Open AccessSelf-Supervised ECG Anomaly Detection Based on Time-Frequency Specific Waveform Mask Feature Fusion
Author(s) -
Chongrui Tian,
Fengbin Zhang
Publication year - 2025
Publication title -
ieee access
Language(s) - English
Resource type - Magazines
SCImago Journal Rank - 0.587
H-Index - 127
eISSN - 2169-3536
DOI - 10.1109/access.2025.3572484
Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation
The imbalance of ECG signal data and the complexity of labeling pose significant challenges for deep learning-based anomaly detection. Traditional contrastive learning approaches for ECG anomaly detection often rely on reconstruction or generation; however, normal signals that resemble abnormal ECG samples may be incorrectly clustered, leading to suboptimal performance. To address this issue, we propose an anomaly detection framework TFMAD that integrates ECG signal mask reconstruction with time-frequency contrastive learning, leveraging the correlation between time- and frequency-domain features for anomaly detection. Specifically, the proposed method incorporates an auto-encoder module, a time-frequency mask module, and a contrastive learning module to extract masked time-frequency domain features of ECG signals. The model then reconstructs the signal using time-frequency feature fusion and employs contrastive learning to structure the feature space, ensuring abnormal distributions are effectively learned. We evaluated this method on six datasets, and the results demonstrate that TFMAD outperforms nine state-of-the-art methods.