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Automatic EEG processing for the early diagnosis of traumatic brain injury

Albert, Bruno, Zhang, Jingjing, Noyvirt, Alexandre, Setchi, Rossitza ORCID:, Sjaaheim, Haldor, Velikova, Svetla and Strisland, Frode 2016. Automatic EEG processing for the early diagnosis of traumatic brain injury. Procedia Computer Science 96 , pp. 703-712. 10.1016/j.procs.2016.08.253

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Traumatic Brain Injury (TBI) is recognized as an important cause of death and disabilities after an accident. The availability a tool for the early diagnosis of brain dysfunctions could greatly improve the quality of life of people affected by TBI and even prevent deaths. The contribution of the paper is a process including several methods for the automatic processing of electroencephalography (EEG) data, in order to provide a fast and reliable diagnosis of TBI. Integrated in a portable decision support system called EmerEEG, the TBI diagnosis is obtained using discriminant analysis based on quantitative EEG (qEEG) features extracted from data recordings after the automatic removal of artifacts. The proposed algorithm computes the TBI diagnosis on the basis of a model extracted from clinically-labelled EEG records. The system evaluations have confirmed the speed and reliability of the processing algorithms as well as the system's ability to deliver accurate diagnosis. The developed algorithms have achieved 79.1% accuracy in removing artifacts, and 87.85% accuracy in TBI diagnosis. Therefore, the developed system enables a short response time in emergency situations and provides a tool the emergency services could base their decision upon, thus preventing possibly miss-diagnosed injuries.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Engineering
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
Publisher: Elsevier
ISSN: 1877-0509
Date of First Compliant Deposit: 16 December 2016
Last Modified: 08 Jul 2023 19:17

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