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

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 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).

Introductory Chapter: Anomaly Detection – Recent Advances, AI and ML Perspectives and Applications

“…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.…”