A blood pressure estimation approach based on single-channel differential features

Objective: Effective blood pressure (BP) management is crucial for early cardiovascular disease treatment. Photoplethysmography (PPG) waveform analysis presents a promising avenue for non-invasive BP estimation. However, existing methods often overlook the impact of PPG feature changes on BP variations, and the effectiveness and physiological interpretability of the features they employ remain largely unclear. Hence, this work introduces a single-channel BP estimation algorithm founded on the PPG differential features (DFs). Methods: A three-month tracking of PPG and BP data was conducted on 90 volunteers, and 68 blood pressurerelated features were extracted from their PPG. The features in the first measurement data were used as baseline features. The differential features were obtained by subtracting the baseline features from the features in the subsequent measurement data through differential feature processing, and 11 DFs for BP estimation were retained using feature selection. Subsequently, four machine learning models were applied for the estimation of systolic (SBP) and diastolic blood pressure (DBP) changes, and finally introduced baseline blood pressure values to achieve BP estimation. Results: Investigating the correlation between PPG DFs and BP changes, we observed a more significant correlation with BP changes for PPG DFs compared to PPG features. Among the machine learning models, the Random Forest model exhibited the most accurate estimation results in blood pressure, achieving standard deviation of the error(STD) of 7.15 mmHg for SBP and 5.30 mmHg for DBP, along with Pearson correlation coefficients (PCC) of 0.90 and 0.86, respectively. Conclusion: This work demonstrates that incorporating PPG feature changes with BP changes achieves higher accuracy in BP estimation, confirming the effectiveness of PPG DFs in BP estimation and providing valuable insights for long-term non-invasive blood pressure monitoring.