SkipDAEformer: A High-Precision Representation Learning Method for Removing Random Mixed Noise in MCG Signals

Automated analytical techniques for magnetocardiography (MCG) are essential for diagnosing and predicting cardiovascular diseases. Clinically acquired MCG signals are often contaminated by various types of noise, which negatively impact subsequent signal analysis. However, traditional methods have limitations in denoising long-term MCG signals with complex spatial structures. We propose a high-precision, robust representation learning method based on skip connection multi-scale feature fusion (SkipDAEformer) for effectively removing random mixed noise in MCG signals. SkipDAEformer integrates attention fusion mechanisms into a basic denoising autoencoder to extract and fuse critical temporal and spatial information from each feature map, thus enhancing the model's ability to capture long-range dependencies and spatial features in MCG signals. Meanwhile, we further supplement and refine the semantic information for the feature maps through a global feature fusion method. By fusing multi-scale features from different skip connections, SkipDAEformer can learn more comprehensive representations of MCG signals, enabling the effective separation of clean signals from noise. Experimental results demonstrate that SkipDAEformer outperforms existing methods in denoising performance, channel consistency, feature consistency, and generalization ability and can be extended to a self-supervised learning framework. In actual noise reduction and diagnostic classification tasks, SkipDAEformer shows superior clinical acceptability and diagnostic value, potentially advancing MCG data analysis.