FGSQA-Net: A Weakly Supervised Approach to Fine-Grained Electrocardiogram Signal Quality Assessment.
IEEE J Biomed Health Inform
; 27(8): 3844-3855, 2023 08.
Article
em En
| MEDLINE
| ID: mdl-37247317
ABSTRACT
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.
Texto completo:
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Base de dados:
MEDLINE
Assunto principal:
Algoritmos
/
Dispositivos Eletrônicos Vestíveis
Limite:
Humans
Idioma:
En
Ano de publicação:
2023
Tipo de documento:
Article