EEGWaveNet: Multiscale CNN-Based Spatiotemporal Feature Extraction for EEG Seizure Detection

Thuwajit, Punnawish; Rangpong, Phurin; Sawangjai, Phattarapong; Autthasan, Phairot; Chaisaen, Rattanaphon; Banluesombatkul, Nannapas; Boonchit, Puttaranun; Tatsaringkansakul, Nattasate; Sudhawiyangkul, Thapanun; Wilaiprasitporn, Theerawit

doi:10.1109/tii.2021.3133307

Cited by 62 publications

(33 citation statements)

References 32 publications

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“…Deep learning baselines are EEGNet, EnK-EEGNet, EEG-WaveNet [49], and CE-stSENet. In addition, we improved EnK-EEGNet by replacing its summation representation matrix R with the average representation matrix in (7), named as Avg-EnK-EEGNet.…”

Section: Experimental Results In Seizure Subtype Classificationmentioning

confidence: 99%

TIE-EEGNet: Temporal Information Enhanced EEGNet for Seizure Subtype Classification

Peng

Zhao

Jiang

et al. 2022

IEEE Trans. Neural Syst. Rehabil. Eng.

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Section: Experimental Results In Seizure Subtype Classificationmentioning

confidence: 99%

TIE-EEGNet: Temporal Information Enhanced EEGNet for Seizure Subtype Classification

Peng

Zhao

Jiang

et al. 2022

IEEE Trans. Neural Syst. Rehabil. Eng.

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“…A comparison with the DLEK-GP [ 35 ] method adopts the classic common spatial pattern (CSP) and discriminative log-Euclidean kernel-based Gaussian process for distinguishing epileptic EEG signals. A further comparison with the EEGWaveNet [ 36 ] approach utilizes trainable depth-wise convolutions as discriminative filters to simultaneously gather features from each EEG channel and separate the signal into multiscale resolution. Lastly, the proposed CE-stSENet [ 37 ] proposes channel-embedding spectral-temporal squeeze-and-excitation network which can capture hierarchical multidomain representations in a unified manner with a variant of squeeze-and-excitation block.…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…For subject-independent classification, also use EEGWaveNet [ 36 ] as one of the base line methods. A representative method with the sparse representation-based epileptic seizure classification based on the dictionary learning with the homotopy (DLWH) algorithm is proposed [ 38 ].…”

Section: Experiments and Resultsmentioning

confidence: 99%

Synthetic Epileptic Brain Activities with TripleGAN

Jie

Zhou

et al. 2022

Computational and Mathematical Methods in Medicine

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Epilepsy is a chronic noninfectious disease caused by sudden abnormal discharge of brain neurons, which leads to intermittent brain dysfunction. It is also one of the most common neurological diseases in the world. The automatic detection of epilepsy based on electroencephalogram through machine learning, correlation analysis, and temporal-frequency analysis plays an important role in epilepsy early warning and automatic recognition. In this study, we propose a method to realize EEG epilepsy recognition by means of triple genetic antagonism network (GAN). TripleGAN is used for EEG temporal domain, frequency domain, and temporal-frequency domain, respectively. The experiment was conducted through CHB-MIT datasets, which operated at the latest level in the same industry in the world. In the CHB-MIT dataset, the classification accuracy, sensitivity, and specificity exceeded 1.19%, 1.36%, and 0.27%, respectively. The crossobject ratio exceeded 0.53%, 2.2%, and 0.37%, respectively. It shows that the established deep learning model of TripleGAN has a good effect on EEG epilepsy classification through simulation and classification optimization of real signals.

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“…In order to investigate the effectiveness of our proposed methods, three common evaluation criteria suggested in [42] are used: classification accuracy, sensitivity, and specificity. Accuracy is computed as:…”

Section: Experimental Evaluationmentioning

confidence: 99%

RS-MSConvNet: A Novel End-to-End Pathological Voice Detection Model

et al. 2022

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Recent studies have reported the success of multi-scale convolution neural network (MSConvNet) model for many classification applications due to its powerful ability of exploring multiscale convolution block to extract multi-scale representations to make a detection. However, a new design based on MSConvNet for pathological voice detection has not been explored. In this paper, we propose RS-MSConvNet, a novel end-to-end MSConvNet model using raw speech for pathological voice detection. The main contribution of the proposed RS-MSConvNet method is to exploit the multi-scale convolution block, followed by spatial-temporal feature block, and fully connected layer as classification. In addition, to further improve accuracy performance, we propose a novel hybrid detection model by integrating the feature extraction ability of the RS-MSConvNet model and the classifier of support vector machine (SVM) method, called RS-MSConvNet-SVM model. The effectiveness of our proposed models is investigated using the TORGO database. The experimental results reveal that the RS-MSConvNet model outperforms other baseline methods in the speaker-independent task. Moreover and as compared to the RS-MSConvNet-SVM model, a further improved accuracy is obtained using the RS-MSConvNet-SVM model. These outcomes exhibit that our proposed models are useful for pathological voice detection. INDEX TERMSPathological voice detection, end-to-end architecture, multi-scale convolution, spatialtemporal feature, hybrid model This article has been accepted for publication in IEEE Access.

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EEGWaveNet: Multiscale CNN-Based Spatiotemporal Feature Extraction for EEG Seizure Detection

Cited by 62 publications

References 32 publications

TIE-EEGNet: Temporal Information Enhanced EEGNet for Seizure Subtype Classification

TIE-EEGNet: Temporal Information Enhanced EEGNet for Seizure Subtype Classification

Synthetic Epileptic Brain Activities with TripleGAN

RS-MSConvNet: A Novel End-to-End Pathological Voice Detection Model

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