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Non-invasive estimation of intracranial pressure in traumatic brain injury (TBI) using fully-anisotropic Morlet wavelet transform and support vector regression

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Abstract

Purpose

This paper aims to estimate the intracranial pressure (ICP) in patients with traumatic brain injuries (TBI) noninvasively using directional features obtained from the texture of brain CT image and support vector regression (SVR) method.

Methods

A fully anisotropic Morlet wavelet transform is performed on brain CT images and optimal feature vectors have been extracted to classify the images into two groups of mild and severe TBI. Genetic algorithms with the fitness functions of support vector machines (SVM) classification accuracy rates have been used to find the optimal feature vector. Finally, SVR is implemented to estimate the ICP of patients with TBI. The results are compared to the ones obtained using Dual Tree complex wavelet transform based directional features.

Results

Features obtained from anisotropic continuous complex wavelet transform are shown to be effective in separating data from two classes of mild and severe TBI. The highest classification accuracy rate of 94.43 percent is achieved. Also, using SVR, the ICP estimation results demonstrate that the proposed algorithm yields excellent performance with a mean absolute error of 4.25 mmHg compared to Dual Tree complex wavelet transform features with the mean absolute error of 5.48 mmHg.

Conclusions

The severity of TBI is assessed non-invasively using brain CT images, and the directional textural features of brain tissue. The proposed algorithm using anisotropic Morlet wavelet features, GA-SVM based feature selection and SVR methods achieves an excellent performance in ICP estimation for TBI severity assessment.

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Author information

Authors and Affiliations

  1. Massachusetts Eye and Ear Infirmary, Boston, MA, 02114, USA

    Babak Seyed Aghazadeh

  2. Mechanical Engineering Department, Worcester Polytechnic Institute, Worcester, MA, 01609, USA

    Babak Seyed Aghazadeh

  3. Computer Science Department, Virginia Commonwealth University, Richmond, VA, 23284, USA

    Sardar Ansari

  4. Mechanical Engineering Department, Virginia Commonwealth University, Richmond, VA, 23284, USA

    Ramana Pidaparti

  5. Department of Computational Medicine and Bioinformatics, Michigan Center for Integrative Research in Critical Care, Ann Arbor, MI, 48109, USA

    Kayvan Najarian

Authors
  1. Babak Seyed Aghazadeh
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  2. Sardar Ansari
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  3. Ramana Pidaparti
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  4. Kayvan Najarian
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Corresponding author

Correspondence to Babak Seyed Aghazadeh.

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Aghazadeh, B.S., Ansari, S., Pidaparti, R. et al. Non-invasive estimation of intracranial pressure in traumatic brain injury (TBI) using fully-anisotropic Morlet wavelet transform and support vector regression. Biomed. Eng. Lett. 3, 190–197 (2013). https://doi.org/10.1007/s13534-013-0102-2

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  • DOI: https://doi.org/10.1007/s13534-013-0102-2

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