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Articles

Autonomic impairment of patients in coma with different Glasgow coma score assessed with heart rate variability

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Pages 496-516 | Received 14 Feb 2018, Accepted 25 Nov 2018, Published online: 12 Feb 2019

References

  • Task_Force_of_ESC_and_NASPE. Heart rate variability. Standards of measurement, physiological interpretation, and clinical use. Task force of the European Society of cardiology and the North American Society of pacing and electrophysiology. Eur. Heart J. 1996;17(3):354–81.
  • Sosnowski M. Heart rate variability. In: Macfarlane PW, van Oosterom A, Pahlm O, Kligfield P,M, Janse M,JC, editors. Comprehensive electrocardiology. London: Springer-Verlag Limited; 2011. p. 1513–674.
  • Cygankiewicz I, Zareba W. Heart rate variability. Handb Clin Neurol. 2013;117:379–93. doi:10.1016/B978-0-444-53491-0.00031-6.
  • Kuusela T. Methodological aspects of heart rate variability analysis. In: Kamath MV, Watanabe MA, Upton ARM, editors. Heart Rate Variability (HRV) signal analysis: clinical applications. I. 1st ed. Boca Raton, London, New York: CRC Press Taylor & Francis Group; 2013. p. 9–46.
  • Riganello F, Cortese MD, Dolce G, Lucca LF, Sannita WG. The autonomic system functional state predicts responsiveness in DOC. J Neurotrauma. 2015;32:1071–77. doi:10.1089/neu.2014.3539.
  • Shimomura C, Matsuzaka T, Koide E, Kinoshita S, Ono Y, Tsuji Y, Kawasaki C, Suzuki Y. Spectral analysis of heart rate variability in the dysfunction of the brainstem. No To Hattatsu. 1991;23(1):26–31.
  • Freitas J, Puig J, Rocha AP, Lago P, Teixeira J, Carvalho MJ, Costa O, de Freitas AF. Heart rate variability in brain death. Clin Auton Res. 1996 Jun 6;3:141–46. doi:10.1007/BF02281900.
  • Conci F, Di Rienzo M, Castiglioni P. Blood pressure and heart rate variability and baroreflex sensitivity before and after brain death. J. Neurol. Neurosurg. Psychiatr. 2001;71(5):621–31.
  • Machado C, Estevez M, Perez-Nellar J, Schiavi A. Residual vasomotor activity assessed by heart rate variability in a brain-dead case. BMJ case reports. 2015. doi:10.1136/bcr-2014-205677.
  • Hildebrandt H, Zieger A, Engel A, Fritz KW, Bussmann B. Differentiation of autonomic nervous activity in different stages of coma displayed by power spectrum analysis of heart rate variability. Eur Arch Psychiatry Clin Neurosci. 1998;248(1):46–52.
  • Baguley IJ, Nott MT, Slewa-Younan S, Heriseanu RE, Perkes IE. Diagnosing dysautonomia after acute traumatic brain injury: evidence for overresponsiveness to afferent stimuli. Arch Phys Med Rehabil. 2009;90(4):580–86. doi:10.1016/j.apmr.2008.10.020.
  • Vakilian AR, Iranmanesh F, Nadimi AE, Kahnali JA. Heart rate variability and QT dispersion study in brain death patients in coma and comatose patients in coma with normal brainstem function. J Coll Physicians Surg Pak. 2011;21(3):130–33.
  • García OD, Machado C, Román JM, Cabrera A, Díaz-Comas L, Rivera B, Grave de Peralta R. Brain death (proceedings of the second international symposium on brain death). In: Machado C, editor. Development in neurology. Vol. 9. Amsterdam: Elsevier; 1995. p. 191–200.1995.
  • Su CF, Kuo TB, Kuo JS, Lai HY, Chen HI. Sympathetic and parasympathetic activities evaluated by heart-rate variability in head injury of various severities. Clin Neurophysiol. 2005;116(6):1273–79. doi:10.1016/j.clinph.2005.01.010.
  • Machado C, Garcia OD, Gutierrez J, Portela L, Garcia MC. Heart rate variability in comatose and brain-dead patients in coma. Clin Neurophysiol. 2005;116(12):2859–60. author reply 60. doi:10.1016/j.clinph.2005.08.016.
  • Machado-Ferrer Y, Estevez M, Machado C, Hernandez-Cruz A, Carrick FR, Leisman G, Melillo R, Defina P, Chinchilla M, Machado Y. Heart rate variability for assessing comatose patients in coma with different Glasgow coma scale scores. Clin Neurophysiol. 2013;124(3):589–97. doi:10.1016/j.clinph.2012.09.008.
