CSGSA-Net: Canonical-structured graph sparse attention network for fetal ECG estimation

Wang, Xu; Han, Yang; Deng, Yamei

doi:10.1016/j.bspc.2022.104556

Cited by 15 publications

(2 citation statements)

References 36 publications

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“…Notably, models leveraging the CycleGAN as a foundational framework outperform other models, underscoring the high-quality extraction of FECG signals by the GAN. [24] 0.061 ± 0.006 0.019 ± 0.005 90.69 ± 0.17 AEDL [35] 0.059 ± 0.002 0.018 ± 0.003 92.09 ± 0.22 CSGSA-Net [36] 0.057 ± 0.003 0.016 ± 0.002 92.27 ± 0.33 CycleGAN [27] 0.042 ± 0.008 0.011 ± 0.004 92.71 ± 0.29 CAA-CycleGAN [28] 0.024 ± 0.003 0.007 ± 0.002 95.34 ± 0.12 this work 0.019 ± 0.004 0.006 ± 0.002 98.01 ± 0.26 Finally, Figure 10 illustrates an image comprising a unit circle (depicted in red) alongside a 3D trajectory (depicted in blue) generated based on data from ADFECGDB r01. As the trajectory approaches one of the P-QRS-T waves, the 3D trajectory exhibits vertical movement, with the limit ring oscillating up and down.…”

Section: Fecg Signal Extraction Quality Assessmentmentioning

confidence: 99%

Enhancing Fetal Electrocardiogram Signal Extraction Accuracy through a CycleGAN Utilizing Combined CNN–BiLSTM Architecture

Yang,

Chen,

2024

Sensors

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The fetal electrocardiogram (FECG) records changes in the graph of fetal cardiac action potential during conduction, reflecting the developmental status of the fetus in utero and its physiological cardiac activity. Morphological alterations in the FECG can indicate intrauterine hypoxia, fetal distress, and neonatal asphyxia early on, enhancing maternal and fetal safety through prompt clinical intervention, thereby reducing neonatal morbidity and mortality. To reconstruct FECG signals with clear morphological information, this paper proposes a novel deep learning model, CBLS-CycleGAN. The model’s generator combines spatial features extracted by the CNN with temporal features extracted by the BiLSTM network, thus ensuring that the reconstructed signals possess combined features with spatial and temporal dependencies. The model’s discriminator utilizes PatchGAN, employing small segments of the signal as discriminative inputs to concentrate the training process on capturing signal details. Evaluating the model using two real FECG signal databases, namely “Abdominal and Direct Fetal ECG Database” and “Fetal Electrocardiograms, Direct and Abdominal with Reference Heartbeat Annotations”, resulted in a mean MSE and MAE of 0.019 and 0.006, respectively. It detects the FQRS compound wave with a sensitivity, positive predictive value, and F1 of 99.51%, 99.57%, and 99.54%, respectively. This paper’s model effectively preserves the morphological information of FECG signals, capturing not only the FQRS compound wave but also the fetal P-wave, T-wave, P-R interval, and ST segment information, providing clinicians with crucial diagnostic insights and a scientific foundation for developing rational treatment protocols.

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Section: Fecg Signal Extraction Quality Assessmentmentioning

confidence: 99%

Enhancing Fetal Electrocardiogram Signal Extraction Accuracy through a CycleGAN Utilizing Combined CNN–BiLSTM Architecture

Yang,

Chen,

2024

Sensors

View full text Add to dashboard Cite

show abstract

“…Since the deep learning-based network has shown excellent results on the computer visions, such as image classification [14]- [16], object detection [17], [18], and semantic segmentation [19]- [21], it is also applied in defects detection on the magnetic tile [22]- [24]. For example, Liang et al propose a loop-shaped fusion convolutional neural network to detect small defects from magnetic tiles [25].…”

Section: Introductionmentioning

confidence: 99%

MVRA-UNet: Multi-View Residual Attention U-Net for Precise Defect Segmentation on Magnetic Tile Surface

Luo,

Cui,

Liao

2023

IEEE Access

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Integrating Contrastive Learning and Cycle Generative Adversarial Networks for Non-invasive Fetal ECG Extraction

Qu,

Song,

Wei

et al. 2024

Pediatr Cardiol

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CSGSA-Net: Canonical-structured graph sparse attention network for fetal ECG estimation

Cited by 15 publications

References 36 publications

Enhancing Fetal Electrocardiogram Signal Extraction Accuracy through a CycleGAN Utilizing Combined CNN–BiLSTM Architecture

Enhancing Fetal Electrocardiogram Signal Extraction Accuracy through a CycleGAN Utilizing Combined CNN–BiLSTM Architecture

MVRA-UNet: Multi-View Residual Attention U-Net for Precise Defect Segmentation on Magnetic Tile Surface

Integrating Contrastive Learning and Cycle Generative Adversarial Networks for Non-invasive Fetal ECG Extraction

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