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

Nor Safira Elaina Mohd Noor,Haidi Ibrahim,Jafri Malin Abdullah,Muhammad Hanif Che Lah

doi:10.3390/biomedinformatics2010007

Nor Safira Elaina Mohd Noor, Haidi Ibrahim + Show 2 more

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https://doi.org/10.3390/biomedinformatics2010007

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Abstract

The computational electroencephalogram (EEG) is recently garnering significant attention in examining whether the quantitative EEG (qEEG) features can be used as new predictors for the prediction of recovery in moderate traumatic brain injury (TBI). However, the brain’s recorded electrical activity has always been contaminated with artifacts, which in turn further impede the subsequent processing steps. As a result, it is crucial to devise a strategy for meticulously flagging and extracting clean EEG data to retrieve high-quality discriminative features for successful model development. This work proposed the use of multiple artifact rejection algorithms (MARA), which is an independent component analysis (ICA)-based algorithm, to eliminate artifacts automatically, and explored their effects on the predictive performance of the random undersampling boosting (RUSBoost) model. Continuous EEG were acquired using 64 electrodes from 27 moderate TBI patients at four weeks to one-year post-accident. The MARA incorporates an artifact removal stage based on ICA prior to RUSBoost, SVM, DT, and k-NN classification. The area under the curve (AUC) of RUSBoost was higher in absolute power spectral density (PSD) in AUCδ = 0.75, AUC α = 0.73 and AUCθ = 0.71 bands than SVM, DT, and k-NN. The MARA has provided a good generalization performance of the RUSBoost prediction model.

Highlights

Traumatic brain injury (TBI) has a tremendous impact on neurological dysfunction and death in young people and children (1–15 years old) worldwide [1,2,3,4]
The present study extends our prior work [40] by including a modification of adding an automatic artifacts rejection method (i.e., multiple artifact rejection algorithm (MARA)), an independent component analysis (ICA)-based algorithm in EEG preprocessing steps and exploring their effects on the predictive performance of the RUSBoost prediction model
The area under the curve (AUC) values were low in absolute power spectral density (PSD) of β (i.e., AUCβ = 0.51) and γ (i.e., AUCγ = 0.54) bands; indicating that the TPrate and TNrate of these frequencies bands were low

Summary

Introduction

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 13–15), approximately 8–10% is graded as moderate (GCS score 9–12) 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 measured using electroencephalography (EEG). EEG records the neuronal activities with non-invasive electrodes fitted on the scalp, allowing the analysis of neuronal activity in five canonical EEG frequency bands: delta δ (

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