Research Article
BibTex RIS Cite

Monitoring Blood Pressure Variability via Chaotic Global Metrics using Local Field Potential Oscillations

Year 2023, , 65 - 77, 31.07.2023
https://doi.org/10.51537/chaos.1262839

Abstract

The intention was to associate blood pressure (BP) variability (BPV) measurements to Local field potentials (LFPs). Thus, assessing how LFPs can co-vary with BPV to permit implantable brain devices (via LFPs) to control output. Elevated BPV is a considerable cardiovascular disease risk factor. Often patients are resistant to pharmacotherapies. An alternative treatment is Deep Brain Stimulation (DBS). Mathematical techniques based on nonlinear dynamics assessed their correlation of BPV chaotic global metrics to LFPs. Chaos Forward Parameter (CFP6) was computed for LFPs, at three electrode depths in the mid-brain and sensory thalamus. Mean, root mean square of the successive differences (RMSSD) and the chaotic global metrics (CFP1 to CFP7) were computed for the BP signal. The right ventroposterolateral (RVPL) nucleus provided a substantial correlation via CFP6 for BP with R-squared up to approximately 79% by means of LFP gamma oscillations. Investigation of BPV via LFPs as a proxy marker might allow therapies to be attuned in a closed-loop system. Whilst all patients were chronic pain patients the chaotic global relationship should be unperturbed. LFPs correlation does not unconditionally predict its causation. There is no certainty DBS in these locations would be therapeutic but can be used as an assessment tool.

Supporting Institution

National Institute of Health (NIHR) Oxford Biomedical Research Centre.

