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

Year 2023, Volume: 5 Issue: 2, 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

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Year 2023, Volume: 5 Issue: 2, 65 - 77, 31.07.2023
https://doi.org/10.51537/chaos.1262839

Abstract

Project Number

Study number 05 Q1605 47

References

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  • 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.
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  • 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.
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  • 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.
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  • 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.
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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 Volume: 5 Issue: 2

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   

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