Multi-scale Masked Autoencoder for Electrocardiogram Anomaly Detection
Journal:
arXiv
Published Date:
Feb 8, 2025
Abstract
Electrocardiogram (ECG) analysis is a fundamental tool for diagnosing
cardiovascular conditions, yet anomaly detection in ECG signals remains
challenging due to their inherent complexity and variability. We propose
Multi-scale Masked Autoencoder for ECG anomaly detection (MMAE-ECG), a novel
end-to-end framework that effectively captures both global and local
dependencies in ECG data. Unlike state-of-the-art methods that rely on
heartbeat segmentation or R-peak detection, MMAE-ECG eliminates the need for
such pre-processing steps, enhancing its suitability for clinical deployment.
MMAE-ECG partitions ECG signals into non-overlapping segments, with each
segment assigned learnable positional embeddings. A novel multi-scale masking
strategy and multi-scale attention mechanism, along with distinct positional
embeddings, enable a lightweight Transformer encoder to effectively capture
both local and global dependencies. The masked segments are then reconstructed
using a single-layer Transformer block, with an aggregation strategy employed
during inference to refine the outputs. Experimental results demonstrate that
our method achieves performance comparable to state-of-the-art approaches while
significantly reducing computational complexity-approximately 1/78 of the
floating-point operations (FLOPs) required for inference. Ablation studies
further validate the effectiveness of each component, highlighting the
potential of multi-scale masked autoencoders for anomaly detection.