Project Number

Study number 05 Q1605 47

References

  • Alkan, A. and M. K. Kiymik, 2006 Comparison of AR and Welch methods in epileptic seizure detection. J Med Syst 30: 413–9.
  • Alkan, A. and A. S. Yilmaz, 2007 Frequency domain analysis of power system transients using Welch and Yule-Walker AR methods. Energy conversion and management 48: 2129–2135.
  • Appiah, K. O. B., M. Nath, L. Manning,W. J. Davison, S. Mazzucco, et al., 2021 Increasing blood pressure variability predicts poor functional outcome following acute stroke. Journal of Stroke and Cerebrovascular Diseases 30.
  • Bacan, G., A. Ribeiro-Silva, V. A. Oliveira, C. R. Cardoso, and G. F. Salles, 2022 Refractory hypertension: a narrative systematic review with emphasis on prognosis. Current Hypertension Reports 24: 95–106.
  • Bar, K. J., S. Berger, M. Metzner, M. K. Boettger, S. Schulz, et al., 2010 Autonomic dysfunction in unaffected first-degree relatives of patients suffering from schizophrenia. Schizophr Bull 36: 1050–8.
  • Bar, K. J., M. K. Boettger, M. Koschke, S. Schulz, P. Chokka, et al., 2007 Non-linear complexity measures of heart rate variability in acute schizophrenia. Clin Neurophysiol 118: 2009–15.
  • Barreto, G. S., F. M. Vanderlei, L. C. M. Vanderlei, and D. M. Garner, 2014 Risk appraisal by novel chaotic globals to HRV in subjects with malnutrition. Journal of Human Growth and Development 24: 243–248.
  • Belair, J., L. Glass, U. an der Heiden, and J. Milton, 1995 Dynamical disease: identification, temporal aspects and treatment strategies of human illness. Chaos: An Interdisciplinary Journal of Nonlinear Science 5: 1–7.
  • Benjamim, C. J. R., Y. Mota de M. Pontes, F. Welington de Sousa Junior, A. A. Porto, C. R. Bueno Junior, et al., 2021 Does bariatric surgery improve cardiac autonomic modulation assessed by heart rate variability: a systematic review. Surgery for Obesity and Related Diseases .
  • Bernardo, A. F., L. C. Vanderlei, and D. M. Garner, 2014 HRV analysis: A clinical and diagnostic tool in chronic obstructive pulmonary disease. Int Sch Res Notices 2014: 673232.
  • Bittar, R. G., I. Kar-Purkayastha, S. L. Owen, R. E. Bear, A. Green, et al., 2005 Deep brain stimulation for pain relief: a metaanalysis. J Clin Neurosci 12: 515–9.
  • Bonatto, C., J. Gallas, and Y. Ueda, 2008 Chaotic phase similarities and recurrences in a damped-driven duffing oscillator. Phys.Rev.E.Stat.Nonlin.Soft.Matter Phys. 77: 026217.
  • Calhoun, D. A., J. N. Booth III, S. Oparil, M. R. Irvin, D. Shimbo, et al., 2014 Refractory hypertension: determination of prevalence, risk factors, and comorbidities in a large, population-based cohort. Hypertension 63: 451–458.
  • Cameron, A. A., I. A. Khan, K. N. Westlund, K. D. Cliffer, and W. D. Willis, 1995 The efferent projections of the periaqueductal gray in the rat: A phaseolus vulgaris-leucoagglutinin study. I. Ascending projections. Journal of Comparative Neurology 351: 568–584.
  • Camm, A., M. Malik, J. Bigger, G. Breithardt, S. Cerutti, et al., 1996 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. Circulation 93: 1043–1065.
  • Carrive, P. and R. Bandler, 1991 Viscerotopic organization of neurons subserving hypotensive reactions within the midbrain periaqueductal grey: a correlative functional and anatomical study. Brain research 541: 206–215.
  • Chang, S., 2010 Physiological rhythms, dynamical diseases and acupuncture. Chin J Physiol 53: 77–90.
  • Cook, R. D. and S.Weisberg, 1982 Residuals and influence in regression. New York: Chapman and Hall.
  • Corrao, G., A. Parodi, F. Nicotra, A. Zambon, L. Merlino, et al., 2011 Better compliance to antihypertensive medications reduces cardiovascular risk. Journal of hypertension 29: 610–618.
  • Das, K., J. Jiang, and J. Rao, 2004 Mean squared error of empirical predictor. The Annals of Statistics 32: 818–840.
  • Dauer, W. and S. Przedborski, 2003 Parkinson’s disease: mechanisms and models. Neuron 39: 889–909.
  • Day, B. P., A. Evers, and D. E. Hack, 2020 Multipath suppression for continuous wave radar via slepian sequences. IEEE Transactions on Signal Processing 68: 548–557.
  • De Souza, N. M., L. C. M. Vanderlei, and D. M. Garner, 2015 Risk evaluation of diabetes mellitus by relation of chaotic globals to HRV. Complexity 20: 84–92.
  • Dirac, P., 1939 New notation for quantum mechanics. Proceedings of the Cambridge Philosophical Society 35: 416.
  • Dolan, E. and E. O’Brien, 2010 Blood pressure variability clarity for clinical practice. Hypertension 56: 179–181.
  • Farkas, E., A. S. Jansen, and A. D. Loewy, 1997 Periaqueductal gray matter projection to vagal preganglionic neurons and the nucleus tractus solitarius. Brain research 764: 257–261.
  • Farkas, E., A. S. Jansen, and A. D. Loewy, 1998 Periaqueductal gray matter input to cardiac-related sympathetic premotor neurons. Brain research 792: 179–192.
  • Frank, G., F. Halberg, R. Harner, J. Matthews, E. Johnson, et al., 1966 Circadian periodicity, adrenal corticosteroids, and the eeg of normal man. J.Psychiatr.Res. 4: 73–86.
  • Galhardo, C., T. Penna, M. A. de Menezes, and P. Soares, 2009 Detrended fluctuation analysis of a systolic blood pressure control loop. New Journal of Physics 11: 103005.
  • Garner, D., M. Alves, B. da Silva, L. de Alcantara Sousa, and V. Valenti, 2020a Chaotic global analysis of heart rate variability following power spectral adjustments during exposure to traffic noise in healthy adult women. Russ J Cardiol 25: 3739.
  • Garner, D., A. Bernardo, and L. Vanderlei, 2021a HRV analysis: Unpredictability of approximate entropy in chronic obstructive pulmonary disease. Series Cardiol Res 3(1): 1–10.
  • Garner, D., N. de Souza, and L. Vanderlei, 2020b Unreliability of approximate entropy to locate optimal complexity in diabetes mellitus via heart rate variability. Series Endo Diab Met. 2: 32–40.
  • Garner, D., F. Vanderlei, L. Vanderlei, V. Valenti, C. J. R. Benjamim,et al., 2022 Chaotic global metric analysis of heart rate variability following six power spectral manipulations in malnourished children. Series Endo Diab Met. 4: 44–58.
  • Garner, D. M., G. S. Barreto, V. E. Valenti, F. M. Vanderlei, A. A. Porto, et al., 2021b HRV analysis: undependability of approximate entropy at locating optimum complexity in malnourished children. Cardiol Young pp. 1–6.