  • Biswas AK, Scott WA, Sommerauer JF, Luckett PM. Heart rate variability after acute traumatic brain injury in children. Crit Care Med. 2000;28(12):3907–12.
  • Haji-Michael PG, Vincent JL, Degaute JP, van de Borne P. Power spectral analysis of cardiovascular variability in critically ill neurosurgical patients in coma. Crit Care Med. 2000;28(7):2578–83.
  • Rapenne T, Moreau D, Lenfant F, Boggio V, Cottin Y, Freysz M. Could heart rate variability analysis become an early predictor of imminent brain death? A pilot study. Anesth. Analg. 2000;91(2):329–36.
  • Gujjar AR, Sathyaprabha TN, Nagaraja D, Thennarasu K, Pradhan N. Heart rate variability and outcome in acute severe stroke: role of power spectral analysis. Neurocrit Care. 2004;1(3):347–53. doi:10.1385/NCC:1:3:347.
  • Proctor KG, Atapattu SA, Duncan RC. Heart rate variability index in trauma patients in coma. J Trauma. 2007;63(1):33–43. doi:10.1097/01.ta.0000251593.32396.df.
  • Cancio LC, Batchinsky AI, Salinas J, Kuusela T, Convertino VA, Wade CE, Holcomb JB. Heart-rate complexity for prediction of prehospital lifesaving interventions in trauma patients in coma. J Trauma. 2008;65(4):813–19. doi:10.1097/TA.0b013e3181848241.
  • Ryan ML, Ogilvie MP, Pereira BM, Gomez-Rodriguez JC, Manning RJ, Vargas PA, Duncan RC, Proctor KG. Heart rate variability is an independent predictor of morbidity and mortality in hemodynamically stable trauma patients in coma. J Trauma. 2011;70(6):1371–80. doi:10.1097/TA.0b013e31821858e6.
  • Cooke WH, Salinas J, Convertino VA, Ludwig DA, Hinds D, Duke JH, Moore FA, Holcomb JB. Heart rate variability and its association with mortality in prehospital trauma patients in coma. J Trauma. 2006;60(2):363–70. doi:10.1097/01.ta.0000196623.48952.0e.
  • Batchinsky AI, Cancio LC, Salinas J, Kuusela T, Cooke WH, Wang JJ, Boehme M, Convertino VA, Holcomb JB. Prehospital loss of R-to-R interval complexity is associated with mortality in trauma patients in coma. J Trauma. 2007;63(3):512–18. doi:10.1097/TA.0b013e318142d2f0.
  • Sykora M, Czosnyka M, Liu X, Donnelly J, Nasr N, Diedler J, Okoroafor F, Hutchinson P, Menon D, Smielewski P. Autonomic impairment in severe traumatic brain injury: a multimodal neuromonitoring study. Crit Care Med. 2016;44(6):1173:81. doi:10.1097/CCM.0000000000001624.
  • Winchel RJ, Hoyt DB. Spectral analysis of heart rate variability in the ICU: A measure of autonomic function. J Surg Res. 1996;63:11–16. doi:10.1006/jsre.1996.0214.
  • Winchell RJ, Hoyt DB. Analysis of heart-rate variability: a noninvasive predictor of death and poor outcome in patients in coma with severe head injury. J Trauma. 1997;43(6):927–33.
  • Morris JA Jr., Norris PR, Ozdas A, Waitman LR, Harrell FE Jr., Williams AE, Cao H, Jenkins JM. Reduced heart rate variability: an indicator of cardiac uncoupling and diminished physiologic reserve in 1,425 trauma patients in coma. J Trauma. 2006;60(6):1165–73. doi:10.1097/01.ta.0000220384.04978.3b.
  • Norris PR, Anderson SM, Jenkins JM, Williams AE, Morris JA Jr. Heart rate multiscale entropy at three hours predicts hospital mortality in 3,154 trauma patients in coma. Shock. 2008;30(1):17–22. doi:10.1097/SHK.0b013e318164e4d0.
  • Norris PR, Ozdas A, Cao H, Williams AE, Harrell FE, Jenkins JM, Morris JA Jr. Cardiac uncoupling and heart rate variability stratify ICU patients in coma by mortality: a study of 2088 trauma patients in coma. Ann Surg. 2006;243(6):804–12. doi:10.1097/01.sla.0000219642.92637.fd.
