Photoplethysmography-Based Blood Pressure Estimation Combining Filter-Wrapper Collaborated Feature Selection With LASSO-LSTM Model

Objective: Currently, BP measurement devices are mainly cuff-based which are not portable or convenient for users. To simplify the measurement of BP, this article proposed a new framework for noninvasive BP estimation using single-channel PPG signals. Methods: Various PPG features that may be related to BP were extracted and a filter-wrapper collaborated feature selection method was used for rejecting irrelevant and redundant features. The features that maximize the correlation with BP were finally selected as the BP-oriented IFS, and a new LASSO-LSTM model was designed to estimate BP from the IFS. Results: Experiments were conducted on a public dataset and a self-collected clinical dataset, respectively. Results demonstrated that the proposed method is superior to previously reported methods in the literature, giving a mean absolute error of 4.95 mmHg for SBP and 3.15 mmHg for DBP which complies with the standard of the AAMI. Conclusion: The proposed filter-wrapper collaborated feature selection method could effectively reject weak correlation and redundant features, and the designed LASSO-LSTM model is capable of learning complicated nonlinear relations between the selected IFS and BP. The proposed method shows improved accuracy of noninvasive BP estimation.