  • Garner, D. M., N. M. de Souza, V. E. Valenti, and L. C. M. Vanderlei, 2019a Complexity of cardiac autonomic modulation in diabetes mellitus: A new technique to perceive autonomic dysfunction. Romanian Journal of Diabetes Nutrition and Metabolic Diseases 26: 279–291.
  • Garner, D. M., N. M. De Souza, and L. C. M. Vanderlei, 2017 Risk assessment of diabetes mellitus by chaotic globals to heart rate variability via six power spectra. Romanian Journal of Diabetes Nutrition and Metabolic Diseases 24: 227–236.
  • Garner, D. M., N. M. de Souza, and L. C. M. Vanderlei, 2018 Heart rate variability analysis: Higuchi and katz’s fractal dimensions in subjects with type 1 diabetes mellitus. Romanian Journal of Diabetes Nutrition and Metabolic Diseases 25: 289–295.
  • Garner, D. M. and B. W. K. Ling, 2014 Measuring and locating zones of chaos and irregularity. J Syst Sci Complex 27: 494–506.
  • Garner, D. M. and B. W.-K. Ling, 2021 Measuring and locating zones of chaos and irregularity by application of high spectral chaotic global variants. International Journal of Bifurcation and Chaos 31: 2150236.
  • Garner, D. M., F. M. Vanderlei, V. E. Valenti, and L. C. M. Vanderlei, 2019b Non-linear regulation of cardiac autonomic modulation in obese youths: interpolation of ultra-short time series. Cardiol Young pp. 1–6.
  • Ghil, M., 1997 The SSA-MTM toolkit: Applications to analysis and prediction of time series. Applications of Soft Computing 3165: 216–230.
  • Gould, S. H., 1995 Variational methods for eigenvalue problems: an introduction to the methods of Rayleigh, Ritz, Weinstein, and Aronszajn. Courier Dover Publications.
  • Gourieroux, C., A. Monfort, E. Renault, and A. Trognon, 1987 Generalised residuals. Journal of econometrics 34: 5–32.
  • Gray, J. B. andW. H.Woodall, 1994 The maximum size of standardized and internally studentized residuals in regression analysis. The American Statistician 48: 111–113.
  • Green, A. L., S.Wang, S. L. Owen, K. Xie, X. Liu, et al., 2005 Deep brain stimulation can regulate arterial blood pressure in awake humans. Neuroreport 16: 1741–5.
  • Henriques, T. S., M. D. Costa, P. Mathur, P. Mathur, R. B. Davis, et al., 2019 Complexity of preoperative blood pressure dynamics: possible utility in cardiac surgical risk assessment. Journal of clinical monitoring and computing 33: 31–38.
  • Hocht, C., 2013 Blood pressure variability: prognostic value and therapeutic implications. ISRN Hypertension 2013.
  • Hodgetts, S. and M. Hausmann, 2022 in Sex/Gender Differences in Brain Lateralisation and Connectivity, Springer.
  • Hwang, W., D. Kang, and D. Kim, 2022 Brain lateralisation feature extraction and ant colony optimisation-bidirectional LSTM network model for emotion recognition. IET Signal Processing 16: 45–61.
  • Jeppesen, J., S. Beniczky, P. Johansen, P. Sidenius, and A. Fuglsang- Frederiksen, 2015 Detection of epileptic seizures with a modified heart rate variability algorithm based on lorenz plot. Seizure 24: 1–7.
  • Jinadasa, S. P., A. Mueller, V. Prasad, K. Subramaniam, T. Heldt, et al., 2018 Blood pressure coefficient of variation and its association with cardiac surgical outcomes. Anesthesia & Analgesia 127: 832–839.
  • Jolliffe, I., 2005 Principal component analysis. Wiley Online Library. Kim, J.-S., S. Park, P. Yan, B.W. Jeffers, and C. Cerezo, 2016 Effect of inter-individual blood pressure variability on the progression of atherosclerosis in carotid and coronary arteries: a post hoc analysis of the normalise and prevent studies. European Heart Journal-Cardiovascular Pharmacotherapy 3: 82–89.
  • Krout, K. E. and A. D. Loewy, 2000 Periaqueductal gray matter projections to midline and intralaminar thalamic nuclei of the rat. Journal of Comparative Neurology 424: 111–141.
  • Lang, A. E. and A. M. Lozano, 1998 Parkinson’s disease. New England Journal of Medicine 339: 1130–1143.
  • Mackey, M. C. and J. G. Milton, 1987 Dynamical diseases. Ann N Y Acad Sci 504: 16–32.
  • Mancia, G., R. Fagard, K. Narkiewicz, J. Redon, A. Zanchetti, et al., 2013 2013 esh/esc guidelines for the management of arterial hypertension: the task force for the management of arterial hypertension of the european society of hypertension (esh) and of the european society of cardiology (esc). Blood pressure 22: 193–278.
  • Marcinkiewicz, M., R. Morcos, and M. Chretien, 1989 CNS connections with the median raphe nucleus: Retrograde tracing with wga-apohrp-gold complex in the rat. Journal of Comparative Neurology 289: 11–35.
  • Matanes, F., M. B. Khan, M. Siddiqui, T. Dudenbostel, D. Calhoun, et al., 2022 An update on refractory hypertension. Current Hypertension Reports 24: 225–234.
  • McAfee, S. S., 2017 Assessing Neuronal Synchrony and Brain Function Through Local Field Potential and Spike Analysis. Thesis. Miles, J., 2005 R-squared, adjusted r-squared. Encyclopedia of statistics in behavioral science .
  • Mustafa, E. R., O. Istratoaie, and R. Musetescu, 2016 Blood pressure variability and left ventricular mass in hypertensive patients. Current health sciences journal 42: 47.
  • Nazaraghaei, F. and K. K. Bhat, 2020 Physiological impacts of ajapajapa yogic meditation on HRV index, RMSSD, PNN50, heart rate and GSR following three-month training course in comparison to FG meditation. Journal of Advanced Medical Sciences and Applied Technologies 5.
  • Newman, D., S. Hilleary, and C. Ginsberg, 1989 Nuclear terminations of corticoreticular fiber systems in rats; pp. 253-264. Brain, behavior and evolution 34: 253–264.
  • Nogueira, M. L., D. M. Garner, F. M. Vanderlei, L. C. de Abreu, and V. E. Valenti, 2017 Higuchi fractal dimension applied to rr intervals during exposure to musical auditory stimulation. Indian J Physiol Pharmacol 61: 211–218.
  • O’Callaghan, E. L., E. C. Hart, H. Sims-Williams, S. Javed, A. E. Burchell, et al., 2017 Chronic deep brain stimulation decreases blood pressure and sympathetic nerve activity in a drug-and device-resistant hypertensive patient. Hypertension 69: 522–528.
  • Osipov, V. and E. Ponizovskaya, 2000 Stochastic resonance in the brusselator model. Phys.Rev.E.Stat.Phys.Plasmas.Fluids Relat Interdiscip.Topics. 61: 4603–4605.