  • Norris PR, Stein PK, Morris JA Jr. Reduced heart rate multiscale entropy predicts death in critical illness: a study of physiologic complexity in 285 trauma patients in coma. J Crit Care. 2008;23(3):399–405. doi:10.1016/j.jcrc.2007.08.001.
  • Mejaddam AY, Birkhan OA, Sideris AC, Van der Wilden GM, Imam AM, Hwabejire JO, Chang Y, Velmahos GC, Fagenholz PJ, Yeh DD, et al. Real-time heart rate entropy predicts the need for lifesaving interventions in trauma activation patients in coma. J Trauma Acute Care Surg. 2013;75(4):607–12. doi:10.1097/TA.0b013e31829bb991.
  • Liu NT, Holcomb JB, Wade CE, Darrah MI, Salinas J. Utility of vital signs, heart-rate variability and complexity, and machine learning for identifying the need for life-saving interventions in trauma patients in coma. Shock. 2014. doi:10.1097/SHK.0000000000000186.
  • Liu NT, Holcomb JB, Wade CE, Salinas J. Improving the prediction of mortality and the need for life-saving interventions in trauma patients in coma using standard vital signs with heart-rate variability and complexity. Shock. 2015. doi:10.1097/SHK.0000000000000356.
  • Naraghi L, Mejaddam AY, Birkhan OA, Chang Y, Cropano CM, Mesar T, Larentzakis A, Peev M, Sideris AC, Van der Wilden GM, et al. Sample entropy predicts lifesaving interventions in trauma patients in coma with normal vital signs. J Crit Care. 2015;30(4):705–10. doi:10.1016/j.jcrc.2015.03.018.
  • Rickards CA, Ryan KL, Ludwig DA, Convertino VA. Is heart period variability associated with the administration of lifesaving interventions in individual prehospital trauma patients in coma with normal standard vital signs? Crit Care Med. 2010;38(8):1666–73. doi:10.1097/CCM.0b013e3181e74cab.
  • Antelmi I, de Paula RS, Shinzato AR, Peres CA, Mansur AJ, Grupi CJ. Influence of age, gender, body mass index, and functional capacity on heart rate variability in a cohort of subjects without heart disease. Am J Cardiol. 2004;93(3):381–85. doi:10.1016/j.amjcard.2003.09.065.
  • Fathizadeh P, Shoemaker WC, Wo CC, Colombo J. Autonomic activity in trauma patients in coma based on variability of heart rate and respiratory rate. Crit Care Med. 2004;32(6):1300–05.
  • Nieminen T, Kahonen M, Koobi T, Nikus K, Viik J. Heart rate variability is dependent on the level of heart rate. Am Heart J. 2007; 154(1):e13. doi:10.1016/j.ahj.2007.04.050.
  • Monfredi O, Lyashkov AE, Johnsen AB, Inada S, Schneider H, Wang R, Nirmalan M, Wisloff U, Maltsev VA, Lakatta EG, et al. Biophysical characterization of the underappreciated and important relationship between heart rate variability and heart rate. Hypertension. 2014;64:1334–43.
  • 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(4504):220–22.
  • Sassi R, Cerutti S, Lombardi F, Malik M, Huikuri HV, Peng CK, Schmidt G, Yamamoto Y, Gorenek B, Lip GY, et al. Advances in heart rate variability signal analysis: joint position statement by the e-Cardiology ESC working group and the european heart rhythm association co-endorsed by the Asia pacific heart rhythm society. Ep Europace. 2015;17(9):1341–53. doi:10.1093/europace/euv015.
  • Huang NE, Shen Z, Long SR, Wu MC, Shih HH, Zheng Q, Yen N, Tung CC, Liu HH. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc R Soc Lond. 1998;454:903–95. doi:10.1098/rspa.1998.0193.
  • Flandrin P, Rilling G, Goncalves P. Empirical mode decomposition as a filter bank. IEEE Signal Process Lett. 2004;11(2):112–14. doi:10.1109/LSP.2003.821662.
  • Huang NE, Young V, Lo MT, Wang YH, Peng CK, Chen X, Wang G, Deng J, Wu Z. The uniqueness of the instantaneous frequency based on intrinsic mode function. Adv Adapt Data Anal. 2013;5(3):1350011–1-14. doi:10.1142/S1793536913500118.
