Cuff-less blood pressure estimation from photoplethysmography signal and electrocardiogram.

In recent studies, the physiological parameters derived from human vital signals are found as the status response of the heart and arteries. In this paper, we therefore firstly attempt to extract abundant vital features from photoplethysmography(PPG) signal, its multivariate derivative signals and Electrocardiogram(ECG) signal, which are verified its statistical significance in BP estimation through statistical analysis t-test. Afterwards, the optimal feature set are obtained by usnig mutual information coefficient analysis, which could investigate the potential associations with blood pressure. The optimized feature set are aid as an input to various machine learning strategies for BP estimation. The results indicates that AdaBoost based BP estimation model outperforms other regression methods. Concurrently, AdaBoost-based model is further analyzed by using the Histograms of Estimation Error and Bland-Altman Plot. The results also indicate the great BP estimation performance of the proposed BP estimation method, and it stays within the Advancement of Medical Instrumention(AAMI) standard. Regarding the British Hypertension Society (BHS), it achieves the grade A for DBP and grade B for MAP. Besides, the experimental result illustrated that our proposed BP estimation method could reduce the MAE and the STD, and improve the r for SBP, MAP and DBP estimation, respectively, which further demonstrates the feasibility of our proposed BP estimation method in this paper.