Title: Predictability of intracranial pressure level in traumatic brain injury: features extraction, statistical analysis and machine learning-based evaluation

Authors: Wenan Chen; Charles H. Cockrell; Kevin Ward; Kayvan Najarian

Addresses: Virginia Commonwealth University, Reanimation Engineering Science (VCURES) Centre, Department of Computer Science, Virginia Commonwealth University, Richmond 23298, VA, USA ' Virginia Commonwealth University, Reanimation Engineering Science (VCURES) Centre, Department of Radiology, Virginia Commonwealth University, Richmond 23298, VA, USA ' Virginia Commonwealth University, Reanimation Engineering Science (VCURES) Centre, Department of Emergency Medicine, Virginia Commonwealth University, Richmond 23298, VA, USA ' Virginia Commonwealth University, Reanimation Engineering Science (VCURES) Centre, Department of Computer Science, Virginia Commonwealth University, Richmond 23298, VA, USA

Abstract: This paper attempts to predict Intracranial Pressure (ICP) based on features extracted from non-invasively collected patient data. These features include midline shift measurement and textural features extracted from Computed axial Tomography (CT) images. A statistical analysis is performed to examine the relationship between ICP and midline shift. Machine learning is also applied to estimate ICP levels with a two-stage feature selection scheme. To avoid overfitting, all feature selections and parameter selections are performed using a nested 10-fold cross validation within the training data. The classification results demonstrate the effectiveness of the proposed method in ICP prediction.

Keywords: intracranial pressure prediction; texture analysis; statistical analysis; machine learning; nested cross validation; intracranial pressure levels; traumatic brain injury; feature extraction; bioinformatics; midline shift measurement; textural features; computed tomography; CT images; feature selection; parameter selection; medical imaging.

DOI: 10.1504/IJDMB.2013.056617

International Journal of Data Mining and Bioinformatics, 2013 Vol.8 No.4, pp.480 - 494

Received: 04 May 2011
Accepted: 04 May 2011

Published online: 20 Oct 2014 *

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