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A lightweight and robust method for electrocardiogram anomaly detection and localization using multi-scale masked
Ya Zhou1, Yujie Yang1, Jianhuang Gan1
1Department of Information Center, Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Insights
This study introduces MMAE-ECG, a novel method for electrocardiogram (ECG) anomaly detection. It efficiently identifies heart condition irregularities without complex preprocessing, offering a more robust and computationally cheaper alternative.
Area of Science:
- Biomedical Engineering
- Artificial Intelligence in Medicine
- Cardiology
Background:
- Electrocardiogram (ECG) analysis is vital for diagnosing cardiovascular diseases.
- Traditional methods often require extensive labeled data and complex preprocessing like R-peak detection.
- Anomaly detection offers a flexible approach for rare and diverse cardiac conditions.
Purpose of the Study:
- To develop an efficient and robust ECG anomaly detection method.
- To overcome the limitations of existing methods relying on complex preprocessing.
- To capture both global and local dependencies in ECG signals effectively.
Main Methods:
- Proposed MMAE-ECG, a multi-scale masked autoencoder.
- Integrated multi-scale masking and attention mechanisms with distinct positional embeddings.
- Utilized a lightweight Transformer encoder and an aggregation strategy for anomaly scoring.
Main Results:
- Achieved state-of-the-art performance in ECG anomaly detection and localization.
- Significantly reduced computational costs: ~1/78 inference FLOPs and 1/18 trainable parameters.
- Demonstrated the effectiveness of multi-scale strategies and aggregation for anomaly detection.
Conclusions:
- MMAE-ECG provides an effective and efficient solution for ECG anomaly detection.
- The multi-scale masked autoencoder approach eliminates the need for complex preprocessing steps.
- This method holds significant potential for improving cardiovascular diagnostics through automated ECG analysis.
Abstract:
Electrocardiogram (ECG) analysis is crucial for diagnosing cardiovascular conditions. While traditional classification models require large volumes of labeled data across multiple disease categories, anomaly detection offers a flexible alternative by identifying deviations from normal patterns-an approach particularly valuable given the rarity and diversity of cardiac conditions. However, existing anomaly detection methods often rely on R-peak detection or heartbeat segmentation, which increases preprocessing complexity and reduces robustness to signal variability. To address these limitations, we propose MMAE-ECG, a multi-scale masked autoencoder designed to capture both global and local dependencies without such preprocessing steps. MMAE-ECG integrates a multi-scale masking strategy and a multi-scale attention mechanism with distinct positional embeddings, enabling a lightweight Transformer encoder to efficiently model ECG signals. Additionally, an aggregation strategy is introduced to improve anomaly score estimation. Experiments demonstrate that MMAE-ECG achieves state-of-the-art performance in both anomaly detection and localization while significantly reducing computational costs. Specifically, it requires only approximately 1/78 of the inference FLOPs and 1/18 of the trainable parameters compared to the previous leading method. Ablation studies further validate the contributions of each component, demonstrating the potential of multi-scale masked autoencoders as an effective and efficient approach for ECG anomaly detection.
