Standard deviation of the difference between the LV-GLS value of the expert and trainees in four consecutive groups of 25 fetal heart clips. حسب Chantelle M. de Vet (16535257)

الموضوعات:

RV-GLS agreement analysis between expert and novice trainees over consecutive quartiles of 25 measurements. حسب Chantelle M. de Vet (16535257)

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S1 File - حسب Changxu Wang (17826805)

الموضوعات:

Baseline patient characteristics and surgical and anesthetic datas. حسب Changxu Wang (17826805)

الموضوعات:

Standard deviation of the difference between the RV-GLS value of the expert and trainees in four consecutive groups of 25 fetal heart clips. حسب Chantelle M. de Vet (16535257)

الموضوعات:

LV-GLS agreement analysis between expert and trainees over consecutive quartiles of 25 measurements. حسب Chantelle M. de Vet (16535257)

الموضوعات:

GLS analysis with 2D-STE software. حسب Chantelle M. de Vet (16535257)

الموضوعات:

ICC of each trainee RV-GLS value compared with the expert in four consecutive groups of 25 fetal heart clips. حسب Chantelle M. de Vet (16535257)

الموضوعات:

ICC of each trainee LV-GLS value compared with the expert in four consecutive groups of 25 fetal heart clips. حسب Chantelle M. de Vet (16535257)

الموضوعات:

Mean of the difference between the RV-GLS value of the expert and trainees in four consecutive groups of 25 fetal heart clips. حسب Chantelle M. de Vet (16535257)

الموضوعات:

Postoperative pain assessment indexes, nausea and vomiting frequency, and postoperative recovery of patients in two groups. حسب Changxu Wang (17826805)

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The flow chart of the study. حسب Changxu Wang (17826805)

الموضوعات:

Mean of the difference between the LV-GLS value of the expert and trainees in four consecutive groups of 25 fetal heart clips. حسب Chantelle M. de Vet (16535257)

الموضوعات:

S1 File - حسب Chantelle M. de Vet (16535257)

الموضوعات:

Comparison of postoperative NLR, PLR, and SII between the two groups. حسب Changxu Wang (17826805)

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PCA-CGAN model parameter settings. حسب Chao Tang (10925)

"…Experiments demonstrate that PCA-CGAN not only achieves stable convergence on a large-scale heterogeneous dataset comprising 43 patients for the first time but also resolves the “dilution effect” problem in data augmentation, avoiding the asymmetric phenomenon where Precision increases while Recall decreases. After data augmentation, the ResNet model’s average F1 score improved significantly, with particularly outstanding performance on rare categories such as atrial premature beats, far surpassing traditional methods like SigCWGAN and TD-GAN. …"

MIT-BIH dataset proportion analysis chart. حسب Chao Tang (10925)

"…Experiments demonstrate that PCA-CGAN not only achieves stable convergence on a large-scale heterogeneous dataset comprising 43 patients for the first time but also resolves the “dilution effect” problem in data augmentation, avoiding the asymmetric phenomenon where Precision increases while Recall decreases. After data augmentation, the ResNet model’s average F1 score improved significantly, with particularly outstanding performance on rare categories such as atrial premature beats, far surpassing traditional methods like SigCWGAN and TD-GAN. …"

Wavelet transform preprocessing results. حسب Chao Tang (10925)

"…Experiments demonstrate that PCA-CGAN not only achieves stable convergence on a large-scale heterogeneous dataset comprising 43 patients for the first time but also resolves the “dilution effect” problem in data augmentation, avoiding the asymmetric phenomenon where Precision increases while Recall decreases. After data augmentation, the ResNet model’s average F1 score improved significantly, with particularly outstanding performance on rare categories such as atrial premature beats, far surpassing traditional methods like SigCWGAN and TD-GAN. …"

PCAECG_GAN. حسب Chao Tang (10925)

"…Experiments demonstrate that PCA-CGAN not only achieves stable convergence on a large-scale heterogeneous dataset comprising 43 patients for the first time but also resolves the “dilution effect” problem in data augmentation, avoiding the asymmetric phenomenon where Precision increases while Recall decreases. After data augmentation, the ResNet model’s average F1 score improved significantly, with particularly outstanding performance on rare categories such as atrial premature beats, far surpassing traditional methods like SigCWGAN and TD-GAN. …"

MIT dataset expansion quantities and Proportions. حسب Chao Tang (10925)

"…Experiments demonstrate that PCA-CGAN not only achieves stable convergence on a large-scale heterogeneous dataset comprising 43 patients for the first time but also resolves the “dilution effect” problem in data augmentation, avoiding the asymmetric phenomenon where Precision increases while Recall decreases. After data augmentation, the ResNet model’s average F1 score improved significantly, with particularly outstanding performance on rare categories such as atrial premature beats, far surpassing traditional methods like SigCWGAN and TD-GAN. …"