Performance Comparison of Machine Learning Techniques for Epilepsy Classification and Detection in EEG Signal

Janghel, Rekh Ram; Rathore, Yogesh

doi:10.1007/978-981-32-9949-8_29

Cited by 8 publications

(12 citation statements)

References 24 publications

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“…However, in the imbalanced data classification problem, the minority classes have a higher classification error cost, so the traditional classification algorithm will result in a large loss. Traditional ML methods are being progressively altered to handle and mitigate the problem of learning from imbalanced datasets, especially for big data (i.e., disease diagnosis, disease prediction, and intelligence patient care) [112].…”

Section: ) Rusboosted Trees Prediction Modelmentioning

confidence: 99%

Improving Outcome Prediction for Traumatic Brain Injury From Imbalanced Datasets Using RUSBoosted Trees on Electroencephalography Spectral Power

Noor¹,

Ibrahim²,

Lah³

et al. 2021

IEEE Access

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Reliable prediction of traumatic brain injury (TBI) outcomes based on machine learning (ML) that is derived from quantitative electroencephalography (EEG) features has renewed interest in recent years. Nevertheless, the approach has suffered from imbalanced datasets. Hence, to get a reliable predictive model for predicting outcomes, specifically in a high proportion of moderate TBI with good outcomes, could be challenging. This work proposes an improved outcome predictive model that combines the absolute power spectral density (PSD) as input features for training random under-sampling boosting decision trees (RUSBoosted Trees) as a classifier. Resting-state, eyes-closed EEG data were obtained from 27 moderate TBI patients with follow-up visits. Patient outcome at 4-10 weeks to 12-month was dichotomized based on the Glasgow Outcome Scale as poor (GOS score ≤ 4) and good outcomes (GOS score = 5). The predictive values of absolute PSD from five frequency bands: δ (0.5-4Hz), θ (4-7Hz), α (7-13Hz), β (13-30Hz) and γ (30-100Hz) were evaluated to identify the most informative predictors for reliable prediction outcomes. RUSBoosted Trees performed best at discriminating patients into two outcomes categories (G-Mean = 92.95%, TP rate =100%, TN rate = 86.4%) of absolute PSD in δ and γ bands, which was excellent compared to the other state-of-the-art methods. The highest area under the curve (AUC) of absolute PSD in δ (AUC δ = 0.97) and γ (AUC γ = 0.95) revealed their predictive values as robust prognostic markers for prediction outcomes. The RUSBoosted Trees presents a promising result in prognosis prediction of highly imbalanced data, making it an accessible prediction tool for clinical decision-making, unlike the black-box approaches.

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Section: ) Rusboosted Trees Prediction Modelmentioning

confidence: 99%

Improving Outcome Prediction for Traumatic Brain Injury From Imbalanced Datasets Using RUSBoosted Trees on Electroencephalography Spectral Power

Noor¹,

Ibrahim²,

Lah³

et al. 2021

IEEE Access

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“…Previous studies that addressed the performance of different supervised machine learning algorithms on decoding different behavioral states found that support vector machines using Gaussian kernel are probably the best selection (Saputro et al, 2019 ; Janghel et al, 2020 ). Therefore, we used the Medium-Gaussian support vector machine algorithm to test the accuracy of prediction at each frequency band.…”

Section: Introductionmentioning

confidence: 99%

Predicting Stimulus Modality and Working Memory Load During Visual- and Audiovisual-Acquired Equivalence Learning

Puszta

Pertich

Giricz

et al. 2020

Front. Hum. Neurosci.

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Scholars have extensively studied the electroencephalography (EEG) correlates of associative working memory (WM) load. However, the effect of stimulus modality on EEG patterns within this process is less understood. To fill this research gap, the present study re-analyzed EEG datasets recorded during visual and audiovisual equivalence learning tasks from earlier studies. The number of associations required to be maintained (WM load) in WM was increased using the staircase method during the acquisition phase of the tasks. The support vector machine algorithm was employed to predict WM load and stimulus modality using the power, phase connectivity, and cross-frequency coupling (CFC) values obtained during time segments with different WM loads in the visual and audiovisual tasks. A high accuracy (>90%) in predicting stimulus modality based on power spectral density and from the theta–beta CFC was observed. However, accuracy in predicting WM load was higher (≥75% accuracy) than that in predicting stimulus modality (which was at chance level) using theta and alpha phase connectivity. Under low WM load conditions, this connectivity was highest between the frontal and parieto-occipital channels. The results validated our findings from earlier studies that dissociated stimulus modality based on power-spectra and CFC during equivalence learning. Furthermore, the results emphasized the importance of alpha and theta frontoparietal connectivity in WM load.

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“…In this task, EEG signals were recorded occasionally from healthy participants and patients with epileptic symptoms [ 20 ]. Seven ML and DL algorithms were used in SD studies: CNN [ 265 , 266 ], SVM [ 116 , 118 , 179 , 180 , 182 , 184 , 191 , 192 , 198 , 200 , 207 , 267 ], KNN [ 189 , 268 , 269 ], ANNs [ 183 , 199 ], RF [ 185 , 187 , 190 ], LDA [ 186 ], and ELM [ 181 ]. However, among the 24 studies focused on seizures, 12 applied the SVM algorithm with various kernels.…”

Section: Discussionmentioning

confidence: 99%

“…Murugappan and Ramakrishnan [ 118 ] have used a hierarchical multi-class SVM (H-MSVM) with an ELM kernel to classify SD. Likewise, linear, and Gaussian kernels have been applied for high-dimensional spaces, respectively [ 198 , 267 ]. The RBF kernel is a common function used in the SVM algorithm applied to different tasks, as described by Hamed et al [ 179 ] and Jaiswal and Banka [ 180 ].…”

Section: Discussionmentioning

confidence: 99%

Neural Decoding of EEG Signals with Machine Learning: A Systematic Review

et al. 2021

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Electroencephalography (EEG) is a non-invasive technique used to record the brain’s evoked and induced electrical activity from the scalp. Artificial intelligence, particularly machine learning (ML) and deep learning (DL) algorithms, are increasingly being applied to EEG data for pattern analysis, group membership classification, and brain-computer interface purposes. This study aimed to systematically review recent advances in ML and DL supervised models for decoding and classifying EEG signals. Moreover, this article provides a comprehensive review of the state-of-the-art techniques used for EEG signal preprocessing and feature extraction. To this end, several academic databases were searched to explore relevant studies from the year 2000 to the present. Our results showed that the application of ML and DL in both mental workload and motor imagery tasks has received substantial attention in recent years. A total of 75% of DL studies applied convolutional neural networks with various learning algorithms, and 36% of ML studies achieved competitive accuracy by using a support vector machine algorithm. Wavelet transform was found to be the most common feature extraction method used for all types of tasks. We further examined the specific feature extraction methods and end classifier recommendations discovered in this systematic review.

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Performance Comparison of Machine Learning Techniques for Epilepsy Classification and Detection in EEG Signal

Cited by 8 publications

References 24 publications

Improving Outcome Prediction for Traumatic Brain Injury From Imbalanced Datasets Using RUSBoosted Trees on Electroencephalography Spectral Power

Improving Outcome Prediction for Traumatic Brain Injury From Imbalanced Datasets Using RUSBoosted Trees on Electroencephalography Spectral Power

Predicting Stimulus Modality and Working Memory Load During Visual- and Audiovisual-Acquired Equivalence Learning

Neural Decoding of EEG Signals with Machine Learning: A Systematic Review

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