Data Sheet 4_Efficacy and safety of Shuxuening injection in intracerebral hemorrhage: a systematic review and meta-analysis.pdf by Wenting Song (822582)

“…Meanwhile, the incidence of AE in the treatment group was lower than that in the control group, with a statistically significant difference [RR = 0.63, 95%CI 0.41, 0.96].…”

Data Sheet 3_Efficacy and safety of Shuxuening injection in intracerebral hemorrhage: a systematic review and meta-analysis.pdf by Wenting Song (822582)

“…Meanwhile, the incidence of AE in the treatment group was lower than that in the control group, with a statistically significant difference [RR = 0.63, 95%CI 0.41, 0.96].…”

Data Sheet 2_Efficacy and safety of Shuxuening injection in intracerebral hemorrhage: a systematic review and meta-analysis.pdf by Wenting Song (822582)

“…Meanwhile, the incidence of AE in the treatment group was lower than that in the control group, with a statistically significant difference [RR = 0.63, 95%CI 0.41, 0.96].…”

Data Sheet 1_Efficacy and safety of Shuxuening injection in intracerebral hemorrhage: a systematic review and meta-analysis.pdf by Wenting Song (822582)

“…Meanwhile, the incidence of AE in the treatment group was lower than that in the control group, with a statistically significant difference [RR = 0.63, 95%CI 0.41, 0.96].…”

Effect of regional nerve block on tourniquet-related injury in pediatric patients undergoing lower limb surgery: a randomized controlled study by Qin Xia (1758646)

“…</p> <p>The present study demonstrated that combining femoral nerve block and sciatic nerve block can alleviate tourniquet-related injuries, decrease opioid consumption, facilitate a quicker and less painful recovery period.…”

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

“…This research addresses core challenges in ECG signal classification—extremely imbalanced data, significant individual physiological differences, and difficulties in long sequence fitting—by proposing a Principal Component Analysis-based Conditional Generative Adversarial Network (PCA-CGAN). …”

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

“…This research addresses core challenges in ECG signal classification—extremely imbalanced data, significant individual physiological differences, and difficulties in long sequence fitting—by proposing a Principal Component Analysis-based Conditional Generative Adversarial Network (PCA-CGAN). …”

Wavelet transform preprocessing results. by Chao Tang (10925)

“…This research addresses core challenges in ECG signal classification—extremely imbalanced data, significant individual physiological differences, and difficulties in long sequence fitting—by proposing a Principal Component Analysis-based Conditional Generative Adversarial Network (PCA-CGAN). …”

PCAECG_GAN. by Chao Tang (10925)

“…This research addresses core challenges in ECG signal classification—extremely imbalanced data, significant individual physiological differences, and difficulties in long sequence fitting—by proposing a Principal Component Analysis-based Conditional Generative Adversarial Network (PCA-CGAN). …”

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

“…This research addresses core challenges in ECG signal classification—extremely imbalanced data, significant individual physiological differences, and difficulties in long sequence fitting—by proposing a Principal Component Analysis-based Conditional Generative Adversarial Network (PCA-CGAN). …”

Experimental hardware and software environment. by Chao Tang (10925)

“…This research addresses core challenges in ECG signal classification—extremely imbalanced data, significant individual physiological differences, and difficulties in long sequence fitting—by proposing a Principal Component Analysis-based Conditional Generative Adversarial Network (PCA-CGAN). …”

PCA-CGAN K-fold experiment table. by Chao Tang (10925)

“…This research addresses core challenges in ECG signal classification—extremely imbalanced data, significant individual physiological differences, and difficulties in long sequence fitting—by proposing a Principal Component Analysis-based Conditional Generative Adversarial Network (PCA-CGAN). …”

Classification model parameter settings. by Chao Tang (10925)

“…This research addresses core challenges in ECG signal classification—extremely imbalanced data, significant individual physiological differences, and difficulties in long sequence fitting—by proposing a Principal Component Analysis-based Conditional Generative Adversarial Network (PCA-CGAN). …”

MIT-BIH expanded dataset proportion chart. by Chao Tang (10925)

“…This research addresses core challenges in ECG signal classification—extremely imbalanced data, significant individual physiological differences, and difficulties in long sequence fitting—by proposing a Principal Component Analysis-based Conditional Generative Adversarial Network (PCA-CGAN). …”

AUROC Graphs of RF Model and ResNet. by Chao Tang (10925)

“…This research addresses core challenges in ECG signal classification—extremely imbalanced data, significant individual physiological differences, and difficulties in long sequence fitting—by proposing a Principal Component Analysis-based Conditional Generative Adversarial Network (PCA-CGAN). …”

PCA-CGAN Model Workflow Diagram. by Chao Tang (10925)

“…This research addresses core challenges in ECG signal classification—extremely imbalanced data, significant individual physiological differences, and difficulties in long sequence fitting—by proposing a Principal Component Analysis-based Conditional Generative Adversarial Network (PCA-CGAN). …”

Structural Diagrams of RF Model and ResNet Model. by Chao Tang (10925)

“…This research addresses core challenges in ECG signal classification—extremely imbalanced data, significant individual physiological differences, and difficulties in long sequence fitting—by proposing a Principal Component Analysis-based Conditional Generative Adversarial Network (PCA-CGAN). …”

PCA-CGAN model convergence curve. by Chao Tang (10925)

“…This research addresses core challenges in ECG signal classification—extremely imbalanced data, significant individual physiological differences, and difficulties in long sequence fitting—by proposing a Principal Component Analysis-based Conditional Generative Adversarial Network (PCA-CGAN). …”

PCA-CGAN Structure Diagram. by Chao Tang (10925)

“…This research addresses core challenges in ECG signal classification—extremely imbalanced data, significant individual physiological differences, and difficulties in long sequence fitting—by proposing a Principal Component Analysis-based Conditional Generative Adversarial Network (PCA-CGAN). …”

Comparison of Model Five-classification Results. by Chao Tang (10925)

“…This research addresses core challenges in ECG signal classification—extremely imbalanced data, significant individual physiological differences, and difficulties in long sequence fitting—by proposing a Principal Component Analysis-based Conditional Generative Adversarial Network (PCA-CGAN). …”