Evaluating of traumatic brain injuries using artificial neural networks

Authors:
İnan Güler;Zeki Gökçil;Eyyüp Gülbandilar
Affiliations:
Gazi University, Faculty of Technical Education, Department of Electronic and Computer Education, Elektronik-Bilgisayar Bolumu, Teknik Egitim Fakultesi, Beşevler 06500, Ankara, Turkey;Gülhane Military Medical School Hospitals, Department of Neurology, Etlik 06018, Ankara, Turkey;Dumlupinar University, Faculty of Engineering, Department of Computer Engineering, 43030 Kütahya, Turkey
Venue:
Expert Systems with Applications: An International Journal
Year:
2009

Citing 3
Cited 2

An Application of Technical of Dimensional Analysis in an Industrial Multivariate Process

CERMA '06 Proceedings of the Electronics, Robotics and Automotive Mechanics Conference - Volume 02
Detection of traumatic brain injuries using fuzzy logic algorithm

Expert Systems with Applications: An International Journal
Multiclass Support Vector Machines for EEG-Signals Classification

IEEE Transactions on Information Technology in Biomedicine

Inverse kinematics of a 6 DoF human upper limb using ANFIS and ANN for anticipatory actuation in ADL-based physical Neurorehabilitation

Expert Systems with Applications: An International Journal
Data mining applied to the cognitive rehabilitation of patients with acquired brain injury

Expert Systems with Applications: An International Journal

Quantified Score

Hi-index	12.05

Visualization

Abstract

The purpose of this study is to develop a diagnostic system to detect the severity of traumatic brain injuries using artificial neural networks. Three layered back propagation neural network with an input layer of 10 nodes whose output providing the inputs to a hidden layer was used. Thirty-two patients with traumatic brain injuries in different age and gender were taken in the study. Electroencephalography, Trauma and Glasgow coma scores were used for evaluating the data. The results obtained from the system were compared with the findings of neurologists. We found a significant relationship between the findings of neurologists and systems output for normal, mild, moderate and severe electroencephalography tracing data. Getting this system in routine use will lead to make a rapid decision for the degree of trauma with electroencephalography and revised trauma score.