Abstract-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 clinicallylabelled 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. Index TermsArtifact removal, Diagnosis, Electroencephalography (EEG), Portable Medical System, Traumatic Brain Injury (TBI).
Wavelet analysis REF is employed to carry on a diagnosis of Traumatic Brain Injury (TBI) by quantitative EEG (qEEG) data and reaches 87.85% of accuracy.
Automatic EEG processing for the early diagnosis of Traumatic Brain Injury
2016 World Automation Congress (WAC)
2016 World Automation Congress (WAC)
Computer Science