Memory-Augmented Autoencoding with Self-Supervised Learning for Unsupervised Detection of Abnormal Physiological Signals.
TL;DR
MAGE is a novel unsupervised anomaly detection framework that integrates multi head memory gating, self-supervised learning, and adversarial training within a unified convolutional autoencoder architecture that consistently outperforms state-of-the-art baselines in standard within-dataset evaluation settings.
Abstract
Detecting abnormal events in physiological signals such as EEG and ECG is critical for early diagnosis of neurological and cardiovascular disorders. However, existing unsupervised anomaly detection methods often suffer from limited representation capacity and weak generalization across diverse signal domains. To address these challenges, we propose MAGE, a novel unsupervised anomaly detection framework that integrates multi head memory gating, self-supervised learning, and adversarial training within a unified convolutional autoencoder architecture. The proposed memory-augmented gating mechanism selectively preserves and adaptively integrates salient features, improving discriminability and robustness over prior memory-based approaches. To further enhance representation learning, a self-supervised auxiliary task based on multiple signal transformations is introduced to encourage structure-aware feature extraction. In addition, transformation-aware adversarial perturbations are incorporated during training to enhance robustness against distribution shifts. Extensive experiments on multiple benchmark EEG and ECG datasets demonstrate that MAGE consistently outperforms state-of-the-art baselines in standard within-dataset evaluation settings, achieving over 98% detection accuracy and superior F1-scores. These results highlight the effectiveness and clinical potential of MAGE for early anomaly detection and continuous health monitoring under realistic deployment conditions. Code available at https://github.com/wzxmodel/MAGE.