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). …”

PCAECG-GAN 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). …”

PCA-CGAN Pseudocode 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). …”

PCA-CGAN Ablation Experiment 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). …”

Acoustic Startle at 28 dpf. by Morgan Barnes (7876373)

“…(C) There is a significant decrease of PPI in the 48+ and 72 + fish (p < 0.0001). …”

Image 1_Structural and acoustic properties of urbanized landscapes adversely affect bird communities in a tropical environment.tiff by Dickson Anoibi Matthew (20287776)

“…Anthropogenic noise had a significant negative relationship with bird abundance and species richness at all levels of physical structures.…”

Table 1_Structural and acoustic properties of urbanized landscapes adversely affect bird communities in a tropical environment.xlsx by Dickson Anoibi Matthew (20287776)

“…Anthropogenic noise had a significant negative relationship with bird abundance and species richness at all levels of physical structures.…”

Table 2_Structural and acoustic properties of urbanized landscapes adversely affect bird communities in a tropical environment.docx by Dickson Anoibi Matthew (20287776)

“…Anthropogenic noise had a significant negative relationship with bird abundance and species richness at all levels of physical structures.…”

Minimal data set. by Ikuo Mikoshiba (9312007)

“…Results indicated that both the DFAT and DFAT + bFGF groups exhibited significantly reduced glottic gaps, increased collagen deposition, and decreased TUNEL-positive apoptotic cells compared to controls. …”

Analyzing the effects of integrated care approaches to address co-occurring depression and diabetes: A systematic review and meta-analysis by Zach W Cooper (19688646)

“…Of 26 studies reporting HbA1c, IC approaches were associated with a significant between-group difference regarding the percent decrease of HbA1c (d = -0.36, 95% CI: -0.52 to -0.21). …”

Supplementary Material for: The Impact of the Selective Cytopheretic Device on Neutrophil-to-Lymphocyte Ratios and Hematological Parameters in AKI: A Pooled Analysis by figshare admin karger (2628495)

“…SCD treated patients demonstrated a statistically significant reduction in NLR vs. control patients at Day 6 (SCD = 13.3 vs. control = 25.7 at day 6; ptrend = 0.011). …”