  • Colominas MA, Schlotthauer G, Torres ME. Improved complete ensemble EMD: A suitable tool for biomedical signal processing. Biomed Signal Process Control. 2014;14:19–29. doi:10.1016/j.bspc.2014.06.009.
  • Huang NE, Hu K, Yang ACC, Chang HC, Jia D, Liang WK, Yeh JR, Kao CL, Juan CH, Peng CK, et al. On Holo-Hilbert spectral analysis: a full informational spectral representation for nonlinear and non-stationary data. Phil Trans R Soc A. 2016;374:20150206.
  • Li X, Li D, Liang Z, Voss LJ, Sleigh JW. Analysis of depth of anesthesia with Hilbert-Huang spectral entropy. Clin Neurophysiol. 2008;119(11):2465–75. doi:10.1016/j.clinph.2008.08.006.
  • Hsu YF, Liao KK, Lee PL, Tsai YA, Yeh CL, Lai KL, Huang YZ, Lin YY, Lee IH. Intermittent theta burst stimulation over primary motor cortex enhances movement-related beta synchronisation. Clin Neurophysiol. 2011;122(11):2260–67. doi:10.1016/j.clinph.2011.03.027.
  • Zheng Y, Wang G, Li K, Bao G, Wang J. Epileptic seizure prediction using phase synchronization based on bivariate empirical mode decomposition. Clin Neurophysiol. 2014;125(6):1104–11. doi:10.1016/j.clinph.2013.09.047.
  • Zhang Y, Ji X, Zhang S. An approach to EEG-based emotion recognition using combined feature extraction method. Neurosci. Lett.. 2016;633:152–57. doi:10.1016/j.neulet.2016.09.037.
  • Amo C, de Santiago L, Barea R, Lopez-Dorado A, Boquete L. Analysis of gamma-band activity from human EEG using empirical mode decomposition. Sensors. 2017;17(5). doi:10.3390/s17050968.
  • Liu Q, Chen YF, Fan SZ, Abbod MF, Shieh JS. Improved spectrum analysis in EEG for measure of depth of anesthesia based on phase-rectified signal averaging. Physiol Meas. 2017;38(2):116–38. doi:10.1088/1361-6579/38/2/116.
  • Salisbury JI, Sun Y. Assessment of chaotic parameters in nonstationary electrocardiograms by use of empirical mode decomposition. Ann Biomed Eng. 2004;32(10):1348–54.
  • Huang Z, Chen Y, Pan M. Time-frequency characterization of atrial fibrillation from surface ECG based on Hilbert-Huang transform. J Med Eng Technol. 2007;31(5):381–89. doi:10.1080/03091900601165314.
  • Corthout J, Van Huffel S, Mendez MO, Bianchi AM, Penzel T, Cerutti S. Automatic screening of obstructive sleep apnea from the ECG based on empirical mode decomposition and wavelet analysis. Conference proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society IEEE Engineering in Medicine and Biology Society Annual Conference, vol. 2008; 2008; Vancouver, BC. 3608–11. doi:10.1109/IEMBS.2008.4649987.
  • Acharya UR, Faust O, Sree V, Swapna G, Martis RJ, Kadri NA, Suri JS. Linear and nonlinear analysis of normal and CAD-affected heart rate signals. Comput Methods Programs Biomed. 2014;113(1):55–68. doi:10.1016/j.cmpb.2013.08.017.
  • Mert A. ECG feature extraction based on the bandwidth properties of variational mode decomposition. Physiol Meas. 2016;37(4):530–43. doi:10.1088/0967-3334/37/4/530.
  • Zheng J, Wang W, Zhang Z, Wu D, Wu H, Peng CK. A robust approach for ECG-based analysis of cardiopulmonary coupling. Med Eng Phys. 2016;38(7):671–78. doi:10.1016/j.medengphy.2016.02.015.
  • Motin MA, Karmakar C, Palaniswami M. Ensemble empirical mode decomposition with principal component analysis: A novel approach for extracting respiratory rate and heart rate from photoplethysmographic signal. IEEE J Biomed Health Inform. 2017. doi:10.1109/JBHI.2017.2679108.
  • Rajesh K, Dhuli R. Classification of ECG heartbeats using nonlinear decomposition methods and support vector machine. Comput Biol Med. 2017;87:271–84. doi:10.1016/j.compbiomed.2017.06.006.
  • Souza Neto EP, Custaud MA, Cejka JC, Abry P, Frutoso J, Gharib C, Flandrin P. Assessment of cardiovascular autonomic control by the empirical mode decomposition. Methods Inf Med. 2004;43(1):60–65.
