Moderate Traumatic Brain Injury Identification from Power Spectral Density of Electroencephalography's Frequency Bands using Support Vector Machine

Lai, Chi Qin; Abdullah, Mohd Zaid; Hamid, Aini Ismafairus Abd; Azman, Azlinda; Abdullah, Jafri Malin; Ibrahim, Haidi

doi:10.1109/icsys47076.2019.8982505

Cited by 4 publications

(9 citation statements)

References 18 publications

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“…The second method for comparison classifies the EEG signal by employing the AdaBoost classifier and is developed by McNerney et al [ 29 ]. The third and fourth methods were our previously developed methods based on SVM [ 91 , 92 ]. In our previous work, the same pre-processing procedure presented in Section 2.2 was used to pre-process the data.…”

Section: Resultsmentioning

confidence: 99%

“…Asserting that the extracted features from the frequency bands can provide valuable data to the classifier, the four comparison methods [ 29 , 30 , 91 , 92 ] used the frequency band-based features. In contrast, the proposed approach in this research does not require any extraction of the features.…”

Section: Resultsmentioning

confidence: 99%

“…Alpha band power and theta band spectral density (PSD) were extracted from the EEG to become the SVM training features for our previous works [ 91 , 92 ]. As expected, they have a lower classification performance as compared with the proposed method (i.e., LSTM ECOC-SVM).…”

Section: Resultsmentioning

confidence: 99%

See 2 more Smart Citations

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

Lai

Ibrahim

Hamid

et al. 2020

Sensors

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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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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

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

Lai

Ibrahim

Hamid

et al. 2020

Sensors

Self Cite

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“…The proposed method is compared with two similar existing methods [24], [25], and two of our previous works [52], [53]. The first method for comparison is the work by Brink et al [24], which uses task-free EEG and Naive Bayes classifier for TBI classification.…”

Section: Comparison Of the Proposed Methods With Existing Workmentioning

confidence: 99%

“…The second method [25] that uses the AdaBoost classifier. From our previous works [52], [53], the same pre-processing procedure presented in Section III was used to pre-process the data. Alpha band power and theta power spectral density (PSD) were extracted to train the SVM classifier.…”

Section: Comparison Of the Proposed Methods With Existing Workmentioning

confidence: 99%

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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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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Moderate Traumatic Brain Injury Identification from Power Spectral Density of Electroencephalography's Frequency Bands using Support Vector Machine

Cited by 4 publications

References 18 publications

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

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

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

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

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