Table_1_Higher oxidative balance score is associated with lower female infertility: a cross-sectional study.DOCX by Xiong Lei (3847897)

“…When OBS was used as a categorical variable, female infertility decreased by 60% in the highest OBS group compared with the lowest OBS group (OR, 0.40; 95% CI, 0.21 to 0.74). …”

Data Sheet 1_Dysregulation of melatonin rhythm in Parkinson’s and Huntington’s disease: a systematic review and meta-analysis.docx by Reema Priyanka Suram (22399759)

“…In manifest HD, both amplitude [RoM = 0.92, 95% CI (0.81 to 1.02); p = 0.00] and acrophase [RoM = 0.92, 95% CI (0.07 to 1.78); p = 0.03] significantly decreased. PD patients with sleep disorders had significantly higher melatonin concentrations than the non-sleep disorder group, with a significant test group difference of p = 0.00. …”

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].…”

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). …”