  • Packiasabapathy, S., V. Prasad, V. Rangasamy, D. Popok, X. Xu, et al., 2020 Cardiac surgical outcome prediction by blood pressure variability indices poincare plot and coefficient of variation: a retrospective study. BMC anesthesiology 20: 1–12.
  • Palatini, P., 2018 Risk of developing foot ulcers in diabetes: contribution of high visit-to-visit blood pressure variability. Journal of hypertension 36: 2132–2134.
  • Parati, G., G. Bilo, and M. Valentini, 2008 Blood pressure vari-ability: methodological aspects, pathophysiological and clinical implications. Manual of Hypertension of the European Society of Hypertension p. 61.
  • Parati, G., J. E. Ochoa, and G. Bilo, 2012 Blood pressure variability, cardiovascular risk, and risk for renal disease progression. Current hypertension reports 14: 421–431.
  • Parati, G., J. E. Ochoa, C. Lombardi, and G. Bilo, 2013 Assessment and management of blood-pressure variability. Nature Reviews Cardiology 10: 143–155.
  • Parati, G., G. S. Stergiou, E. Dolan, and G. Bilo, 2018 Blood pressure variability: clinical relevance and application. The Journal of Clinical Hypertension 20: 1133–1137.
  • Patel, N., S. Javed, S. Khan, M. Papouchado, A. Malizia, et al., 2011 Deep brain stimulation relieves refractory hypertension. Neurology 76: 405–407.
  • Peng, C. K., S. Havlin, H. E. Stanley, and A. L. Goldberger, 1995 Quantification of scaling exponents and crossover phenomena in nonstationary heartbeat time series. Chaos 5: 82–7.
  • Percival, D. and A.Walden, 1993 Spectral Analysis for Physical Applications: Multitaper and Conventional Univariate Techniques. Cambridge University Press, New York.
  • Pezard, L., J.-L. Nandrino, B. Renault, F. El Massioui, J.-F. Allilaire, et al., 1996 Depression as a dynamical disease. Biological Psychiatry 39: 991–999.
  • Pierce, D. A. and D. W. Schafer, 1986 Residuals in generalized linear models. Journal of the American Statistical Association 81: 977–986.
  • Pincus, S., 1995 Approximate entropy (apen) as a complexity measure. Chaos: An Interdisciplinary Journal of Nonlinear Science 5: 110–117.
  • Ploner, M., C. Sorg, and J. Gross, 2017 Brain rhythms of pain. Trends in cognitive sciences 21: 100–110.
  • Prigogine, I., 1962 Non-equilibrium statistical mechanics.. New York: Interscience.
  • Rangasamy, V., T. S. Henriques, X. Xu, and B. Subramaniam, 2020 Preoperative blood pressure complexity indices as a marker for frailty in patients undergoing cardiac surgery. Journal of cardiothoracic and vascular anesthesia 34: 616–621.
  • Richman, J. S. and J. R. Moorman, 2000 Physiological time-series analysis using approximate entropy and sample entropy. Am J Physiol Heart Circ Physiol 278: H2039–49.
  • Rothwell, P. M., S. C. Howard, E. Dolan, E. O’Brien, J. E. Dobson, et al., 2010 Effects of beta-blockers and calcium-channel blockers on within-individual variability in blood pressure and risk of stroke. The Lancet Neurology 9: 469–480.
  • Schmitt, L., J. Regnard, and G. P. Millet, 2015 Monitoring fatigue status with HRV measures in elite athletes: an avenue beyond RMSSD. Frontiers in physiology 6: 343.
  • Shannon, C. E., 2001 A mathematical theory of communication. ACM SIGMOBILE Mobile Computing and Communications Review 5: 3–55.
  • Silke, B., M. A. Frais, K. A. Midtbo, S. P. Verma, S. Sharma, et al., 1987 Comparative hemodynamic dose-response effects of five slow calcium channel-blocking agents in coronary artery disease. Clin Pharmacol Ther 42: 381–7.
  • Slepian, S., 1978 Prolate spheroidal wave functions, fourier analysis and uncertainty, v, the discrete case. Bell Syst Tech J 57: 1371– 1430.
  • Spallone, V., 2018 Blood pressure variability and autonomic dysfunction. Current Diabetes Reports 18: 137.
  • Srinivasan, V., N. Udayakumar, H. Liang, and K. Anandan, 2022 A hybrid approach for analysis of brain lateralisation in autistic children using graph theory techniques and deep belief networks. International Journal of Biomedical Engineering and Technology 39: 40–64.
  • Stoco-Oliveira, M. C., A. L. Ricci-Vitor, L. M. Vanzella, H. B. Valente, V. E. d. S. Silva, et al., 2021 Parkinson’s disease effect on autonomic modulation: an analysis using geometric indices. Arquivos de Neuro-Psiquiatria 79: 114–121.
  • Subasi, A., 2007 Selection of optimal AR spectral estimation method for eeg signals using cramer-rao bound. Comput.Biol.Med. 37: 183–194.
  • Thomson, D. J., 1982 Spectrum estimation and harmonic analysis. Proceedings of the IEEE 70: 1055–1096.
  • Tuchler, M., A. C. Singer, and R. Koetter, 2002 Minimum mean squared error equalization using a priori information. IEEE Transactions on Signal processing 50: 673–683.
  • Vautard, R., P. Yiou, and M. Ghil, 1992 Singular-spectrum analysis - a toolkit for short, noisy chaotic signals. Physica D 58: 95–126.
  • Voss, A., V. Baier, S. Schulz, and K. J. Bar, 2006 Linear and nonlinear methods for analyses of cardiovascular variability in bipolar disorders. Bipolar Disord 8: 441–52.
  • Wajnsztejn, R., T. D. De Carvalho, D. M. Garner, L. C. M. Vanderlei, M. F. Godoy, et al., 2016 Heart rate variability analysis by chaotic global techniques in children with attention deficit hyperactivity disorder. Complexity 21: 412–419.
  • Wang, Z. and A. C. Bovik, 2009 Mean squared error: Love it or leave it: a new look at signal fidelity measures. IEEE signal processing magazine 26: 98–117.
  • Webb, A. J., A. Lawson, K. Wartolowska, S. Mazzucco, and P. M. Rothwell, 2021 Progression of beat-to-beat blood pressure variability despite best medical management. Hypertension 77: 193– 201.
  • Whitmer, D., C. de Solages, B. C. Hill, H. Yu, and H. Bronte-Stewart, 2013 Resting beta hypersynchrony in secondary dystonia and its suppression during pallidal deep brain stimulation in DYT3+ l ubag dystonia. Neuromodulation: Technology at the Neural Interface 16: 200–205.
  • Zhou, T. L., R. M. Henry, C. D. Stehouwer, T. T. van Sloten, K. D. Reesink, et al., 2018 Blood pressure variability, arterial stiffness, and arterial remodeling: The maastricht study. Hypertension 72: 1002–1010.
Year 2023, , 65 - 77, 31.07.2023
https://doi.org/10.51537/chaos.1262839

