A novel interpretable feature set optimization method in blood pressure estimation using photoplethysmography signals

•The paper extracts features from PPG and its derivatives, while also incorporating statistical information from subjects. All 172 features from 10 dimensions are provided in Appendix A.•The SHAP algorithm is utilized to enhance the interpretability of the feature optimization process.•The optimized LightGBM model is combined with SHAP to calculate the SHAP values in each feature.•The paper proposes an interpretable method for feature importance ranking. The most appropriate number of features is obtained based on the performance of the model and feature importance. Appendix B provides the ranking of all features.•The results show that all evaluation metrics of the model improved after feature optimisation, achieving accurate SBP and DBP estimation.