  • Maestri R, Pinna GD, Balocchi R, D’Addio G, Ferrario M, Porta A, Sassi R, Signorini MG, La Rovere MT. Clinical correlates of non-linear indices of heart rate variability in chronic heart failure patients in coma. Biomedizinische Technik Biomedical Eng. 2006;51(4):220–23. doi:10.1515/BMT.2006.041.
  • Maestri R, Pinna GD, Porta A, Balocchi R, Sassi R, Signorini MG, Dudziak M, Raczak G. Assessing nonlinear properties of heart rate variability from short-term recordings: are these measurements reliable? Physiol Meas. 2007;28(9):1067–77. doi:10.1088/0967-3334/28/9/008.
  • Maestri R, Pinna GD, Accardo A, Allegrini P, Balocchi R, D’Addio G, Ferrario M, Menicucci D, Porta A, Sassi R, et al. Nonlinear indices of heart rate variability in chronic heart failure patients in coma: redundancy and comparative clinical value. J Cardiovasc Electrophysiol. 2007;18(4):425–33. doi:10.1111/j.1540-8167.2007.00728.x.
  • Shafqat K, Pal SK, Kumari S, Kyriacou PA. Empirical mode decomposition (EMD) analysis of HRV data from locally anesthetized patients in coma. Conference proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society IEEE Engineering in Medicine and Biology Society Annual Conference. vol. 2009; 2009; Minneapolis, MN. 2244–47. doi:10.1109/IEMBS.2009.5335000.
  • Ebrahimi F, Setarehdan SK, Ayala-Moyeda J, Nazeran H. Automatic sleep staging using empirical mode decomposition, discrete wavelet transform, time-domain, and nonlinear dynamics features of heart rate variability signals. Comput Methods Programs Biomed. 2013;112(1):47–57. doi:10.1016/j.cmpb.2013.06.007.
  • Yeh JR, Peng CK, Lo MT, Yeh CH, Chen SC, Wang CY, Lee PL, Kang JH. Investigating the interaction between heart rate variability and sleep EEG using nonlinear algorithms. J. Neurosci. Methods. 2013;219(2):233–39. doi:10.1016/j.jneumeth.2013.08.008.
  • Chang CC, Hsiao TC, Hsu HY. Frequency range extension of spectral analysis of pulse rate variability based on Hilbert-Huang transform. Med Biol Eng Comput. 2014;52:343–51. doi:10.1152/jn.01120.2015.
  • Chang CC, Hsu HY, Hsiao TC. The interpretation of very high frequency band of instantaneous pulse rate variability during paced respiration. Biomed Eng Online. 2014; 13(1):46. doi:10.1186/475-925X-13-46.
  • Mohebbi M, Ghassemian H. Predicting termination of paroxysmal atrial fibrillation using empirical mode decomposition of the atrial activity and statistical features of the heart rate variability. Med Biol Eng Comput. 2014;52(5):415–27. doi:10.1007/s11517-014-1144-z.
  • Schiecke K, Wacker M, Benninger F, Feucht M, Leistritz L, Witte H. Advantages of signal-adaptive approaches for the nonlinear, time-variant analysis of heart rate variability of children with temporal lobe epilepsy. Conference proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society IEEE Engineering in Medicine and Biology Society Annual Conference. vol. 2014; 2014; Chicago, IL. 6377–80. doi:10.1109/EMBC.2014.6945087.
  • Altan G, Kutlu Y, Allahverdi N. A new approach to early diagnosis of congestive heart failure disease by using Hilbert-Huang transform. Comput Methods Programs Biomed. 2016;137:23–34. doi:10.1016/j.cmpb.2016.09.003.
  • Gupta P, Sharma KK, Joshi SD. Fetal heart rate extraction from abdominal electrocardiograms through multivariate empirical mode decomposition. Comput Biol Med. 2016;68:121–36. doi:10.1016/j.compbiomed.2015.11.007.
  • Lin YC, Lin YH, Lo MT, Peng CK, Huang NE, Yang CC, Kuo TB. Novel application of multi dynamic trend analysis as a sensitive tool for detecting the effects of aging and congestive heart failure on heart rate variability. Chaos. 2016;26(2):023109. doi:10.1063/1.4941673.
  • Bravi A, Longtin A, Seely AJ. Review and classification of variability analysis techniques with clinical applications. Biomed Eng Online. 2011;10:90. doi:10.1186/1475-925X-10-90.