Abstract

Project Number

Study number 05 Q1605 47

References

  • Alkan, A. and M. K. Kiymik, 2006 Comparison of AR and Welch methods in epileptic seizure detection. J Med Syst 30: 413–9.
  • Alkan, A. and A. S. Yilmaz, 2007 Frequency domain analysis of power system transients using Welch and Yule-Walker AR methods. Energy conversion and management 48: 2129–2135.
  • Appiah, K. O. B., M. Nath, L. Manning,W. J. Davison, S. Mazzucco, et al., 2021 Increasing blood pressure variability predicts poor functional outcome following acute stroke. Journal of Stroke and Cerebrovascular Diseases 30.
  • Bacan, G., A. Ribeiro-Silva, V. A. Oliveira, C. R. Cardoso, and G. F. Salles, 2022 Refractory hypertension: a narrative systematic review with emphasis on prognosis. Current Hypertension Reports 24: 95–106.
  • Bar, K. J., S. Berger, M. Metzner, M. K. Boettger, S. Schulz, et al., 2010 Autonomic dysfunction in unaffected first-degree relatives of patients suffering from schizophrenia. Schizophr Bull 36: 1050–8.
  • Bar, K. J., M. K. Boettger, M. Koschke, S. Schulz, P. Chokka, et al., 2007 Non-linear complexity measures of heart rate variability in acute schizophrenia. Clin Neurophysiol 118: 2009–15.
  • Barreto, G. S., F. M. Vanderlei, L. C. M. Vanderlei, and D. M. Garner, 2014 Risk appraisal by novel chaotic globals to HRV in subjects with malnutrition. Journal of Human Growth and Development 24: 243–248.
  • Belair, J., L. Glass, U. an der Heiden, and J. Milton, 1995 Dynamical disease: identification, temporal aspects and treatment strategies of human illness. Chaos: An Interdisciplinary Journal of Nonlinear Science 5: 1–7.
  • Benjamim, C. J. R., Y. Mota de M. Pontes, F. Welington de Sousa Junior, A. A. Porto, C. R. Bueno Junior, et al., 2021 Does bariatric surgery improve cardiac autonomic modulation assessed by heart rate variability: a systematic review. Surgery for Obesity and Related Diseases .
  • Bernardo, A. F., L. C. Vanderlei, and D. M. Garner, 2014 HRV analysis: A clinical and diagnostic tool in chronic obstructive pulmonary disease. Int Sch Res Notices 2014: 673232.
  • Bittar, R. G., I. Kar-Purkayastha, S. L. Owen, R. E. Bear, A. Green, et al., 2005 Deep brain stimulation for pain relief: a metaanalysis. J Clin Neurosci 12: 515–9.
  • Bonatto, C., J. Gallas, and Y. Ueda, 2008 Chaotic phase similarities and recurrences in a damped-driven duffing oscillator. Phys.Rev.E.Stat.Nonlin.Soft.Matter Phys. 77: 026217.
  • Calhoun, D. A., J. N. Booth III, S. Oparil, M. R. Irvin, D. Shimbo, et al., 2014 Refractory hypertension: determination of prevalence, risk factors, and comorbidities in a large, population-based cohort. Hypertension 63: 451–458.
  • Cameron, A. A., I. A. Khan, K. N. Westlund, K. D. Cliffer, and W. D. Willis, 1995 The efferent projections of the periaqueductal gray in the rat: A phaseolus vulgaris-leucoagglutinin study. I. Ascending projections. Journal of Comparative Neurology 351: 568–584.
  • Camm, A., M. Malik, J. Bigger, G. Breithardt, S. Cerutti, et al., 1996 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. Circulation 93: 1043–1065.
  • Carrive, P. and R. Bandler, 1991 Viscerotopic organization of neurons subserving hypotensive reactions within the midbrain periaqueductal grey: a correlative functional and anatomical study. Brain research 541: 206–215.
  • Chang, S., 2010 Physiological rhythms, dynamical diseases and acupuncture. Chin J Physiol 53: 77–90.
  • Cook, R. D. and S.Weisberg, 1982 Residuals and influence in regression. New York: Chapman and Hall.
  • Corrao, G., A. Parodi, F. Nicotra, A. Zambon, L. Merlino, et al., 2011 Better compliance to antihypertensive medications reduces cardiovascular risk. Journal of hypertension 29: 610–618.
  • Das, K., J. Jiang, and J. Rao, 2004 Mean squared error of empirical predictor. The Annals of Statistics 32: 818–840.
  • Dauer, W. and S. Przedborski, 2003 Parkinson’s disease: mechanisms and models. Neuron 39: 889–909.
  • Day, B. P., A. Evers, and D. E. Hack, 2020 Multipath suppression for continuous wave radar via slepian sequences. IEEE Transactions on Signal Processing 68: 548–557.
  • De Souza, N. M., L. C. M. Vanderlei, and D. M. Garner, 2015 Risk evaluation of diabetes mellitus by relation of chaotic globals to HRV. Complexity 20: 84–92.
  • Dirac, P., 1939 New notation for quantum mechanics. Proceedings of the Cambridge Philosophical Society 35: 416.
  • Dolan, E. and E. O’Brien, 2010 Blood pressure variability clarity for clinical practice. Hypertension 56: 179–181.
  • Farkas, E., A. S. Jansen, and A. D. Loewy, 1997 Periaqueductal gray matter projection to vagal preganglionic neurons and the nucleus tractus solitarius. Brain research 764: 257–261.
  • Farkas, E., A. S. Jansen, and A. D. Loewy, 1998 Periaqueductal gray matter input to cardiac-related sympathetic premotor neurons. Brain research 792: 179–192.
