To address the SMOTE algorithm′s noise sensitivity and physical distortion in pulse wave imbalance processing, this study proposes a CEEMDAN-enhanced CP-SMOTE that decomposes preprocessed pulse waves into primary/secondary layers for stratified sample generation, effectively eliminating residual noise. By integrating adaptive distance metrics and constrained supervision mechanisms aligned with pulse wave characteristics, the algorithm ensures physiologically authentic sample generation while enhancing inter-class discriminability. Evaluations on proprietary and public PPG-BP datasets with four classifiers demonstrate CP-SMOTE′s superiority: 1.51%-18.25% improvements in AUC, G-mean, and F1 scores on proprietary data, CP-SMOTE consistently outperformed SMOTE-based algorithms across key metrics including AUC,G-mean, and F1-score, with improvements ranging from 1.51% to 18.25% on proprietary data,and minimum 1.43% gains in Accuracy(2.24%),G-mean (1.47%) and AUC (1.43%) on public data, confirming its effectiveness in mitigating physical distortion and noise interference compared to SMOTE variants.