google Scholar

54.

Badilini F, Maison-Blanche P, Coumel P. Heart rate variability in passive tilt test: comparative evaluation of autoregressive and FFT spectral analyses. Pacing Clin Electrophysiol 1998;21:1122–32.CrossrefGoogle Scholar

55.

Karemaker JM. Counterpoint: respiratory sinus arrhythmia is due to the baroreflex mechanism. J Appl Physiol 2009;106:1742–3.CrossrefGoogle Scholar

56.

Pichon A, Roulaud M, Antoine-Jonville S, de Bisschop C, Denjean A. Spectral analysis of heart rate variability: interchangeability between autoregressive analysis and fast Fourier transform. J Electrocardiol 2006;39:31–7.CrossrefGoogle Scholar

57.

Ryan ML, Ogilvie MP, Pereira BMT, Gomez-Rodriguez JC, Manning RJ, Vargas PA, et al. Heart rate variability is an independent predictor of morbidity and mortality in hemodynamically stable trauma patients. J Trauma Inj Infect Crit Care 2011;70:1371–9.CrossrefGoogle Scholar

58.

Machado C, Estévez M, Pérez-Nellar J, Gutiérrez J, Rodríguez R, Carballo M, et al. Autonomic, EEG, and behavioral arousal signs in a PVS case after zolpidem intake. Can J Neurol Sci 2011;38:341–44.CrossrefGoogle Scholar

59.

Riganello F, Dolce G, Sannita WG. Heart rate variability and the central autonomic network in the severe disorder of consciousness. J Rehab Med 2012;44:495–501.CrossrefGoogle Scholar

60.

Machado C, Estévez M, Rodríguez R, Pérez-Nellar J, Chinchilla M, Defina P, et al. Zolpidem arousing effect in persistent vegetative state patients: Autonomic, EEG and behavioral assessment. Curr Pharm Des, 2014;20:4185–202.Google Scholar

## Comments (0)

General note:By using the comment function on degruyter.com you agree to our Privacy Statement. A respectful treatment of one another is important to us. Therefore we would like to draw your attention to our House Rules.