  • Frank, G., F. Halberg, R. Harner, J. Matthews, E. Johnson, et al., 1966 Circadian periodicity, adrenal corticosteroids, and the eeg of normal man. J.Psychiatr.Res. 4: 73–86.
  • Galhardo, C., T. Penna, M. A. de Menezes, and P. Soares, 2009 Detrended fluctuation analysis of a systolic blood pressure control loop. New Journal of Physics 11: 103005.
  • Garner, D., M. Alves, B. da Silva, L. de Alcantara Sousa, and V. Valenti, 2020a Chaotic global analysis of heart rate variability following power spectral adjustments during exposure to traffic noise in healthy adult women. Russ J Cardiol 25: 3739.
  • Garner, D., A. Bernardo, and L. Vanderlei, 2021a HRV analysis: Unpredictability of approximate entropy in chronic obstructive pulmonary disease. Series Cardiol Res 3(1): 1–10.
  • Garner, D., N. de Souza, and L. Vanderlei, 2020b Unreliability of approximate entropy to locate optimal complexity in diabetes mellitus via heart rate variability. Series Endo Diab Met. 2: 32–40.
  • Garner, D., F. Vanderlei, L. Vanderlei, V. Valenti, C. J. R. Benjamim,et al., 2022 Chaotic global metric analysis of heart rate variability following six power spectral manipulations in malnourished children. Series Endo Diab Met. 4: 44–58.
  • Garner, D. M., G. S. Barreto, V. E. Valenti, F. M. Vanderlei, A. A. Porto, et al., 2021b HRV analysis: undependability of approximate entropy at locating optimum complexity in malnourished children. Cardiol Young pp. 1–6.
  • Garner, D. M., N. M. de Souza, V. E. Valenti, and L. C. M. Vanderlei, 2019a Complexity of cardiac autonomic modulation in diabetes mellitus: A new technique to perceive autonomic dysfunction. Romanian Journal of Diabetes Nutrition and Metabolic Diseases 26: 279–291.
  • Garner, D. M., N. M. De Souza, and L. C. M. Vanderlei, 2017 Risk assessment of diabetes mellitus by chaotic globals to heart rate variability via six power spectra. Romanian Journal of Diabetes Nutrition and Metabolic Diseases 24: 227–236.
  • Garner, D. M., N. M. de Souza, and L. C. M. Vanderlei, 2018 Heart rate variability analysis: Higuchi and katz’s fractal dimensions in subjects with type 1 diabetes mellitus. Romanian Journal of Diabetes Nutrition and Metabolic Diseases 25: 289–295.
  • Garner, D. M. and B. W. K. Ling, 2014 Measuring and locating zones of chaos and irregularity. J Syst Sci Complex 27: 494–506.
  • Garner, D. M. and B. W.-K. Ling, 2021 Measuring and locating zones of chaos and irregularity by application of high spectral chaotic global variants. International Journal of Bifurcation and Chaos 31: 2150236.
  • Garner, D. M., F. M. Vanderlei, V. E. Valenti, and L. C. M. Vanderlei, 2019b Non-linear regulation of cardiac autonomic modulation in obese youths: interpolation of ultra-short time series. Cardiol Young pp. 1–6.
  • Ghil, M., 1997 The SSA-MTM toolkit: Applications to analysis and prediction of time series. Applications of Soft Computing 3165: 216–230.
  • Gould, S. H., 1995 Variational methods for eigenvalue problems: an introduction to the methods of Rayleigh, Ritz, Weinstein, and Aronszajn. Courier Dover Publications.
  • Gourieroux, C., A. Monfort, E. Renault, and A. Trognon, 1987 Generalised residuals. Journal of econometrics 34: 5–32.
  • Gray, J. B. andW. H.Woodall, 1994 The maximum size of standardized and internally studentized residuals in regression analysis. The American Statistician 48: 111–113.
  • Green, A. L., S.Wang, S. L. Owen, K. Xie, X. Liu, et al., 2005 Deep brain stimulation can regulate arterial blood pressure in awake humans. Neuroreport 16: 1741–5.
  • Henriques, T. S., M. D. Costa, P. Mathur, P. Mathur, R. B. Davis, et al., 2019 Complexity of preoperative blood pressure dynamics: possible utility in cardiac surgical risk assessment. Journal of clinical monitoring and computing 33: 31–38.
  • Hocht, C., 2013 Blood pressure variability: prognostic value and therapeutic implications. ISRN Hypertension 2013.
  • Hodgetts, S. and M. Hausmann, 2022 in Sex/Gender Differences in Brain Lateralisation and Connectivity, Springer.
  • Hwang, W., D. Kang, and D. Kim, 2022 Brain lateralisation feature extraction and ant colony optimisation-bidirectional LSTM network model for emotion recognition. IET Signal Processing 16: 45–61.
  • Jeppesen, J., S. Beniczky, P. Johansen, P. Sidenius, and A. Fuglsang- Frederiksen, 2015 Detection of epileptic seizures with a modified heart rate variability algorithm based on lorenz plot. Seizure 24: 1–7.
  • Jinadasa, S. P., A. Mueller, V. Prasad, K. Subramaniam, T. Heldt, et al., 2018 Blood pressure coefficient of variation and its association with cardiac surgical outcomes. Anesthesia & Analgesia 127: 832–839.
  • Jolliffe, I., 2005 Principal component analysis. Wiley Online Library. Kim, J.-S., S. Park, P. Yan, B.W. Jeffers, and C. Cerezo, 2016 Effect of inter-individual blood pressure variability on the progression of atherosclerosis in carotid and coronary arteries: a post hoc analysis of the normalise and prevent studies. European Heart Journal-Cardiovascular Pharmacotherapy 3: 82–89.
