Key result
FGSQA-Net, a weakly supervised fine-grained ECG signal quality assessment method, produced an average AUC of 0.975, outperforming the state-of-the-art beat-by-beat method.
Why the study?
Due to the lack of fine-grained labels, current ECG signal quality assessment can only evaluate signal quality at a coarse scale.
FGSQA-Net is a novel, weakly supervised deep learning architecture that effectively performs fine-grained ECG signal quality assessment, achieving an average AUC of 0.975.
OBJECTIVE: Due to the lack of fine-grained labels, current research can only evaluate the signal quality at a coarse scale. This article proposes a weakly supervised fine-grained electrocardiogram (ECG) signal quality assessment method, which can produce continuous segment-level quality scores with only coarse labels. METHODS: A novel network architecture, i.e. FGSQA-Net, is developed for signal quality assessment, which consists of a feature shrinking module and a feature aggregation module. Multiple feature shrinking blocks, which combine residual CNN block and max pooling layer, are stacked to produce a feature map corresponding to continuous segments along the spatial dimension. Segment-level quality scores are obtained by feature aggregation along the channel dimension. RESULTS: The proposed method was evaluated on two real-world ECG databases and one synthetic dataset. Our method produced an average AUC value of 0.975, which outperforms the state-of-the-art beat-by-beat quality assessment method. The results are visualized for 12-lead and single-lead signals over a granularity from 0.64 to 1.7 seconds, demonstrating that high-quality and low-quality segments can be effectively distinguished at a fine scale. CONCLUSION: FGSQA-Net is flexible and effective for fine-grained quality assessment for various ECG recordings and is suitable for ECG monitoring using wearable devices. SIGNIFICANCE: This is the first study on fine-grained ECG quality assessment using weak labels and can be generalized to similar tasks for other physiological signals.
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Liu et al. (2023) studied ECG signal quality assessment. FGSQA-Net vs. State-of-the-art beat-by-beat quality assessment method was evaluated on Average AUC for signal quality assessment. FGSQA-Net, a weakly supervised fine-grained ECG signal quality assessment method, produced an average AUC of 0.975, outperforming the state-of-the-art beat-by-beat method.
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