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Table 4 Reviewed papers that used GANs in epilepsy studies

From: Generative adversarial networks in EEG analysis: an overview

Study

Purpose

Dataset

GAN type

Evaluation metrics

Results

(with GAN)

Wei et al., 2019 [67]

Proposes an automatic epileptic EEG detection method

CHB-MIT Scalp

WGANs

• Classification accuracy

• Sensitivity

• Specificity

•  + 2.51%

•  + 1.43%

•  + 3.59%

You et al., 2020 [68]

Solve the class imbalance problem of epileptic seizures detection

Private dataset

DCGAN

an anomaly detector

• AUROC

• Sensitivity

• False detection rate

• 93.93 1% (With Gram Matrix)

• 96.3%

• 0.14 per hour

Pascual et al., 2021 [70]

Overcome scarcity of epileptic seizures EEG signals and address the privacy concerns

EPILEPSIAE project [73]

Epilepsy GAN

• Classification accuracy

• Synthetic data Recall values

• Geometric mean of sensitivity and specificity

•  + 1.3%

• median: + 3.2%

•  + 1.3%

Truong et al., 2019 [74]

Predict seizures with an unsupervised algorithm

CHB-MIT

 + 

Freiburg Hospital

 + 

EPILEPSIAE

DCGAN

feature extractor

• Classification accuracy

CHB-MIT: 61.53%

Freiburg Hospital: 53.84%

EPILEPSIAE 13.33% (with AUC above 80%)

Usman et al. 2021 [71]

Solve the class imbalance problem of epileptic seizures predictor

CHBMIT

GAN

• Sensitivity

• Specificity

• Anticipation time

• 93%

• 92.5%

• Average 32 min

Usman et al., 2021 [75]

Overcome the challenge of accurate prediction of epileptic seizures

CHBMIT

 + 

American epilepsy society-Kaggle

seizure prediction

GAN

Classification accuracy

• CHB-MIT: + 1.74%

• IEEG: achieved 95.53%

Sensitivity

• CHB-MIT: + 1.56%

• IEEG: 94.27%

Specificity

• CHB-MIT: + 1.93%

• IEEG: 95.81%

Average

Anticipation

Time

• CHB-MIT: 1.34 m

Salazar et al., 2021 [76]

Improve seizure prediction performance with extreme data scarcity

Private dataset “Barcelona test”

GAN + vector Markov Random Field (vMRF)

Classification accuracy

NA

Rasheed et al. 2021 [77]

Improve seizure prediction performance

Epilepsyecosystem + CHB-MIT

DCGAN

• Sensitivity

• AUC

•  + 15%

•  + 6:10%