  • Krout, K. E. and A. D. Loewy, 2000 Periaqueductal gray matter projections to midline and intralaminar thalamic nuclei of the rat. Journal of Comparative Neurology 424: 111–141.
  • Lang, A. E. and A. M. Lozano, 1998 Parkinson’s disease. New England Journal of Medicine 339: 1130–1143.
  • Mackey, M. C. and J. G. Milton, 1987 Dynamical diseases. Ann N Y Acad Sci 504: 16–32.
  • Mancia, G., R. Fagard, K. Narkiewicz, J. Redon, A. Zanchetti, et al., 2013 2013 esh/esc guidelines for the management of arterial hypertension: the task force for the management of arterial hypertension of the european society of hypertension (esh) and of the european society of cardiology (esc). Blood pressure 22: 193–278.
  • Marcinkiewicz, M., R. Morcos, and M. Chretien, 1989 CNS connections with the median raphe nucleus: Retrograde tracing with wga-apohrp-gold complex in the rat. Journal of Comparative Neurology 289: 11–35.
  • Matanes, F., M. B. Khan, M. Siddiqui, T. Dudenbostel, D. Calhoun, et al., 2022 An update on refractory hypertension. Current Hypertension Reports 24: 225–234.
  • McAfee, S. S., 2017 Assessing Neuronal Synchrony and Brain Function Through Local Field Potential and Spike Analysis. Thesis. Miles, J., 2005 R-squared, adjusted r-squared. Encyclopedia of statistics in behavioral science .
  • Mustafa, E. R., O. Istratoaie, and R. Musetescu, 2016 Blood pressure variability and left ventricular mass in hypertensive patients. Current health sciences journal 42: 47.
  • Nazaraghaei, F. and K. K. Bhat, 2020 Physiological impacts of ajapajapa yogic meditation on HRV index, RMSSD, PNN50, heart rate and GSR following three-month training course in comparison to FG meditation. Journal of Advanced Medical Sciences and Applied Technologies 5.
  • Newman, D., S. Hilleary, and C. Ginsberg, 1989 Nuclear terminations of corticoreticular fiber systems in rats; pp. 253-264. Brain, behavior and evolution 34: 253–264.
  • Nogueira, M. L., D. M. Garner, F. M. Vanderlei, L. C. de Abreu, and V. E. Valenti, 2017 Higuchi fractal dimension applied to rr intervals during exposure to musical auditory stimulation. Indian J Physiol Pharmacol 61: 211–218.
  • O’Callaghan, E. L., E. C. Hart, H. Sims-Williams, S. Javed, A. E. Burchell, et al., 2017 Chronic deep brain stimulation decreases blood pressure and sympathetic nerve activity in a drug-and device-resistant hypertensive patient. Hypertension 69: 522–528.
  • Osipov, V. and E. Ponizovskaya, 2000 Stochastic resonance in the brusselator model. Phys.Rev.E.Stat.Phys.Plasmas.Fluids Relat Interdiscip.Topics. 61: 4603–4605.
  • Packiasabapathy, S., V. Prasad, V. Rangasamy, D. Popok, X. Xu, et al., 2020 Cardiac surgical outcome prediction by blood pressure variability indices poincare plot and coefficient of variation: a retrospective study. BMC anesthesiology 20: 1–12.
  • Palatini, P., 2018 Risk of developing foot ulcers in diabetes: contribution of high visit-to-visit blood pressure variability. Journal of hypertension 36: 2132–2134.
  • Parati, G., G. Bilo, and M. Valentini, 2008 Blood pressure vari-ability: methodological aspects, pathophysiological and clinical implications. Manual of Hypertension of the European Society of Hypertension p. 61.
  • Parati, G., J. E. Ochoa, and G. Bilo, 2012 Blood pressure variability, cardiovascular risk, and risk for renal disease progression. Current hypertension reports 14: 421–431.
  • Parati, G., J. E. Ochoa, C. Lombardi, and G. Bilo, 2013 Assessment and management of blood-pressure variability. Nature Reviews Cardiology 10: 143–155.
  • Parati, G., G. S. Stergiou, E. Dolan, and G. Bilo, 2018 Blood pressure variability: clinical relevance and application. The Journal of Clinical Hypertension 20: 1133–1137.
  • Patel, N., S. Javed, S. Khan, M. Papouchado, A. Malizia, et al., 2011 Deep brain stimulation relieves refractory hypertension. Neurology 76: 405–407.
  • Peng, C. K., S. Havlin, H. E. Stanley, and A. L. Goldberger, 1995 Quantification of scaling exponents and crossover phenomena in nonstationary heartbeat time series. Chaos 5: 82–7.
  • Percival, D. and A.Walden, 1993 Spectral Analysis for Physical Applications: Multitaper and Conventional Univariate Techniques. Cambridge University Press, New York.
  • Pezard, L., J.-L. Nandrino, B. Renault, F. El Massioui, J.-F. Allilaire, et al., 1996 Depression as a dynamical disease. Biological Psychiatry 39: 991–999.
  • Pierce, D. A. and D. W. Schafer, 1986 Residuals in generalized linear models. Journal of the American Statistical Association 81: 977–986.
  • Pincus, S., 1995 Approximate entropy (apen) as a complexity measure. Chaos: An Interdisciplinary Journal of Nonlinear Science 5: 110–117.
  • Ploner, M., C. Sorg, and J. Gross, 2017 Brain rhythms of pain. Trends in cognitive sciences 21: 100–110.
