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Research Papers

Wavelet Analysis of Head Acceleration Response Under Dirac Excitation for Early Oedema Detection

[+] Author and Article Information
V. Kostopoulos1

Applied Mechanics Laboratory, Department of Mechanical Engineering and Aeronautics, University of Patras, Patras University Campus, GR 265 00 Patras, Greecekostopoulos@mech.upatras.gr

T. H. Loutas, C. Derdas

Applied Mechanics Laboratory, Department of Mechanical Engineering and Aeronautics, University of Patras, Patras University Campus, GR 265 00 Patras, Greece

E. Douzinas

Department of Critical Care, Medical School, University of Athens, Evangelismos Hospital, 45-47 Ipsilandou Street, 106 75 Athens, Greece

1

Corresponding author.

J Biomech Eng 130(2), 021017 (Apr 03, 2008) (8 pages) doi:10.1115/1.2903432 History: Received December 30, 2006; Revised January 16, 2008; Published April 03, 2008

The present work deals with the application of an innovative in-house developed wavelet-based methodology for the analysis of the acceleration responses of a human head complex model as a simulated diffused oedema progresses. The human head complex has been modeled as a structure consisting of three confocal prolate spheroids, whereas the three defined regions by the system of spheroids, from the outside to the inside, represent the scull, the region of cerebrospinal fluid, and the brain tissue. A Dirac-like pulse has been used to excite the human head complex model and the acceleration response of the system has been calculated and analyzed via the wavelet-based methodology. For the purpose of the present analysis, a wave propagation commercial finite element code, LS-DYNA 3D , has been used. The progressive diffused oedema was modeled via consecutive increases in brain volume accompanied by a decrease in brain density. It was shown that even a small increase in brain volume (at the level of 0.5%) can be identified by the effect it has on the vibration characteristics of the human head complex. More precisely, it was found that for some of the wavelet decomposition levels, the energy content changes monotonically as the brain volume increases, thus providing a useful index of monitoring an oncoming brain oedema before any brain damage appears due to uncontrolled intracranial hypertension. For the purpose of the present work and for the levels of brain volume increase considered in the present analysis, no pressure increase was assumed into the cranial vault and, associatively, no brain compliance variation.

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Copyright © 2008 by American Society of Mechanical Engineers
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Figures

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Figure 1

ICP against brain volume increase (BUI) in the case of a brain oedema (1)

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Figure 2

14 cutout view of the model with boundary conditions and contact interface. Component 1 represents the brain, 2 the CSF, and 3 the skull

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Figure 3

Location of Dirac excitation

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Figure 4

Resultant Dirac force-time profile

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Figure 5

Normalized nodal acceleration for brain volume increase by 0.5%

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Figure 6

Wavelet-based signal processing methodology

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Figure 7

db10 wavelet (amplitude versus samples)

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Figure 8

Eight-level decomposition of the 0.5% brain volume increase signal (acceleration in m∕s2 versus samples)

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Figure 9

Fourier transform of each decomposition level (fast Fourier transform (FFT) amplitude versus frequency in Hz)

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Figure 10

a8 1evel energy versus brain volume increase

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Figure 11

Wavelet level 9 energy versus brain volume increase

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