Key result
Novel LSTM/ECOC-SVM architecture with resting-state EEG achieves 100% accuracy classifying non-severe TBI versus healthy subjects.
Why the study?
Traumatic brain injury is frequently misused in fraudulent insurance claims, driving the need for an instant classification system that addresses the immobility limitations of CT and MRI.
Does a novel classification architecture using LSTM and ECOC-SVM with resting-state EEG accurately classify non-severe TBI patients from healthy subjects?
Does a novel classification architecture using LSTM and ECOC-SVM with resting-state EEG accurately classify non-severe TBI patients from healthy subjects?
A novel machine learning architecture combining LSTM and ECOC-SVM with resting-state EEG achieved 100% accuracy in distinguishing non-severe TBI patients from healthy subjects.
Traumatic brain injury (TBI) is one of the common injuries when the human head receives an impact due to an accident or fall and is one of the most frequently submitted insurance claims. However, it is often always misused when individuals attempt an insurance fraud claim by providing false medical conditions. Therefore, there is a need for an instant brain condition classification system. This study presents a novel classification architecture that can classify non-severe TBI patients and healthy subjects employing resting-state electroencephalogram (EEG) as the input, solving the immobility issue of the computed tomography (CT) scan and magnetic resonance imaging (MRI). The proposed architecture makes use of long short term memory (LSTM) and error-correcting output coding support vector machine (ECOC-SVM) to perform multiclass classification. The pre-processed EEG time series are supplied to the network by each time step, where important information from the previous time step will be remembered by the LSTM cell. Activations from the LSTM cell is used to train an ECOC-SVM. The temporal advantages of the EEG were amplified and able to achieve a classification accuracy of 100%. The proposed method was compared to existing works in the literature, and it is shown that the proposed method is superior in terms of classification accuracy, sensitivity, specificity, and precision.
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Lai et al. (2020) studied Non-severe traumatic brain injury. LSTM network with ECOC-SVM using resting-state EEG vs. Existing works in the literature was evaluated on Classification accuracy. A novel classification architecture using LSTM and ECOC-SVM with resting-state EEG achieved 100% accuracy in classifying non-severe traumatic brain injury patients versus healthy subjects.
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