  • Prigogine, I., 1962 Non-equilibrium statistical mechanics.. New York: Interscience.
  • Rangasamy, V., T. S. Henriques, X. Xu, and B. Subramaniam, 2020 Preoperative blood pressure complexity indices as a marker for frailty in patients undergoing cardiac surgery. Journal of cardiothoracic and vascular anesthesia 34: 616–621.
  • Richman, J. S. and J. R. Moorman, 2000 Physiological time-series analysis using approximate entropy and sample entropy. Am J Physiol Heart Circ Physiol 278: H2039–49.
  • Rothwell, P. M., S. C. Howard, E. Dolan, E. O’Brien, J. E. Dobson, et al., 2010 Effects of beta-blockers and calcium-channel blockers on within-individual variability in blood pressure and risk of stroke. The Lancet Neurology 9: 469–480.
  • Schmitt, L., J. Regnard, and G. P. Millet, 2015 Monitoring fatigue status with HRV measures in elite athletes: an avenue beyond RMSSD. Frontiers in physiology 6: 343.
  • Shannon, C. E., 2001 A mathematical theory of communication. ACM SIGMOBILE Mobile Computing and Communications Review 5: 3–55.
  • Silke, B., M. A. Frais, K. A. Midtbo, S. P. Verma, S. Sharma, et al., 1987 Comparative hemodynamic dose-response effects of five slow calcium channel-blocking agents in coronary artery disease. Clin Pharmacol Ther 42: 381–7.
  • Slepian, S., 1978 Prolate spheroidal wave functions, fourier analysis and uncertainty, v, the discrete case. Bell Syst Tech J 57: 1371– 1430.
  • Spallone, V., 2018 Blood pressure variability and autonomic dysfunction. Current Diabetes Reports 18: 137.
  • Srinivasan, V., N. Udayakumar, H. Liang, and K. Anandan, 2022 A hybrid approach for analysis of brain lateralisation in autistic children using graph theory techniques and deep belief networks. International Journal of Biomedical Engineering and Technology 39: 40–64.
  • Stoco-Oliveira, M. C., A. L. Ricci-Vitor, L. M. Vanzella, H. B. Valente, V. E. d. S. Silva, et al., 2021 Parkinson’s disease effect on autonomic modulation: an analysis using geometric indices. Arquivos de Neuro-Psiquiatria 79: 114–121.
  • Subasi, A., 2007 Selection of optimal AR spectral estimation method for eeg signals using cramer-rao bound. Comput.Biol.Med. 37: 183–194.
  • Thomson, D. J., 1982 Spectrum estimation and harmonic analysis. Proceedings of the IEEE 70: 1055–1096.
  • Tuchler, M., A. C. Singer, and R. Koetter, 2002 Minimum mean squared error equalization using a priori information. IEEE Transactions on Signal processing 50: 673–683.
  • Vautard, R., P. Yiou, and M. Ghil, 1992 Singular-spectrum analysis - a toolkit for short, noisy chaotic signals. Physica D 58: 95–126.
  • Voss, A., V. Baier, S. Schulz, and K. J. Bar, 2006 Linear and nonlinear methods for analyses of cardiovascular variability in bipolar disorders. Bipolar Disord 8: 441–52.
  • Wajnsztejn, R., T. D. De Carvalho, D. M. Garner, L. C. M. Vanderlei, M. F. Godoy, et al., 2016 Heart rate variability analysis by chaotic global techniques in children with attention deficit hyperactivity disorder. Complexity 21: 412–419.
  • Wang, Z. and A. C. Bovik, 2009 Mean squared error: Love it or leave it: a new look at signal fidelity measures. IEEE signal processing magazine 26: 98–117.
  • Webb, A. J., A. Lawson, K. Wartolowska, S. Mazzucco, and P. M. Rothwell, 2021 Progression of beat-to-beat blood pressure variability despite best medical management. Hypertension 77: 193– 201.
  • Whitmer, D., C. de Solages, B. C. Hill, H. Yu, and H. Bronte-Stewart, 2013 Resting beta hypersynchrony in secondary dystonia and its suppression during pallidal deep brain stimulation in DYT3+ l ubag dystonia. Neuromodulation: Technology at the Neural Interface 16: 200–205.
  • Zhou, T. L., R. M. Henry, C. D. Stehouwer, T. T. van Sloten, K. D. Reesink, et al., 2018 Blood pressure variability, arterial stiffness, and arterial remodeling: The maastricht study. Hypertension 72: 1002–1010.
There are 99 citations in total.

Details

Primary Language English
Subjects Clinical Sciences
Journal Section Research Articles
Authors

David Garner 0000-0002-8114-9055

Shouyan Wang This is me 0000-0002-9776-8539

Ashley Raghu This is me 0000-0002-3866-3833

Vitor Valenti This is me 0000-0001-7477-3805

Tipu Aziz This is me 0000-0001-9128-8668

Alexander Green This is me 0000-0002-7262-7297

Project Number Study number 05 Q1605 47
Early Pub Date May 10, 2023
Publication Date July 31, 2023
Published in Issue Year 2023

Cite

APA Garner, D., Wang, S., Raghu, A., Valenti, V., et al. (2023). Monitoring Blood Pressure Variability via Chaotic Global Metrics using Local Field Potential Oscillations. Chaos Theory and Applications, 5(2), 65-77. https://doi.org/10.51537/chaos.1262839

Chaos Theory and Applications in Applied Sciences and Engineering: An interdisciplinary journal of nonlinear science 23830 28903   

The published articles in CHTA are licensed under a Creative Commons Attribution-NonCommercial 4.0 International License Cc_by-nc_icon.svg