Hybrid Machine Learning Scheme for Classification of BECTS and TLE Patients Using EEG Brain Signals

Yang, Wonsik; Joo, Minsoo; Kim, Yujaung; Kim, Se Hee; Chung, Jong-Moon

doi:10.1109/access.2020.3038948

Cited by 11 publications

(5 citation statements)

References 35 publications

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“…Further, [24] addressed the classification of epileptiform discharges in EEG through SVM in a reduced dimensional space, resulting in high sensitivity. A differentiation between ''Benign Epilepsy with Centrotemporal Spikes'' (BECTS) and ''Temporal Lobe Epilepsy'' (TLE) was achieved in [25] by creating feature matrices post signal decomposition, followed by SVM-based feature selection and decision tree ensemble classification. The complexity of EEG signals was approached using signal decomposition methods like ''Empirical Mode Decomposition'' (EMD), ''Discrete Wavelet Transform'' (DWT), and ''Dual-tree Complex Wavelet Transform'' (DTCWT) in [26], employing classifiers like SVM, k-NN, and ''Linear Discriminant Analysis'' (LDA) on the CHB-MIT dataset, culminating in perfect accuracy.In [27], the authors differentiated between normal and epileptic seizure signals employing Discrete Wavelet Transform (DWT) and arithmetic coding on the Bonn University dataset [28].…”

Section: Related Workmentioning

confidence: 99%

Evaluating CNN Methods for Epileptic Seizure Type Classification Using EEG Data

Rivera,

Sanchis,

Corcho

et al. 2024

IEEE Access

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Section: Related Workmentioning

confidence: 99%

Evaluating CNN Methods for Epileptic Seizure Type Classification Using EEG Data

Rivera,

Sanchis,

Corcho

et al. 2024

IEEE Access

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“…During the training process, an SA provides feedback about the prediction accuracy. SA is widely used in data classification process for different applications such as early detection and prediction of diabetes [ 24 , 25 , 26 , 27 ], prediction of Alzheimer’s Disease [ 28 , 29 , 30 , 31 ], detection of Acute Respiratory Distress Syndrome [ 32 , 33 , 34 ] and EEG Signal Processing [ 35 , 36 , 37 , 38 ].…”

Section: Supervised Machine-learning Approachesmentioning

confidence: 99%

EMD-Based Method for Supervised Classification of Parkinson’s Disease Patients Using Balance Control Data

et al. 2022

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There has recently been increasing interest in postural stability aimed at gaining a better understanding of the human postural system. This system controls human balance in quiet standing and during locomotion. Parkinson’s disease (PD) is the most common degenerative movement disorder that affects human stability and causes falls and injuries. This paper proposes a novel methodology to differentiate between healthy individuals and those with PD through the empirical mode decomposition (EMD) method. EMD enables the breaking down of a complex signal into several elementary signals called intrinsic mode functions (IMFs). Three temporal parameters and three spectral parameters are extracted from each stabilometric signal as well as from its IMFs. Next, the best five features are selected using the feature selection method. The classification task is carried out using four known machine-learning methods, KNN, decision tree, Random Forest and SVM classifiers, over 10-fold cross validation. The used dataset consists of 28 healthy subjects (14 young adults and 14 old adults) and 32 PD patients (12 young adults and 20 old adults). The SVM method has a performance of 92% and the Dempster–Sahfer formalism method has an accuracy of 96.51%.

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“…(Li et al, 2018 ; Bharath et al, 2019 ). In a previous study, SVM was able to distinguish temporal lobe epilepsy from benign epilepsy in healthy controls or central temporal spikes (Jin and Chung, 2017 ; Sriraam and Raghu, 2017 ; Yang et al, 2020 ). In addition, the use of SVM could also distinguish between resected and unresected regions based on preoperative interictal MEG data in epileptic patients.…”

Section: Introductionmentioning

confidence: 98%

Classification of partial seizures based on functional connectivity: A MEG study with support vector machine

Wang

Zhang

et al. 2022

Front. Neuroinform.

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Temporal lobe epilepsy (TLE) is a chronic neurological disorder that is divided into two subtypes, complex partial seizures (CPS) and simple partial seizures (SPS), based on clinical phenotypes. Revealing differences among the functional networks of different types of TLE can lead to a better understanding of the symbology of epilepsy. Whereas Although most studies had focused on differences between epileptic patients and healthy controls, the neural mechanisms behind the differences in clinical representations of CPS and SPS were unclear. In the context of the era of precision, medicine makes precise classification of CPS and SPS, which is crucial. To address the above issues, we aimed to investigate the functional network differences between CPS and SPS by constructing support vector machine (SVM) models. They mainly include magnetoencephalography (MEG) data acquisition and processing, construction of functional connectivity matrix of the brain network, and the use of SVM to identify differences in the resting state functional connectivity (RSFC). The obtained results showed that classification was effective and accuracy could be up to 82.69% (training) and 81.37% (test). The differences in functional connectivity between CPS and SPS were smaller in temporal and insula. The differences between the two groups were concentrated in the parietal, occipital, frontal, and limbic systems. Loss of consciousness and behavioral disturbances in patients with CPS might be caused by abnormal functional connectivity in extratemporal regions produced by post-epileptic discharges. This study not only contributed to the understanding of the cognitive-behavioral comorbidity of epilepsy but also improved the accuracy of epilepsy classification.

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Hybrid Machine Learning Scheme for Classification of BECTS and TLE Patients Using EEG Brain Signals

Cited by 11 publications

References 35 publications

Evaluating CNN Methods for Epileptic Seizure Type Classification Using EEG Data

Evaluating CNN Methods for Epileptic Seizure Type Classification Using EEG Data

EMD-Based Method for Supervised Classification of Parkinson’s Disease Patients Using Balance Control Data

Classification of partial seizures based on functional connectivity: A MEG study with support vector machine

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