LSTM network as a screening tool to detect moderate traumatic brain injury from resting-state electroencephalogram

Abstract

Fraud and abuse in insurance and health care claims have received major attention as they can cause increasing losses of revenue. Processing health care claims requires extensive workloads because the staffs have to investigate the legibility of the report. For the investigation of brain injury claims, the related insurance company may request medical images of the brain from the hospital and subsequently get opinions from the medical staff. Conventionally, computed tomography (CT) or magnetic resonance imaging (MRI) is utilized for this purpose. However, to perform a CT scan or an MRI scan for every patient that requested medical claims is impractical due to the limited resources. Thus, we proposed a screening approach that uses the resting-state electroencephalogram (EEG) recordings as the input to a long short-term memory (LSTM) network. This LSTM architecture can classify the resting state EEG into two classes, which are either as a moderate traumatic brain injury (TBI) patient or a healthy person. Experimental results show that the proposed approach is able to outperform two similar recent works by achieving a classification accuracy of 74.33%.