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Analysis of a Non-invasive Intracranial Pressure Monitoring Method in Patients with Traumatic Brain Injury

Part of the Acta Neurochirurgica Supplement book series (NEUROCHIRURGICA,volume 126)


Objective: We aimed to compare the invasive (iICP) and a non-invasive intracranial pressure (nICP) monitoring methods in patients with traumatic brain injury, based on the similarities of the signals' power spectral densities.

Materials and methods: We recorded the intracranial pressure of seven patients with traumatic brain injury admitted to Hospital São João, Portugal, using two different methods: a standard intraparenchymal (iICP) and a new nICP method based on mechanical extensometers. The similarity between the two monitoring signals was inferred from the Euclidean distance between the non-linear projection in a lower dimensional space (ISOMAP) of the windowed power spectral densities of the respective signals. About 337 h of acquisitions were used out of a total of 608 h. The only data exclusion criterion was the absence of any of the signals of interest.

Results: The averaged distance between iICP and nICP, and between arterial blood pressure (ABP) and nICP projections in the embedded space are statistically different for all seven patients analysed (Mann-Whitney U, p < 0.05).

Conclusions: The similarity between the iICP and nICP monitoring methods was higher than the similarity between the nICP and the recordings of the radial ABP for all seven patients. Despite the possible differences between the shape of the ABP waveform at radial and parietal arteries, the results indicate—based on the similarities of iICP and nICP as functions of time—that the nICP method can be applied as an alternative method for ICP monitoring.


  • Non-invasive
  • Intracranial pressure
  • Arterial blood pressure
  • Dimensionality reduction
  • Fourier transform

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  1. Lee KR, Hoff JT. Intracranial pressure. In: Youmans JR, editor. Youmans neurological surgery, vol. 1. 4th ed. Philadelphia: WB Saunders; 1996. p. 491–518.

    Google Scholar 

  2. Kashif FM, Verghese GC, Novak V, Czosnyka M, Heldt T. Model-based noninvasive estimation of intracranial pressure from cerebral blood flow velocity and arterial pressure. Sci Transl Med. 2012;4:129ra44.

    CrossRef  PubMed  PubMed Central  Google Scholar 

  3. Ragauskas A, Daubaris G, Dziugys a, Azelis V, Gedrimas V. Innovative non-invasive method for absolute intracranial pressure measurement without calibration. Acta Neurochir Suppl. 2005;95:357–61.

    CAS  CrossRef  PubMed  Google Scholar 

  4. Barone DG, Czosnyka M. Brain monitoring: do we need a hole? An update on invasive and noninvasive brain monitoring modalities. Sci World J. 2014;2014:1–6.

    CrossRef  Google Scholar 

  5. Padayachy LC. Non-invasive intracranial pressure assessment. Childs Nerv Syst. 2016;32:1–11.

    CrossRef  Google Scholar 

  6. CJJ A, Van Eijndhoven JH, Wyper DJ. Cerebrospinal fluid pulse pressure and intracranial volume-pressure relationships. J Neurol Neurosurg Psychiatry. 1979;42:687–700.

    CrossRef  Google Scholar 

  7. Hashimoto M, Higashi S, Tokuda K, Yamamoto Y, Yamashita J. Changes of intracranial pressure and pulse wave form induced by various mechanical stresses upon intracranial hemodynamics. In: Avezaat CJJ, van Eijndhoven JHM, Maas AIR, Tans JTJ, editors. Intracranial pressure. VIII SE—79. Heidelberg: Springer; 1993. p. 367–71.

    CrossRef  Google Scholar 

  8. Ferreira MCPD. Multimodal brain monitoring and evaluation of cerebrovascular reactivity after severe head injury. Porto: University of Porto; 2015.

    Google Scholar 

  9. Fan JY, Kirkness C, Vicini P, Burr R, Mitchell P. Intracranial pressure waveform morphology and intracranial adaptive capacity. Am J Crit Care. 2008;17:545–54.

    PubMed  Google Scholar 

  10. Scalzo F, Hamilton R, Hu X. Real-time analysis of intracranial pressure waveform morphology. In: Chen K-S, editor. Adv Top Neurol Disord InTech. 2012;99–128.

    Google Scholar 

  11. Cabella B, Vilela GHF, Mascarenhas S, Czosnyka M, Smielewski P, Dias C, Colli BO (2016) Validation of a new noninvasive intracranial pressure monitoring method by direct comparison with an invasive technique. Acta Neurochirurgica. Supplement 2016;122:93–96.

    Google Scholar 

  12. Allen J. Short term spectral analysis, synthesis, and modification by discrete Fourier transform. IEEE Trans Acoust Speech Signal Process. 1977;25(3):235–8.

    Google Scholar 

  13. Tenenbaum JB, de Silva V, Langford JC. A global geometric framework for nonlinear dimensionality reduction. Science (New York, N.Y.), 290(5500), 2319–23.

    Google Scholar 

  14. Jones E, Oliphant T, Peterson P. Scipy: open source scientific tools for Python. 2001.

  15. Hunter JD. Matplotlib: a 2D graphics environment. IEEE Comput Sci Eng. 2007;9:90–5.

    CrossRef  Google Scholar 

  16. Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, et al. Scikit-learn: machine learning in Python. J Mach Learn Res. 2011;12:2825–30.

    Google Scholar 

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Funded by FAPESP, PAHO, CNPq and Ministry of Health of Brazil.

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We declare that we have no conflict of interest.

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Correspondence to G. Frigieri .

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Frigieri, G. et al. (2018). Analysis of a Non-invasive Intracranial Pressure Monitoring Method in Patients with Traumatic Brain Injury. In: Heldt, T. (eds) Intracranial Pressure & Neuromonitoring XVI. Acta Neurochirurgica Supplement, vol 126. Springer, Cham.

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