Classification of Non-Severe Traumatic Brain Injury from Resting-State EEG Signal Using LSTM Network with ECOC-SVM

Abstract

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.

A Pilot Investigation of Visual Pathways in Patients with Mild Traumatic Brain Injury

“…High amplitude slow waves and a loss of the peaks in the PSD in the classical ranges (beta—gamma frequency ranges) would bring a shift in the PSD nearer to 1/f, with a slope near 4. We attribute these properties to a sustained loss of the large-scale synaptic integration that normalizes by cooperative interactions through competitive inhibition and prevents the breakdown of the large-scale organization of the neocortex [ 11 , 31 , 32 ]. We would expect mTBI instances to cause dominant local neural activity, which would imply a diminished global integration through the sustained loss of long-range correlated activity, also seen in seizure activity due to the power in both the low and high theta ranges [ 31 , 32 ].…”

Deep learning algorithm performance compared to experts in visual evaluation of interior vena cava collapse on ultrasound to determine intravenous fluid need in dehydration management

“…This appears to be the first instance of LSTM DL architecture being applied to pediatric IVC measurement. The majority of prior LSTM work has focused on ECG and EEG analysis as well as computed tomography image analysis [16][17][18]. These imaging types pose different challenges to analysis than due to limited number of frames requiring analysis and change occurring over a short period of time.…”

Prediction of Recovery from Traumatic Brain Injury with EEG Power Spectrum in Combination of Independent Component Analysis and RUSBoost Model

“…Traumatic brain injury (TBI) has a tremendous impact on neurological dysfunction and death in young people (i.e., younger than 45 years old) and children (1-15 years old) worldwide [1][2][3][4]. Most TBI is graded based on initial Glasgow Coma Scale (GCS) as mild (GCS score [13][14][15], meanwhile approximately 8-10% is graded as moderate (GCS score [9][10][11][12] or severe (GCS score 8 or less) [5,6] when recorded during the emergency room admission [7]. The effects of TBI on brain electrical activity, due to injury on a number of ionic channels, electrical generators and network dynamics involved in the distribution and coordination of electrical energy, can be easily measured using electroencephalography (EEG).…”

“…Numerous research groups have been working on developing a TBI-diagnostic model based on quantitative EEG (qEEG) features (i.e., differentiation between mild TBI (mTBI) and no mTBI) [12][13][14][15][16][17][18] but have yet to develop a TBI-prognostic (i.e., prediction TBI recovery) tool effectively that has gained widespread attention. Quantitative EEG analyses use computationally derived features that highlight specific components of EEG with numerical values [19].…”