Anomaly Detection in Medical Time Series with Generative Adversarial Networks: A Selective Review
Miloš Cekić
Abstract: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 tim… Show more
“…In healthcare, Cekić et al [1] shed light on the importance of anomaly detection in medical time series data, such as electrocardiography (ECG) and electroencephalography (EEG). They highlight the use of Generative Adversarial Networks (GANs) for this purpose.…”
Section: The Role Of Ai and ML In Anomaly Detectionmentioning
“…In healthcare, Cekić et al [1] shed light on the importance of anomaly detection in medical time series data, such as electrocardiography (ECG) and electroencephalography (EEG). They highlight the use of Generative Adversarial Networks (GANs) for this purpose.…”
Section: The Role Of Ai and ML In Anomaly Detectionmentioning
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