  • Viola AU, Tobaldini E, Chellappa SL, Casali KR, Porta A, Montano N. Short-term complexity of cardiac autonomic control during sleep: REM as a potential risk factor for cardiovascular system in aging. PLoS ONE. 2011;6(4):e19002. doi:10.1371/journal.pone.0019002.
  • Clark MT, Rusin CG, Hudson JL, Lee H, Delos JB, Guin LE, Vergales BD, Paget-Brown A, Kattwinkel J, Lake DE, et al. Breath-by-breath analysis of cardiorespiratory interaction for quantifying developmental maturity in premature infants. J Appl Physiol. 2012;112(5):859–67. doi:10.1152/japplphysiol.01152.2011.
  • Takahashi AC, Porta A, Melo RC, Quiterio RJ, Da Silva E, Borghi-Silva A, Tobaldini E, Montano N, Catai AM. Aging reduces complexity of heart rate variability assessed by conditional entropy and symbolic analysis. Intern Emerg Med. 2012;7(3):229–35. doi:10.1007/s11739-011-0512-z.
  • Torres ME, Colominas MA, Schlotthauer G, Flandrin P A complete ensemble empirical mode decomposition with adaptive noise. 36th Int Conf on Acoustics, Speech and Signal Processing ICASSP. Prague, Czech Republic 2011.
  • Li H, Kwong S, Yang L, Huang D, Xiao D. Hilbert-huang transform for analysis of heart rate variability in cardiac health. IEEE/ACM Trans Comput Biol Bioinf. 2011;8(6):1557–67. doi:10.1109/TCBB.2011.43.
  • Schiecke K, Wacker M, Piper D, Benninger F, Feucht M, Witte H. Time-variant, frequency-selective, linear and nonlinear analysis of heart rate variability in children with temporal lobe epilepsy. IEEE Trans Biomed Eng. 2014;61(6):1798–808. doi:10.1109/TBME.2014.2307481.
  • Marple LS. Computing the discrete-time “analytic” signal via fft. IEEE Trans Signal Process. 1999;47(9):2600–03. doi:10.1109/78.782222.
  • Estevez M Matlab code of the Hilbert Marginal Spectrum (HMS). 2017 [accessed 2017 Jan 10]. https://www.researchgate.net/publication/320161261.
  • 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(3):386–92.
  • Montes-Brown J, Estévez-Báez M, Velázquez L. Disfunción autonómica cardiovascular en la Ataxia Espinocerebelosa. Madrid, España: Publicia; 2015.
  • Sivakumar SS, Namath AG, Tuxhorn IE, Lewis SJ, Galan RF. Decreased heart rate and enhanced sinus arrhythmia during interictal sleep demonstrate autonomic imbalance in generalized epilepsy. J Neurophysiol. 2016;115:1988–99. doi:10.1152/jn.01120.2015.
  • Percival D, Walden T. Spectral analysis for physical applications. Cambridge, UK: Cambridge University Press; 1993.
  • Wichert S, Fokianos K, Strimmer K. Identifying periodically expressed transcripts in microarray time series data. Bioinformatics. 2004;20:5–20.
  • Yeh JR, Sun WZ, Shieh JS, Huang NE. Intrinsic mode analysis of human heartbeat time series. Ann Biomed Eng. 2010;38(4):1337–44. doi:10.1007/s10439-010-9939-z.
  • Veerabhadrappa ST, Vyas AL, Anand S Higher order spectral analysis of heart rate variability in pregnancy and postpartum. Conference proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society IEEE Engineering in Medicine and Biology Society Annual Conference. vol. 2013; 2013; Osaka, Japan. 2575–78. doi:10.1109/EMBC.2013.6610066.
  • Chang CC, Kao SC, Hsiao TC, Hsu HY. Assessment of autonomic nervous system by using empirical mode decomposition-based reflection wave analysis during non-stationary conditions. Physiol Meas. 2014;35(9):1873–83. doi:10.1088/0967-3334/35/9/1873.
  • Sacha J. Why should one normalize heart rate variability with respect to average heart rate. Front Physiol. 2013;4:306. doi:10.3389/fphys.2013.00306.
  • Sacha J. Interaction between heart rate and heart rate variability. Ann Noninvasive Electrocardiol. 2014;19(3):207–16. doi:10.1111/anec.12148.
