gms | German Medical Science

72. Jahrestagung der Deutschen Gesellschaft für Neurochirurgie (DGNC)
Joint Meeting mit der Polnischen Gesellschaft für Neurochirurgie

Deutsche Gesellschaft für Neurochirurgie (DGNC) e. V.

06.06. - 09.06.2021

About the stochastic features during intracranial pressure transients – the Allan deviation

Zur Charakterisierung von transienten Hirndruckänderungen mittels der Allan Deviation

Meeting Abstract

  • presenting/speaker Hans E. Heissler - Medizinische Hochschule Hannover, Klinik für Neurochirurgie, Hannover, Deutschland
  • Mesbah Alam - Medizinische Hochschule Hannover, Klinik für Neurochirurgie, Hannover, Deutschland
  • Manolis Polemikos - Medizinische Hochschule Hannover, Klinik für Neurochirurgie, Hannover, Deutschland
  • Joachim Kurt Krauss - Medizinische Hochschule Hannover, Klinik für Neurochirurgie, Hannover, Deutschland

Deutsche Gesellschaft für Neurochirurgie. 72. Jahrestagung der Deutschen Gesellschaft für Neurochirurgie (DGNC), Joint Meeting mit der Polnischen Gesellschaft für Neurochirurgie. sine loco [digital], 06.-09.06.2021. Düsseldorf: German Medical Science GMS Publishing House; 2021. DocP211

doi: 10.3205/21dgnc492, urn:nbn:de:0183-21dgnc4926

Veröffentlicht: 4. Juni 2021

© 2021 Heissler et al.
Dieser Artikel ist ein Open-Access-Artikel und steht unter den Lizenzbedingungen der Creative Commons Attribution 4.0 License (Namensnennung). Lizenz-Angaben siehe http://creativecommons.org/licenses/by/4.0/.


Gliederung

Text

Objective: The Allan deviation (AD) has not been generally used as a tool to determine the statistical properties of intracranial pressure (ICP) up to now. This fact is remarkable because concordance to modern analytic methods applied to ICP (i.e., the wavelet variance) exists. AD becomes important as it provides insight into the signal’s properties in time, as is the degree of ICP's phase modulation originated by the cardiac “oscillator.” This variability is the time difference between pulse-pressure subpeaks p1 and p2 (∆t12). We hypothesised AD to differ in stochastic properties between dynamic ICP changes and episodes of undynamic controls.

Methods: In 12 episodes of 50 min with distinct dynamics, ICP was recorded for diagnostis in patients with idiopathic intracranial hypertension (IIH). Offline analysis of PP waveforms was carried out, extracting the ∆t12 parameter. Nine out of twelve episodes were assigned to 3 groups of notable ICP changes. Three episodes were rated controls because of no change in ICP dynamics. Allan deviation was calculated for nonoverlapping dyadic intervals (1,2,4...1024 samples). Linear regression was computed to determine stochastic properties within correlated group members' subsets (presets: high effect size, R2>0.81, N=4, p<0.05).

Results: Allan variance did not differ among groups; however, in longer intervals, differences between controls and dynamic episodes became apparent. A difference was found in the profile of AD, which showed almost concave, converging, and linear (controls) behavior over time intervals. For episodes of greater than 32 samples, controls showed minimum AD values. Stochastically, the dominant signal property was white noise, which was found in 6/144 subsets. Another 3 subgroups depict random walk and irregular properties. Seven subsets showed no effect size. The remaining subsets could not be unambiguously attributed to any stochastic properties.

Conclusion: The Allan deviation is a statistic portraying the dynamics through ICP measurements or derived parameters of it. The AD algorithm focussed on the nonstationarity. This limited study of an ICP parameter showed that different ICP dynamics had stochastic properties besides the deterministic ones. As irregularity was the leading signal characteristic, ICP should not only be rated by the magnitude and its fluctuations in time but also by the stochastic properties, which adds to the assessment of ICP curves as experienced in the patients with IIH and with other changes of ICP dynamics.