Auxiliary Classifier Generative Adversarial Network for Interictal Epileptiform Discharge Modeling and EEG Data Augmentation

Geng, David; Chen, Zhe

doi:10.1109/ner49283.2021.9441359

Cited by 4 publications

(6 citation statements)

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“…Figure 2 , orange segments in the outer ring). Two studies aimed to detect an ongoing seizure or its onset ( Haradal et al, 2018 ; Wei et al, 2019 ), two studies aimed to predict an upcoming seizure ( Niu et al, 2021 ; Rasheed et al, 2021 ), and the last one aimed at detecting spikes occurring between seizures ( Geng and Chen, 2021 ). Note that all five epilepsy studies had a within-subjects design, i.e., all patients contributed data for all classes.…”

Section: Resultsmentioning

confidence: 99%

“…The reviewed studies achieved the data-label classification by extending the architecture of the existing discriminator ( cf. Figure 3 dashed elements) ( Panwar et al, 2020 ; Geng and Chen, 2021 ). Note that other methods exist, e.g., utilizing an extra classifier that gets only the generated data as input ( Liao and Dong, 2022 ).…”

Section: Resultsmentioning

confidence: 99%

“…Finding two- or three-dimensional mappings of the data that represent its distribution are popular methods that were also applied in some of the reviewed studies. However, the three studies that did show convincing 2D distributions of the time-series data ( Fahimi et al, 2021 ; Geng and Chen, 2021 ; Xu et al, 2022 ) all worked with time locked data, e.g., the form of the ERP was represented in 2D space and not a continuous EEG signal. The only study showing a 2D representation of continuous time-series data with a principal component analysis (PCA) ( Kalashami et al, 2022 ) could neither demonstrate a class nor dataset separation in this representation.…”

Section: Discussionmentioning

confidence: 99%

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Generation of synthetic EEG data for training algorithms supporting the diagnosis of major depressive disorder

Carrle,

Hollenbenders,

Reichenbach

2023

Front. Neurosci.

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IntroductionMajor depressive disorder (MDD) is the most common mental disorder worldwide, leading to impairment in quality and independence of life. Electroencephalography (EEG) biomarkers processed with machine learning (ML) algorithms have been explored for objective diagnoses with promising results. However, the generalizability of those models, a prerequisite for clinical application, is restricted by small datasets. One approach to train ML models with good generalizability is complementing the original with synthetic data produced by generative algorithms. Another advantage of synthetic data is the possibility of publishing the data for other researchers without risking patient data privacy. Synthetic EEG time-series have not yet been generated for two clinical populations like MDD patients and healthy controls.MethodsWe first reviewed 27 studies presenting EEG data augmentation with generative algorithms for classification tasks, like diagnosis, for the possibilities and shortcomings of recent methods. The subsequent empirical study generated EEG time-series based on two public datasets with 30/28 and 24/29 subjects (MDD/controls). To obtain baseline diagnostic accuracies, convolutional neural networks (CNN) were trained with time-series from each dataset. The data were synthesized with generative adversarial networks (GAN) consisting of CNNs. We evaluated the synthetic data qualitatively and quantitatively and finally used it for re-training the diagnostic model.ResultsThe reviewed studies improved their classification accuracies by between 1 and 40% with the synthetic data. Our own diagnostic accuracy improved up to 10% for one dataset but not significantly for the other. We found a rich repertoire of generative models in the reviewed literature, solving various technical issues. A major shortcoming in the field is the lack of meaningful evaluation metrics for synthetic data. The few studies analyzing the data in the frequency domain, including our own, show that only some features can be produced truthfully.DiscussionThe systematic review combined with our own investigation provides an overview of the available methods for generating EEG data for a classification task, their possibilities, and shortcomings. The approach is promising and the technical basis is set. For a broad application of these techniques in neuroscience research or clinical application, the methods need fine-tuning facilitated by domain expertise in (clinical) EEG research.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Generation of synthetic EEG data for training algorithms supporting the diagnosis of major depressive disorder

Carrle,

Hollenbenders,

Reichenbach

2023

Front. Neurosci.

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“…Figure 2, orange segments in the outer ring). Two studies aimed to detect an ongoing seizure or its onset (Haradal et al, 2018;Wei et al, 2019), two studies aimed to predict an upcoming seizure (Niu et al, 2021;Rasheed et al, 2021), and the last one aimed at detecting spikes occurring between seizures (Geng and Chen, 2021). Note that all five epilepsy studies had a within-subjects design, i.e., all patients contributed data for all classes.…”

Section: Use Case For Classification and Eeg Paradigmmentioning

confidence: 99%

“…The reviewed studies achieved the data-label classification by extending the architecture of the existing discriminator (cf. Figure 3 dashed elements) (Panwar et al, 2020;Geng and Chen, 2021). Note that other methods exist, e.g., utilizing an extra classifier that gets only the generated data as input (Liao and Dong, 2022).…”

Section: Class Differentiationmentioning

confidence: 99%

Translational Applications of Neuroimaging

2024

Frontiers Research Topics

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Frontiers is more than just an open access publisher of scholarly articles: it is a pioneering approach to the world of academia, radically improving the way scholarly research is managed. The grand vision of Frontiers is a world where all people have an equal opportunity to seek, share and generate knowledge. Frontiers provides immediate and permanent online open access to all its publications, but this alone is not enough to realize our grand goals. Frontiers journal seriesThe Frontiers journal series is a multi-tier and interdisciplinary set of openaccess, online journals, promising a paradigm shift from the current review, selection and dissemination processes in academic publishing. All Frontiers journals are driven by researchers for researchers; therefore, they constitute a service to the scholarly community. At the same time, the Frontiers journal series operates on a revolutionary invention, the tiered publishing system, initially addressing specific communities of scholars, and gradually climbing up to broader public understanding, thus serving the interests of the lay society, too. Dedication to qualityEach Frontiers article is a landmark of the highest quality, thanks to genuinely collaborative interactions between authors and review editors, who include some of the world's best academicians. Research must be certified by peers before entering a stream of knowledge that may eventually reach the public -and shape society; therefore, Frontiers only applies the most rigorous and unbiased reviews. Frontiers revolutionizes research publishing by freely delivering the most outstanding research, evaluated with no bias from both the academic and social point of view. By applying the most advanced information technologies, Frontiers is catapulting scholarly publishing into a new generation. What are Frontiers Research Topics?Frontiers Research Topics are very popular trademarks of the Frontiers journals series: they are collections of at least ten articles, all centered on a particular subject. With their unique mix of varied contributions from Original Research to Review Articles, Frontiers Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area.

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Unsupervised feature learning based on autoencoder for epileptic seizures prediction

Wang

Cui

et al. 2023

Appl Intell

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Auxiliary Classifier Generative Adversarial Network for Interictal Epileptiform Discharge Modeling and EEG Data Augmentation

Cited by 4 publications

References 6 publications

Generation of synthetic EEG data for training algorithms supporting the diagnosis of major depressive disorder

Generation of synthetic EEG data for training algorithms supporting the diagnosis of major depressive disorder

Translational Applications of Neuroimaging

Unsupervised feature learning based on autoencoder for epileptic seizures prediction

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