  • Sacha J, Barabach S, Statkiewicz-Barabach G, Sacha K, Muller A, Piskorski J, Barthel P, Schmidt G. How to strengthen or weaken the HRV dependence on heart rate–description of the method and its perspectives. Int J Cardiol. 2013;168(2):1660–63. doi:10.1016/j.ijcard.2013.03.038.
  • Estévez-Báez M, Machado C, Leisman G, Brown-Martínez M, Jas-García JD, Montes-Brown J, Machado-García A, Carricarte-Naranjo C. A procedure to correct the effect of heart rate on heart rate variability indices: description and assessment. Int J Disabil Hum Dev. 2015. doi:10.101515/ijdhd-2015-0014.
  • van Roon AM, Snieder H, Lefrandt JD, de Geus EJ, Riese H. Parsimonious correction of heart rate variability for its dependency on heart rate. Hypertension. 2016. doi:10.101161/HYPERTENSIONAHA11608053.
  • Xia L. A very high frequency index of heart rate variability for evaluation of left ventricular systolic function and prognosis in chronic heart failure patients in coma using five-minute electrocardiogram. J Geriatr Cardiol. 2009;6(4):213–17.
  • Ozyilmaz I, Ergul Y, Tola HT, Saygi M, Ozturk E, Tanidir IC, Tosun O, Ozyilmaz S, Gul M, Guzeltas A, et al. Heart rate variability improvement in children using transcatheter atrial septal defect closure. Anatolian J Cardiol. 2015. doi:10.5152/akd.2015.5922.
  • Estevez M, Machado C, Montes-Brown J, Jas-García JD, Leisman G, Schiavi A, Machado-García A, Carricarte-Naranjo C, Carmeli E. Very high frequency oscillations of heart rate variability in healthy humans and in patients in coma with cardiovascular autonomic neuropathy. Adv Exp Med Biol. 2017. doi:10.1007/5584_2018_154.
  • Norris PR, Morris JA Jr., Ozdas A, Grogan EL, Williams AE. Heart rate variability predicts trauma patient outcome as early as 12 h: implications for military and civilian triage. J Surg Res. 2005;129(1):122–28. doi:10.1016/j.jss.2005.04.024.
  • Goldstein B, Toweill D, Lai S, Sonnenthal K, Kimberly B. Uncoupling of the autonomic and cardiovascular systems in acute brain injury. Am J Physiol. 1998;275(4 Pt 2):R1287–92.
  • King DR, Ogilvie MP, Pereira BM, Chang Y, Manning RJ, Conner JA, Schulman CI, McKenney MG, Proctor KG. Heart rate variability as a triage tool in patients in coma with trauma during prehospital helicopter transport. J Trauma. 2009;67(3):436–40. doi:10.1097/TA.0b013e3181ad67de.
  • Garcia-Gonzalez MA, Fernandez-Chimeno M, Ramos-Castro J. Bias and uncertainty in heart rate variability spectral indices due to the finite ECG sampling frequency. Physiol Meas. 2004;25(2):489–504.
  • Merri M, Farden DC, Mottley JG, Titlebaum EL. Sampling frequency of the electrocardiogram for spectral analysis of the heart rate variability. IEEE Trans Biomed Eng. 1990;37(1):99–106. doi:10.1109/10.43621.
  • Ellis RJ, Zhu B, Koenig J, Thayer JF, Wang Y. A careful look at ECG sampling frequency and R-peak interpolation on short-term measures of heart rate variability. Physiol Meas. 2015;36(9):1827–52. doi:10.1088/0967-3334/36/9/1827.
  • Daskalov IK, Christov I. Improvement of resolution in measurement of electrocardiogram RR intervals by interpolation. Med Eng Phys. 1997;1(9):375–79. doi:10.1016/S1350-4533(96)00067-7.
  • 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 Jun;45(6):698–715. doi:10.1109/10.678605.
  • Estévez M, Machado C, Leisman G, Estévez-Hernández T, Arias-Morales A, Machado A, Montes-Brown J. Spectral analysis of heart rate variability. Int J Disabil Hum Dev. 2015. doi:10.1515/ijdhd-2014-0025.
  • Yen JL. On nonuniform sampling of bandwidth-limted signals. Trans Circuit Theory. 1956;CT-3:251–57. doi:10.1109/TCT.1956.1086325.
  • Clifford GD, Tarassenko L. Quantifying errors in spectral estimates of HRV due to beat replacement and resampling. IEEE Trans Biomed Eng. 2005;52(4):630–38. doi:10.1109/TBME.2005.844028.
