Screening of Moderate Traumatic Brain Injury from Power Feature of Resting State Electroencephalography using Support Vector Machine

Lai, Chi Qin; Abdullah, Mohd Zaid; Abdullah, Jafri Malin; Azman, Azlinda; Ibrahim, Haidi

doi:10.1145/3362752.3362758

Cited by 7 publications

(15 citation statements)

References 21 publications

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“…Comparing to other existing methods, the proposed method reaches a high accuracy of 72.46%, which stands out compared to the work by den Brink et al [24], McNerney et al [25], Cao et al [26], and our previous work [27]. By using the same dataset, these approaches achieve the mean cross-validation accuracies of 59.05%, 54.00%, 51.17%, and 49.64%, respectively, as shown in Table 7.…”

Section: Discussionsupporting

confidence: 59%

“…e parameters are the learning rate and mini batch size. Trained CNN models are then compared with Naive Bayes [24], AdaBoost classifier [25], SVM (MRMR) [26], and SVM (power) [27].…”

Section: Resultsmentioning

confidence: 99%

“…e third method that is compared is the work done by Cao et al [26] that uses SVM. e fourth method that is compared is our previous work that proposed an EEG-based SVM classifier using alpha band power for moderate TBI detection [27]. For a fair comparison, the same dataset and training procedure are used.…”

Section: Comparison Of the Proposed Methods With Existing Workmentioning

confidence: 99%

“…Previously, we also has proposed one method to classify moderate TBI patients and healthy subjects using the restingstate eye-closed EEG [27]. Similar to the work of Cao et al [26], our method is also using SVM as our computational intelligence method.…”

Section: Introductionmentioning

confidence: 93%

“…e parameters are the learning rate and the mini batch size. Our method is further compared to existing stateof-art approaches and our previous work [27].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Detection of Moderate Traumatic Brain Injury from Resting-State Eye-Closed Electroencephalography

Lai

Ibrahim

Hamid

et al. 2020

Computational Intelligence and Neuroscience

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Traumatic brain injury (TBI) is one of the injuries that can bring serious consequences if medical attention has been delayed. Commonly, analysis of computed tomography (CT) or magnetic resonance imaging (MRI) is required to determine the severity of a moderate TBI patient. However, due to the rising number of TBI patients these days, employing the CT scan or MRI scan to every potential patient is not only expensive, but also time consuming. Therefore, in this paper, we investigate the possibility of using electroencephalography (EEG) with computational intelligence as an alternative approach to detect the severity of moderate TBI patients. EEG procedure is much cheaper than CT or MRI. Although EEG does not have high spatial resolutions as compared with CT and MRI, it has high temporal resolutions. The analysis and prediction of moderate TBI from EEG using conventional computational intelligence approaches are tedious as they normally involve complex preprocessing, feature extraction, or feature selection of the signal. Thus, we propose an approach that uses convolutional neural network (CNN) to automatically classify healthy subjects and moderate TBI patients. The input to this computational intelligence system is the resting-state eye-closed EEG, without undergoing preprocessing and feature selection. The EEG dataset used includes 15 healthy volunteers and 15 moderate TBI patients, which is acquired at the Hospital Universiti Sains Malaysia, Kelantan, Malaysia. The performance of the proposed method has been compared with four other existing methods. With the average classification accuracy of 72.46%, the proposed method outperforms the other four methods. This result indicates that the proposed method has the potential to be used as a preliminary screening for moderate TBI, for selection of the patients for further diagnosis and treatment planning.

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Section: Discussionsupporting

confidence: 59%

“…e parameters are the learning rate and mini batch size. Trained CNN models are then compared with Naive Bayes [24], AdaBoost classifier [25], SVM (MRMR) [26], and SVM (power) [27].…”

Section: Resultsmentioning

confidence: 99%

Section: Comparison Of the Proposed Methods With Existing Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 93%

“…e parameters are the learning rate and the mini batch size. Our method is further compared to existing stateof-art approaches and our previous work [27].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Detection of Moderate Traumatic Brain Injury from Resting-State Eye-Closed Electroencephalography

Lai

Ibrahim

Hamid

et al. 2020

Computational Intelligence and Neuroscience

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Convolutional Neural Network with Hidden Markov Model to Identify Non-severe Traumatic Brain Injury from Electroencephalography

Lai

Azman

Abdullah

et al. 2022

Lecture Notes in Electrical Engineering

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Convolutional Neural Network Utilizing Error-Correcting Output Codes Support Vector Machine for Classification of Non-Severe Traumatic Brain Injury From Electroencephalogram Signal

et al. 2021

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A sudden blow or jolt to the human brain called traumatic brain injury (TBI) is one of the most common injuries recorded in the health insurance claim. Generally, computed tomography (CT) or magnetic resonance imaging (MRI) is required to identify the trauma's severity. Unfortunately, CT and MRI equipment are bulky, expensive, and not always available, limiting their use in TBI detection. Therefore, as an alternative, this study presents a novel classification architecture that can classify non-severe TBI patients from healthy subjects by using resting-state electroencephalogram (EEG) as the input. The proposed architecture employs a convolutional neural network (CNN), and error-correcting output codes support vector machine (ECOC-SVM) to perform automated feature extraction and multi-class classification. In this architecture, complex feature selection and extraction steps are avoided. The proposed architecture attained a high-performance classification accuracy of 99.76%, potentially being used as a classification approach to preventing healthcare insurance fraud. The proposed method is compared to existing studies in the literature. The outcome from the comparisons indicates that the proposed method has outperformed the benchmarked methods by presenting the highest classification accuracy and precision.

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Screening of Moderate Traumatic Brain Injury from Power Feature of Resting State Electroencephalography using Support Vector Machine

Cited by 7 publications

References 21 publications

Detection of Moderate Traumatic Brain Injury from Resting-State Eye-Closed Electroencephalography

Detection of Moderate Traumatic Brain Injury from Resting-State Eye-Closed Electroencephalography

Convolutional Neural Network with Hidden Markov Model to Identify Non-severe Traumatic Brain Injury from Electroencephalography

Convolutional Neural Network Utilizing Error-Correcting Output Codes Support Vector Machine for Classification of Non-Severe Traumatic Brain Injury From Electroencephalogram Signal

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