Generative Adversarial Network Based Semi-supervised Learning for Epileptic Focus Localization

Daoud, Hisham; Bayoumi, Magdy

doi:10.1109/bibm52615.2021.9669695

Cited by 2 publications

(2 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The additional benefit of the synthetic procedure, as the authors point out, was deidentification of the original data and significant improvement in data privacy. Multiple additional groups have applied similar data augmentation approaches with various modifications, including different feature extraction methods, different generator and discriminator architectures, different loss functions, utilization of LSTM/GRU cells or attention instead of CNNs, and the application of different classifiers [126][127][128][129][130][131][132][133][134][135][136][137][138][139][140][141][142][143].…”

Section: Gans In Eeg Epilepsy Detectionmentioning

confidence: 99%

Anomaly Detection in Medical Time Series with Generative Adversarial Networks: A Selective Review

Cekić

2024

Artificial Intelligence

View full text Add to dashboard Cite

Anomaly detection in medical data is often of critical importance, from diagnosing and potentially localizing disease processes such as epilepsy to detecting and preventing fatal events such as cardiac arrhythmias. Generative adversarial networks (GANs) have since their inception shown promise in various applications and have been shown to be effective in cybersecurity, data denoising, and data augmentation, and have more recently found a potentially important place in the detection of anomalies in medical time series. This chapter provides a selective review of this novel use of GANs, in the process highlighting the nature of anomalies in time series, special challenges related to medical time series, and some general issues in approaching time series anomaly detection with deep learning. We cover the most frequently applied GAN models and briefly detail the current landscape of applying GANs to anomaly detection in two commonly used medical time series, electrocardiography (ECG) and electroencephalography (EEG).

show abstract

Section: Gans In Eeg Epilepsy Detectionmentioning

confidence: 99%

Anomaly Detection in Medical Time Series with Generative Adversarial Networks: A Selective Review

Cekić

2024

Artificial Intelligence

View full text Add to dashboard Cite

show abstract

“…In seizure detection research, Pascual et al used GAN to generate synthetic seizure-like signals to develop seizure detection systems [6]. Daoud and Bayoumi adopted GAN for the localization of epileptic lesions, distinguishing between focused and unfocused EEG channels [7].…”

Section: Introductionmentioning

confidence: 99%

Epileptic Seizure Detection Based on Semi-supervised Generative Adversarial Network

Liu

2023

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

Since the manual diagnosis of electroencephalograph (EEG) recordings requires a lot of labor and material costs for clinical seizure detection, the annotation for seizure data is of great challenge for seizure detection. To tackle the issue of small samples of epilepsy-labeled data, we propose a semi-supervised generative adversarial network-based seizure detection method. To begin with, a Butterworth filter is used to preprocess the EEG, and the filtered EEG signal is input into the SGAN model. Finally, the output of the SGAN model is subjected to post-processing operations including averaging filtering and threshold comparison, and the discriminative result of whether the tested EEG is a seizure is output. The method achieved an average sensitivity of 90.36%, an average specificity of 93.72%, and an average accuracy of 93.72% in the CHB-MIT EEG dataset. Experiments demonstrate that the semi-supervised generative adversarial network has more accurate classification performance in epilepsy detection.

show abstract

Generative Adversarial Network Based Semi-supervised Learning for Epileptic Focus Localization

Cited by 2 publications

References 10 publications

Anomaly Detection in Medical Time Series with Generative Adversarial Networks: A Selective Review

Anomaly Detection in Medical Time Series with Generative Adversarial Networks: A Selective Review

Epileptic Seizure Detection Based on Semi-supervised Generative Adversarial Network

Contact Info

Product

Resources

About