  • Cersosimo M, Benarroch E. Central control of autonomic function and involvement in neurodegenerative disorders. In: Buijs R, Swaab D, editors. Handbook of clinical neurology, the autonomic nervous system. Edinburgh London New York Oxford Philadelphia St Louis Sydney Toronto: Elsevier B.V.; 2014. p. 117.
  • Baguley IJ, Heriseanu RE, Felmingham KL, Cameron ID. Dysautonomia and heart rate variability following severe traumatic brain injury. Brain Inj. 2006;20(4):437–44. doi:10.1080/02699050600664715.
  • Baguley IJ, Heriseanu RE, Nott MT, Chapman J, Sandanam J. Dysautonomia after severe traumatic brain injury: evidence of persisting overresponsiveness to afferent stimuli. Am J Phys Med Rehabil. 2009;88(8):615–22. doi:10.1097/PHM.0b013e3181aeab96.
  • Fernandez-Ortega JF, Prieto-Palomino MA, Garcia-Caballero M, Galeas-Lopez JL, Quesada-Garcia G, Baguley IJ. Paroxysmal sympathetic hyperactivity after traumatic brain injury: clinical and prognostic implications. J Neurotrauma. 2012;29(7):1364–70. doi:10.1089/neu.2011.2033.
  • Marthol H, Intravooth T, Bardutzky J, De Fina P, Schwab S, Hilz MJ. Sympathetic cardiovascular hyperactivity precedes brain death. Clin Auton Res. 2010;20:363–69. doi:10.1007/s10286-010-0072-8.
  • Fugate J, Rabinstein A, Wijdicks F. Blood pressure patterns after brain death. Neurology. 2011;77:399. doi:10.1212/WNL.0b013e3182270444.
  • Korpelainen JT, Huikuri HV, Sotaniemi KA, Myllyla VV. Abnormal heart rate variability reflecting autonomic dysfunction in brainstem infarction. Acta Neurol Scand. 1996;94(5):337–42.
  • Bernardi L, Keller F, Sanders M, Reddy PS, Griffith B, Meno F, Pinsky MR. Respiratory sinus arrhythmia in the denervated human heart. J Appl Physiol. 1989;67:1447–55. doi:10.1152/jappl.1989.67.4.1447.
  • Bernardi L, Salvucci F, Suardi R, Solda PL, Calciati A, Perlini S, Falcone C, Ricciardi L. Evidence for an intrinsic mechanism regulating heart rate variability in the transplanted and the intact heart during submaximal dynamic exercise? Cardiovasc Res. 1990;24(12):969–81.
  • Mateo J, Serrano P, Bailón R, Olmos S, García J, Del Río A, Ferreira I, Laguna P. ECG-based clinical indexes during exercise test including repolarization, depolarization and HRV. Comput Cardiol. 2001;28:309–12.
  • Mateo J, Serrano P, Bailón R, Olmos S, García J, Del Río A, Ferreira I, Laguna P. Heart rate variability measurements during exercise test may improve the diagnosis of ischemic heart disease. 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society. October 25- 28 2001; Istanbul, Turkey. Turkey 2001.
  • Bailon R, Mateo J, Olmos S, Serrano P, Garcia J, Del Rio A, Ferreira IJ, Laguna P. Coronary artery disease diagnosis based on exercise electrocardiogram indexes from repolarisation, depolarisation and heart rate variability. Med Biol Eng Comput. 2003;41(5):561–71. doi:10.1007/BF02345319.
  • Toledo E, Pinhas I, Aravot D, Akselrod S. Very high frequency oscillations in the heart rate and blood pressure of heart transplant patients. Med Biol Eng Comput. 2003;41(4):432–8.
  • Toledo E, Pinhas I, Aravot D, Almog Y, Akselrod S. Functional restitution of cardiac control in heart transplant patients. Am J Physiol Regulatory Integrative Comp Physiol. 2002;282:R900–R8.
  • Pinhas I, Toledo E, Aravot D, Akselrod S. Bicoherence analysis of new cardiovascular spectral components observed in heart-transplant patients in coma: statistical approach for bicoherence thresholding. IEEE Trans Biomed Eng. 2004;51(10):1774–83. doi:10.1109/TBME.2004.831519.
  • Riganello F. Responsiveness and the autonomic control two-way interaction in disorders of consciousness. In: Moni MM, Samnita WG, editors. Brain function and responsiveness in disorders of consciousness. Switzerland: Springer International Publishing; 2016. p. 145–155.

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